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 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.