So far this series of posts has described generic inputs to calculate the value of a marketing measurement project. Ordinarily an internal expert or a consultant would help define the values for those inputs. But let’s assume we’re in the self-service world where non-experts provide those values for themselves. What advice can we give them?
The first is to define the project scope. That is, you want to limit the analysis to the customers, revenues and costs that will actually be affected by the project. For example, inputs related to a Web analytics project should only include the number of Web customers, Web revenues and Web costs. But even this case isn’t so simple: if the project is measuring something specific, such as paid search results, then it may affect just a subset of all Web customers. It’s easy to overestimate the scope of a project’s impact, which in turn overstates the expected benefit. The more precise you can be in your understanding of the actual customers affected by a project, the more you’ll be able to build a realistic estimate of its impact.
A second useful consideration is the actual mechanism that will provide the expected change. Because we are talking about marketing measurement projects, all the project provides is better information about business results. Unless this information can be used to change something, it won’t have any impact. So, for example, a system to uncover immediate opportunities is worthless if you can’t react in less than a week. Similarly, a model that shows the value of changes in the marketing mix has no value if the mix won’t be changed for political reasons. A close look at how the new information will be used will allow a more precise estimate of the expected changes in value.
A third consideration is your ability to project the impact of your changes. Most marketing measurement projects can show the relative performance of current marketing programs, but only some can estimate the impact of incremental investments in those programs. This can be a particular issue where there are constraints on program expansion, such as limited advertising inventory. Where projections of incremental returns are not immediately available, you may be able to conduct additional research or use standards rules of thumb such as the square root rule to make reasonable estimates.
Finally, you need to consider the time horizon of your analysis. Most MPM projects will require a significant initial investment followed by lower, recurring operating costs. The benefits will probably follow a very different pattern, starting slowly and then growing over time. The formulas I’ve presented can be extended to create this sort of projection by doing separate calculations for a sequence of time periods. But in many cases a much simpler approach is acceptable, basically of estimating the benefit for a “mature” program for a fixed time period such as one year. This can be compared with program investment to calculate annual ROI or pay-back period.
Note that even a “simple” calculation may need to consider long-term impacts. For example, a program that acquires new customers more effectively will benefit from the full lifetime value of those customers, not simply their first-year revenue. This is where the idea of “mature” value comes in. It implies looking at a slice of results across all customer groups after the program has been in place for some time. Doing this math correctly can be fairly complex. But, again, you may be able to save effort by making some simplifying assumptions. In the case of a program that let’s you spend more efficiently on acquisitions, this might mean assuming the number of new customers will remain the same because you reduce your acquisition budget. Since the number of new customers doesn’t change, the later values also remain the same. The value of the program thus equals the savings in acquisition cost—a much simpler item to estimate. (Of course, this will underestimate the actual program value if you will in fact be maintaining your acquisition budget and acquiring more customers. Whether this matters depends on the situation.)
There are certainly other considerations that could apply in a given situation. Identifying them all may not be possible. But even if you could list them, the resulting document would be too long to be practical in a self-service situation. So we have to hope that our self-service consumers are able to complete the process on their own, or at least recognize when it’s time to call for expert help.
Thursday, August 28, 2008
Tuesday, August 26, 2008
Measuring the Value of a Marketing Measurement Project - Part 2
The first post in this series explained why I might create a standard spreadsheet to measure the value of a marketing performance measurement (MPM) project. In a traditional consulting engagement, this measurement would be a custom analysis tailored specifically to the project at hand. In the self-service world, this is an unavailable luxury. I’m starting with value measurement because I’m incurably linear and the first question to answer about a project is what value the client hopes to receive. The answer drives everything else.
In creating a generic project value form, the trick is to define a set of categories that are specific enough to be useful yet broad enough to cover all the possible cases. My inner Platonist wants to start with a general value formula such as value=revenue – costs, and then subdivide each element. But how would you know if the subcomponents corresponded to the value drivers of actual MPM projects? It’s better to start with a sample of MPM projects, identify their value drivers, and then see if these can be joined as components of a single formula. (Philosophy majors everywhere will recognize the difference between deductive and inductive reasoning. But I digress.)
A reasonable list of typical MPM projects would include marketing mix models, brand value studies, response measurements, Web analytics, social media measurement, and operational process measurements. Except for the final category, these all help allocate marketing resources to the most effective use. In contrast, operational processes help the department perform its internal functions more efficiently. This distinction immediately suggests breaking the value formula into two primary components: value received and marketing operations.
Of course, value received is the more important of the two, particularly if the calculation includes non-overhead marketing costs such as advertising, discounts and channel promotions. One way to subdivide value is to consider that a typical marketing plan will be divided among customer acquisition, development and retention programs. Of these, acquisition and retention focus on number of customers, while development focuses on value per customer. It therefore makes sense to calculate value as the product of these factors (i.e., value= number of customers x value per customer). Since many companies are more product-oriented than customer-oriented, value per customer could further be divided into value per unit and units per customer. Value per unit, in turn, could be split into revenue per unit, product cost per unit (cost of goods sold, shipping, etc.), and marketing cost per unit.
A single value for “number of customers” doesn’t really capture the dynamic between acquisition and retention rates, so it too must be broken into pieces. The basic formula is number of customers = (existing customers + customers added – customers lost).
The result of all this is a value formula with the following elements:
net value = value received – marketing operations cost
value received = number of customers x units per customer x value per unit
number of customers = existing customers + customers added - customers lost
units per customer (possibly broken down by product mix)
value per unit =revenue per unit – product cost per unit – marketing cost per unit
Now let’s do a reality check against our list of MPM projects:
- marketing mix models include product mix, pricing, advertising, and channel promotions as their major components.
- product mix is covered by revenue per unit and/or units per customer.
- pricing is covered by revenue per unit and/or marketing cost per unit, depending on how you treat discounts, coupons, etc.
- advertising is covered by marketing cost per unit
- channel promotions are covered by product cost per unit and/or marketing cost per unit
- brand value studies measure the relation of consumer attitudes to behaviors such as trial, retention and consumption rates. These are covered by existing customers, customers added, customers lost, and possibly by units per customer. A more formal sales funnel could easily fit into this section of the formula if appropriate.
- response measurements are covered by customers added and marketing cost per unit.
- Web analytics projects encompass a range of objectives such as lower cost per order, improved conversion rates and higher revenue per visitor. These are covered respectively by marketing cost per unit, number of new customers, and a combination of units per customer and revenue per unit. Other objectives could also probably be covered by the formula components.
- social media measurements are like brand value measurements: they relate messages to attitudes to behaviors. They would also be covered by changes in customer numbers and units per customer.
- operational process measurements are covered directly by marketing operations cost
So it looks like the proposed set of variables lines up reasonably well with the value drivers of typical MPM projects. This means that project planners should be able to define the expected benefits in terms of these variables without too many intermediate calculations. Once they’ve done this, the actual value calculation is purely mechanical.
The final set of inputs would look like this (with apologies for poor formatting):
inputs..................current value....expected value......change
Number of Customers:
existing customers........xxxx............xxxx...................xxxx
+ customers added........xxxx............xxxx...................xxxx
- customers lost.............xxxx............xxxx...................xxxx
Units per customer:......xxxx............xxxx...................xxxx
Value per Unit:
revenue per unit............xxxx............xxxx...................xxxx
- product cost per unit...xxxx............xxxx...................xxxx
- marketing cost/unit.....xxxx............xxxx...................xxxx
Marketing Ops Cost.......xxxx............xxxx...................xxxx
Based on those inputs, the final calculation is:
Value Received (= Number of Customers x Units per Customer x Value per Unit)
- Marketing Ops Cost
= Net Value
OK, so now we have a formula that calculates business value using inputs relevant to marketing measurement projects. But the real work is deciding what values to apply to those inputs. Part 3 of this sequence will talk about that.
In creating a generic project value form, the trick is to define a set of categories that are specific enough to be useful yet broad enough to cover all the possible cases. My inner Platonist wants to start with a general value formula such as value=revenue – costs, and then subdivide each element. But how would you know if the subcomponents corresponded to the value drivers of actual MPM projects? It’s better to start with a sample of MPM projects, identify their value drivers, and then see if these can be joined as components of a single formula. (Philosophy majors everywhere will recognize the difference between deductive and inductive reasoning. But I digress.)
A reasonable list of typical MPM projects would include marketing mix models, brand value studies, response measurements, Web analytics, social media measurement, and operational process measurements. Except for the final category, these all help allocate marketing resources to the most effective use. In contrast, operational processes help the department perform its internal functions more efficiently. This distinction immediately suggests breaking the value formula into two primary components: value received and marketing operations.
Of course, value received is the more important of the two, particularly if the calculation includes non-overhead marketing costs such as advertising, discounts and channel promotions. One way to subdivide value is to consider that a typical marketing plan will be divided among customer acquisition, development and retention programs. Of these, acquisition and retention focus on number of customers, while development focuses on value per customer. It therefore makes sense to calculate value as the product of these factors (i.e., value= number of customers x value per customer). Since many companies are more product-oriented than customer-oriented, value per customer could further be divided into value per unit and units per customer. Value per unit, in turn, could be split into revenue per unit, product cost per unit (cost of goods sold, shipping, etc.), and marketing cost per unit.
A single value for “number of customers” doesn’t really capture the dynamic between acquisition and retention rates, so it too must be broken into pieces. The basic formula is number of customers = (existing customers + customers added – customers lost).
The result of all this is a value formula with the following elements:
net value = value received – marketing operations cost
value received = number of customers x units per customer x value per unit
number of customers = existing customers + customers added - customers lost
units per customer (possibly broken down by product mix)
value per unit =revenue per unit – product cost per unit – marketing cost per unit
Now let’s do a reality check against our list of MPM projects:
- marketing mix models include product mix, pricing, advertising, and channel promotions as their major components.
- product mix is covered by revenue per unit and/or units per customer.
- pricing is covered by revenue per unit and/or marketing cost per unit, depending on how you treat discounts, coupons, etc.
- advertising is covered by marketing cost per unit
- channel promotions are covered by product cost per unit and/or marketing cost per unit
- brand value studies measure the relation of consumer attitudes to behaviors such as trial, retention and consumption rates. These are covered by existing customers, customers added, customers lost, and possibly by units per customer. A more formal sales funnel could easily fit into this section of the formula if appropriate.
- response measurements are covered by customers added and marketing cost per unit.
- Web analytics projects encompass a range of objectives such as lower cost per order, improved conversion rates and higher revenue per visitor. These are covered respectively by marketing cost per unit, number of new customers, and a combination of units per customer and revenue per unit. Other objectives could also probably be covered by the formula components.
- social media measurements are like brand value measurements: they relate messages to attitudes to behaviors. They would also be covered by changes in customer numbers and units per customer.
- operational process measurements are covered directly by marketing operations cost
So it looks like the proposed set of variables lines up reasonably well with the value drivers of typical MPM projects. This means that project planners should be able to define the expected benefits in terms of these variables without too many intermediate calculations. Once they’ve done this, the actual value calculation is purely mechanical.
The final set of inputs would look like this (with apologies for poor formatting):
inputs..................current value....expected value......change
Number of Customers:
existing customers........xxxx............xxxx...................xxxx
+ customers added........xxxx............xxxx...................xxxx
- customers lost.............xxxx............xxxx...................xxxx
Units per customer:......xxxx............xxxx...................xxxx
Value per Unit:
revenue per unit............xxxx............xxxx...................xxxx
- product cost per unit...xxxx............xxxx...................xxxx
- marketing cost/unit.....xxxx............xxxx...................xxxx
Marketing Ops Cost.......xxxx............xxxx...................xxxx
Based on those inputs, the final calculation is:
Value Received (= Number of Customers x Units per Customer x Value per Unit)
- Marketing Ops Cost
= Net Value
OK, so now we have a formula that calculates business value using inputs relevant to marketing measurement projects. But the real work is deciding what values to apply to those inputs. Part 3 of this sequence will talk about that.
Sunday, August 24, 2008
Measuring the Value of a Marketing Measurement Project - Part 1
I’ve been toying recently with the notion that traditional consulting is being replaced by a self-service model. In this vision, “clients” would fill out standard scorecards to guide them through an analysis, and then use the results to tell them what to do next. For example, a “gap analysis” scorecard might list various Web analytics capabilities; seeing which were missing from the client’s own company would show where it is weak. Obviously an expert must still create the scorecards and define the actions implied by different answers. But once this is done, the consultant is out of the picture and largely out of a job.
I’m not yet certain whether this truly makes sense. Consultants have always had forms of their own, so that part isn’t new. Creating “intelligent” forms that present recommendations based on user entries is something that takes a bit of technology, but nothing major. You could do it in Excel. In any case, it’s logically no different from looking up the answers in a book. So there’s nothing significantly new there, either.
If anything material has actually changed, it’s the attitudes of the erstwhile clients. People today are simply used to looking up information for themselves instead of relying on experts for the answers. So maybe they’ve just now become ready for a self-service solution that could have been provided long ago.
As a professional consultant, I don’t find this a pleasant prospect. I certainly believe that my experience, intuition and judgment can’t be captured in a set of simple decision rules. But what I believe doesn’t matter: it’s what the paying customers believe. If they convince themselves that selecting a vendor can as automated as selecting an airline ticket, then I will be as obsolete as your local travel agent.
Of course, the way to avoid that fate is to demonstrate added value, and I do try. Still, it never hurts to hedge your bets. So I’ve been thinking about what kind of forms I’d need if my business evolved away from traditional consulting towards a self-service model.
(Incidentally, I’m very eager to coin the phrase “Consulting as a Service” to describe this approach. It’s a bit redundant, since consulting has always been a service. But it does capture the remote-access, plug-and-play nature of the thing, not to mention sounding delightfully trendy. Or is the whole “[Whatever]-as-a-Service” patter already passé? How about “cloud-based consulting” instead?)
The next post in this series will look at a specific spreadsheet: one to measure the value of a proposed marketing performance measurement project.
I’m not yet certain whether this truly makes sense. Consultants have always had forms of their own, so that part isn’t new. Creating “intelligent” forms that present recommendations based on user entries is something that takes a bit of technology, but nothing major. You could do it in Excel. In any case, it’s logically no different from looking up the answers in a book. So there’s nothing significantly new there, either.
If anything material has actually changed, it’s the attitudes of the erstwhile clients. People today are simply used to looking up information for themselves instead of relying on experts for the answers. So maybe they’ve just now become ready for a self-service solution that could have been provided long ago.
As a professional consultant, I don’t find this a pleasant prospect. I certainly believe that my experience, intuition and judgment can’t be captured in a set of simple decision rules. But what I believe doesn’t matter: it’s what the paying customers believe. If they convince themselves that selecting a vendor can as automated as selecting an airline ticket, then I will be as obsolete as your local travel agent.
Of course, the way to avoid that fate is to demonstrate added value, and I do try. Still, it never hurts to hedge your bets. So I’ve been thinking about what kind of forms I’d need if my business evolved away from traditional consulting towards a self-service model.
(Incidentally, I’m very eager to coin the phrase “Consulting as a Service” to describe this approach. It’s a bit redundant, since consulting has always been a service. But it does capture the remote-access, plug-and-play nature of the thing, not to mention sounding delightfully trendy. Or is the whole “[Whatever]-as-a-Service” patter already passé? How about “cloud-based consulting” instead?)
The next post in this series will look at a specific spreadsheet: one to measure the value of a proposed marketing performance measurement project.
Thursday, August 7, 2008
External Data Will Help To Explain Customer Behavior
Let’s continue a bit with last week’s post about capturing all the marketing messages received by customers and prospects. Although this is becoming easier and less expensive, it will never be completely simple or free. So there will always be a need to measure the cost of gathering the information against its value.
The value will ultimately be two-fold. Retrospectively, the information will help to measure the impact of past messages, enabling optimal allocation of marketing investments. This works at the aggregate level, and is traditional marketing measurement.
But we also want to use information proactively, to help target individuals. Consumption of marketing messages, particularly in online media, is closely related to customer choices about which Web pages to visit or which offers to accept. This behavior provides vital insights into the customer’s current state of mind and, thus, which future treatments are most likely to be productive. Here we’re moving into a different type of marketing measurement, which involves predictions of individual behavior.
I suppose we could debate whether this type of activity, which itself is not new, really should be labeled as marketing measurement. But the particular point I had in mind today was even complete information about a customer’s interactions with the company will not be enough to accurately predict behavior. There are many external factors as well. So individual-level predictions will be much more useful if they can be based on a combination of internal and external data.
This isn’t a particularly brilliant insight, or a new one. But what is new is the greater availability of external information, such as data about individuals’ backgrounds, news about their companies, and public attitudes revealed through online discussions such as forums, blogs, and reviews. I haven’t compiled a comprehensive list of the information sources, but companies like Jigsaw (online directory of individuals and companies), InsideView (aggregation of news about companies) and Twing (tracking of online discussions) keep popping up. Any predictive system would benefit from incorporating their contents. Benefits include gaining background on customers and prospects (which should yield insights into the approaches most likely to be effective), and identifying specific events (which might prompt new needs for particular products).
The most important value from these sources will come from better sales results. But retrospective marketing measurement will also benefit because it will more precisely identify the factors that impact the results of a given marketing effort. Thus, the focus will increasingly shift from “whether” a particular marketing program worked, to “when” (i.e., under which conditions) it works. This will guide both treatments of specific individuals and the over-all allocation of marketing resources. Once that happens, the distinction between retrospective and proactive marketing measurement will be less important: they will effectively be the same thing.
The value will ultimately be two-fold. Retrospectively, the information will help to measure the impact of past messages, enabling optimal allocation of marketing investments. This works at the aggregate level, and is traditional marketing measurement.
But we also want to use information proactively, to help target individuals. Consumption of marketing messages, particularly in online media, is closely related to customer choices about which Web pages to visit or which offers to accept. This behavior provides vital insights into the customer’s current state of mind and, thus, which future treatments are most likely to be productive. Here we’re moving into a different type of marketing measurement, which involves predictions of individual behavior.
I suppose we could debate whether this type of activity, which itself is not new, really should be labeled as marketing measurement. But the particular point I had in mind today was even complete information about a customer’s interactions with the company will not be enough to accurately predict behavior. There are many external factors as well. So individual-level predictions will be much more useful if they can be based on a combination of internal and external data.
This isn’t a particularly brilliant insight, or a new one. But what is new is the greater availability of external information, such as data about individuals’ backgrounds, news about their companies, and public attitudes revealed through online discussions such as forums, blogs, and reviews. I haven’t compiled a comprehensive list of the information sources, but companies like Jigsaw (online directory of individuals and companies), InsideView (aggregation of news about companies) and Twing (tracking of online discussions) keep popping up. Any predictive system would benefit from incorporating their contents. Benefits include gaining background on customers and prospects (which should yield insights into the approaches most likely to be effective), and identifying specific events (which might prompt new needs for particular products).
The most important value from these sources will come from better sales results. But retrospective marketing measurement will also benefit because it will more precisely identify the factors that impact the results of a given marketing effort. Thus, the focus will increasingly shift from “whether” a particular marketing program worked, to “when” (i.e., under which conditions) it works. This will guide both treatments of specific individuals and the over-all allocation of marketing resources. Once that happens, the distinction between retrospective and proactive marketing measurement will be less important: they will effectively be the same thing.
Friday, August 1, 2008
'Total Marketing Measurement' Is Closer Than You Think
As I’ve mentioned previously, most of my research these days is targeted at demand generation systems. So I was a little surprised that one of the demand generation products, ActiveConversion, positioned itself as a “total marketing measurement” system, complete with three letter abbreviation of TMM. A quick glance at the company Web site, followed by a conversation with president Fred Yee, confirmed that ActiveConversion has functions similar to other demand generation products: it sends email campaigns, tracks the resulting Web visits, nurtures leads until they are mature, and then turns them over to sales. ActiveConversion is a little unusual in not providing tools to build Web pages. Instead, it tracks behavior by embedding tags pages built externally. But this makes little practical difference, and the vendor will add page generation functions later this year.
Why, then, does ActiveConversion call itself a TMM? I think it’s mostly a bit of innocent marketing patter, but it also reflects the system’s ability to track Web visitors’ behavior in great detail and report it to salespeople. This is common among demand management products, but not a feature of traditional Web analytics, which is more about group behaviors such as how many times each page is viewed. In some sense, this detailed individual could reasonably be described as “total” measurement.
In practice, the “total” measurement by demand generation systems is limited to behavior on company Web pages. This is far from the complete set of interactions between a company and its customers. But as the scope of recorded transactions expands relentlessly—something I personally consider an Orwellian nightmare, but see as inevitable—it’s worth contemplating a world where “total” measurement truly does capture all behaviors of each individual. At that point, marketers will no longer be able to hide behind the traditional barrier of not knowing which messages reached each customer. This will leave them face to face with the need to take all this data and make sense of it.
Much as I love technology, I suspect there are significant limits to how accurately it will be able to measure marketing results at the individual level. But meaningful predictions should be possible for groups of people, and will yield substantial improvements in marketing effectiveness compared with what we have today. But this will only happen if marketers make a substantial investment in new measurement techniques, which in turn requires that marketers believe those techniques are important. The only way they will believe this is to see early successes, which is why marketers must start working today to find effective approaches, even if they are based on partial data. After all, it’s certain that the quality of the information will improve. What’s in question is whether marketers make good use of it.
Why, then, does ActiveConversion call itself a TMM? I think it’s mostly a bit of innocent marketing patter, but it also reflects the system’s ability to track Web visitors’ behavior in great detail and report it to salespeople. This is common among demand management products, but not a feature of traditional Web analytics, which is more about group behaviors such as how many times each page is viewed. In some sense, this detailed individual could reasonably be described as “total” measurement.
In practice, the “total” measurement by demand generation systems is limited to behavior on company Web pages. This is far from the complete set of interactions between a company and its customers. But as the scope of recorded transactions expands relentlessly—something I personally consider an Orwellian nightmare, but see as inevitable—it’s worth contemplating a world where “total” measurement truly does capture all behaviors of each individual. At that point, marketers will no longer be able to hide behind the traditional barrier of not knowing which messages reached each customer. This will leave them face to face with the need to take all this data and make sense of it.
Much as I love technology, I suspect there are significant limits to how accurately it will be able to measure marketing results at the individual level. But meaningful predictions should be possible for groups of people, and will yield substantial improvements in marketing effectiveness compared with what we have today. But this will only happen if marketers make a substantial investment in new measurement techniques, which in turn requires that marketers believe those techniques are important. The only way they will believe this is to see early successes, which is why marketers must start working today to find effective approaches, even if they are based on partial data. After all, it’s certain that the quality of the information will improve. What’s in question is whether marketers make good use of it.
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