Web Analytics Association Articles RSS Feed

Dynamic Customer Management and the Value of One-to-One Marketing

Thu, 14 Jan 2010 12:00:00 GMT

Khan, Romana, Lewis, Michael, Singh, Vishal (2008). Dynamic Customer Management and the Value of One-to-One Marketing. Marketing Science, Vol. 28, No. 6. 17 pages, pp 1063-1079.

Reviewed by Jim Novo, 2010

Executive Summary:

The concept of one-to-one marketing is intuitively appealing, but there is little research that investigates the value of individual-level marketing relative to segment-level or mass marketing. In this paper, the authors investigate the financial benefits of and computational challenges involved in one-to-one marketing. They investigate the impact of customizing promotions on the two most important consumer decisions: the decision to buy from the store and expenditure level. The modeling approach accounts for two sources of consumers’ responsiveness to various marketing mix elements: cross-sectional differences across consumers and temporal differences within consumers based on the purchase cycle.

A series of policy simulations show that for an online retail business, customizing promotions leads to a significant increase in profits relative to current practice of uniform promotions to all customers.

Specifically, they find for this online retail business:

Customizing offers based on purchase cycle (Recency or weeks since last purchase) contributes more to profitability than exploiting variations across consumers using previous transactional content (segmenting by purchase category, basket size, demographics, etc.). This is important because the computational burden of implementing the dynamic optimization to account for variations across consumers is far greater than accounting for purchase cycle.
A substantial number of customers purchase without a promotion of any kind. Offering any promotion to these customers substantially reduces the profitability of a campaign, and targeting by purchase cycle is key to avoiding this problem.
Free shipping tends to be the most profitable promotion for re-acquiring lapsed customers, whereas discounts are the most effective tool for managing active customers. Offering the “wrong” promotion (e.g. free shipping to active customers) substantially reduces the profitability of a campaign.
Customizing offers by previous transactional content in addition to purchase cycle increases profitability further, with customizing at the individual level outperforming customizing at the segment. However, gains in profit using individual level targeting when accounting for costs might not exceed the gains relative to cost by segment targeting; outcomes need to be tested.

Review:

This is an incredibly rich study and I highly recommend a personal review for WAA members involved with online commerce. There is a ton of detail on how the different promotions affect response and order size, in addition to how these parameters interact with purchase cycle to variously contribute to profit.

For those not used to discussing purchase cycle as a segmentation variable, I offer this chart on purchase rate (not response rate) from the paper:

What you are looking at is a model constructed from actual test results. The model maps probability of purchase by 4 groups of online customers, by weeks since last purchase. Three of these groups are being offered promotions – Coupons, Free Shipping, and a Reward program. The Baseline group is offered no promotions.

Example: Looking at the Baseline (lowest) curve, with week = 0 being the last purchase date, and remembering these customers receive no promotions: about 3.3% of customers will make their next purchase 1 week later; about 5% of customers will make their next purchase 2 weeks later; about 5.5% of customers will make their next purchase 3 weeks later, and so on.

Please recognize that there is a “Natural” purchase rate, as represented by this “Baseline” group – those offered no promotion. This natural purchase rate peaks at about 4 weeks, and after 4 weeks of no purchases, the likelihood to purchase again begins to fall each week that no purchase is made.

Your business model has a chart that looks similar to this one. The peak may be different, the slope may be different, but the general characteristics will be the same. The Baseline group is often called the “control” group, and is simply a sample of the population that receives no promotion, which allows you to measure the natural purchase rate and revenue generated from these buyers.

The chart above shows what Marketers mean when they talk about “Lift”, as opposed to response. Let’s say the response to a campaign may be 8% from buyers 4 weeks into the cycle. If the natural purchase rate for people receiving a campaign is 4% at that same 4 week point in the purchase cycle, then the campaign is only responsible for generating 4% of behavior – literally 50% of the “response” to the campaign. The Baseline or control group tells you the natural buying rate and revenue generated from natural buyers in each point of the purchase cycle, which starts at last purchase date (week = 0 in chart).

This also means that when you do a financial analysis of your campaigns, you should only be taking credit for the Lift caused by the campaign. Said another way, the full cost of the campaign should be applied against only those sales the campaign is responsible for generating, in the above example, the 4% rather than the 8%. As you might expect, this cost allocation against the true performance of the campaign can dramatically affect profitability.

And this is why segmenting customers by purchase cycle contributes more profitability to a campaign than segmenting by transactional content like category, basket size, demographics, and so forth. The timing of the offer is a more powerful determinant of profitability than the content of the offer.

Why is this important to you?

Because if you believe in the power of interactive to “pull” customers in, if you believe that usability and customer centricity really matter, then it follows you should be thrilled to have a high natural purchase rate. In fact, increases in natural purchase rate can be used to prove that customer centricity drives increased profitability.

Logically, if you accept the above premise, “push” campaigns will encounter higher levels of natural demand as a business becomes more customer-centric. Which means that as your business becomes more customer-centric, you should rely on more and more on purchase cycle targeting to drive higher profitability.

Impact of Different Promotions

An example of how to take action on purchase cycles is represented in the study, where free shipping tends to be the most profitable approach for re-acquiring lapsed customers, and discounts are the most effective tool for managing active customers. Look at the graph above to see how this works.

On the left side of the graph, when weeks since last purchase are low, you can see purchase incidence is higher in the “Coupon” group than the “Freeship” group; the Coupon line is higher than the Freeship group so the delta versus the Natural buying rate is greater for Coupons than for Free Shipping.

If you follow the Coupon line down to the right, you can see it drops below the Freeship line at 6 weeks with no purchase and in the out weeks, closely approaches the purchase incidence of Natural buyers. This is happening while the Freeship group maintains a significant delta to the purchase incidence of Natural buyers. If the purchase cycle analysis for your business looked exactly like this one, what should this data mean to you? Primarily two things:

In order to maximize purchase rate, customers who are offered Coupons and are non-responsive after 6 weeks should then be offered Free Shipping.
Offering a Coupon after 20 weeks of non-response generates very little lift in purchase rate; virtually all the responders are Natural buyers who would have purchased anyway. This means you are probably generating negative profit after campaign and discount cost on these efforts.

I think it’s worth repeating again that purchase cycle (or more broadly, LifeCycle, to include analysis of any action including visits, log-ins, downloads, etc.) curves will not look exactly like this one for your business, and the optimal timing of switching offers by purchase cycle likely won;t be the same.

However, having seen these same types of curves many times over my 15+ years working with online businesses, I can tell you this kind of work is worthy of your attention and effort – and especially so if your company is actively working on becoming more customer-centric. The more successful you are in pulling customers back to you, the more attention you should pay to purchase cycle segmentation to drive company profitability.

If you believe a fundamental part of your business model is to be “interactive”, time since last interaction – perhaps you’d prefer the term “dis-engagement” – is one of those most powerful segmentation approaches you can use.

The Effectiveness of Combining Online and Print Advertisements: Is the Whole Better than the Individual Parts?

noemail@webanalyticsassociation.org — Tue, 12 Jan 2010 18:00:00 GMT

Wakolbinger, Lea M., Michaela Denk, Klaus Oberecker. (2009). The Effectiveness of Combining Online and Print Advertisements: Is the Whole Better than the Individual Parts? Journal of Advertising Research, Vol. 49, No. 3. 12 pp 360-372.

Reviewed by Jason Dong, 2010

Executive Summary:

Marketers have increasingly made online marketing an integral part of their multi-channel communications strategies. There are certainly advantages to using online marketing including economies of scale, direct fulfillment, exceptional targeted advertising capabilities, and of course the ability to track advertising.

Wakolbinger et al. have taken this knowledge of increased spending and usage of online marketing and set out to determine whether a multi-channel marketing approach is more effective than using any one single channel for a marketing campaign. Their hypothesis was that a combination of marketing channels should a) yield higher values in advertising effectiveness (measured via five different recall and recognition metrics) and b) between print and online individually, neither when used individually, show any difference in influence on advertising effectiveness when directly compared against each other.

Their experiment was carried out on university commerce students in Austria. They created a fictitious nonprofit organization (NPO) and placed ads promoting the NPO in a local newspaper and its corresponding website. Different groups of students were assigned to different test conditions. All of the students were then surveyed to determine whether they were able to recall the NPO ads.

The experiment yielded results that were contrary to the authors’ hypotheses. There was no discernable difference in brand recall or recognition between the print-only and online-only groups. This was consistent with previously conducted studies. The more interesting conclusion was that there was also no statistically verifiable conclusion that the combination of print and online was more effective than using print or online alone.

Finally, the authors conceded there was certainly room for further research regarding the effectiveness of mixing and matching different advertising mediums and look forward to more research being conducted on integrated marketing communications with online playing a significant role.

Review:

Overall, this article was full of very useful information of online and traditional marketers alike. Executing integrated marketing communications strategies and plans involve numerous players from different business areas. These campaigns also cost a lot of money as each channel individually has unique costs to produce a final marketing deliverable as part of the larger campaign. Thus, it makes complete sense that marketers will want to spend their money most effectively and results like what was derived from this experiment are good to know and may serve to dispel preconceived notions that the more channels a customer is bombarded with the same message isn’t necessarily better.

For the web analyst, there are already inherent challenges in determining what advertising element to attribute ultimate conversion to from an analytics perspective. This challenge becomes more complex when dealing with multichannel marketing campaigns with online components integrated into each tactic (i.e. online discount codes on print material, vanity URLs in television ads, tracking codes on external online ads, etc.) and trying to determine which tactic to assign “credit” to for conversion. As a result, it sometimes becomes very difficult to analyze, report, and make recommendations either in the middle or end of campaign. This article reinforces the need for the web analyst to work that much closer with their marketing counterparts and be active in the planning and execution of multichannel campaigns. The web analyst brings with them a wealth of knowledge of how previous campaigns have performed and the analysis and recommendations need to be disseminated to those who formulate and decide on how a company markets their products. The web analyst would also know for companies that sell multiple products that online may not always be the best to have in the mix.

Whatever the campaign’s objective is, whether it is awareness or sales or both, it is incumbent both traditional and online marketers to use the appropriate mix of tactics for their marketing plans by performing due diligence in knowing who their customers are and using the right channels to reach them. It is especially important not to get caught up in the hype of spending frivolously on placing online ads everywhere given the steady press from various sources that internet ad spending is on the rise. I highly recommend this article to online and traditional marketing practitioners who are involved in managing and executing integrated marketing communications plans.

A single copy of the full journal reviewed above is available to members of the Web Analytics Association. To request a copy, email .

12-Jan-10 2:00 PM

Viewpoint: Now or Never – An Urgent Call to Action for Consensus On New Media Metrics

noemail@webanalyticsassociation.org — Tue, 12 Jan 2010 18:00:00 GMT

Wurtzel, Alan. (2009). Viewpoint: Now or Never – An Urgent Call to Action for Consensus On New Media Metrics. Journal of Advertising Research, Vol. 49, No. 3. 3 pp 263-265.

Reviewed by Jason Dong, 2010

Executive Summary:

The issue of standardization of media (including web) metrics has become increasingly important to address and find solutions for. Wurtzel does a good job outlining where the metric standardization problems in television originated and how that has spilled over into internet advertising. He outlines four major problems facing media: 1. Constantly new metrics from new platforms dilute an already cloudy soup of existing metrics; 2. There is no coordinated effort to leverage every research provider’s best assets; 3. There are no standardization standards anywhere; and 4. There is a need to measure cross-platform performance to exploit the good in each platform.

He concludes his opinion piece by offering up some solutions that mainly centre on the ideas of bipartisanship, collaboration, and transparency. He also cites the players that will be critical in achieving a consensus on new media metrics. They include trade organizations, national governments, and research providers.

Review:

Wurtzel does a good job outlining a continuing problem both in the television/media world as well as online. Differences in definitions of metrics with the same label used across different audience measurement tools down to different web analytics tools plagues the quality and ultimate conclusions of analysis and reporting being done by web analysts. You don’t have to go far to learn of the frustration of web analysts when you ask of their opinion on the many different ways a “visit” can be defined.

The author makes valid points in the approach towards standardization: All parties with vested interest put aside short term self-interest in the name of accuracy and stability of metrics. He also states legacy consequences if action is not taken now. The same can be said for web analytics metrics where if definitions of even the simplest of metrics are not standardized, it will be very difficult, if not impossible, to conduct analyses on historical web performance for anything.

While this article being read by the members and friends of the Web Analytics Association seems like preaching to the choir, it is still an interesting read for those interested in web analytics standards given that the solutions that Wurtzel proposes ultimately has cross-platform implications.

A single copy of the full journal reviewed above is available to members of the Web Analytics Association. To request a copy, email .

12-Jan-10 2:00 PM

Assumptions, Explanation, and Prediction in Marketing Science: “It’s the Findings, Stupid, Not the Assumptions"

noemail@webanalyticsassociation.org — Mon, 04 Jan 2010 13:00:00 GMT

Tsang, Eric W. K. (2009). Assumptions, Explanation, and Prediction in Marketing Science: “It’s the Findings, Stupid, Not the Assumptions”. Marketing Science (28). 5 pp 986-990.

Reviewed by Christopher Berry, 2010

Executive Summary:

Tsang references a previous debate in Marketing Science on whether analytic models need to have realistic assumptions and stakes out a position that modifies Shugan’s “It’s the Findings, Stupid, Not the Assumptions” point of view in 2007.

Tsang reasons that the realism of an assumption is a continuous variable – not binary. That is to say, an assumption isn’t necessary unrealistic or realistic – the realism of assumptions is more like a thermometer than it is a light-switch.

Tsang suggests that: “Authors should discuss the probable impacts of the assumptions on their findings, predictions, or implications, and if possible, test the sensitivity by varying the assumptions and compare results...The burden of proof should lie on those who use the assumption.” He goes onto suggest: “As the field advances, efforts should be directed toward making assumptions more realistic…More realistic assumptions result in better theories.”

Tsang concludes: “although Shugan (2007) rightly stresses that it is inappropriate to dismiss a model or theory based only on the realism of its assumptions, realism does matter, and it matters a great deal for model building and theory development.”

This is an important conclusion. Instead of building theories and models based on unrealistic assumptions, like a house of cards that falls down when a door slams, we ought to be building a brick house. Building on a solid foundation of assumptions ensures the long-term predictive value of a discipline.

Review:

Implications for Practitioners

I’d have to retype the article in its entirety to do it justice. The summary above highlights the most relevant bits for web analysts.

Web analysts are frequently asked to make assumptions. Making unrealistic assumptions has gotten us into deep trouble in the past. For instance, the assumption that a unique visitor really represented a unique person was intensely flawed. And yet, for reasons that have been well documented, we as an industry stretched the assumption for the sake of precision. We as practitioners are frequently pressured to bend on assumptions – one way or another. We ought to become self-aware of why that happens and use sensitivity models to communicate the consequences.

Web analysts make predictions about the future. Optimization is not possible without making a range of predictions about the future and selecting the best scenario to guide action (Even A/B testing falls into this definition). Any prediction is fraught with unknowns and uncertainty. We use assumptions to simplify those unknowns, and to a large extent, quantify them.

For instance, a common question is how many more sales would we get if we employed [generic tactic X]. That kind of analysis requires reasoning and preferably accurate evidence for previous circumstances. We lack a common a pool of findings in our industry. Many analysts lack a common pool of evidence at best, and at worst, a sanitized version of what was a success and what was not. Regardless of how much data we have, we still make assumptions for the purposes of making predictions.

Tsang makes reference to the fact that not all sciences are predictive in nature. For instance, Earth Scientists are great at explaining earthquakes after they’ve happened, but are seldom predictive. Indeed, (tongue-in-cheek), many web analysts are indeed deployed like Earth Scientists. Like Earth Scientists, in certain circumstances, it’s worth pushing assumptions to the limit of believability so as to understand the range of what’s possible.

We should take advantage of those types of models to communicate with non-analysts. We can treat assumptions like a continuous variable and push the limits if it helps us make better predictions in the future, so long as those assumptions return to Earth in the final analysis and recommendation. The next generation of visualization software could assist us in communicating the bounds of what’s likely when given a set of assumptions – going beyond the spreadsheet.

Understanding and acknowledging that assumptions exist in our own analytical world will free us to challenge each other on those assumptions more often. We will all be better off for it.

I recommend that Web Analytics Practitioners read this article.

A single copy of the full journal reviewed above is available to members of the Web Analytics Association. To request a copy, email .

4-Jan-10 9:00 AM

Are Your Business Goals Aligned with Insights drawn from Your Web Analytics?

noemail@webanalyticsassociation.org — Sun, 03 Jan 2010 15:00:00 GMT

The world of online marketing is evolving fast and 2010 promises to be explosive in terms of social media engagement. The extent of accurate measurement of these efforts is going to play a major deciding role in differentiating all business successes. Measuring your web engagements , be it the website or the social media efforts, isn’t about the volume; it’s about identifying the critical few performance indicators that aligns with your goals, assists with insights and eventually drives the business forward.

Web Analytics is the biggest weapon available to businesses of all size in their battle for insights. Yet, while currently about 70% of consumers are shopping online at least once, researches have revealed that nearly 60% of the businesses still under utilize this critical power house of online customer information. The biggest data management revolution in recent years has happened in the form of free Analytics tools from Google & Yahoo. A lot more followed, but Google Analytics still commands the major market share due to the simplicity of its installation and constantly expanding robust reporting features.

I have always been a huge fan of simple & holistic approach to solutions.

Here’s a basic 4 step analysis model I’ve been following for quite some time and currently implement at Curve Trends Marketing. This has more than enabled me to gain business insights and action on:

Prospect Acquisition, Retention & Conversion
Nurture Existing Customers

while deriving outcomes in terms of

Lower Costs
Increased Revenue
Improved Customer Experience

Step 1 - Assess the Current State of your Business	Gain Insights and Action on Prospect Acquisition, Retention & Conversion Nurture Existing Customers	Step 2 - Identify goals, establish benchmarks & report on basic key metrics that drives visitor behavior
Step 4 - Answer the “Why” in Customer Behavior - Qualitative Analysis , Usability Testing & Competitive Analysis	Derive Outcomes Lower Costs Increased Revenue Improved Customer Experience	Step 3 - Customize the “Critical Few” actionable "Key Performance Indicators” Dashboard that is Unique to Your Goals & Your Customers

If you are currently new to the concept of Web Analytics tools or just getting acquainted with customer behavior through click stream analysis, you might want to start taking those tiny incremental steps of establishing your basic metrics first.

Basic Metrics

Visitors & Unique Visits
Bounce Rates
Exit Rate
Sources Of Traffic
Popular Content & Landing pages

But once you are ready to dive deeper into your customer world, it becomes imperative that you start pursuing the advanced segmentation & analysis.

No advanced analysis can be complete without understanding the “why” in customer behavior. Why did they come to your web site? Why did they react the way they did? Why did they leave? Did they get confused with the layout? What were they crazy about? Customer surveys, benchmarking, A/B testing (where you test 2 different versions of the web page) are some common channels for answering these questions. Capturing the voice of customer (VOC), will enhance your user experience & improve customer satisfaction.

Advanced Analysis

Segmentation
Understanding Intent of Visit
Customized Traffic Source Analysis
Multichannel Measurement Including Offline Efforts & Social Media Campaigns

They say “keep your friends close, but your enemies closer”. Unless you understand the modus operandi of your competition, you will not be able to set yourself apart. Competitive Intelligence helps to understand industry trends that integrates with the insights derived from Web Analytics and enhances your outcomes. There are several free tools including Google’s Insights for Search available to help you accomplish your competitive analysis.

Whatever you do, always remember the key to your success lies with identifying your unique Key Performance Indicators (KPIs), for objectives you have set for your online actions, before starting to monitor and measure them.

Still unsure? Or are you already an Analytics Ninja? Let us know your role and how you are leveraging this powerful tool to drive your business.

Wishing you a safe ,successful & measurable 2010!

-Bibi

3-Jan-10 11:00 AM

The Most Important Analytics Reference Document: Definitions

noemail@webanalyticsassociation.org — Wed, 04 Nov 2009 20:00:00 GMT

There is an “analytics” document which is often overlooked in the operational rush to measure and then analyze the results of the Web effort. It is a simple reference document that will help you understand and use the metrics available to you. In fact, it will tell you what information is available to you. It is also invaluable in helping your IT department to understand what information you need and which they need to plan for. This article will discuss what to include in a “Data Definitions” document.

The Data Definitions document is a simple list of all the metrics that are collected about your site. It reflects your information architecture. It may be owned by Marketing, IT, the Analytics group, the Information Architect, or some other group within the organization. It will benefit all. It is particularly important for large organizations with many people and for Web sites collecting large sets of data about their pages. This document is guaranteed to save hours and hours of time, effort, and massive amounts of misunderstanding.

Variables

The first step in creating your document is to list all the variables you collect about your site. A variable is just a term that designates a container into which data values are passed. Below are a few examples for a Media site. These are the “Business Names” for the variables and they should be short and matter-of-fact descriptive. (No marketing buzz words, in other words.)

Business Name

Page Name

Site Name

Content Hierarchy

Topic

Page Type

Application/Tool

Module + Link

Server + Beacon

Sponsor ID

Collecting this list should be a relatively straight forward task. The list of variables will be in your tracking systems. If you do not know what they are already, you’re Analytics Product Manager or your IT department should be able to provide them, even if they don’t yet have sensible Business names.

Definitions

The heart of this document is information about what the data means. This goes a long way to make sure everyone within the organization is on the same page about what is being tracked and why. More specifically, each variable should have the following information:

What is it?
Provide a brief description of the type of information the variable is supposed to contain. You should be clear and concise. Make sure you are not sacrificing clarity for trivia that adds no real value. Direct and succinct declarative sentences are almost always better.
How is it used?
This is a high level description of how the Business will use the information, the reason for all the effort involved to collect it. Again, you should be clear and concise. If you can’t identify specific action items that will be taken as a result of knowing this information, it may only be data noise and not worth collecting.
The format of the values.
Sometimes the values have a specific format or form they follow. Provide the expected format.
Example values.
Provide some example values. This goes a long way to helping everyone understand the data and what can be expected.
Data population rules.
Values are often set when certain conditions exist or there are rules for how the value is determined. Provide a brief description of these rules so everyone has a clear understanding of what is being tracked.
How is the value collected?
Values can be set by different systems and in different ways or even manually. Indicate where the value comes from and what person or group owns the system.
Who decides the value?
Identify who or what group decides what a given value will be. It may be a system that determines the specific value. This will provide the go-to people for questions about the value.
Related documentation.
Often there is more information in other documentation; information that is too big or tangential for the definitions document but that staff should be aware of. Add a link to that documentation.

The various items in this list will likely come from multiple sources within your organization. For example, the Business Managers, Marketing, your Analytics Department, or your Developers. Here are two examples of descriptions:

Simple Example 1:

Definition

Publication Source identifies the publication company and the product line for third party content. It is used by Editorial to track the effectiveness of content and manage the third-party contractual obligations.

Format: [companyName]-[publicationSource]

Example values: “timewarner-cookinglight”, “mrthstwrt-everydayfood”

It is a concatenation of two values passed from Documentum: Company Name and Publication Source. The values are separated by a “-“. If no value is passed, the beacon will pass a value of “ntc”. The beacon will change all values to lower case.

The values are determined by the Affiliate group and selected in the CMS to be set on the page.

Complex Example 2:

Definition

This variable can contain several different values related to Boards, Blogs, or similar Community applications and depending on the site.

Main Site:

Boards: This is the Alpha-Numeric code identifying the message board action taken by the visitor.

Format: command=[command id]

Example Values: “command=view_thread_summary”, “command=view_category_folder”, “command=read_thread&threadid=8964bb9c”

Examples of board actions include: Post, Edit, View, etc. The individual thread views (read_thread) are identified by individual thread ID. The other commands are roll ups across all threads

The values are passed from the WebCrossing system and the values are system values.

Blog: The variable contains the name of the blog and is a roll up of several blog pages. (Note the individual pages are passed in Page Name).

Format: blog:[blog-name]

Example values: “blog:all-rabbits”, “blog:life-with-dogs”, “blog:sexual-health-of-livestock”

The values are passed from the WebCrossing system and the values are determined by Editorial staff, specifically the blog moderator.

Pet Health Community: The variable contains the identifier of an individual thread.

Format: blog: ph-[threadID]

Example value: ph-dis-147/3

The values are passed from the WebCrossing system and the thread ID is system determined.

Lifestyles Site:

Boards: This is the code identifying the message board action taken by the visitor.

Format: command=[command id]

Example Values: “command=view_discussion”, “command=view_folder”, “command=reply_to_message”

The command values are unique to this implementation and will not collide with Main Site values. The values are passed from the WebCrossing system and are system determined.

Blog: The variable contains the name of the blog and is a roll up of several blog pages. (Note the individual pages are passed in Page Name).

Format: blog:[Vanity-uri]

Example values: “blog:thedifferential”, “blog:runningbackwards”, “blog:rblakeley”, “blog:worsthomerecipies”

Each time a blog page name is passed into the Page Name variable, pass the Vanity URI into this variable. The vanity uri is the vanity URL without the domain.

The values are passed from the Web Crossing system. The vanity URI is derived by the system from the user entered blog name.

Variable System Names

The variable names that your systems know and that your Development staff knows may not be the Business names. To make sure everyone knows what is being talked about, these system names should be included for each variable. This provides an essential mapping across various systems and individuals (think of it as a kind of Rosetta stone). This information will come from your System Managers and Developers.

In the example below, the first column is Omniture based, the second is for internal scripting, and the third is for the Documentum CMS (Content Management System). The systems you use will likely be different, but you get the concept.

Omniture Field Name	De Field	CMS	Business Name
Account	s_cid		Account
pageName	s_pn	From Documentum: Page Name (de_w_nm)	Page Name
pageType	s_er		Error Page Indicator
channel	s_chn		Undefined
Hier1	s_hier1		Content Hierarchy
Prop1	s_site		Site Name
Prop2	s_subject s_cn is a deprecated value.	Documentum: Primary Subject Code (de_r_prm_id)	Subject

Putting It Together

Here is how all our example columns will lay out:

Omniture Field Name	De Field	CMS	Business Name	Definition
Value	Value	Value	Value	Value

This can become a big document. I have one on legal sized paper that runs 29 pages, printed on both sides. It may take some effort to create and maintain this document.. But you can see how useful it is to have a Data Definitions reference document. (Granted, the title of this article is a little exaggerated). If your organization does not already have one, you should make the effort to create one or have one created.

Lastly, once created, be sure it is put in a place on your network that everyone can find and access it.

Robert Blakeley is the product manager for WebMD’s analytics tool. Mr. Blakeley has worked in the Internet industry since 1993 and has worked with many companies and government agencies. These include the Direct Marketing Association, International Council of Shopping Centers, Atlantic City and the City University of New York. He can be reached at rblakeley@webmd.net. More articles by Robert Blakeley can be found at www.rblakeley.com/webwork/articles.shtml. ã 2008 Robert Blakeley.

4-Nov-09 4:00 PM

Modeling the Determinants and Effects of Creativity in Advertising

noemail@webanalyticsassociation.org — Tue, 06 Oct 2009 12:00:00 GMT

Smith, Robert E, MacKenzie, Scott B., Yang, Xiaojing, Buchholz, Laura M., Darley, William K. (2007). Modeling the Determinants and Effects of Creativity in Advertising. Marketing Science, Vol. 26, No. 6. pp. 819-833, 15 pages.

Reviewed by Christopher Berry, October 2009

Executive Summary:

Smith et al get straight to the point in their opening line: “Marketing researchers and practitioners agree that creativity is one of the essential elements for advertising success in a cluttered marketplace”.

Their study is aimed at defining ad creativity, measuring it, and examining its determinants.

The article examines previous methodologies of defining ad creativity and produces a better one. They identify two concepts in their literature review, divergence and relevance, as being the primary variables in defining creativity. They define divergence being ‘originality’ – how much a particular ad stands out from the pack. They define ‘relevance’ as “the extent to which at least some ad/brand elements are meaningful, useful, or valuable to the consumer”.

They infer that ads with low divergence and high relevance are easy to miss and ignore in a cluttered landscape, while ads with high divergence and high relevance are the most effective.

The authors do not (refreshingly) start from scratch. Instead, they go about refining previous definitions of both divergence and relevance using a survey methodology to gradually reduce the number of questions pertaining to each group and their relative impacts on ‘creativity’. These include questions like “The product or brand was meaningful to me”, “The ad demanded my attention”, and “The production elements of the ad were of high quality”.

Then they showed students a number of ads which included award winning ones – making the assumption that award winning ads would be judged more creative. Students were asked the shortlist of questions based on the authors’ operational definition of creativity and evaluated the effectiveness of their model. Their model is a series of questions, grouped thematically into groups and indexed, then treated like independent variables to explain variation in a dependent variable: creativity. They found that award winning ads indeed tended to be more creative.

They found that divergence is the leading indicator of creativity. Interestingly, they found that creativity was very strongly correlated with attention to the ad, but not to purchase intent.

Review:

The article will be of particular interest to web analytics practitioners and managers in very brand oriented companies.

A practitioner can borrow from this methodology to add detail to their analysis of why different pieces of creative worked, and why some were less than successful. It might also inform a range of informed hypotheses that could be progressively A/B tested. They might do this by using the survey questions (helpfully listed in Appendix A) and evaluate the ads themselves, or, use the questions to survey customers directly themselves. The answers could be put into a spreadsheet or statistical software and compared with the actual performance of specific ads, be it display, video, social, search, or on-site promotion. An analyst could then correlate creativity to various metrics, including attention, clickthrough, and ultimately conversion. Not that the analysis would be completely fair or necessarily perfect in assigning ROI, but it would yield actionable insight.

The authors go one step further, where I think web analytics should go: “In addition, it seems time for marketing research to move beyond the attentional effects of ad creativity and investigate its role in the persuasion process.”

Practitioners steeped in the direct response world might question the real role of creativity in ads. Indeed, much of the dialogue in Search Engine Marketing (SEM) world (possibly the closest thing to ‘direct’ marketers we have in digital) focuses on the questions of rank, price, and keyword relevancy. There are 95 precious characters that go into a Google Adword Unit. How important is divergence? How important is relevance? There’s an opportunity, using this framework, to explore the effects of creativity on conversion. The same could be said for landing page optimization from a purely web analyst point of view.

I would recommend this article to web analytics practitioners and managers who are particularly concerned with the role of ad creativity and its correlation to various metrics: including ROI.

A single copy of the full journal reviewed above is available to members of the Web Analytics Association. To request a copy, email .

6-Oct-09 8:00 AM

Firm-Created Word-of-Mouth Communication: Evidence from a Field Test

Mon, 21 Sep 2009 14:00:00 GMT

Godes, David, Mayzlin, Dina., (2009). Firm-Created Word-of-Mouth Communication: Evidence from a Field Test. Marketing Science, Vol. 28, No. 4. 19 pages.

Reviewed by Jim Novo, 2009

Executive Summary:

The authors investigate the effectiveness of a firm proactively managing customer-to-customer communication. In particular, they are interested in proving how, if at all, a firm should go about effecting a meaningful word-of-mouth (WOM) communications program. This is done through two different data collection schemes: a large scale, 15 market test through BzzAgent with a client restaurant chain, and also through a controlled online experiment. The results are somewhat counterintuitive and may change the way web analysts and Marketers should be thinking about WOM and social analysis, particularly if there is a hard monetary investment in the WOM program.

Specially, the researchers are trying to answer 2 questions:

What kind of WOM maximizes incremental Sales?

The answer: WOM created by less loyal (not highly loyal) customers, and occurring between acquaintances (not friends). Though perhaps surprising, this result is often found in Marketing program measurement; Sales would occur anyway without the program, especially among best customers. Said another way, the results demonstrate the pitfalls of not using control groups (people not exposed to the campaign) to accurately measure Marketing effectiveness.

Which kinds of people are most effective at creating the WOM above?

The answer: “Opinion Leaders” or “Fans” are not as effective in spreading WOM that drives incremental Sales because these efforts are “preaching to the choir”, per #1 above. The networks that opinion leaders or fans have are likely to already know about the Product from pre-existing conversations, and spending money on creating a campaign to reach these people is ineffective because the social communication has already taken place.

In sum, if you want to invest in a WOM program that will drive Sales you would not have received anyway, you want the WOM conversations happening, as the authors say, “where none would have naturally occurred otherwise”.

As is typical of academic research and testing, there is an extensive review of the results of other WOM Marketing studies all the way back to the 1970s upon which the hypothesis for this test was formulated.

Review:

This is a classic piece of research that is not only helpful for the evidence and results produced, but also demonstrates a great many ideas and techniques that should be employed in Marketing analysis. Some of the concepts could very well be used to bring more precise definitions and measurement practices for WOM and the social construct in general to the web analytics community.

The discussion of the difference between the need for a persuasive argument versus building awareness is something web analysts should keep in mind so they can make sure they understand the real needs of the Marketer or Product Manager. For products with high awareness already, what is really needed to increase Sales in persuasion of the people already aware, not more awareness. New products with zero awareness obviously need increased awareness.

Per this study, this persuasion versus awareness question affects the choice of who to recruit for WOM campaigns. Loyal customers are the best persuaders and are best used when the product already has high awareness. If you want to drive sales through increased awareness – the goal of many WOM campaigns online – you should be recruiting less loyal customers and encouraging them to talk not to their friends, but to their acquaintances. This approach appears to be contrary to the “opinion leader” or “fan” approach now thought of as best practices. Because of this, a lot of books on social marketing may need to be rewritten, at least as they pertain to generating incremental Sales…

At the very least, some of the discussions around tracking or proving the value of social media need to change given the results of these tests. Seems to me a test like this that is carefully executed using the scientific method is what social advocates have been dreaming of, yet the results don’t lean in the direction these folks generally support. It will be interesting to read their reactions, if any. After all, many online Marketers really don’t care if programs generate profit and are more comfortable following the ancient offline mantra of “any exposure is good”.

As a practical implementation matter, the above suggests changes to the design of many WOM programs and any incentives provided, depending on the goal of the WOM program. The most common program structure – to look for opinion leaders that have lots of “followers” - generates more sales when the product already has high awareness, a situation that requires persuasion. Yet online, this program structure is often used to introduce brand new products. The implication is perhaps this: when launching a new product, the Social programs focused on Opinion Leaders or Fans should be implemented after Advertising has created awareness to maximize their effectiveness.

The more difficult question to answer from an implementation perspective is this: if you want to generate awareness, how do you recruit less loyal customers (not fans) and have them spread the word not to their friends, but acquaintances?

One answer to this question is forced by one of the brilliant ideas in the test design. The authors used BzzAgent in perhaps an unexpected way – not as the hip, cool people who are thought leaders with lots of followers, but as a group of people who were not customers of the product at all and are simply paid to spread the word. It is this group that generated the highest incremental sales.

The opposing group – best customers in the restaurant loyalty program – did not generate as much incremental Sales activity as the BzzAgent crew. This makes sense because among the loyalty program customers, most of their good friends probably were already aware of the restaurant through casual conversation. In other words, the available audience “to be made aware for the first time” was much smaller with loyal customers.

There were some non-controllable issues in the field test that may have affected the outcome, such as different demographics between the BzzAgent people and loyal customers of the restaurant chain. This is common in field tests, but contrary to what we usually see with success stories outlined in by vendors in the online marketing space, these issues were actively researched by the authors and completely disclosed, leaving it up to the reader to decide if these issues nullified the results in any way. Extensive mathematical simulations were run and the potential for any of these issues to significantly affect results discarded.

Personally, I would have preferred to see split market testing, where not all 15 markets in the chain were involved in the test and results compared to this not-involved control group. I’d guess the nature of the work done by BzzAgent and the operating methodology of the restaurant precluded this structure for the test.

Plus, the authors counter these challenges with a very clever solution - creating an online test where they completely controlled all variables. The design of this test was ingenious, but more importantly, the results confirmed those of the field test – the effectiveness of WOM programs depend a lot on whether a product needs awareness or persuasion to drive incremental Sales, who exactly is doing the buzzing, and to whom. Once again, generating conversations where they did not already exist is the key to driving incremental Sales, and this is most likely to occur when less loyal customers spread WOM to acquaintances.

At the risk of repeating myself but understanding the results of this study will be met with a lot of skepticism, this kind of effect is seen in Marketing measurement all the time, and especially so when Marketing to best customers. So the results are not really surprising in any way; it is often difficult to drive incremental Sales when Marketing to best customers because they are highly likely to buy anyway without any Marketing effort, and interactivity simply amplifies this phenomenon. The results of this study fit right into the existing model hand and glove.

The good news for online social buzz advocates is this: online WOM can drive incremental Sales – which is no surprise, given the decades of data from offline studies – and a scientifically designed and executed study proves that. The not so good news is WOM drives incremental Sales when implemented very differently than the way most people currently approach the challenge. The question, as always with Marketing programs like this, is did you make more money that you spent? I’d venture a guess there are a lot of “preaching to the choir” programs going on in social right now that are not profitable.

As a Marketing person, this is what the research means to me. WOM will spread all by itself among Opinion Leaders and Fans, so one should be careful with investing in this area and be clear that persuasion is the result, not awareness. On the other hand, WOM for awareness can be invested in when it’s somewhat intrusive, passed by less loyal customers to acquaintances. In other words, WOM drives awareness when the execution is similar to Advertising, creating conversations where they did not already exist. But this notion of “Intrusive Social” creates somewhat of a Paradox for many of the Social advocates who view intrusive practices to be “anti-Social”. One has to wonder if when it comes to generating awareness, Advertising might be the better way to go.

This research is a great piece of work from a couple of very creative analysts, and well worth your time to review. Also, a hat tip to BzzAgent for being so open with their practices and sharing the data for this very important study.

If you are primarily a Marketing person and more interested in the end Behavior than the Analytics, just skip over the Math sections, as these submissions are peer-reviewed and would not be published if the Math (or test design, for that matter) were faulty. As such, in your pile of research on Social, this piece should be given a lot of weight. Junk science doesn’t make it to publication in the academic journal world – as opposed to many of those blogs you probably read!

A single copy of the full journal reviewed above is available to members of the Web Analytics Association. To request a copy, email .

21-Sep-09 10:00 AM

September 2009 Featured Article: Unica

noemail@webanalyticsassociation.org — Tue, 08 Sep 2009 14:00:00 GMT

Using web analytics to complete the circle of Life Time Value, not a replacement.

You’ve had your web analytics solution for a while, you’re finally getting around to all those great features and functions that you emphasized to justify resources and expense.... but what about the actual data/information itself that your web site has been pumping into the solution for months/years? Are you getting the most out of that strategic asset?

One of the most exciting things about analytics is being able to manipulate data and address specific business issues with the functionality available at your fingertips (theoretically) yet often times this isn’t the case in practice. For a variety of reasons, practitioners spend a great deal of time engaged in the manual and technical tasks of set-up and validation to answer a historical reporting question asked for by the business days ago. How is that us analysts can break through both of those barrier?

Driving down the latency from a business person’s request to a valid data-driven response?
Offering business people an increasing choice of intelligence, including both historical reports and analysis (what happened?) as well as predictive analysis (what do we believe is going to happen and which visitors do we believe are going to exhibit a certain behavior in the future?)

More and more often companies are asking web analysts to quantify the value of visitor. As practitioners in the data mining world are well aware, this is a complex analysis to perform and not something typically found falling out of a “report”. What they also know, however, is that an accurate LTV in conjunction with other predictive attributes often becomes the basis for implementing highly targeted “Longitudinal” (staged messaging over a period time) messaging strategies. The great news in today’s market is that the tools to perform this critical analysis are finding their way into the toolbox of the web analyst and stand to forever enhance both the their standing and value to senior executives and stakeholders.

What are those tools and how can web analysts begin figuring out how to use them?

The primary tools are not just software products, though software plays a vital role.

First of all, an analyst needs an analytical data mart of web behavioral information at the individual visitor level. This type of analysis is not done in the aggregate.

Secondly, the analyst needs to understand the nature of customer’s engagement with their company and fully understand the data itself. In order to use data to model behavior, the analyst’s role is to see through the noise and run valid experiments using raw data, derived variables, roll-ups, etc.

Third, the analyst needs at least 2 critical software capabilities:

They need an analytical solution that gives them the flexibility to manipulate the data and visualize it as you would in traditional reporting, but it must be highly dynamic- this is a real-time iterative business problem, not a batch reporting one.
They need an easy to use statistical mining application that can consume the working file of analytical data, determine the appropriate statistical algorithm and variables for answering the question at hand, and the automated ability to build/train/deploy a completed model. The last critical piece of necessary functionality/capability is part technical, part business process. The analyst needs to champion a closed-loop process for enhancing their web data mart with their predictions and tracing actual behavior (regardless of channel) to determine the success of their predictive experiment.

One of the most exciting aspects of Unica’s entrée to the web analytics marketplace is our deep experience in helping companies implement this sort of analytical rigor and the very unique set of web analytical and predictive solutions that can help a web analyst who is today doing reporting become a web analyst who is briefing executives on how customers and visitors are going to behave in the future and how much they are worth to the company.

About the Authors

Lee Isensee, Marketing Architect, has been in online delivery, ecommerce, and analytics for 15 years and is highly regarded for his work in web analytics and attrition measurement, conversion optimization as well as enterprise data warehouse integration.

Rick Fuller, Account Manager, has been in the marketing automation and web analytics space for a decade. Rick's background and experience includes BI/OLAP, online personalization, web analytics and traditional direct marketing technology solutions.

8-Sep-09 10:00 AM

Can Using Real-Time, Raw Data Be Cost Effective? Opening Up the Discussion

noemail@webanalyticsassociation.org — Tue, 11 Aug 2009 20:00:00 GMT

Web Analytics Guru Avinash Kaushik has outlined in his book that the ROI you can get from leveraging real-time, raw data is so low it does not make sense for most companies to implement it or care about it. His conclusions are not based on the inherent inutility of using real-time raw data but rather the cost and complexity hurdles that have to be surmounted in order to get the kind of ROI that would make this type of analytics solutions justifiable.

And since most of us are fans and students of his book Web Analytics: An Hour a Day (I include myself in both of these categories) we do not take issue with this idea; also we do not take issue with it because his book is so chock full of pearls of wisdom that everyone gleans some insights from it regardless of their level of experience.

However I want to open up for discussion on how working with real time raw data can be cost effective. And, it can provide greater ROI if and only if the analytics product’s surrounding eco-system is set up in a way that enables the data to be leveraged effectively. In fact, my theory is that:

Real Time Data Can be Leveraged Cost Effectively
Both Real Time and Raw Data have Beneficial Repercussions throughout every Analysis
Both Real Time and Raw Data Create More Actionable Metrics
Real Time Raw Data Ensures Greater Accuracy (no caching) and Timeliness
Real Time Raw Data Future-Proofs Your Analytics Solution

I absolutely agree with Avinash Kaushik that real-time data does not matter to a company unless they will actually take action on the reports; that is what in the long run drives the ROI of the tool.

In general however the ROI on any analytics tool will be greater if each person on every team that touches the website has the power and the understanding to create little frequent changes; i.e the power of crowd-sourcing for managing a large, dispersed multi-national website is immensely greater than having one smart analyst in one location interpreting all the data. No matter how smart they are, one analyst can never react in a timely enough manner, nor in a localized enough manner, nor even be able to digest all the complex statistics fast enough to glean the insights that create the actions that drive the ROI.

Therefore, if each person on each team can react daily to even just one real time data point, you will be creating a competitive advantage over those that have only one analyst in a lagged time frame. The actions will be created by those that are familiar with the data and in a time frame that provides real monetary value ( Just think of the rapid changes that create additional revenue in eCommerce, Travel, News Content, Trend Marketing, etc.)

Therefore the tool you choose needs to complement the real time data with an easy to use interface and unique user management so that each marketing person can have the metrics that matter to them provided to them on a daily basis. If each person is responsible for certain KPIs ( Key Performance Indicators) and the tool provides an intuitive enough interface or an export capability so that these people will potentially not even have to learn how to use the tool, then you have a winning combination. Real time data with unique user management and easy to understand reporting is very effective and can drive the ROI required. However, you have to have the complete eco-system in one tool and at a reasonable price point in order to make implementing it worthwhile.

And, even more importantly, I would agree with Avinash that you need to also ask yourself whether your website gets enough visitors exhibiting the right behavior to ensure that the real time data choices you make will result in statistically significant outcomes. He does confirm that “..statistical significance is not just about raw numbers..” but more about the impact of the changes you can make in real time. However, if it is clear that your business can impact the ROI by leveraging real time data, then you should also be looking for a tool that can handle significant volumes of data in real time with ease (i.e. as much as 1B page impressions a month) If you have a large amount of data that you are processing then even smaller changes you make can have quite a large monetary impact.

The same idea is pervasive about leveraging raw data for analytics – i.e. it might be great in theory, but provides poor ROI because it is too costly.

However, having the raw data enables complete flexibility in every analysis, as well as future proofs your analytics solution – increasing the tools’ long term value and again providing more effective ROI.

When all data can be correlated in any way in all time periods, this enables much more detailed views, segmentation, and most importantly, retro-active on-the-fly queries. In other tools you need to know ahead of time what sort of analysis you think will be important in the future; with the raw data you can slice and dice the data any way you would like at any moment. You can add any special unique metrics in any time frame. Therefore leveraging raw data becomes imperative as you compare and contrast different campaign success metrics in different time periods ( Even Avinash Kaushik cites that as your “..strategic objectives evolve [y]ou should expect a 20% churn in your main KPIs every six months…if they are not changing at least that much, either not a single dimension of your business has changed on the Web in that time (highly unlikely) or your KPIs are stale.. ” p.349.)

In addition, raw data’s ability to provide a granular enough view so that you will get user level data also makes the ROI on the tool much higher than those tools giving you aggregated information. Who wouldn’t want to receive a timely email with a special discount coupon for a pair of pants that you had just put in your shopping cart but then abandoned because they were too expensive?

Thus, the level of detail that raw data provides creates action oriented metrics – not vanity metrics. For example, knowing that your conversion rate is increasing is great and can make a website owner proud (i.e. vanity metrics) but what are the next steps that one should take to continue to help increase this traffic? High conversion rates could be the result of non-reproduce- able actions, such as moving out a deeply discounted product line. With a raw data based analytics solution, you can drill down to find out exactly how this conversion rate was increased and by which visitor segments on which search engines, thus enabling the online marketer to know exactly what action to take next (i.e. buy more keywords on certain search engines, reach out to certain publishers, etc.)

Thus, granular, extensive, sophisticated data analysis along with the ability to perform any retro-active analysis on the fly is the key to providing better ROI and getting longer term value from your analytics tool.

In sum, real-time raw data actually can be cost effective if the attributes of the tool enable your team to leverage action-oriented metrics now as well as any unknown future metrics at a reasonable price point.

About Cecily Robyn Lough

Cecily Robyn Lough is currently Director of International Sales at Webtrekk GmbH in Berlin, Germany. She has over 15 years experience in pulling actionable insights from online marketing data. She believes that Webtrekk’s current analytics solution does have the ability to make raw, real-time data cost effective for web sites that need up to a billion pieces of data segmented on-the-fly. Please contact her (details below) for more information or connect up with her at at Webtrekk and LinkedIn.

11-Aug-09 4:00 PM

Path Data in Marketing: An Integrative Framework

noemail@webanalyticsassociation.org — Tue, 14 Jul 2009 13:00:00 GMT

Hui, Sam K., Fader, Peter S., Bradlow, Eric T., (2009) Path Data in Marketing: An Integrative Framework and Prospectus for Model Building. Marketing Science, Vol 28, pp. 320-335. 16 pages.

REVIEWED BY JOSE DAVILA, JULY 2009

Executive Summary

By analyzing different marketing scenarios or “domains of data collection” (grocery shopping, eye tracking, web browsing, information acceleration), the authors attempt to formally define a path in the context of marketing. Their main objective is to propose a “unifying framework” that will facilitate further research on the topic. This is accomplished while showcasing the importance of path data in the future of marketing research.

The authors define paths as “records of consumer movements in a spatial configuration” or as “a conscious agent’s movement in a physical or simulated environment that is observable”.

As part of the suggested framework, two primary dimensions are identified: “characteristics of spatial configuration” and “the agent”. Specific characteristics of the actual spatial configuration, either if it is physical or nonphysical, continuous or discrete, as well as the existence of constrains, will influence the model and the approach of the analysis. Defining agent factors such as the level of (1) “social interaction”, (2) ”goal-directedness” and (3) “forward-looking-behavior” should also be considered.

Based on literature review, references to relevant related research sources are introduced. Several examples in the context of “retail/service environments”, “advertising studies”, “e-commerce”, “experimental research” and areas outside marketing are also considered. In the next section important operational issues, considerations and restrictions for researchers working with any form of marketing path are identified.

Finally, the authors conclude on potential directions of future research in the field.

Review

There are several the reasons why this article can appeal to web analysts, solution vendors and online marketing researchers:

The unified vision of marketing based paths proposed is interesting by its own nature. Including traditional and digital marketing scenarios on the same bucket can be useful to validate and generate awareness on the web analytics practices and the online marketing channel. Being aware of these similarities can help analysts to better communicate their ideas within the organization. 
Taking a look at the differences and similarities among heterogenous marketing scenarios can be useful for any practitioner with previous marketing experience who is looking to better understand the online channel.
The article also opens the door to consider the study of path related data “in conjunction with other data sources” and external information to gain deeper understanding of consumer behavior. Studying path data across different channels, different websites and path types could be one of the future developments in this field. 
The path data collected at the moment is under-exploited. Advocates of this practice insist on the potential opportunity for research and business optimization. One of the main goals of this article is to promote and facilitate further path analysis research. In this context several areas of the web analytics practice, such as behavioral targeting, conversion analysis, website morphing, predictive analytics or A/B or split testing (testing scenarios, ideas for segmentation, results interpretation) can benefit. 
This article can also provide valuable input for web analytics vendors and may help to design better ways to collect, analyze and present path related data.

The “commonalities across domains are rarely recognized and appreciated”, the authors state. A unified framework is proposed as a solution to this misunderstanding. If we assume that web analytics is not a closed and isolated system, we would be able to recognize synergies from other disciplines that at first sight can look completely unrelated. “Borrowing tools” from other domains could in some cases make the difference.

Diverse backgrounds, concerns, areas of expertise and experience can always throw a different light on a subject. In this respect, it is interesting to point out how one of the key defining sub-dimensions for an agent, the level of social interaction, is approached for a web browsing experience.

For environments like retail the influence of peer interaction modifying the shopper (agent) behavior is evident. It can encourage or discourage the purchase. However, web browsing is considered by the authors an activity where “little interaction among agents” happens. Not enough interactivity to even make it part of the model.

This can be true if we assume that web browsing experience happens in an isolated and deterministic context. Is this entirely true? I wonder how realistic this assumption becomes in an environment where the interaction with peers through social media, use of mobile devices or similar activities is dramatically increasing. It may be time to review the model for web path analysis to incorporate more social interactions. On the other hand, perhaps the influence of this new factor is minimal or totally irrelevant to the model.

In any case this article brings to the table interesting “thought provoking metaphors” and invites you to think outside the commonly accepted boundaries on how to extract more insights from path analysis environments.

A single copy of the full journal reviewed above is available to members of the Web Analytics Association. To request a copy, email Lindsay De Santis.

14-Jul-09 9:00 AM

Marketing Science and the Financial Crisis

noemail@webanalyticsassociation.org — Tue, 07 Jul 2009 16:00:00 GMT

Bradlow, Eric T., (2009). Marketing Science and the Financial Crisis. Marketing Science, Vol 28. Page 201.

Reviewed by Jason Dong, July 2009

Executive Summary:

Eric Bradlow from The Wharton School submits that despite the state of the economy, the journal Marketing Science has actually experienced a surge in number of articles submitted and a decrease in turnaround time for acceptance.

This segues nicely into his editorial to encourage the research and thinking in the field (rather than the journal) of Marketing Science. In addition, he names a number of issues where the intersection of the discipline and the financial crisis could make for very interesting and relevant research and findings. These include Customer Lifetime Value, Structural (business and marketing) Models, Choice Modeling, “No Choice” option, and whether past lessons on Marketing Strategy are outdated.

He concludes by stating that given the worldwide economic turmoil, now is not the time for Marketing Science (the discipline and the journal itself) to sit idle. He believes that the current market condition has provided a glorious opportunity for the discipline to raise its profile as it is very involved in studying how consumers optimize, act, and think during economically challenging times.

Review:

Although brief, this editorial’s message is clear: The current market conditions are a time for lesser known, valuable marketing sub-disciplines to come to the forefront. With advertising and marketing budgets being watched more closely, the focus on optimization and frugalness becomes more imperative. Thus, it makes sense for fields like marketing science and I would argue, web analytics to become more prominent and visible. Both require multidisciplinary thinking and are highly relevant to marketing optimization.

There are certainly components in each of the potential research problems and paradigms that Bradlow mentions for Marketing Science (the journal) that web analytics and online marketing also play a role in. Bradlow mentions Customer Lifetime Value, the utility function of the corporation, and choice modeling. The web analyst can investigate, solve, and write about similar problems with analytics using metrics and data such as attrition rates, unique/repeat/single visits, revenue per visit, and customer registration funnels.

He also sees “No choice option”, and potential obsolescence of learned marketing strategy as research problems. The web analyst sees path analyses, fallout reports, and hypothesis testing results for optimization.

Regardless of the potential research problems and areas that marketing science (the discipline) covers, the message that Bradlow tries to convey has equal bearing and importance to web analytics during tough economic times: Seize this opportunity to show the world the enormous value that the qualitative side of marketing brings to the bottom line.

I recommend this article to all web analytics practitioners and I challenge you to go through a similar exercise that Bradlow did and think about web analytic-specific research problems and the effect that more WA literature will have in both academia and marketing in general.

A single copy of the full journal reviewed above is available to members of the Web Analytics Association. To request a copy, email .

7-Jul-09 12:00 PM

Does your team spend more time using the web analytics solution than making optimizations?

noemail@webanalyticsassociation.org — Fri, 12 Jun 2009 02:00:00 GMT

Six months ago, your organization decided to go for a sophisticated web analytics solution. The implementation was carried out under the supervision of a product consulting team. The Product vendor suggested your organization to send their employees for 1-week training. Your management decided to send 1 representative from each department i.e. Online Marketing, Merchandising, E-Commerce and Operations. After going through a grilling 1-week of training, your team comes back to their individual departments with 1 question in their mind i.e. What's Next?

While, your management is thinking about, how to create a culture in the organization where, more and more employees participate in creating opportunities for optimization, and help us in achieving a 10-15% increase in the site revenue.

The team starts using the tool based on the training provided by the vendor but, despite of spending 3-4 Hrs/Week for 3 months, no significant lift in the traffic and revenue was observed. The management is confused and frustrated with the tool capabilities but, not focusing on important aspect i.e. how the tool is being utilized by individual departments?

Here are certain questions; I would recommend management should ask their individual departments?

What % of time your employees spend in observing the basic clickstream metrics i.e. Visits, Unique Visitors, Revenue, Orders, AOV, Basket Size, Average Selling Price etc.?
What % of their time they spend in reconciliation between in-house transactional system and the web analytics solution?
What % of their time they spend in reading and interpreting the data?

You will be surprised to know that, more than 90% of your team time gets spend towards above activities instead of focusing on following areas, which have a potential to create opportunities for optimization for e.g.

Try to understand the Pathing Behavior of your online visitors.
Keep an eye on the return frequency of your online visitors.
Check the bounce rate on your key landing pages.
Look at the navigational behavior of your campaign visitors and segment it based on different channels so that; you could smartly optimize your marketing budget
Measure the value of your key landing pages and check their contribution in the conversion process
Calculate the lifetime value of your paid search keywords and check the trend month over month. May be you are wasting too much money on your paid search channel and all the credit is getting cannibalized by your super affiliates or organic channel
Your marketing initiatives may not be driving the incremental sales month over month but, they might be contributing to the store sales. So; keep an eye on what % of your online traffic is engaging with the store locator?
Check the % of Null Searches happening on the site and identify the search keywords and the referring pages.
Is your team spending the entire focus on a 1-2% of your online visitors who are entering in to the conversion funnel rather than, understanding the behavior of remaining 97% visitors, who are not even reaching your shopping cart? Is there is a mismatch between your visitor objectives and the merchandising strategy?

As always, I appreciate your comments, feedback and ideas for the new post. Please let me know, how your team is spending their time using the tool?

11-Jun-09 10:00 PM

Website Morphing

noemail@webanalyticsassociation.org — Tue, 09 Jun 2009 13:00:00 GMT

Hauser, John R., Urban, Glen L., Liberali, Guilherme., Braun, Michael, (2009) Website Morphing. Marketing Science, Vol 28, pp. 202-223. 21 pages.

Reviewed by Christopher Berry, May 2009

Executive Summary:

The authors define website morphing as changing “the website automatically by matching characteristics to customers’ cognitive styles”. They define a cognitive style as “a person’s preferred way of gathering, processing, and evaluating information”. They note four technical challenges, namely, “(1) For first-time visitors, a website must morph based on relatively few clicks’ otherwise, the customer sees little benefit. (2) Even if we knew a customer’s cognitive style, the website must learn which characteristics are best for which customers (in terms of sales or profit). (3) To be practical, a system needs prior distributions on parameters. (4) Implementation requires a real-time working system (and the inherently difficult web programming.” They proceed to solve these problems using their preferred mathematical solution.

Of particular interest is the classification of clicks based on area of the page: content area, content purpose, and navigation – and the use of this data in real-time.

They also launch into a case study by British Telecom (BT) and the math behind it, namely Gittins and Bayesian loops, concluding “The Bayesian updating enables customers to reveal their cognitive styles through their clickstreams. Together, the Gittins and Bayesian loops automate morphing (after a priming study).” The authors estimate that if their method were implemented system wide for BT, the increases in conversion would “represent approximately $80 million in additional revenue”.

In sum, they describe a process for automatically modifying the look and feel of a website based on performance and cognitive styles.

Review:

The paper is enormously important for web analytics practitioners for at least four reasons.

The first is that it provides an example of web analytics being used in real-time to improve the user experience. Incremental improvements to a website can be very challenging to execute in many medium to large organizations. The ability to set meta-meta rules for experiences and to incrementally adjust those rules based on the analytics opens a new dimension of applicability for web analytics.

The second is that finally, perhaps, the Internet Channel can live up to some of the initial 1995 hype. Supposedly, we were supposed to be able to do what Direct Mail marketers have been able to do for decades – careful segmentation and experiential differentiation. So far, many (if not most) websites, serve up one-size-fits-all experiences, or, we differentiate using very early forms of personalization. The underlining theme of morphing is to adjust the experience to the manner in which a user thinks.

The third is that web analytics, as it is currently practiced, does not rely on many statistical methods. Not all variants of website morphing will make use of the authors preferred course of Gittins plus Bayesian loops. Web analytics, as a practice, will face the dual pressures of having to acquire a new skill set and simultaneously explain the methods to non-practitioners.

Initially, I believed that website morphing would pose a direct threat to hosted vendors. While retaining hosted analytics solutions in addition to a morphing analytical system might be psychologically unsatisfying from a management perspective (“why can’t one system do it all?”), page view level analytics, even without morphing information embedded therein, can still be of some value to the organization. It can holistically report how, in the past, the website has performed.

Morphing represents an additional layer of data - a complication. Many tools do not take into consideration the effect of an A/B or a multivariate test over a specific period of time even within the same vendor toolset, so the deficiency and complication is already present. If there are 16 cognitive profiles and 16 matching morphs, with a single A/B test therein, there would be 32 unique experiences to measure. Third party analytics could tell the organization how the website morph is performing in aggregate, but significant changes to the existing pageview paradigm would need to be made for the third party provider to offer data with respect to why which morph, and which version of the morph, performed better.

Fourth, website morphing will require an initial set of inputs that will be heavily, though not exclusively, informed by web analytics derived insights. The preparation of these customer profiles and the pre-populating of experiences will represent an enormous opportunity for web analysts to shine in an interdisciplinary process. Web analysts risk a loss of leadership if they neglect the opportunity or do not seek a major ownership stake in the technology.

In sum, the paper is important, and I would recommend a read by web analytics practitioners and managers.

A single copy of the full journal reviewed above is available to members of the Web Analytics Association. To request a copy, email .

9-Jun-09 9:00 AM

WAA Public Sector Survey Analysis

noemail@webanalyticsassociation.org — Thu, 04 Jun 2009 19:00:00 GMT

The WAA Public Sector SIG conducted a literature search to determine the state of public sector analytics, and then conducted a survey of 124 participants from four types of public organizations: government (including both U.S. - and non-U.S. government agencies), non-profit, academic, and foundation. The survey respondents included WAA and non-WAA members. The research team conducted a follow-up phone survey to obtain additional details about survey respondents.

In the interest of establishing a baseline set of data that could serve as a foundation for research, the WAAPS-SIG divided the study into three questions:

How are Web site managers in the public and non-profit sectors measuring the performance of their Web sites?
Can any KPIs used by many non-commerce organizations be used to measure the impact, effectiveness, and contributions of all non-commerce Web sites?
Can we develop a series of benchmarks for the key dimensions of visitor online interaction with the Web sites in the public and non-profit sectors?

Members, you can download a copy of this analysis, non-members, you can purchase this document for $50 until July 11, 2010.

4-Jun-09 3:00 PM

Do Paid Search Campaigns Generate Repeat Purchases?

noemail@webanalyticsassociation.org — Thu, 14 May 2009 05:00:00 GMT

Customer Acquisition and Customer Retention are the two most important terms that retailers and brands talk about in their monthly, quarterly and yearly management meetings. The outcome of these meetings often generates tremendous pressure on the Agencies to optimize their marketing budget across different campaigns & channels, explore new channels for customer acquisition and raise the switching cost for the existing customers by building brand loyalty and trust.

As Pete Blackshaw (EVP, Nielson) has also mentioned in his recent hardcover, Satisfied Customers Tell Three Friends, Angry Customers Tell 3,000. So; it's very important for the retailers and brands to understand the very first conversion experience of the customer, and plan out their retention strategy based on that because, every touchpoint of the customer has an objective behind it, and when you correlate that information with the channel they came from, it makes the picture clearer.

To provide some more context around this, let's take an example:

Four months ago, I was searching for a Book reader on Google and saw a PPC AD for Amazon Kindle. I clicked on the AD and got redirected to the KINDLE Product Page. I Read the Product Description, Looked at the Product Demo and finally, went through 25 Reviews from the existing customers. After reading the reviews, I decided to place an order. On the Order Confirmation page, I saw a Delivery Window of 45 Days for my merchandise, and it really freaked me out but still, I decided to place an order considering the value proposition.

Right after the order is placed, I received an Order Confirmation Email within 15 minutes stating that, my order has accepted.

After 20 days, I received an email from Amazon that, they have upgraded my order to Kindle 2, and asked me for my permission to be a BETA Customer. After looking at the DEMO and features of Kindle 2, I decided to be a BETA Customer. This email raised my expectation and desire to own the product ASAP.

After 10 days, I received the product and I was pretty happy because, I was expecting it atleast after 45 days but, they delivered with in 30 days.

The day I got the Shipment of my merchandise, I received a CROSS SELL Email from AMAZON, recommending me to buy 3 accessories, e.g. a Leather Cover, an extra battery and the New Best Sellers. This really made me excited and amazed because, when I opened my Kindle Box, I got nervous and concerned about, how would I carry this product and maintain it because, it's WHITE and so Delicate. I was thinking about going to the AMAZON and looking for a cover but, they knew it what would be my first reaction after opening the box, and they just hit the nail at the right spot.

I opened the email, clicked on the Leather Cover and placed an order and you know what, I got an extra 10% off because of an Order Level Promotion running on the site for the repeat purchasers. This is great customer experience!

Now, if we look at this story from the online marketer perspective, it was a wonderful execution of Search Engine and Email Marketing strategy. The online marketer bid for the "Book Reader" keyword and assigned this keyword to the "Sony Reader" and "Amazon Kindle Reader" AD Groups. Customer clicked on the Amazon Kindle AD, which generated a Clickthrough data against that Keyword. When the customer converted, it gave marketer the conversion data against that Keyword, Campaign and AD Group.

There was a latency period of 30 days between the first and second purchase activity. It could have been a week if, the delivery window would be 3-7 days. This is a big opportunity for optimization for Amazon, and I am sure they would be working in this direction.

The second conversion activity was driven by a well targeted, personalized and timely email campaign execution.

Here is a question for the Online Marketers: Should the credit of the Kindle Cover be allocated to the "book reader" keyword or not? If yes, what should be the decent latency period between 2 touchpoints and if not, what percent of your paid search visitors converts in the single session?

Here is a million dollar question for the analytics consultants, how do you measure this activity in your analytics solution?

Based on my experience with Google, Coremetrics and Omniture, none of these tools have answered this question well. Even though Omniture provides a lot of flexibility in regards to setting up the allocation and expiry values against your tracking codes, that's still not enough. Recently, Omniture has come up an enhancement to their Cross Visit Participation plug-in which offers 3 new metrics – Cross Visit Participation Orders, Cross Visit Participation Revenue and Cross Visit Participation Assists at the Campaign Level.

About Sushant Ajmani

I am still in discussion with the Omniture Engineering Services regarding measuring latent purchases, so I will explain the reporting piece in the next post on my personal Blog.

As always, I appreciate your feedback and comments and ideas for the next post. If you want to check my previous posts, please visit my blog http://conversionpath.typepad.com/.

14-May-09 1:00 AM

Do you know the Lifetime Value of your Paid Search keywords?

noemail@webanalyticsassociation.org — Sat, 09 May 2009 20:00:00 GMT

Over the last 18 months, lots of retailers have cut down their online marketing budget, and forced their marketing agencies to revisit their customer acquisition strategy, and focus on 2 things i.e. Optimization of Marketing Spend across traditional marketing channels, and Explore New and Efficient channels, which creates brand awareness and promote collaboration and repeat purchase behavior.

Inspite of the fact that, the Social Networks like Facebook and Tweeter have evolved tremendously over the last 12 months, traditional marketing channels such as Paid Search, Email, Affiliates and Comparison Engine still gets the major share of the marketing budget, and among these traditional channels, Paid Search still gets the major attention considering the shopping behavior of the online visitors, which often starts with the major search engines such as Google, Yahoo and MSN.

Considering the value proposition of the Paid Search Channel and their contribution to the bottomline, it's a pretty tough job for the Online Marketers to revisit their Paid Search strategy and optimize various Campaigns and AD Groups making sure, it doesn't impact the current ROI. To assist my Online Marketer friends in this optimization endeavor, I am sharing some of my thoughts which might help them in cutting down their marketing spend against inefficient and poor performing keywords, and divert that savings towards exploring new channels for customer acquisition.

Just like an Online Customer, every keyword that the Online Marketer bid on the major search engines has a Lifetime Value associated to it. Depending on, what categories your search keywords fall, the Lifetime Value varies for each keyword. If you are "Major Brand" and bidding for the Brand specific keywords, your keywords might have a longer Lifetime Value but, if you are a "Category Seller", bidding for the General, Category or Style specific keywords, which generally have a pretty higher CPC rate, and a pretty high cannibalization cost, these keywords might have a lower Lifetime Value for your organization. The reason for that is, the competitive landcape is becoming cluttered, aggressive and brutal. Initially, an online retailer was competing with other online retailers but now, the Comparison Engines and even your Super Affiliates are in the game too, and bidding for the same keywords to earn better commission. It has not only make the sales cycle longer but, on the other hand diverted a % of qualified traffic to the competitors

Now the question is, how to determine the Lifetime Value of your Paid Search Keywords and Pull the Plug once the Juice stops coming?

The definition of Lifetime Value here is, what is the Net Present Value of the Profit that you will realize on the average keywords in the coming months.

For the ease of analysis, let's pick the Top 5 keywords for the current month and sort them in a descending order based on their current month Clickthrough Rate (Clicks/Impressions). Create a Trend Report for last 6 months and calculate the % drop or appreciation in the CTR for these 5 keywords.

Filter out those keywords which are showing a constant drop in the CTR % month over month. Calculate the Avg. Revenue per Click using your Web Analytics Solution and compare this number with the Cost Per Click you paid to the Search Engines for these keywords in the last 6 months. The reason we are doing this comparison because, if your Paid Search AD has a Poor Clickthrough Rate and the quality of your Landing Page content is poor, your CPC will increase overtime and that might drain all your marketing revenue to Google's Pocket.

Take these keywords and search for them on Google to see, what ADs are competing with yours. You will be surprised to find that, among your competitors, your channels partners are also bidding for the same keywords and some of them might be your Super Affiliates.

If on an average 10% decline in the CTR happening since last 6 months, and the "Avg. Revenue/Click" is lower than the Keyword CPC, then you should stop bidding for these keywords because, they are just 4-5 months away from their demise, and it's better you save the money from going in to drain.

Repeat the same exercise for those keywords which are showing an appreciation in the CTR since last 6 months but, have a poor "Avg Revenue/Click". For these keywords, check the Pathing Behavior of the visitors after landing on the site, and determine the major Exit Points. Compare these Exit Points with the Objectives of your Paid Search Campaigns and ask yourself a question, are my campaigns in sync with the visitor objectives? and if not, then bring them in sync before wasting more money.

After all, we don't want unqualified traffic on our site. We want our visitors to enter the conversion funnel.

Remember, the defintion of conversion can vary from retailer to retailer. For your site it might be Order Conversion but for others, it might be Catalog Request, Newsletter Signup or may be a Store Locator Search.

The above scenario doesn't include 2 key aspects of online world i.e. the Return Frequency of the online visitor, and the Latency of the Paid Search Channel. It will be incorrect to assume that, every visitor who comes from the major search engines on your site converts in the same session because, Paid Search Channels have an average 30 Day Latency, and if we include that in the above example you will be surprised to find that, your other marketing channels might be cannibalizing your paid search credit and leaving you with higher CTR and a Poor Conversion. In the next post, we will talk about the Latency of the Channels and how to allocate credit of the purchase to the right channel?

As always, I welcome your comments and feedback and ideas for future blog posts.

9-May-09 4:00 PM

Review: The Web Analytics Report 2009

noemail@webanalyticsassociation.org — Tue, 05 May 2009 11:00:00 GMT

The bottom line, quite frankly, is to know if, The Web Analytics Report 2009 report should be purchased? Is it worth the time to absorb the content or the cost? Due to the sheer amount of research and information different types of readers can glean, then the short answer is yes.

This report is for the project professionals and the purse string guardians...

Many who will hear about this report will already be web analytics professionals, but the intended audience not only includes them but goes beyond as there is a larger business environment to consider. This report is for the project professionals and the purse string guardians where implied information on time lines, dependencies and costs are useful. It’s for the curious, the executive types and for those in the implementation trenches where they can find great vendor comparisons, overall education and concepts worth knowing.

This report, like it’s subject matter, is a useful and potentially sophisticated tool in and of itself. What may push this report into the must-have pile is the attempt to compare vendors and tools to non-biased industry standards as proposed by the Web Analytics Association (WAA). There’s some good work here that can only help the education and the implementation practice as a whole and the discovery of industry trends.

It’s worth having a better vertical and horizontal view of the subject; especially if the reader gains better knowledge of what her peers are up to.

Perhaps 400+ pages seems like a lot in its entirety but there are two important concepts to keep in mind. First, the introduction directs readers to their proper sections based off their needs. That’s great if you only have a moment and need to satisfy your niche concerns. The second, is that (in my opinion) reports are a faster read than a like paged, popular novel. While that may only be my own bias, my recommendation is to not be intimidated by page count alone. Take a look through the whole report after you’ve read your recommended section. It’s worth having a better vertical and horizontal view of the subject; especially if the reader gains better knowledge of what his peers are up to. There’s a greater potential for coordinating efforts and uncovering many of the moving pieces in the multi-faceted world of web analytics.

The next few lines will hint at what’s inside and specifically why the information is valuable. There’s:

Part 1 – How to Use This Report,
Part 2 – What is Web Analytics?,
Part 3 – Business Case for Web Analytics,
Part 4 – Web Analytics Technology and Features,
Part 5 – The Web Initiative: Purchasing and Implementation,
Part 6 – Web Analytics Software Vendors
and finally the appendices.

Throughout the report there is some obvious effort to educate the reader about web analytics tasks at hand in addition to the industry trends as a whole.

On a side note: The reader needs to be aware of the industry changes currently underway. There is a marked increase in the types of technologies and how they perform on the web. There’s also a rising comfort level with business professionals and our online customers as they are increasingly savvy when it comes to the web, doing business online or having fun.

Under this reality of ever increasing web usage and the blind necessity to try and monetize absolutely everything whether it makes sense or not, the web analytics vendors are rightfully pushing boldly into new areas. Some of these newer areas (while far from perfect) include the merging of divergent online/offline information and processes – essentially trying to create the single source, uber-marketing utopia.

At its core the report successfully positions itself as the foundation source material for educating both stakeholders and decision makers.

Regardless of all the tools and their flavors, working in this environment does get exceedingly complicated exceptionally fast. As in many things, but especially for these costly, politically charged web projects where the web is still the shiny toy and gets lots of attention, the buyer must be aware and be able to look past the hype and shiny brochures with a level head. The report does a decent job planting the seed for critical thinking into our collective heads.

This report is a great first step towards education. Perhaps I could have begun and ended this review with that statement? It is also an excellent resource for allowing one professional to peer over the fence at his peers, peek at the vendors and gain some knowledge of the web analytics industry as a whole. At its core the report successfully positions itself as the foundation source material for educating both stakeholders and decision makers. It arms them with good information and critical arguments to pass up and down the chain. This report should sit on any of the decision maker’s desks as a reference and as proof of her due diligence. The ultimate question seems to be “how” and “when” web analytics initiatives will be carried out…not “if”. Prepare yourself.

CMS Watch The Web Analytics Report 2009 is available at: http://www.cmswatch.com/Analytics/Report/. WAA Members get a 10% discount for the initial purchase, and 50% off subsequent years.

Feedback

The Web Analytics Report 2009 feedback is encouraged at this email: editor@cmswatch.com.

5-May-09 7:00 AM

Do the first 3 touchpoints make all the difference?

noemail@webanalyticsassociation.org — Sun, 26 Apr 2009 14:00:00 GMT

Few years ago, I learned an important fact about the online retail industry and that was, on average, every online customer visits a retailer site at least 2-3 times before he goes for a final conversion. However, the definition of conversion can vary from retailer to retailer depending on their business model, online/offline store presence, type of merchandise they deal in, competitive landscape, online marketing mix and on top of that, what their target customer segment is and their objectives?

Recently, I unsurfaced some really interesting facts while doing an analysis for one of my client, and I thought it would be better to share these facts with other members in this association and listen their perspective.

20% of the monthly revenue comes from the first touchpoint visitors.
The next 30% comes from second touchpoint visitors.
The next 28% comes from third touchpoint visitors.

If we aggregate the above numbers you will notice that, the first 3 touchpoints of the visitor makes all the difference, and if the retailer is not able to assist the visitor in his conversion process, then he might lose this visitor to his competitor.

The above % can vary from retailer to retailer depending on the marketing mix, and how the retailer hand hold the visitor from one marketing channel to another until the customer converts. Now the question is, what marketing channels makes the most difference and what should be the recency between the first 3 touchpoints to generate the above results? To learn more about this, check the second part of this article.

26-Apr-09 10:00 AM

The Market Valuation of Internet Channel Additions

noemail@webanalyticsassociation.org — Tue, 14 Apr 2009 00:00:00 GMT

Geyskens, Inge., Gielens, Katrijn., Dekimpe, Marnik G. (2002). The Market Valuation of Internet Channel Additions. Journal of Marketing, Vol 66., pp. 102-119.. 18 pages.

Reviewed by Christopher Berry, April 2009

Executive Summary:

The authors focus on how the stock market responds when a company adds the Internet to its marketing mix. They note demand issues: demand and price level, and supply issues: physical distribution costs and transaction costs, and ask if the net effect is performance enhancing or performance destroying.

The authors build a framework around the firm (channel power, intensity of experience, scope of experience, firm size), introduction strategy (order of entry, publicity) and marketplace (product-demand growth, channel-demand growth). They train their framework on European newspapers, citing that it “...offers an interesting setting in which to apply our framework. First, it represents a mature, old-economy industry that faces rising costs, falling revenues and increasing retail power.”

They found that the stocks of newspapers that added an Internet channel went up marginally after an Internet channel launch. They conclude “Adding an Internet channel to an entrenched channel system is a double-edged strategy”. They argue that the addition saps goodwill from entrenched channels, that it isn’t likely to generate demand but instead “may cause cannibalization and/or brand-damaging interchannel conflict”. They also find “that firms of any size can successfully enter the playing field”, causing firm size to be less of an issue, but that “firms should indeed be fast” in deploying to the channel.

Review:

The article is interesting because much has been written lately on the destruction of newspapers by the Internet.

This general framework, in making the decision to expand to the Internet, is useful. While many companies have websites, many of them are still, strictly speaking, brochureware or non-transactional catalogs. The decision to add real commerce and transactional functionality is not one to take lightly. Web analytics, used to evaluate the existing performance of a web presence, is a valuable input into that decision - but it is just one factor among many. Carefully considering demand, price, physical distribution costs and transaction costs are a very good start.

This article is not recommended for web analytics practitioners who are not actively engaged in evaluating Internet channel expansions.

A single copy of the full journal reviewed above is available to members of the Web Analytics Association. To request a copy, email .

13-Apr-09 8:00 PM

Web Analytics Association Articles RSS Feed

Dynamic Customer Management and the Value of One-to-One Marketing

Executive Summary:

Review:

Why is this important to you?

Impact of Different Promotions

Related Readings

The Effectiveness of Combining Online and Print Advertisements: Is the Whole Better than the Individual Parts?

Executive Summary:

Review:

Viewpoint: Now or Never – An Urgent Call to Action for Consensus On New Media Metrics

Executive Summary:

Review:

Assumptions, Explanation, and Prediction in Marketing Science: “It’s the Findings, Stupid, Not the Assumptions"

Executive Summary:

Review:

Implications for Practitioners

Are Your Business Goals Aligned with Insights drawn from Your Web Analytics?

Basic Metrics

Advanced Analysis

The Most Important Analytics Reference Document: Definitions

Variables

Definitions

Variable System Names

Putting It Together

Modeling the Determinants and Effects of Creativity in Advertising

Executive Summary:

Review:

Firm-Created Word-of-Mouth Communication: Evidence from a Field Test

Executive Summary:

Review:

September 2009 Featured Article: Unica

Using web analytics to complete the circle of Life Time Value, not a replacement.

Can Using Real-Time, Raw Data Be Cost Effective? Opening Up the Discussion

About Cecily Robyn Lough

Path Data in Marketing: An Integrative Framework

Marketing Science and the Financial Crisis

Executive Summary:

Review:

Does your team spend more time using the web analytics solution than making optimizations?

Website Morphing

Executive Summary:

Review:

WAA Public Sector Survey Analysis

Do Paid Search Campaigns Generate Repeat Purchases?

About Sushant Ajmani

Do you know the Lifetime Value of your Paid Search keywords?

Review: The Web Analytics Report 2009

Feedback

Do the first 3 touchpoints make all the difference?

The Market Valuation of Internet Channel Additions

Executive Summary:

Review: