This brings us to the next set of “gears” in our Insight Engine: the investment tactics.  The ‘tactics’ are the narrowly-defined, individual elements of marketing campaigns, product release plans, sales initiatives, relationship building programs, variable service offerings, etc. They represent, and are framed as, investments that are made in order to impact customer behavior.  They combine to form larger investment plans and scenarios and should collectively (often with some creativity) account for the total costs of those scenarios.

In order to mesh with the other pieces of our model, the investment tactics need to be defined in such a way as to:

1.) Relate to individual customers (i.e., the cost of the tactic can be allocated across the customer / consumer set it is meant to influence)

2.) Include variable components that are clearly defined (such as “size of target population,” “duration of impact,” and/or “cost of unit of investment” [a ‘unit,’ where applicable, might be defined as a direct mail piece, a loyalty “point,” a promotional incentive, a service interaction, etc.] )

3.) Provide a way to scale components (i.e., scales or value sets are defined for such variable factors as number of direct mail pieces, amount of incentive, size of target population, types of feature changes, etc.)

4.) Have identified mindset targets (i.e., the decision criteria they aim to influence, such as “have control of the process,” or “feel respected,” or “admire the brand”)

5.) (Initially) include estimates of the degree of impact the investment tactic is expected to have on targeted mindsets and behaviors.

Any type of investment that is aimed at impacting customer behavior can be incorporated into this framework (which, if investment efficiency is your goal, should constitute a major chunk of your total expenditures.)  All marketing, sales, and service, and most R&D activities likely fit into this category.  While there’s certainly no need to boil the ocean on Day 1 (a model focused on a single initiative is generally the place to start), it’s useful to get a sense of the potentially broad applicability of a long-term model… i.e., all the areas in which it could improve investment efficiencies.

While linking individual investment tactics to individual mindset targets is a largely subjective process, the rigor of simply going through this process can already begin to strengthen investment efficiency.  Each investment has a cost.  In order for any investment to generate a return, it has to create an impact on customer behavior (we’ve already excluded investments in internal efficiency).  That behavior is determined by whether the customer’s decision criteria are met.  Providing, then, an explicit case for how an investment tactic is going to influence each (known) customer decision criterion (and knowing the relative impact of each criterion on each behavior) allows for rapid, intuitive vetting of proposed tactics.  Placing these assessments back in the context of the customer “stories” allows yet another level of intuitive assessment.

While the “gut check” method is a good place to start, an on-going measurement system will start to provide hard data to fill in these impacts.  Even when new initiatives don’t fully resemble the old, a database of initiative characteristics, along with measured impacts and qualitative post mortems that are well-categorized, will ensure estimates always trend toward greater accuracy as time goes on.

The key is to define the behaviors and their associated metrics and algorithms in a way that ensures they are:

1.) Clearly measurable (ideally they are already being measured in some form)

2.) Indicative of direct, individual customer action (i.e., something a customer does or does not decide to do).  This also typically ensures they have traceable impact on financial value.

3.) Driven by an isolated set of decision criteria (more on this in a minute…)

4.) MECE (that is, “mutually exclusive, collectively exhaustive,” so that adding together the individual impacts of every behavior gives you a sum that is exactly equal to the total financial impact of all consumer behavior).  This is where the puzzle comes in!

Essentially, we’re trying to link how “value” is measured (e.g., revenue, profit, market share) with how individual customers behave (e.g., “buy product,” “use service,” “refer a friend.”)Now as you might be able to guess, this can become an onerously complex exercise if you let it.  Linking traditional accounting metrics to this ‘behavior perspective’ can become a headache if you take it so far as to parse all the components involved in margin calculations and try and map to every metric in a product-oriented accounting system.  My perspective on this one is somewhat like the Web 2.0 philosophy of perpetual beta.  Build it basic but good enough to show its value, put it in play, and start a cycle of feedback and iteration until it “evolves” into what you need it be.  Start with behaviors based on existing measures of product margins, make the puzzle fit as best you can with an understood margin of error, and leave second and third order considerations out until you have broad buy-in and clear justification in place for addressing them.  Trust me, the level of insight you’ll see generated from even a ‘bubble gum and bailing wire’ Insight Engine will be highly useful.

Let’s look at a few simple examples of behaviors.  Let’s pretend we’re a company that sells widgets over the internet.  You might start your list of desirable customer behaviors with “buy widgets.”  This meets our first criteria above… almost certainly our company is already tracking how many widgets it sells.  And it meets our second criteria as well… customers decide on whether or not to purchase widgets and then act on that decision.  It’s actually not that strong on the 3^rd criteria, that the decision be based on just one set of decision criteria, for reasons that are somewhat subtle.  Typically, marketers break out the “trial” stage from the “repeat purchase” stage, largely because they are often driven by different decision criteria (for example, ‘reputation’ may matter more to the first purchase decision and ‘satisfaction’ more to the second).  For the same reason, we’ll be better served when we go to link these behaviors with customer decision criteria if we break “buy widgets” into the distinct behaviors of “Try” and “Buy Again.”  Let’s take it a step further…. say that widgets are something a typical customer buys every few months.  Instead of “Buy Again,” we may want to define a “typical customer profile” and have our post-trial purchase behavior be “Become a Regular Customer.”  Doing this let’s us then establish other customer behaviors relative to our “typical” baseline, such as “Buy More Widgets [than average customers do],” “Buy More Often,” “Stay a Regular Customer Longer.”  This then lets us focus on what differentiates a typical customer from an avid customer, which can be a source of invaluable insight (think 80/20 rule). 

For each of these behaviors, we need a way to measure it and determine its financial impact.  For example, the behavior of “Try” can be measured by the number of initial purchases by first time customers during a specified time interval, such as in a month.  (Note that this definition requires us to keep track of who’s buying what, which might not be the right way to go for all businesses; another example of the need for customization).  Our value algorithm may be as simple as the net margin on a widget (if we’ve chosen profit as our financial impact measure).  “Buy More Often” might be measured as the number of purchases per year relative to the typical (baseline) customer.  We might need two value algorithms here, based on whether we’re using our model to predict future impacts or account for things that have already happened.  The prospective measure might be linked to the margin on an average purchase order, while the retrospective measure might take into account whether increased frequency dilutes purchase order size by linking to an average purchase order amount for purchases made above the baseline.  As you can see, this is inevitably an iterative process (although really not as complicated as I’m making it sound.)

And the MECE challenge… As I implied before, there really is no formula for this; it just takes practice and iteration, communication and vetting.  The behaviors have to be measurable and meaningful and their links to financial value mappable.  Defining an average customer baseline can be very helpful, as it allows you to build behavior algorithms related to customer quantity (e.g., “Become a New Customer,” “Stay Longer as a Customer”) using an “average customer value” parameter.  You then don’t have to contend with multiplicative impacts when also including incremental existing customer behaviors (e.g., “Buy More,” “Buy Different Product Lines,” etc.)

While assigning value algorithms to behaviors such as “Buy a Widget for the First Time” are pretty straightforward, more complex behaviors can also certainly be incorporated into our model.  In the marketing-cluttered world we live in, one of the most powerful value drivers today is customer referral.  Here, the metric may be number (or percent) of new customer trials resulting from referrals.  Again, there’s a data tracking component required for measuring retrospective impacts, but more and more businesses seemed to have already incorporated this one in a standard upfront question of, “Where did you hear about us?”  The value algorithm might weight the value of an average customer with the likelihood of becoming an average customer and credit all customers generated by referral to that behavior. 

Sometimes value comes from a source not directly related to the customer actions that are a focus of your business.  For example, if you have a website that primarily generates revenues from on-site advertising, the customer behaviors you are most interested in aren’t necessarily those of “click ad,” which is easy to link to revenue, but those of “visit site” and behaviors that lead to deeper levels of engagement with your content so as to promote loyalty and return visits.  In these cases, it’s often helpful to calculate and assign a dollar value to an intermediate metric, such as “revenue per page visit,” (which can be determined based on an estimated likelihood of clicking ads) and which provides a base for behaviors such as “visit more pages,” “visit more often,” etc.

My apologies for going into the weeds in a few places in this post.  Perhaps in a later post I’ll pick an example and work it through as that may better demonstrate it’s not nearly as complicated as I’ve made it sound.

To build this connection end to end, it actually helps to start in the middle, between how customers feel and your bottom line. The relevant question here is, “What are the things that customers need to do to drive the value of your business?” Are your revenues generated directly from customer sales? Or do you generate revenues from advertisers that pay to sponsor your site because consumers visit it? How much does your business rely on people spreading the word?

It usually doesn’t take much to make a list of all these desired “behaviors.” For example, “buy product A,” “use service B,” “refer friends,” “pay on time,” etc. There are some challenges to this exercise when it comes time to isolate their independent impacts on financial performance, but I’ll save that for a later post. For now, a basic idea of customer behaviors will do.

In this glorious internet age, it’s become an increasingly easy (and cheap) exercise to gather reliable, multiple choice-type survey information from current and prospective customers.  If structured properly, we can get out of this one of the key ingredients in our model: measures of how the different aspects of mindset impact the likelihood of customers to behave in the ways we’d like them to. If we survey enough people, we can even look at how these levels of impact play out differently among different groups of customers (e.g., how different people are motivated in different ways).

I can’t state enough how important it is to get the right mindset measures in place. That story exercise I keep harping on really makes a difference! I did a bunch of work in the ‘90’s with one of the original contributors to the Service Profit Chain, an ambitious model that looked to quantitatively link investments made in employees to how “satisfied” employees were to how they performed their jobs to how “satisfied” this made customers to how customers then behaved to how this impacted the bottom line. Whew! It was always interesting to put this all together for companies, and we always found evidence that this chain of events does measurably exist. But sometimes we would get excited about relationships between factors that weren’t all that big (that would say, for instance, that one factor in the chain could be seen to account for <20% of the outcome of the next factor). A lot of studies done since have found that “satisfaction” isn’t generally the best predictor of behavior (although all of the suggested alternatives still suffer from being generic, rather than business-specific, mindset indicators). While using even weak metrics can still demonstrate important impacts, using good metrics can actually make the model useable.

From the research, we should be able to walk away with a decent set of starter values for our model that link how people feel to how they are likely to behave.  And if we’ve gathered information from enough people, we should have separate sets of impact factors for distinct groups of customers. The model will be strongest if we choose to distinguish customers (to segment them) based on these impact values*, as they take into account both their motivations and their behavioral tendencies. Just as getting the right mindset measures is a critical factor, defining customer groupings in just the right way is also a critical driver of our engine’s efficiency.

While the survey method is a great way to get us started, the strongest model will have evolved over time from measures of actual customer behavior. With a well-developed measurement framework in place (and, after some time up and running, the utility of the model clearly demonstrated), we then have reason to build out data collection and analysis processes aimed at updating and refining the measures of impact in the model.

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*Footnote: Technically speaking, if we’ve structured the research so as to be able to statistically derive impact coefficients (vs. asking respondents to provide them outright) then these variables don’t exist in a one-to-one relationship with respondents so can’t be used in a segmentation exercise. However, there are ways to cluster respondents based on their combined answers to individual mindset and behavior questions and thereby “sort” them according to impact similarities

A lot of customer research will look at factors such as “price” and “value” as drivers of customer mindset. The problem with these types of indicators is that, really, they don’t tell you much. If a customer tells you they don’t buy your product because it’s too expensive, all they’re really saying is that it’s not worth enough for them to pay that price. But why is it not worth enough to them? If you’re trying to sell an over-priced commodity, the answer is pretty obvious. Otherwise, one has to assume, you’ve priced your products the way you have for a reason. Somehow you’re not tripping the wire in the consumer mind that says “worth it.” Why not?

If you’ve got your stories right, then this will become apparent. Perhaps you’re trying to sell them an information service, and while the information would be useful to them, they’re resentful of all the information that gets dumped in their inboxes and all the sorting it requires. It may be that their issue isn’t “price” at all… it could be delivery method. They may not consider the service worth the price in that form, but that doesn’t mean they wouldn’t pay for it as, perhaps, continuous access to an access-as-needed web portal.

Instead of “price,” a better mindset lever in this case would be “control,” as in do they feel they have adequate say in how they access and use your product. As you might guess, it’d be pretty hard to come up with this as a key behavior driver off the top of your head. But that again is why the stories are so valuable….they illuminate what’s actually happening in the consumer decision-making process. While there’s something of an art to extracting just the right set of metrics from a collection of stories, it’s nonetheless a much more robust starting point than a list of generic indicators that are ultimately capable of only providing limited insight.

If you’re familiar at all with Six Sigma, TQM, Balanced Scorecards or any of the other management trends from the latter half of the last century, then you’ve heard the old saw “you get what you measure.” In other words, if you really want to control the attitudes that drive customers to make your business profitable, you need to keep tabs on those attitudes. But even that’s not enough…if you really want to make the customer perspective a part of the way your company makes its strategic and investment decisions, you need to “link it up” to the things that matter to those decision makers (like “increasing market share,” or “improving ROI’s,” or “driving revenues.”) I’ve too often seen elaborate “customer satisfaction” programs that hassle customers at every instance to find out “Did you like that? Are you satisfied?” that never make a dent in any decision-making outside the research department (if they even count for much there). Or research results (that cost a pretty penny to put together) sit in fat dusty binders on a shelf or (only slightly better) be splayed out on dashboards that no one ever looks at.

So the challenge is two-fold: to keep yourself firmly planted in the customer mind and to keep the customer perspective firmly planted in your business. The way to do that? Good metrics.

The right metric model should:

1.) Incorporate measures of customer attitudes that reflect the actual drivers of customer behavior

2.) Account for exactly how those attitudes impact customer behavior (as in their measurable impact)

3.) Link directly to “strategic” metrics, such as revenue, profit, and market share

4.) Be readily attainable from customers

Tomorrow I’ll share some more on what it takes to put together a model like this.

So how can you get inside the heads of your customers and potential customers, to find out why they make the decisions they do and even what would encourage them to potentially make different choices? Tell their stories.

Stories are the fundamental way we make sense of the world. It’s generally how we create our identities (“This is where I started from and here’s what happened to me along the way…”). It’s how we communicate values (e.g., Bible stories), entertain ourselves (books, television), and learn new things (“it’s like when …”). I even read an article recently about how our presidential candidates may be competing more on “literary narrative” than anything else.

Stories “catalyze understanding”… they force the listener to participate in figuring out what is being said…”what’s her point in telling me this story?”… which means the listener first has to relate it to their own experience….to put themselves in the story.

Stories also illuminate needs and motivations that might otherwise be hard to articulate, and meeting unarticulated needs can often lead to breakthrough opportunities (think Netflix and iPod). It seems a simple concept, to step into the customers’ shoes, but surprisingly few companies are good at it. According to Patricia Seybold (who has many bright things to say about understanding customers), “[Most companies are] so focused on fine-tuning their own offerings that they’ve failed to see how those products and services fit into the real lives of their customers.“

The idea behind telling the customer story is to put you into their story. If you can relate convincingly to their story… who they are, what motivates them, the context of their daily lives, how they come into contact with your product or service, how they perceive it and why, what they do and why… then you’ve got it. You should be able to predict the outcome of customer-impacting investment scenarios just by checking your gut. Hey, if you can identify so well with Tony Soprano, you can “get” your customers.

A recent company I worked with, a firm that sold consumer software to retail banks, had, before I started, numerous product demos, pro forma arguments, positive research results (even from live pilots) and a good-sized sales team with some impressive credentials. Yet they still hadn’t managed a new client sale in a year and half. After talking with some people both inside and outside the company, I helped them to tell the customer story (through a video demo) that finally created the “I get it” moment for clients and customers alike (and even, to a surprising degree, for the employees!). This simple story led directly to 6 new client sales in the next 6 months, in affect doubling the company’s annual revenues for the year. That’s the power of story.

So why don’t they? Because it’s not an easy thing to master. You may already be familiar with some of the limitations of typical market research. Did you know that approximately 80% of new products and services fail within 6 months or fall far short of expected results, even with the green light from market research? And focus groups, one of the most popular ways to test a new product or service offering, can cost hundreds of thousands to conduct (even more if you’re trying to get a truly representative sampling of customers).

Traditional quantitative research doesn’t tend to be much better. The good old standby, “satisfaction” scores have been shown time and again to be poor indicators of actual customer behavior. And even if they were a decent indicator, they don’t tell you much about what to do to change anything.

Another issue is that it’s very hard to be inside trying to see what you look like from the outside… it’s hard from within a company to see it the way customers do. A Bain study from 2004 showed that while 80% of firms believed they offered a “superior experience” to their customers, only 8% of their customers, on average, felt that way.

And finally, you can’t just “buy” successful innovation. Apple doesn’t spend proportionally more on R&D than do it’s competitors. A study done by Bain Capital looked at around 100 successful and unsuccessful software startups and found that what differentiated these firms was NOT the amount they spent on sales and marketing, NOT the amount they spent on R&D, NOT whether they were first to market, and NOT whether, even, they had the “best” product.

The difference? The successful startups were those that had a clear sales message that resonated with customers and had brought customer feedback into product development early on.