2010 Nov 01

New Techniques for Marketing Measurement
David M. Raab
Information Management
November / December 2010

It’s fitting that the most famous quote about marketing measurement – “Half the money I spend on advertising is wasted. The trouble is, order I don’t know which half.” — is itself unreliable: although it’s typically attributed to John Wanamaker, pilule there is no definitive citation.(1) Nor has much changed since Wanamaker’s time: to paraphrase what Mark Twain (2) didn’t say about the weather, everybody still talks about marketing measurement but few do anything about it.

The problem isn’t lack of data. Marketers today can track the messages received and actions taken by their customers in more detail than Wanamaker could imagine. It’s true that the information is still not complete or fully accurate, but even perfect information would not yield the answer. The problem is fundamental: even if we knew everything that happened to a customer and everything that the customer did, we couldn’t calculate the how individual events affected the final result. All we see is what a particular set of inputs will produce in aggregate. It’s like following a recipe in a cook book: you know what the combination of ingredients will produce, but not how each one contributes.

Measuring the impact of individual marketing programs requires breaking apart the combination of inputs and testing the effect of changing them individually. This is why even complete data cannot solve the measurement problem. Only tests allow marketers to measure the incremental impact of specific activities.

There’s nothing new about this insight. But, with the glaring exception of direct marketing, testing has traditionally been expensive and inexact. As a result, marketers limited testing to a handful of major issues and sought additional insights from statistical correlations such as marketing mix models. But mix models are effective only in industries with very large customer bases and marketing budgets, and even there they can only measure broad effects.

In today’s data-rich environment, testing is much easier. Marketers can more often build databases of their customers and track many of the messages those customers see and respond to. Even formerly anonymous media such as broadcast and print advertising often drive consumers to trackable behaviors on the Web.

But tests face another obstacle: the time lag between many marketing contacts and a customer’s eventual purchase. Although revenue is the ultimate measure of marketing success, many marketing efforts occur early in the purchase process. Waiting until a purchase is completed before measuring their results introduces a lengthy delay. It also allows time for intervening events that could affect the final outcome. For both these reasons, marketers need a different measure they can read more quickly.

Much of the current innovation in marketing measurement is directed as finding such measures. The general approach is to divide the buying process into stages and to measure the impact of marketing efforts on moving prospects from one stage to the next. This is similar to the sales funnel or pipeline traditionally monitored by sales automation systems, except that that it begins earlier in the purchase process. It’s also similar to psychological models such as AIDA (awareness, interest, desire, action), which are often used to guide brand advertising.

The difference from past psychological models is that marketers are now tracking the status of individual consumers. This is what today’s new data sources make possible. In the past, companies rarely knew more about non-customers than basic demographics (age, income, gender, location). But these can’t track movement through purchase stages. Movement must be inferred from fresh information, whether provided directly through survey responses or inferred from behaviors such as Web site visits. Behavior is a much richer source because there’s more of it and it’s not limited by what prospects choose to provide.

In sum, then, marketers are finding they can greatly improve marketing measurement by using behavior to track consumers through stages in the purchase process, and using tests to correlate changes in this movement with changes in marketing programs. This creates several technical requirements, including:

– identifying individuals consistently over time and across channels, so you can build a database of their behaviors and marketing contacts. This database provides the input for tracking their movement through the purchase stages.

– data mining and analytical tools that uncover patterns, such as x Web visits within the past Y days, which indicate a prospect’s location in the purchase process.

– scoring systems that can apply complex patterns and other information to identify the current stage of thousands or millions of prospects on a continuous basis and to identify movement from one stage to the next. (In practice, movement does not always follow a fixed sequence: some prospects will stagnate, move backwards or skip ahead several stages at once.)

– tagging of content by stage, to help the scoring system. While the volume of behavior is important, the specific information consumed gives much more insight into a prospect’s current state of mind. Three visits to the white paper library means they’re just doing some research, while three visits to the contract terms means they’re seriously considering a purchase. Although content tagging can be major task, the tags are also needed to help select the right message for each individual. So the measurement system should be able to use tags that are already in place.

– maintaining a history of previous statuses, since they cannot be reconstructed using only current information. This is a classic “slowly changing dimension” in data warehouse terms.

Converting this data into actual measurements adds still more requirements:

– testing features including random selection, tagging of test group members, and test/control reports.

– stage movement measures such as average time per stage and continuation rates from one stage to the next.

– correlation of marketing contacts with prospect stages. This is used to calculate the marketing cost per stage and to show the impact of different contacts on stage progression

– integration of purchase history with prospect profiles, needed for revenue-related calculations such as return on investment.

– projections of future revenues from the current prospect pool. This uses the number of prospects in each stage, the expected continuation rates from one stage to the next, and the expected value of ultimate purchases. It is used to forecast business results to estimate the financial impact of differences revealed by tests.

Few of these capabilities are wholly new to marketing systems. Most are required for other purposes such as offer selection and segmentation. But making them available for measurement requires exposing them in different ways, so marketers and IT staff still need to ensure they are available for measurement projects.

The advantages of stage-based measurement make it worth the trouble. Breaking the buying process into stages lets marketers understand what’s driving results, identify problem areas and find opportunities for improvement. It lets them measure the true incremental value of individual marketing contacts, replacing arbitrary “revenue attribution” on the first or last touch or simplistic fractional weighting. Perhaps most important, stage-based measurement provides near-immediate feedback, allowing marketers to quickly reallocate resources to the most productive programs.

Stage-based measurement isn’t a complete solution to all marketing measurement problems. It has a short-term, incremental focus that may not capture the deeper value of branding programs. It measures each marketing program in isolation, making it difficult to assess interactions among several programs. And it often relies on the assumption that changes at one stage in the buying process ripple through to the end more or less undiminished. This last assumption is particularly dangerous, because the opposite is often true: positive changes at one stage often have negative consequence later on. (For example, a free introductory offer may attract more orders but fewer conversions to paid customers.) Sophisticated marketers will be aware of these issues and compensate for them. They are a reasonable price to pay for finally knowing which half of your marketing budget is wasted.

1. The Quote Verifier: Who said what, where, and when, Ralph Keyes, New York, NY: St. Martin’s Press, 2006, pg 2
2. It was more likely Charles Dudley Warner. See http://quoteinvestigator.com/2010/04/23/everybody-talks-about-the-weather/

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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics. He can be reached at draab@raabassociates.com.

2010 Jul 01

Autonomous Marketing Messages
David M. Raab
Information Management
July / August 2010

Here’s a metaphysical puzzle: can you send a marketing message before it’s created?  The answer used to be no: messages remained unchanged after they were sent, salve either because they were broadcast and vanished immediately or because they were physically persistent but inert, like a printed catalog or recorded TV ad.  Even messages that were dynamically tailored to a specific individual and situation were rendered and then frozen before they were sent.  A Web site might adjust its offers over time, but each offer was itself fixed.

Because marketers knew the contents of each message when they sent it, the only subsequent information they needed was who received it and how they reacted.  Indeed, most marketing measurement boils down to answering those two exceedingly difficult questions.

But marketers today face an added challenge: capturing the message itself.  Paradox notwithstanding, an increasing number of messages can now change after they’re created.  Consider:

– Adobe’s latest design software, CS5, can create ads that send different messages to different individuals and record the results.  Specifically, Web designers can embed the testing, segmentation and automated optimization of Adobe’s recently-acquired Omniture Web analytics system.  The solution relies on the Omniture server to execute tests and store results.  But the next logical step is to embed test logic, tracking and automated self-optimization within ad itself so it can function when a server connection is unavailable.  This would result in a truly autonomous marketing message.

– Vendors including smartFocus, Genius.com and Genoo have extended social media sharing to tag each item with the ID of the individual who shared it, so they can be credited as the source of later visits by recipients.  In other words, if Jane posts a link to this article on Twitter, marketers will later know not just which visitors came from Twitter, but also which came from Jane’s Twitter post.  This lets them measure how much traffic Jane generates and identify the members of Jane’s social network.  In effect, the original message is being modified by adding the identity of each sharer, which must then be captured with responses.

– Barcodes on products and advertisements are being linked via mobile phone applications to Web sites that vary their content based on location.  Here, the original message is being enhanced with the viewer’s location and, perhaps, actual identity.  One obvious use is to deliver different offers based on local weather and competitive promotions.  Vendor StickyBits make a buzzword triple play by adding social media to mobile and geo-targeting, with separate social media sites for different locations of the same UPC code.  Since the social site also evolves, marketers can only know the message received by each consumer if they capture a snapshot of the site as it appeared to each visitor.  They must also capture whatever contextual variables (time of day, weather, competition, current promotions, etc.) play into offers and results.

These examples point to the emergence of autonomous marketing messages: communications that are launched into the world to operate more or less independently, occasionally phoning home like a dutiful college student to report results and perhaps get some advice.

The concept poses challenges for everyone involved.  For marketers already struggling with the transition from one-way broadcasts to peer-based communities, it’s a further loss of control.  For technologists serving those marketers, it’s another set of delivery systems and reporting systems to manage.  For marketing analysts, it’s a new type of data to incorporate.

But the concept also creates new opportunities for success.  Messages that can track their own movement from consumer to consumer can provide important insights into the always-mysterious connection between messages sent and resulting customer behavior.  Autonomous viewer logs, testing and optimization can enhance media where continuous real-time connections to central servers are unavailable.  Periodic contacts with central servers let the applications download their information and update their libraries of offers, models and business rules.  Combining mobile, location and social media provides rich information about consumer behavior, along with direct opportunities to deliver highly targeted messages.

Autonomous messages add to the flood of data already generated by digital marketing.  This increases the need for ways to load, store, access and analyze tremendous volumes at reasonable cost and speed.  Similarly, the complex and variable structure of the new data reinforces the existing demand for technologies that can easily incorporate new data types and models.  Autonomous messages also require improvements in techniques to automatically uncover significant patterns within the data and infer appropriate marketing treatments.

The major new challenge posed by autonomous messaging is portability.  Autonomous systems must somehow incorporate decision rules, self-adjusting analytics, alternative treatments and data capture mechanisms while making minimal demands on host resources.  This is a particular issue in mobile environments, where bandwidth, storage and processing power are scarce.   The messages must also find efficient ways to exchange data with central servers.

Customer identification is another issue.  Autonomous messaging could ease some privacy concerns by tracking and responding to behaviors without sharing them externally.  But it also extends to platforms where customer identification is more difficult than usual, making it still harder to gain the most value from data that marketers have permission to use.

Progress will be incremental.  The immediate future will see hybrids that combine different aspects of autonomy with centralized techniques.  Marketers and technologists will need to assess the strengths and weaknesses of each approach and look for opportunities to combine them to deliver solutions more powerful than any one method provides by itself.

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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics.  He can be reached at draab@raabassociates.com.

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2010 May 01

Bridging the Gap between Online and Database Marketing
David M. Raab
Information Management
May / June 2010

Database marketing is based on sending messages to known individuals.  This has always been in sharp contrast to conventional mass media, cure such as television and print advertising, ailment where the marketer did not know exactly whom they were reaching.

But the introduction of online marketing has blurred this distinction.  Online marketers often know a great deal about the people they interact with, diagnosis even when they don’t know their actual name and address.  This means that they can gather, analyze and react to data in ways similar to database marketers.  But without personal identifiers, online marketers cannot integrate this data into the individual profiles that are the heart of a conventional marketing database.

One result has been a surprising separation between online and database marketers.  Even though both rely heavily on technology and apply disciplined analytical approaches, each has developed its own universe of service and software vendors.  Database marketers rely on marketing services agencies and marketing automation systems whose core competency is building and managing customer databases.  Online marketers rely on search marketing, Web analytics and Web site development vendors who are skilled at attracting traffic and tailoring Web treatments to visitor behaviors.

Email and ecommerce are exceptions: online activities dominated by database marketing vendors and techniques.  But they merely prove the rule, since both face situations where personal identities are known and it’s possible to build a conventional marketing database.

Another result has been continued fragmentation among online marketing subspecialties.  Search engine optimization, paid search marketing, site personalization, Web display ads, mobile marketing, downloadable applications and social media are usually managed separately, even though they rely in part on traffic statistics from same Web analytics systems.

Some fragmentation is inevitable.  New varieties of online marketing appear so quickly that marketers must rely on internal or external specialists for quick deployment.  But this fragmentation, as well as the separation from conventional database marketing, imposes extra costs and prevents consistent treatments for individual customers.

Of course, if it were easy to integrate online with offline data, the database marketers would have been doing it all along.  Social media can help by providing an additional source of personal identifiers that can link individuals across sources.  But what’s really needed is a change in attitude: one that recognizes it’s worth centralizing information even when it cannot be tied to a specific individual.

This is a radical switch for database marketers who have spent their careers looking for better ways to identify individuals.  But they (and the rest of us) need to adjust to a concept of “semi-anonymous” marketing, which means being able to reach individuals who share certain characteristics even if you don’t know who they are.

To bring home the importance of this concept, let’s look at the types of information available in online marketing channels.

– cookies are the primary means of tagging individuals who visit a Web site.  By itself, a cookie only identifies a computer, but it can be linked to additional information that’s either observed (e.g. pages visited) or provided by the visitor (e.g. registration).  This data can be stored within the cookie or, preferably, in a database linked to the cookie ID.  This means you can use a cookie to, say, show an ad with a discount coupon to someone who previously discarded a shopping cart, even if you don’t know who that person is.  In other words, even anonymous cookies let you put people into identifiable marketing segments and send them appropriate messages.

– IP address (showing where a user has connected to the Internet) and other information provided with each Web visit (browser type, operating system, etc.) can sometimes act as a proxy identifier for individuals, since many systems keep their IP address over time.  However, this is controversial in privacy circles and not wholly reliable.  Yet even discarding this approach, IP address can often be traced to a corporate account owner (this works for business computers, not for home computers which typically connect through an IP address registered to a phone company or other Internet service provider).  And nearly all IP addresses can be mapped to a geographic location, which in turn can be linked to geo-demographic databases such as Nielsen PRIZM clusters.  Again, this information can be used to target messages to unidentified individuals.

– mobile phone location is known to the phone network operator, although how much they share with marketers depends on privacy and commercial considerations.  It’s certainly possible to target messages to people within a certain geographic area, either in network-based advertising or through user-downloaded apps.  More advanced but still semi-anonymous applications, such as targeting based on whether someone is outside of their usual territory, are possible but demand more data retention.

– social media support many kinds of marketing, including advertising based on member profiles and groups, direct messages where a prior relationship exists, monitoring public activities by usernames, and linking usernames to email and other personal identity information.  The opportunities depend on the particular medium and the operator’s terms of service, but the general point is it’s worth building a history that may later become useful even if you can’t market to it directly today.

As marketers centralize their online information, technical demands will increase.  Marketing databases not only become much larger, but they will hold more kinds of information, much of it less structured than the traditional customer and transaction records.  There will be increased opportunities to use sophisticated matching techniques to associate specific individuals with semi-anonymous information, although this can raise privacy concerns.

But even if an addressable individual is never identified, marketers will gain by integrating information across channels at the level of the semi-anonymous segments themselves.  This will allow them to identify similarities in interests and behaviors, which in turn will lead to coordinated messages and clearer understanding of results.   The ultimate impact will be to help unify the marketing departments themselves, ending the fragmentation that detracts from business performance.

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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics.  He can be reached at draab@raabassociates.com.

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