0 MMA Modernizes Marketing Mix Models

I’ve been spending a lot of time recently looking at marketing measurement systems. This means that you, Dear Reader, will be spending a lot of time reading about them. A good place to start is Marketing Management Analytics, known to its friends as MMA.

MMA was founded in 1989 and is one of the pioneers in marketing mix modeling. Mix models remain the heart of the company’s business. But while traditional mix models look at direct correlations between advertising and sales, MMA’s current approach takes a more layered view. This includes what the company calls “multistage” attribution, which looks at intermediate touchpoints between an advertisement and the final purchase, and “customer cascade analysis”, which measures the long-term impact of advertisements on brand equity. The company has also beefed up its consulting services to help make its findings more actionable.

MMA’s foray into attribution is intriguing, since it puts the company into some degree of competition with attribution specialists like VisualIQ, Adometry, and ClearSaleing. But MMA works with aggregate data such as total spend and impressions, with a major emphasis on mass media like television. Those other vendors work primarily with data about individual buyers, which comes largely from digital and direct media. MMA's clients are traditional mass media advertisers, in consumer packaged goods, automotive, financial services, retail, pharmaceuticals, and communications, and it is working for CMOs who are allocating budgets across channels. The other vendors' clients are concentrated in ecommerce and they are answering more tactical questions about spending within the digital channels. What they all share is the goal of measuring the incremental impact of expenditures in specific media.

MMA recently released the latest version of its flagship software, Avista.  The system is still focused on traditional marketing mix models, although it can incorporate the "multistage" approach of measuring the impact of one channel on another.  The new release, Avista 8, was designed to make it easier for marketers and media planners work directly with the system, rather than relying on technical experts. The main interface displays curves that represent the relationship between spending on each tactic and final sales. Marketers use sliders to adjust the spending levels and the system then estimates the sales that would result.

Avista can also run optimization routines to automatically find the most effective spending mix. Users can limit how much spending on any one tactic can increase or decrease, can create groups of tactics that draw from a shared budget, and can choose the target of the optimization (maximum profit with a given budget, minimum spend to reach a target revenue level, etc.). Outputs can show details by brand, product, region, sales channel and time period. Users can save scenarios and compare them to each other. Once they’ve chosen a scenario, Avista can convert it to a high-level media plan for buyers to execute.

The system also has a forecasting feature that runs the same models but also lets users change assumptions about factors other than marketing spend, such as weather, competitive behavior, and distribution channels. Results can be displayed on reports, which in turn can be assembled into custom dashboards.

MMA also offers its clients a data access tool called MarketView, which lets them view and lightly analyze the data assembled as model inputs. This is a popular service by itself, because model inputs often include data the marketers have never seen before. Giving them early access helps to speed the modeling process by letting them verify the quality of the data.

0 Top Five Metrics for Revenue Generation Marketers

Marketing measurement is a perennially popular topic. I myself have just completed a white paper on Top Five Metrics for Revenue Generation Marketers, sponsored by LeadMD, and touched on it in a separate Gleanster study, Revenue Performance Management - The Evolution of Marketing Automation. With both of these on my mind, I also paid new attention to Eloqua’s list of five key revenue performance indicators (listed in the ‘Take a tour’ graphic on this page). The obvious question was whether these three sources agreed about what’s important.

The answer is: not exactly. The following table compares the top metrics from each paper, with analogous items on the same row:

The only item that’s clearly shared across all three lists is the number of leads generated, and even that takes a bit of squinting to include Eloqua’s measure of “reach”, which is really the number of leads currently at different stages. You could also argue that close rate and conversion rates are pretty much the same thing, and therefore also present on all three lists. (Again, a bit of squinting is required). Three of the other items appear just twice (return on investment, revenue, and time to close). The remaining three occur just once.

What accounts for the inconsistencies? I’d say mostly it’s the nature of the lists. The Gleanster list is from a survey of what marketers actually do: it’s no accident that nearly all items are quite easy to calculate. (Return on investment is a glaring exception, and I very much doubt that 73% of marketers actually calculate it today. So let’s just assume that figure is aspirational.)

The other two lists are prescriptive: that is, they show what an expert feels should be done, not what marketers actually do. Look closely, and you'll see that the lists are quite similar.  Four of the five measures are shared.  Even the two non-matching items are related: my fifth item is cost and Eloqua's is return, which is a combination of cost and revenue. 

The apparent difference between the lists is that mine looks more simplistic. It starts with a three-part formula for calculating revenue: (number of leads) x (close rate) x (revenue per closed lead). A fourth factor, cost, combines with revenue to create return on investment. The fifth factor, time, is needed to forecast revenue by period.

Eloqua’s list breaks those same factors down by stages. That is, instead of a single close rate is has a set of conversion rates from one stage to the next. It similarly breaks number of leads into reach (number of leads at each stage), revenue into value (expected revenue from leads at each stage), and time into velocity (number of days spent at each stage). This makes total sense, and if you read my paper, you’ll see that I recommend breaking the measures into stages in almost exactly the same way.* The reason is that reporting on stages gives much greater insight into what’s working well or poorly, and thus helps marketers to see where they should make changes. Providing this sort of actionable information is probably the most important purpose for any measurement system.

In short, Eloqua and I pretty much agree on what marketers should measure. Now if the marketers themselves would join the consensus.

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* The paper also gives plenty of sage advice on how to actually build a system based on these measures.

0 Survey of Surveys: Budgets and Process are Main Barriers to Marketing Technology Success

I recently gave a Web presentation comprised almost entirely of slides from different surveys. This was a bit of an experiment and, sad to say, it didn’t seem terribly successful. I did weave the slides into a nice little story line – marketers know they need better technology, poor data is the root of their problem, and we know how to solve this – but even that wasn’t enough. Pity.

Still, preparing the slides gave me a chance to scan the surveys in my archives, which was entertaining in its own little way. Many surveys ask similar questions, which gave me some choices during my preparation. But I didn’t look carefully at how they compare.

Today I’ll do that. I’ve chosen one of the most popular questions: what are the barriers to marketing technology adoption? I have versions of this from seven different surveys within the past year.

Of course, each survey uses different terms. To make the comparison, I collapsed the various answers into a few reasonably-distinct categories, committing a certain amount of shoe-horning along the way. I then recorded where each answer ranked in each survey, compiled the results, and did a crude ranking with a combination of mathematical wizardly and body english.  (Multiple answers for the same survey indicate I placed several questions into the same category.)

Results are below.  I've shaded the first ranked answers in orange and the second and third ranked answers in yellow.

My first observation was the sheer inconsistency of the answers. Budget issues emerged as a clear number one, but they reached that rank on just four of the seven surveys and ranked quite low on the other two that included them. The second-ranked item (marketing process) was never listed first; it ended where it did because it had the most twos and threes. No other item was ranked first more than once or in the top three more than twice.

Things made a bit more sense when I looked at the survey audiences. Winterberry and Forrester were specifically about online marketing, Gleanster and Marketing Sherpa were B2B surveys, and IBM and the two CMO Council studies were of general marketers. Since most B2B marketing is also online, it makes sense to look at the first four as one group and the other three as another.

Now we see some interesting consistencies:

• Budget isn’t much of an issue for the online and B2B marketers, but dominant for the mixed marketers.

• Marketing process and marketing staff skills are major concerns for online and B2B but rarely mentioned by the mixed marketers.

• Senior management support, and to a lesser extent IT support and technology capabilities, are significant barriers for mixed marketers but don’t slow down the online and B2B groups.

• Metrics, organizational silos, and the economy are cited occasionally by both groups but don’t seem to be major issues for either.

So there’s a fairly coherent picture after all.

• Online and B2B marketers are struggling to keep up with a rapidly changing marketplace, meaning their biggest problems are people and process. The importance of their work is obvious enough that budgets and senior management support are generally available. They have the technical savvy and independence to avoid issues with IT support and organizational silos.

• Mixed marketers, working in traditional channels, still struggle with budgets, metrics, and senior management. They have mature marketing organizations, so process and skills are in place, at least for traditional programs. They do struggle more with IT, technology, and organizational silos, because they lack their own technical skills and have limited clout in the organization.

• Everybody says they care about metrics but it's rarely a top priority.


Or at least that’s my take. I’ve displayed the actual surveys below – if you reach other conclusions or spot any other patterns, let me know.

0 Coremetrics Offers a Foggy View of Lifecycle Analysis

I stumbled over an Adexchanger interview yesterday with John Squire, the Chief Strategy Officer of IBM Coremetrics. It first caught my eye because the headline read “IBM’s Vision for the Marketer”, which is always a topic of interest. Then I noticed it was touting new reporting feature called Coremetrics Lifecycle, which the company describes as “the industry’s first application geared to enable online marketers to track and understand how customers progress through long-term conversion lifecycles.”

This was intriguing. On one hand, I’ve seen plenty of systems that track customers through the buying process, including Eloqua, Marketo, Leadformix, Clear Saleing, C3 Metrics, and Encore Media Metrics. So the claim to be first is questionable. But, on the other hand, seeing another vendor offer this sort of analysis reinforces the importance of the concept.

But a closer look at Lifecycle itself was disappointing. The product does allow tracking of individual Web site visitors over time, which is the foundation of lifecycle analysis. But, in my opinion, a lifecycle tracking system reports on movement of customers across stages within the lifecycle. That is, it shows conversions from one stage to the next. This implies reports that show the previous stages of customers who enter a new stage (“where they came from”), and show the destinations of customers who leave a stage (“where they went”). These are typically represented as a matrix showing all combinations of previous and current stages, or a flow chart that highlights the most common before-and-after pairs.

Lifecycle does none of this. Rather, it lets users define any number of segmentation schemes and count the number of customers in each segment. It does report how many customers entered each segment during a specified time period, but not where they came from. In fact, there is no requirement for a logical progression from one segment to the next, which to me is what a lifecycle implies.

Lifecycle has some other useful features. It can report on the most common marketing treatments received by people who moved into a segment, giving some insight into treatment effectiveness. It calculates the average number of days and Web sessions that customers spend in a segment, which is a limited velocity measure. It also lets users select segment members and send them messages through Coremetrics’ products for email, display ad retargeting, and Web site personalization, although it's not clear the process can be automated.

But a proper lifecycle analysis tool would go much further. It would calculate the end-to-end completion rates, show the drop-off from one stage to the next, estimate the incremental impact of specific treatments, project future segment counts, and show changes in these measures over time. So while I’m pleased that Coremetrics is promoting the concept of lifecycle analysis, I’m disappointed that its product doesn’t deliver a real lifecycle measurement solution.

Addendum - June 19, 2011

After the original post and IBM's comment on it, I reviewed the Lifecycle product with the Coremetrics team. This uncovered no substantive errors in the original post, although a couple of points could have been stated more clearly.

- the system supports two types of lifecycles, one requiring that customers progress through the stages in sequence and one that does not. Users specify the type when they set up a new lifecycle. In both cases, the stages are defined by selection rules created by the user.

- there is a limit of six stages per lifeycle.

- for sequential lifecycles, the system will warn the user if the selection rules are not inherently sequential. (An inherently sequential rule might be based on the number of purchases made; you can't make three purchases without having previously made two. Other stage definitions, such as downloading a white paper or leaving a comment, might come in any order and, therefore, are not inherently sequential.)

- in a sequential lifecycle, the system will not allow customers to advance outside of sequence even if the definitions would allow it. Nor does it report on customers who would qualify for a later stage but cannot reach it because they didn't qualify for a previous one.

- the system's primary report shows the number of customers within each stage during a specified date range. Think of this as an inventory. A "Migrator" report shows how many customers entered their current stage during the report period: for example, there were 500 customers in stage 3, of whom 200 first entered stage 3 during this period. This gives some sense of movement, but it's not the classic funnel analysis showing the percentage of customers in each stage who eventually move to the next stage.

- users can run the standard reports against "segments", which could be defined as anything including a cohort of customers who entered the system during a specified time period. A Lifecycle inventory report for such a cohort would show how many customers reached each stage and got no further. This is the information needed to build a classic funnel analysis, although users would have to extract the data and manipulate it to produce an actual funnel report. This would be done outside of Coremetrics, because there is no end-user report writer.

- reports show the average number of days and Web sessions it takes customers to reach each stage (i.e., since they first entered the system), not the number of days and sessions spent in each stage, as I wrote originally.

- users do have the option to create a recurring process that automatically selects customers in a particular stage and sends them an email or other message. The system could apply a few rules to this process, such as eliminating people who had been selected previously. But more sophisticated controls would be handled outside of Coremetrics, in the message delivery system.

- the system can profile customers in each stage against many attributes (products purchased, geography, social network membership, etc.) in addition to marketing contents received. But, as I wrote originally, the reporting only shows the percentage of customers in each stage who match a particular attribute: this is far from measuring influence, for reasons I'll explain in a future post.

- we confirmed that the system doesn't do projections of future inventory counts, report on out-of-sequence customer movements, or allow customers to migrate backwards into lower-ranked stages (as might happen if stages were based on recency or ratios).

I'm happy to have clarified these matters but none of this changes my original assessment: Lifecycle is a useful product that falls far short of serious life stage analysis.

0 [x+1] NexTargeting Conference: Cross-Channel Attribution and Online Ad Scalability Remain Hot Topics

Continuing my adventures in online ad measurement, I attended [x+1]’s NexTargeting Summit last week. This reinforced and refined my conclusions from last month’s OMMA Metrics conference, which identified the burning industry issues as:

- better understanding of the interactions between online and offline events (both advertising and results), and

- better scalability for successful online advertising programs.

The online / offline connection was covered by MarketShare CEO Jon Vein, who presented studies that showed including the “indirect impact” of online display ads can dramatically improve their reported return on investment. He also said his firm has found that marketing mix models with complete data can explain as much as 98% of the variance in revenue, while optimization based on mix models can typically improve marketing effectiveness by 10% to 15%. Although I don’t recall Vein mentioning it during his actual presentation, he did tell me in a side conversation that his firm purchased JovianData last year in order to expand its ability to work with individual-level data. MarketShare and [x+1] announced an alliance last month to combine MarketShare’s cross-channel analytics with [x+1]’s digital targeting.

Scalability was covered [x+1] itself, which announced extension of its Media+1 audience targeting platform to combine information from direct media buys and ad exchanges. The relationship between that extension and scalability is a bit complicated, but it boils down to this: combined information lets marketers control the number of ads served to individual consumers across both types of media buys, which segment-level purchases do not. This means that marketers can expand their budgets by targeting ads to new individuals (=effective scaling) rather than bombarding the same people with more messages (=ineffective scaling). That this mimics the reach and frequency measures used in traditional mass marketing (i.e., television) is a happy bonus.

I’ve skipped some of the subtleties of the Media+1 product. These include tracking the degree of overlap between the audiences of different direct-buy Web purchases; identifying optimal message frequency by customer segment; using direct-buy Web sites to establish a base of impressions and then supplementing these on a customer-by-customer basis through real time bidding on ad exchanges; and using scorecards to track performance after initial customer acquisition. The bottom line on Media+1’s beta client was reallocating 40% of the online ad budget to achieve a 20% improvement in results.

[x+1] also used the conference to announce an even broader product, called [x+1] Origin, scheduled for release this summer. This will build a customer-level data hub that combines data and sends targeted messages across display ads, Web site, email, and mobile. I asked [x+1] CEO John Nardone whether it’s actually possible to identify the same customer across all those channels, and he said it’s not an issue in many cases, since you can often give the customer a reason to log in or otherwise identify herself.

Of course, the big exception is acquisition, which seems like a pretty big exception indeed. (“Other than that, how did you like the play, Mrs. Lincoln?”) But tracking mechanisms do get better all the time and there’s plenty of value in better treatment within existing customer relationships. So it’s definitely a good start.

0 OMMA Metrics Conference: Online Ads Must Prove Real Value To Succeed

I took a break from my usual obsessions yesterday to attend the New York edition of MediaPost’s OMMA Metrics and Measurement conference. It was a good chance to dive into this particular sector of the marketing analytics universe. (Another version of the program will be presented in San Francisco in July; the company also live streams a free Webcast. You can also download selected presentations from yesterday.)

If there was an overriding theme to the event, it was frustration that online advertising isn’t attracting as much money as it should. There was more than a little ”TV-envy”: the feeling that TV gets more advertising because buying is based on simple, widely-accepted audience measures. Some speakers argued for duplicating this situation, by removing some middlemen and creating standard online audience measures.

Others pointed to a deeper issue: that online marketers can’t measure the value of their efforts in terms of revenue or brand metrics like awareness and preference. In this view, TV buyers accept simple measures like Gross Rating Points because these measures have proven over time to correlate with real business results. Media mix modeling has more recently confirmed this. But except for direct response, online media can’t show the same relationship. This forces online marketers to report endless (but never complete) data about who saw what and how they acted, in the hopes that piling on enough details will somehow make advertisers happy. It never does.

This is the online version of the old joke about the drunk who loses his keys in the alley but looks for them under the streetlamp “because the light is better”. Moral of story: no volume of irrelevant data can substitute for the information you really need.

In the case of online advertising, the dark alley is the connections between ad placements and final business results. Several speakers touched on parts of this. IBM’s Yuchun Lee gave an opening keynote that highlighted the pervasive influence of online information over all customer activities, not just online purchases. Adometry’s Steve O’Brien explicitly stated that attribution must measure the incremental impact of each marketing effort on final results (although I think he limited this to online results). ForeSee Results’ Larry Freed stressed the need to trace all online and offline behaviors to understand their true role in final outcomes. Others cited studies where careful measurement found that indirect results showed online to be much more powerful than direct attribution alone.

Yesterday’s speakers also raised the problem of scalability: that is, being able to duplicate and expand on success. This is one area where TV envy makes sense, because it’s easy to add more Gross Rating Points and be reasonably sure of getting the expected results. Online ad buying is more like buying print ads or mailing lists: you may have some sense of the audience demographics, but the only way to really know how it will perform is to run a test. But this isn't a measurement problem: simple, standard measures that hide true audience differences are only going to be unreliable predictors of actual results. What’s really needed are better testing methods to predict as quickly and cheaply as possible how each new audience will perform. The trick is you’re not just looking at immediate response, but all of those indirect effects that are so tricky to capture in the first place. Now you have to predict them in advance as well as measure them after the fact.

Nobody said it would be easy.

0 Fractional Response Attribution is Worse Than Nothing

Summary: Should companies apply fractional revenue attribution when more sophisticated methods are impractical? I think not: it gives inaccurate results that could result in bad decisions. Better to avoid financial measures at all if you can't do them properly.

I spent most of the past week in San Francisco at overlapping conferences for the Direct Marketing Association and Marketo. My Marketo presentation was based on the marketing measurement white paper I recently wrote for them, which argues that measurement should be based on tracking buyers through stages in the purchase process. One corollary to this is not attributing fractions of revenue among different marketing touches. The analogy I’m currently using is baking a cake – it doesn’t make sense to assign partial credit for the final flavor to different ingredients: the recipe as a whole either works or doesn’t. Only testing can determine the impact of making changes.

Given this mindset, I was more than a little surprised to attend a DMA panel discussion where two of the more sophisticated marketing measurement vendors described their systems as providing fractional attribution. Both vendors also offer more advanced methods and both made clear that they used such methods in appropriate situations. But they seemed to feel that when adequate data is not available, fractional attribution is better than nothing.

I certainly understand their attitude. Many of the business-to-business marketers at the Marketo conference have exactly this problem: their data volumes are too small to accurately measure the incremental impact of most marketing programs. The best suggestion I can make is that they run whatever tests their volumes make practical. I’d further suggest that testing may actually be more practical than they realize if they actively and creatively look for opportunities to do it.

But, again, the vendors on my panel knew this. The examples they gave were situations where companies had previously attributed all marketing revenue to the “last touch” before an actual purchase or other conversion event. They used fractional attribution to help people (marketers and those who fund them) see that other contacts also contribute to those final results. The practical goal was to justify funding for early-stage programs that such as search engine optimization and display advertising that precede that “last touch” itself.

I’m all in favor of recognizing that early-stage contacts have value. But I still feel that assigning a fundamentally arbitrary financial value to those contacts is a mistake. The main danger is that people who don’t know any better may use these numbers to allocate marketing funds to the more “productive” uses. Such figures are not accurate enough to support such decisions.

I’d rather use non-monetary measures such as correlations between different kinds of touches and ultimate results. These can highlight the connections between early and later touches without providing financial values that are easily misapplied. Maybe this is just wishful thinking, but perhaps refusing to provide unreliable financial metrics will even highlight the need for tests that can provide truly meaningful ones—thus helping marketers to make the necessarily investments.

So what do you think: is fractional revenue attribution of reasonable compromise or a harmful distraction? Let me know your thoughts.

0 Four Must-Have Metrics for Marketing Measurement

Summary: Four critical metrics tell you most of what you need to show the value of your marketing efforts and to optimize your results. And, here's a funny picture.

There’s still time to sign up for my October 7 Webinar on stage-based marketing measurement (sponsored by Marketo and hosted by the American Marketing Association). During my extensive, um, research, I was very pleased to find the following picture to illustrate the concept of stages:


I like this picture both because it's amusing (a major priority) and also because it illustrates that stage definitions are constructed, not discovered. (I suppose the proper science is that evolutionary stages are objective facts, in which case our monkey friend in the photo simply has it wrong. But the deeper point still stands: whether it’s evolutionary stages or purchasing stages, someone imposes conceptual order on the jumble of reality.)*

If the picture isn't enough reason to attend, the Webinar will also present four essential metrics of stage-based marketing measurement. (Quick review: stage-based measurement tracks the ability of marketing programs to move leads through stages in the purchase process. This is more meaningful than attributing some fraction of the final revenue directly to each program. I’ll cover this in the Webinar and also discuss it in a recent whitepaper Winning the Marketing Measurement Marathon).

In case you can’t attend the Webinar, I thought I’d share the four metrics here.

1. Marketing ROI.
Purpose: to show the company’s return on its marketing investment.
Inputs: marketing costs and marketing-related revenue.
Metric: return on investment (= revenue / cost)
Comment: As with any ROI calculation, the trick here is to determine which costs are associated with which revenues. It’s always hard for marketers to know which revenues they helped to generate, but I’ll assume a database or digital environment that identifies the treatments applied to individuals and their actual purchases. In this situation, marketing ROI is calculated by summing all marketing costs for a cohort of customers sharing some common feature such as original source, acquisition date range or first purchase date. Note that a meaningful calculation must also include spending on people who never purchase, so a cohort based on purchase dates must somehow include non-buyers.

2. Program ROI
Purpose: measure the relative performance of individual marketing programs.
Inputs: incremental marketing cost, incremental revenue
Metric: incremental ROI
Comment: Obviously the key word here is “incremental”. Marketing programs exist in the context of other activities that influence buyer behavior. The only thing you can really measure is the incremental change that occurs when a particular program is added or removed from the mix. Combined with incremental costs, this gives an incremental ROI for the program. Spending more on high ROI programs and less on low ROI programs is how marketers optimize their results. Remember, though, that ROI is just one part of the equation. In practice, marketers must balance it against considerations such as revenue goals and marketing budgets.

Incremental measurement requires formal tests that compare performance of two similar groups which differ only in whether they received a particular program. These tests can cover any type of program, including nurture programs that don’t acquire new names. Proper measurement must track through the end of the buying cycle, since a program’s impact on early stages might vanish or even be reversed at later stages. One common example: a free introductory offer that yields higher initial response but doesn't add to the final number of paying customers.

3. Stage Results
Purpose: understand movement of leads through the buying stages
Inputs: marketing costs per stage, conversions (= number of leads that move to the next stage), conversion time (= time in stage before conversion to next stage; a.k.a. velocity), lead inventory (=number of leads in each stage)
Metrics: conversion rate, cost per conversion, average conversion time
Comment: These statistics describe how leads are moving from one stage to the next. The information is used to project future behaviors, to identify problem stages, to track changes in stage performance, and to compare the effects of marketing programs. Where leads in different cohorts (based on original source, acquisition date, marketing treatments, etc.) behave differently, statistics should be gathered separately for each cohort.

One statistic you can't calculate is the ROI for stage investments. This is counter-intuitive: stage ROI should be possible because you're making investments at each stage and the investments produce leads with higher values. But in fact the aggregate value of a cohort of leads remains the same as they move through the stages; all that happens is that unproductive (i.e., valueless) leads drop out. That is, even though the value per lead increases, there is no increase in the value of all leads combined. Without a value change, you can’t calculate a return on investment.

(Actually, there is a bit of value change as leads move through the stages because leads in later stages will need less additional investment to reach the final sale. But the expected revenue for the cohort stays constant. Of course, to the extent that a particular marketing program creates an incremental change in total value, this can be measured like any other program ROI.)

4. Revenue Forecast
Purpose: estimate future period revenues (by week, month, quarter, etc.) from the current lead inventory.
Inputs: lead inventory per stage, conversion rate per stage, conversion time per stage
Metric: revenue forecast by period
Comment: Revenue projections are among the most critical of corporate statistics. The stage-based approach allows more accurate projections of revenue over time, starting with the current lead inventory and known stage statistics. If the projections can distinguish marketing-generated leads from other leads, they can also give a concrete measure of the value that marketing has provided to the organization. If leads from different cohorts behave differently, the projections need to use separate assumptions for each group.

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* Platonists and creationists, with their respective theories of absolute Forms and divinely-created immutable species, might argue that species actually do have an independent existence. They're wrong.

0 Webinar and White Paper on Marketing Measurement

Marketo yesterday released Winning the Marketing Measurement Marathon, a white paper that I wrote for them. This was timed to coincide release of their new Revenue Cycle Explorer, which adds advanced reporting to their Revenue Cycle Analytics line. (See my August 3 post for more details on Revenue Cycle Analytics.)

I'll also be speaking with Marketo in an October 7 Webinar hosted by American Marketing Association. Please join us.

0 Marketo's Enterprise Edition and Revenue Cycle Management: Looking Under the Hood

Summary: Marketo continues to follow its own path. Enterprise Edition adds the complex security needed by large organizations but sticks to simple campaign flows. Revenue Cycle Management blazes an important new trail for others to follow.

I finally caught up with Marketo for a briefing on their Enterprise Edition (announced in March) and Revenue Cycle Analytics (announced in May). Since both are somewhat old news, and Marketo describes them in detail on its Web site, I’ll just make a few comments.

Executive Edition shows what Marketo believes is needed to service large marketing organizations. The most extensive enhancements provide finer-grained control over user rights. This is critical in large organizations, where regional and product groups may be responsible for different market segments and where users will have different functional specialties and approval authorities. Enterprise Edition supports these by adding user roles, “lead partitions” to control access to database segments and “workspaces” to make Marketo objects (contents, campaigns, lists, etc.) available to different user groups. User roles (but not lead partitions or workspaces) are now available in Marketo’s Professional Edition as well.

These changes are a big advance over earlier versions of Marketo, which distinguished only between users and administrators and let all users access pretty much everything. Enterprise Edition also adds a “sandbox” environment for training, testing and development – the sort of things that small companies might do on a live system, but large organizations cannot safely allow.

The other major big-company need that Enterprise addresses is more sophisticated integration with other corporate systems. Related features include LDAP integration with enterprise security systems and a Web services API to call Marketo functions and access its data.

Perhaps most interesting is that Marketo did NOT expand the complexity of its actual campaign flows. These remain fundamentally linear: that is, all leads follow the same flow from step 1 to step 2 to step 3, etc. Rules within each step can deliver different treatments to different segments, but everyone still moves to the same next step unless they leave the campaign altogether. Other enterprise-level marketing automation systems can create different branches within their campaigns, so different segments follow entirely separate paths. This makes it easier to design and visualize fundamentally different treatments for different types of leads, something that matters more in a large enterprise with many different lead types. I’ve always considered branching campaign flows to be one of the key requirements for an enterprise-level marketing automation system. It seems that Marketo disagrees.

(Actually, Marketo disagrees with much of the preceding paragraph. Everything in it is factually accurate, but I'm happy to clarify that (1) several campaigns can run simultaneously, sending leads through different flows and (2) steps within Marketo campaigns can remove leads or send them to other campaigns (3) Marketo can connect several campaigns to produce the same flows as single branching campaign in other systems.)

Revenue Cycle Analytics breaks some important new ground. As I commented in an earlier post on purchase funnel measurement, Marketo’s approach is not conceptually unique. The basic idea is to track leads through stages in a purchase funnel, which is similar to pipeline reporting in many sales automation systems. It just starts earlier in the process.

However, Marketo's implementation brings this reporting to a new level. Most specifically, Marketo has introduced a star-schema reporting database, which I’m pretty sure no other marketing automation system currently offers. (Market2Lead had something similar but is no longer sold.) This is important because the structure of an operational marketing database, which most B2B marketing automation systems also use for reporting, makes it hard or impossible to do the necessary time-based analysis.

Other components are similarly sophisticated. These include graphical models that track movement of leads through the stages, detailed analytics with specialized measures such as conversion rates and speeds, statistical projections based on current inventory and historical flow rates, and executive dashboards. The models capture more than a simple linear pipeline: they support skipping and backwards flows among stages, splits within flows for different lead types, complex stage definitions, and transitional stages where leads are processed and reassigned.

Marketo is also tackling the difficult issue of allocating revenue to multiple individuals and marketing touches. Its methods are not particularly advanced: credit can be spread evenly or based on marketing-assigned weights. But no one else has found a much better solution, particularly at the low volumes of most B2B marketing programs.

My only real complaint is that you can't actually buy it all today. Marketo is releasing Revenue Cycle Analytics in stages. The database itself was available for the May announcement and the modeling engine was released in July. Initial analytics are set for delivery this month (August), with the really cool projections and dashboards out during the first half of next year. This delay could prove costly, since funnel-based marketing measurement is a hot topic and other vendors could well build or partner to deploy something similar in the interim.

Pricing of Revenue Cycle Analytics starts at $1,500 per month and grows with database size. Incidentally, I don’t think they’ve published that figure anywhere before, so there’s a bit of news in this post after all. Huzzah.

0 Using a Purchase Funnel to Measure Marketing Effectiveness: Better than Last-Click Attribution But Far From Perfect

Summary: Many vendors are now proposing to move beyond "last click" attribution to measure the impact of advertising on movement of customers through a sequence of buying stages. This is a definite improvement but not a complete solution.

Marketers have long struggled to measure the impact of individual promotions. Even online marketing, where every click can be captured, and often tracked back to a specific person, doesn’t automatically solve the problem. Merely tracking clicks doesn’t answer the deeper question of the causal relationships among different marketing contacts.

Current shorthand for the issue is “last click attribution” – as in, “why last click attribution isn’t enough”. Of course, vendors only start pointing out a problem when they’re ready to sell you a solution. So it won’t come as a surprise that a new consensus seems to be emerging on how to measure the value of multiple marketing contacts.

The solution boils down to this: classify different contacts as related to the different stages in the buying process and then measure their effectiveness at moving customers from one stage to the next. This is no different from the “sales funnel” that sales managers have long measured, nor from the AIDA model (awareness, interest, desire, action) that structures traditional brand marketing. All that’s new, if anything, is the claim to assign a precise value to individual messages.

Examples of vendors taking this approach include:

- Marketo recently announced new "Revenue Cycle Analytics" marketing measurement features with its customary hoopla. The conceptual foundation of Marketo’s approach is that it tracks the movement of customers through the buying stages. Although this itself isn’t particularly novel, Marketo has added some significant technology in the form of a reporting database that can reconstruct the status of a given customer at various points in the time. Although this is pretty standard among business intelligence systems, few if any of Marketo's competitors offer anything similar.

- Clear Saleing bills itself as an “advertising analytics platform”. Its secret sauce is defining a set of advertising goals (introducer, influencer, or closer) and then specifying which goal each promotion supports. Marketers can then calculate their spending against the different goals and estimate the impact of changes in the allocation. Credit within each goal can be distributed equally among promotions or allocated according to user-defined weights. While such allocation is a major advance for most marketers, it’s still far from perfect because the weights are not based on directly measuring each ad's actual impact.

- Leadforce1 offers a range of typical B2B marketing automation features, but its main distinction is to infer each buyer's position in a four-stage funnel (discovery, evaluation, use, and affinity) based on Web behaviors. The specific approach is to link keywords within Web content to the stages and then track which content each person views. The details are worth their own blog post, but the key point, again, is that the contents are assigned to sales stages and the system tracks each buyer’s progress through those stages. Although the primary focus of LeadForce1 is managing relationships with individuals, the vendor also describes using the data to assess campaign ROI.

Compared with last click attribution, use of sales stages is a major improvement. But it’s far from the ultimate solution. So far as I know, none of the current products does any statistical analysis, such as a regression model, to estimate the true impact of messages at either the individual or campaign level. They either rely on user-specified weights or simply treat all messages within each stage as a group. This lack of detail makes campaign optimization impossible: at best, it allows stage optimization.

Even more fundamentally, stage analysis assumes that each message applies to a single marketing stage. This is surely untrue. As brand marketers constantly remind us, a well-designed message can increase lifetime purchases among all recipients, whether or not they are current customers. It’s equally true that some messages affect certain stages more than others. But to ignore the impact on all stages except one is an oversimplification that can easily lead to false conclusions and poor marketing decisions.

Stage-based attribution has its merits. It gives marketers a rough sense of how spending is balanced across the purchase stages and lets them measure movement and attrition from one stage to the next. Combined with careful testing, it could give insight into the impact of individual marketing programs. But marketers should recognize its limits and keep pressing for solutions that measure the full impact of each program on all their customers.

0 CMO Survey: Measurement Isn't Our Top Priority

I’ve spent a lot of time looking at surveys to understand marketers’ priorities. Another one crossed my desk today, taken by Aprimo at Argyle Executive Forum’s 2010 CMO Spotlight Forum: Retail and Consumer Goods & Services on April 29, 2010 in New York.

The results are the most puzzling yet. The survey seems nice and simple: three questions with five answers each, and the answers contain similar categories. But the most common answer to each question suggests a different priority:

Q: What is driving the highest degree of change to your marketing strategies?
A: Creating more compelling customer/prospect experiences (37%)

Q: What is the CMO’s biggest challenge today?
A: Integrating and tracking multiple channels (37%)

Q: What is most broken in marketing?
A: Correlating marketing activities to revenues (39%)

So which is it, folks? Customer experience, channel integration or marketing measurement? It's nice to know that I could cite whichever I like if I have a particular point to support. But mostly this suggests that CMOs are just plain confused.

I suppose a more subtle interpretation would be that marketers know that correlation of activities with revenues is their "most broken" process, but consider fixing it less important than integrating multiple channels. You could argue this supports the case I made in my last two posts that marketers haven't invested in measurement because they have other priorities.

The table below gives a more complete view of the results, with color-coding of related answers across categories. You might see a bit of a pattern if you look hard enough: integration and measurement show up in four of top six cells. And I suppose the #3 rank of measurement in the "biggest challenge" category reinforces my argument about its low priority.

Goals Driving Most Change

Biggest CMO Challenge Today

Most Broken in Marketing

create compelling experiences 37%

integrate & track multiple channels 37%

correlate activity w/revenue 39%

ROI / accountability 27%

do more with less 28%

lack of channel integration 27%

digital marketing 18%	accountability / measurement 18%	too many silos 15%

integrate channels 17%

control messages in social media 11%

perceived lack of marketing value 10%

streamline operations 1%

keep up with social media 6%

channel-consistent messaging 10%

You can download the survey results and take a more detailed CMO survey if you're so inclined. Aprimo seems to be setting up some sort of community as well, although I couldn't find any actual discussion to date.

0 Why Marketers Don't Measure: A Test to Find Out

Last week's post Why Marketers Don't Measure generated some interesting debate on whether the problem is lack of time or lack of knowledge. It dawns on me that this should be a testable question -- something the assembled measurement gurus should find congenial.

My initial thought is an a/b test of email headlines, one offering "quick and easy ways to improve your marketing measurement" (i.e., time) and the other offering "learn how to do a better job measuring your marketing results" (i.e., knowledge). These could offer a book, Webinar, white paper or something else; what matters is which value proposition is more attractive, which would be measured simply through the open rate. Come to think of it, this could also be a split test in paid search or display ads.

I don't happen to have a suitable event upcoming to actually test this against, but perhaps someone out there could give it a try and share the results? Or can you think of a better test to answer the time vs. knowledge question?

0 Why Marketers Don't Measure

I had a small epiphany the other day when someone recommended that one of my clients needed a marketing measurement project. As author of The Marketing Performance Measurement Toolkit and a frequent speaker on the topic, I was surprised to find I didn’t like the idea. The problem was that this particular client had other marketing challenges that were more pressing. Even though their measurement could indeed be improved, a measurement project at this time would have been a distraction.

This got me to thinking. If I, a certified measurement guru, rejected a measurement project because we had other priorities, how much more likely are other marketers to make the same judgment? By coincidence – or was it? – I was speaking on the very topic a few days later, so I polled the audience. Sure enough, heads nodded vigorously: yes, they really understand the value of better measurement. But they just didn’t have time to set up a major effort.

It’s no news that marketers are busy. What makes this interesting (to me, at least) is that marketers have for years listed better measurement as a top priority but made little actual progress. When asked about obstacles, they generally come up with reasons like lack of data or measurement technology (For example, see the 2009 Marketing Performance Advantage study from CMG Partners and Chadwick Martin Bailey.) Since these are problems that can be solved with funding, they suggest that the root cause is that marketers doubt measurement is worth the investment or don’t know how to do it.

If ignorance is the problem, then education is the solution. This has long been my premise as a measurement evangelist: if only I could convince marketers that measurement is truly important and help them learn how to do it, they would take the plunge.

But if the real problem is lack of time, then education doesn't matter. My current thinking is that most marketers do sincerely want to improve their measurement programs and would even spend money to do it but just don’t have the time to set things up.

My analogy is a speedometer. We all recognize the benefits of a speedometer and use the speedometer built into our car, but few people would buy a speedometer by itself or attend seminars or buy books on speedometer design. We might glance at the speedometer when we buy a new car, but aren’t likely to give it much weight in our purchase decision. Similarly, I think marketers recognize that measurement is important and will use the measurement tools they have available, but few buy stand-alone measurement systems or make measurement a major factor in their product selection.

If I’m right about this, marketing system vendors are in an awkward position. They know that marketers are likely to use only the measurement tools their products provide, and thus that they should build in strong measurement capabilities to help their clients succeed. But they also know that marketers won’t buy their products because they have better measurement or pay extra for measurement features. So the software companies have no incentive to invest in better measurement capabilities.

Economists are familiar with this sort of market failure. It’s why seatbelts are required by law – because many buyers won’t pay extra for them despite their proven value. (Speedometers too, come to think of it.) Despite this, some marketing software includes extensive measurement features and some vendors have even attempted to differentiate their products with those features. I haven’t asked how that’s working out, but suspect it hasn’t been a major factor in many purchase decisions. (If any vendors care to comment on this point, I'd appreciate it.)

Consultants like myself have it easier. Although most marketers won’t spend either time or money on better measurement, there are enough others willing to pay consultants (basically trading time for money) for at least some of us to make a living.

The implications for me as a writer and speaker are a bit more pointed. Lectures aimed at inspiring or educating marketers about measurement are probably off target. Instead, marketers need concrete advice on how to do better measurement with their existing tools with a minimal investment in time. Such advice will lead to tactical and incremental projects rather than a grand unified measurement vision. But so long as it moves marketers in the right direction, it’s worthwhile.

0 Real Examples of Social Media ROI

Summary: some published examples of "hard" ROI from social media.

As part of the preparation for next Tuesday’s Webinar with 1to1 Media and Neolane (register here), I poked around for some concrete examples of ROI from social media. Here’s what I found.

Socialnomics blog by Erik Qualman offers a dynamic video with 33 “salient examples and data points” about social media ROI. Some are pretty vague but the concrete ones include:

- Wine TV Library gained 1,800 new customers from Twitter

- Lenovo attributed a 20% reduction in call center activity to use of a community website for answers

- Burger King received 32 million media impressions from a Facebook app promotion costing less than $50,000

- Genius.com reports that 24% of its social media leads convert to sales opportunities

- Moonfruit sales of its Web hosting service increased 20% on a $15,000 social media investment

Jacob Morgan cites a Computerworld article describing how online community platform vendor Reality Digital generated 72 leads over the first three months of its social media project, at a cost of roughly $9,000. The company expected this to yield at least one sale which would cover the entire annual cost of the program.

ReadWriteWeb reports “a Cisco study in 2004 found that 43% of visits to online support forum are in lieu of opening up a support case through standard methods.”

Socialtext corporate blog cites an estimate by TransUnion CTO John Parkinson that his $50,000 investment in Socialtext has avoided $2.5 million in tech spending by helping users share ideas on how to solve their problems more cheaply.

10e20 corporate blog gives three examples of social media conversion:

- response to a LinkedIn group query became a 10e20 client

- a "couple of hours per week" spent social bookmarking the contents of an online magazine at StumbleUpon and other sites drove “10’s of thousands of visitors as opposed to hundreds”, resulting in much higher ad pay-per-click ad revenue

- major national fashion brand invested the equivalent of "one mid-level employee’s salary" to run a dedicated social media presence, yielding 75,000 fans and followers and “several hundred thousand dollars in new sales in three months of marketing” as well as reaching a new audience, improving public relations and customer service, and gaining feedback for product development

HubSpot's The State of Inbound Marketing 2010 survey found that 41% to 46% of the companies using Twitter, LinkedIn, Facebook or a company blog had acquired a customer from that channel.

Predictive Marketing Blog by Bob Hodgson reported that eight Tweets by a high tech conference with 350 followers generated 10 completed registrations worth $15,000.

I also found plenty of insightful content that doesn’t include specific numbers. In general, there are two schools of thought on social media ROI: some think it really must be tied to revenue and profits to be meaningful; others argue just as passionately that different measures are appropriate depending on the program objective.

Truth be told, my heart is with the “revenue and profits” school. But I suspect it may be too simplistic, so I do accept alternative measures as a valid alternative. The problem with tying social media to "hard" ROI is this often relies on complex intermediate calculations, which are subjective in themselves. That being the case, alternative measures are not necessarily less valid; it depends on the details. (Fallacy alert: just because neither is perfect, it doesn’t follow that both are equally bad).

In any case, here are a few discussions I found particularly worthwhile:

A SlideShare presentation from Peashoot (a social media campaign manager) listing different metrics for different campaigns. These are good examples even though there are no actual results.

An eConsultancy blog post sharing comments on social media ROI from a collection of British experts.

Another eConsultancy post listing ten specific ways to measure social media success.

0 Survey Suggests Marketers Are Moving from Paid to Social Media

Summary: a new survey suggests that marketers are less focused on lead generation than on final sales, growing current customers and building online communities. I’m not sure I trust the data, but it’s a pretty picture nevertheless.

I don’t know quite what to make of the 2009 Survey on Marketing, Media and Measurement released earlier this month by custom content company King Fish Media.

- On one hand, it’s a rare opportunity to see data from business, rather than consumer, marketers. (Of the 230 respondents, 52% were pure B2B and another 36% were mixed B2B and business-to-consumer.) So I'd really like to believe it.

- But on the other hand, the sample seems dangerously unrepresentative: 44% said their organization’s primary industry was “publishing/media/advertising/marketing”, which is vastly higher than the real-world proportion. Presumably this was the result of the survey method – an online survey based on email invitations to the lists of King Fish and co-sponsors HubSpot, Junta42 and Upshot Institute. In addition to the industry skew, this probably reached a group that’s much more online-oriented than marketers as a whole.

The best I can do is to treat the results very carefully: assuming that this group shares some characteristics of the broader universe, but keeping in mind that some answers might reflect its atypical composition. Here goes.

1. Marketing Measurement Practices

The group reported using three broad types of marketing success measurements:

- 91% measured new customers acquired or leads generated.
- 63% measured customer retention or sales from current customers or lapsed customers.
- 54% measured brand-marketing-style metrics such as awareness, perception or intent.

Directionally, this seems about right: more marketers focus on new business than on existing customers, and brand-style measurements are less common than business results. The figures for existing-customer measurements are higher than I would expect, but perhaps that’s because publishing marketers are more directly responsible for renewals than business marketers in general.

Another oddity was that more people report measuring new customers (77%) than leads (73%). An optimist would treat this as evidence that marketers are adopting an end-to-end vision (as they should) rather than ending their responsibility when a lead is handed over to sales. But think the more likely cause is that marketers in publishing are more likely to sell directly (i.e., without a sales force) than in other industries.

Incidentally, the survey also found that 73% of respondents had guidelines in place to measure marketing success, but just 50% said their company requires a measurement plan as part of its program approval process. Treat this as you wish: is it impressive that 73% have measurement guidelines or frightening that 27% do not? Also bear in mind that 91% were at least using measurement on acquisition programs (some, apparently, without standard guidelines). So I think we can conclude that basic measurement is widespread, although its quality and consistency are questionable.

2. Spending on Acquisition vs. Existing Customers

Media spending by purpose was distributed:

- 56% for new leads
- 33% for retention
- 10% for other

This is interesting because I don’t recall seeing other data showing this split. The actual numbers show much more spending on retention than I would have expected. As with the measurement figures, this probably reflects the business of the survey responders.

3. Media Preferences

The main thrust of the survey was how marketers view different media. Marketers were asked to rate "the most effective way to communicate with customers and prospects", with separate answers for each. Here are the results:

	for prospects/leads	for current customers
corporate Web site	75%	70%
social media	73%	72%
custom content and media	70%	77%
face-to-face events	69%	62%
white papers / e-books	67%	52%
Webcasts and virtual trade shows	64%	51%
e-mail marketing	58%	78%
online advertising	42%	13%
direct mail promotions	33%	34%
print advertising	33%	17%
broadcast advertising	10%	11%

If there’s a pattern here, it’s that awareness-generating media (e-mail, direct mail and online/print/broadcast advertising) rank shockingly low, especially for prospecting. Apart from using email for customer communications, the respondents gave their highest rankings to the corporate Web site, social media, and custom content.

But how, exactly, can they attract traffic for the Web site, social media message and custom content if they don’t reach out to new audiences? I can think of (at least) two answers:

- they can’t, and the answers just reflect an infatuation with online media. I’m not saying the respondents are poor marketers: chances are they really do use the low-ranked media, but don’t consider them terribly effective. (Other answers in the survey suggest the same thing, showing that budgets are moving away from the low-ranking media to the high-ranked categories.)

- they can, by using social media and custom media in the awareness- and traffic-building roles previously handled by paid advertising. Put another way, the traditional first steps of generating awareness and interest are handled by the community rather than by marketers themselves. In this world, marketing’s role becomes to nurture communities of enthusiasts and evangelists, and then to meet the needs of prospects attracted by the community. This is what I meant in my September 23 post about community-centric marketing replacing customer centricity. (Can I coin CBM as a new acronym for Community Based Marketing?)

Obviously the second possibility is more intriguing. It’s surely correct to some degree, although the Big Question is how quickly and how far marketers’ role will shift. Given my concerns about this survey, I wouldn’t treat its results as definitive answers. But they're still tasty food for thoughts.

0 More Surveys Agree: Web and Non-Web Data Must Be Integrated

It’s not that I’m obsessive, but just to gnaw a bit more on last week’s bone about the coming unification of Web and other marketing data...

- a recent survey sponsored by enterprise marketing automation vendor Unica found that “integration with other marketing solutions” was the most commonly cited web analytics challenge (46%).

This was followed by “verifying accuracy of data (inflation/deflation)” (41%) and “not comprehensive/missing types of data” (32%). It’s interesting that the question was about Web analytics in particular – even analyzing Web results by itself requires non-Web data.

- another survey by another marketing automation vendor, Alterian, found the most-commonly cited top obstacle in online marketing (25% of respondents) was “integration of online with database marketing and offline channels”.

Now, this isn’t exactly the same answer as the Unica survey, since the question isn’t limited to data integration. But Alterian also reported that “lack of ability to assess or manage internal infrastructure and culture challenges (25%) and the integration of all the technology to power the cycle (20%) were identified as the biggest factors in implementing the customer engagement cycle.” So clearly data and system integration are indeed primary concerns for multi-channel marketing.

For still more on this topic, see today’s post on the MPM Toolkit blog, describing a very detailed report in eMarketer about Online Brand Measurement. This provides still more evidence for the need for integration of Web and non-Web data, in addition to other issues.

Case closed.

0 Web Analytics Is Dead. So Is Customer Centricity. I Need a Drink.

Summary: Web analytics is merging into the broad world of marketing measurement across all media, which itself is shifting focus from tracking individuals to understanding group behavior. Although Web analytics and marketing automation vendors are currently wrestling over who will house customer data, both are likely to lose custody to enterprises who want to control their data for themselves.

Even as analysts are still sorting through the implications of last week’s acquisition of Omniture by Adobe, the industry saw two additional important announcements this week: Omniture combining its data with comScore to help measure Web advertising audiences, and Nielsen working with Facebook to poll consumers on advertising impact.

Both announcements share several interesting features: they don't rely on traditional Web analytics (tracking page views); they involve vendors who report data from consumer panels; and they relate to measuring advertising measurement. Maybe they coincided simply because the big Advertising Week conference is now under way. But I think they, along with the Omniture/Adobe deal itself, hint at something more profound: the end of Web analytics as we know it.

Ok, that may not be as earth-shattering as the end of several other things you might imagine. But many marketers are just getting their arms around traditional Web analytics. So it’s worth warning them that things are about to change again.

Smarter Content on the Way

Let’s start with Omniture/Adobe. After thinking about it for a week (and hearing what the participants said), I think the main purpose of the deal was to let Adobe build content that was more intelligent in two ways:

- First, the content will be inherently “instrumented” to report how, when, where and which people are consuming it, using techniques go beyond traditional Web analytics methods (server logs, Javascript tags and cookies). This is needed because content increasingly exists outside of plain vanilla Web pages that can be tracked with conventional techniques. Problems include Flash, video, audio and other non-HTML Web content; venues like mobile, digital video recorders and interactive TV; widgets that migrate through social networks; and plain old cookie deletion. Web analytics vendors are striving to extend their technologies to capture these, but at some point you have to look for a different basic model. Content that can itself “phone home” rather than relying on the carrier medium could be the long-term answer.

- Second, content will be self-optimizing. This involves built-in tests and, perhaps, a sort of swarm intelligence where subsequent views are actually modified based on previous results reported by the content itself. Imagine a widget with a built-in A/B headline test: every time someone accesses it, the widget offers one headline or the other, and reports the result to a central server. (This could be done without transmitting personally identifiable information, so privacy issues are minimal.) Once a pattern emerges, the server could instruct the distributed widgets to only display the winning headline, or, better still, to start a new test. Even without a central server, each copy of the widget could still run its own test and, assuming it’s accessed enough times, adjust all by itself.

Who Owns The Data?

So far so good, and I think that’s plenty of reason to justify spending $1.8 billion for Omniture. But I also think Adobe was interested in the fact that Omniture controls so much of its clients’ data.

There is a battle brewing over that issue: like most Web analytics vendors, Omniture stores Web traffic data on its own servers and sells clients the ability to access that data. That didn’t raise any particular business issues when Web data was viewed largely in isolation. But as the Web takes an increasingly central role in customer contacts, marketers need to merge that Web data with their other customer information. Indeed, if you want to use that data to help guide customer interactions across channels, the data must be not just centralized but also updated in real-time. That means marketers either give all their non-Web data to their Web analytics vendor, or directly capture Web analytics data on their own servers.

The Web analytics vendors see this future and recognize that they’ll be more important to their clients, and thus able to charge higher fees, if they hold everything. Marketing automation vendors and in-house IT groups see the same future and recognize the threat to their own positions. Thus, the business-oriented marketing automation vendors (i.e., demand generation vendors: Eloqua, Silverpop, Marketo, etc.) already capture Web behavior in their own systems and integrate it directly with customer management. The big consumer-oriented marketing automation vendors (SAS, Teradata, Unica) also offer Web analytics, although perhaps less tightly integrated.

The problem here is that the Web analytics vendors and demand generation vendors are largely SaaS systems, so both hold the integrated customer data outside of the client’s own data center. It’s not clear that clients – especially big enterprise clients – will continue to accept this. The consumer-oriented marketing automation vendors already largely support on-premise configurations, so they may be the real winners in this battle. Yet bear in mind that looming behind the marketing automation vendors are the enterprise CRM vendors, who also offer largely on-premise installations. They may eventually gobble up the marketing automation business and the Web analytics data along with it. In that case, Web analytics vendors who hope to become rich stewards of centralized customer databases will be very disappointed.

Customer-Centricity Is Obsolete

I also think there may be one still deeper trend at play here, although this is more speculative. Let’s call it a shift from customer-centric to community-centric marketing. Since customer centricity has been the ultimate goal of marketers, or at least marketing gurus, for several decades, I expect some skepticism.

But think of it this way: marketing has always been about deploying and propagating messages to consumers. In recent years, we’ve striven and become technically more able to target those messages directly at individuals. Yet messages were never really limited to one person. Even if they were delivered privately, they could be shared directly through conversation, physical and electronic pass-along, and indirectly as consumers discussed their experiences with the company in general. Thus, there has always been a community component to marketing campaigns. In cases such as word of mouth programs, this was even the primary objective.

Today, of course, that sort of sharing has become increasingly important for every marketing project. Thus marketers have more need to track the secondary impact of their messages on the larger community. Happily, they also have more technology to do the tracking.

Here’s what’s interesting, though. Marketers will never be able to trace the exact path of each message from one person to another. And even if the data were available, there were no privacy constraints and they could handle the volume, marketers would still face the insurmountable challenge of that multiple messages influence final behavior. That is, they could never meaningfully say that one particular message was the single reason a customer did something. All they can ever do is to look at the many different messages a customer probably received and compare these with actual behavior. If the customer did what you wanted, the messages somehow worked.

If this sounds familiar, it should: it’s the classic problem faced by brand marketers in measuring the value of their investments. Their solution has always been to measure intermediate variables such as consumer attitudes, and to measure these through samples rather than by polling everyone in the market.

This brings us full circle to the panel-based attitudinal research at the core of the Omniture/comScore and Nielsen/Facebook deals. Once you recognize that what’s most important is the broad community impact of your marketing efforts, you fall back on those types of measures rather than attempting the impossible, and impossibly expensive, task of tracing the exact path followed by each individual. In other words, what we’re seeing here is not some Mad Men-style reversion to obsolete brand marketing behaviors, but a recognition that modern marketing is community-driven, so its measurements must be as well.

Now, if I can just find a reason to bring back the three-martini lunch….

0 Acxiom Uses Social Media Data to Segment Email Lists

Summary: Acxiom's new social media marketing tool gathers public data about social media links and uses it to segment email lists. It's a different, and arguably more practical, approach to helping marketers take advantage of social media.

Acxiom last week released a new “social media marketing” solution called Relevance-X Social.

The press release is frustratingly vague (“With the ability to engage socially active customers and prospects in their preferred networks, marketers can link that knowledge to relevant communications that ignite conversations on behalf of the brand.”) But, on talking to the company, it turns out there is a pretty interesting product here. (Disclosure: I am a consultant to Acxiom, although I had nothing to do with this product).

What Acxiom has done – and this is so Acxiom – is to ignore the content posted in people’s social media comments or profiles, and just capture the “hard” information about links between people and membership in groups. Apparently (and I’m taking Acxiom’s word on this), this data is publicly available from most social networks (Twitter, Facebook, MySpace, LinkedIn, Plaxo and some more specialized ones) once you know someone’s email address. So Acxiom has taken its own database of more than 500 million email addresses and found the connections for each.

Relevance-X then accepts a marketer’s own email list – presumably its customers or prospects – and returns information from its own database about the matching names. This avoids at least some privacy and spam issues, since marketers are only given information about people they already have some type of relationship with.

The main application of this information is sending targeted emails. Thus, a bank might send one message to customers who belonged to a financial planning group, and a different message to customers who don’t. Other segmentation might be based on the total number of connections, membership in the company's own fan group, or information the company already knows from other sources.

The key point here is that Acxiom is using social media to execute traditional database marketing. This is quite different from most social media marketing products, which boil down to monitoring for posts on specified topics, responding to individuals, or to publishing messages to groups through the network itself. In a way, it seems rather old-fashioned to use social media data as a basis for outbound marketing. But for marketers struggling to find a practical use for social media, it's better than many alternatives.

(As Ed Park points out in a comment below, other vendors including RapLeaf and Unbound Technology also build similar databases by capturing social media links.)

Relevance-X includes two other components. One is the ability to tag the content it publishes – such as links within emails or messages posted to group pages – so marketers can track response. This is done with standard page tags and browser cookies, so what’s important here is not the technology but the ability to measure results. Again, this is something that traditional database marketers consider essential – and that other social media products sometimes struggle to accomplish.

The other component is a separate social media monitoring service that tracks keyword mentions, sentiments and trends, but on an aggregate basis rather than by tracking individuals. Acxiom is using a third party product for this. The goal is to supplement the direct response tracking with a more general measure of marketing program impacts.

Pricing for Relevance-x Social is based on the number of relationships (typically email addresses) researched and on the number, size and complexity of the campaigns being managed. It can be purchased on a campaign-by-campaign basis or annual subscription. Pricing for a basic campaign could start at around $25,000.