Why Proponents Of Klout Are Missing the Big Picture

Jay Baer wrote a blog post titled Why Critics of Klout Are Missing the Big Picture in which he argues that  “influence measures help business create order from chaos.” Baer goes on to write:

“What’s important is to recognize that more and more and more and more of our behaviors occur online and often with the social media realm. And if companies are going to succeed in a chaotic, real-time environment, they need some mechanism – even a flawed one – to triage promotion and reaction. So yeah, Klout isn’t perfect. But instead of rehashing the same old “look how screwed up their formula is” argument, let’s focus instead on how advanced metrics will enable companies to deliver highly specific interactions with customers based on perceived influence.”

My take: Baer makes a good and valid point. But I think Baer and I might disagree on what the “big picture” is.

Baer’s definition of big picture  seems to be “making sense of chaos.” My notion of the big picture is “making the right decisions.”

And, using my definition, what I see are marketers making questionable business decisions based on people’s Klout score.

The best example I can give you to demonstrate this is the bank that’s reserving the best spots in its parking lots for its customers with a high Klout score.

Let me state this is no uncertain terms, and aim it directly at the bank with which I do business:

If you reserve the best spots in your parking lot for some pimply-faced 25 year old (who spends too much time on Facebook and Twitter and has somehow managed to get himself a high Klout score) instead of for me, then I’m pulling my millions out of your bank.

If you think I’m kidding, try me. And I’ll also pull my kids’ accounts (they’re Gen Yers, btw — not little kids), too.  THEN you’ll learn who has INFLUENCE. And when dear-old Mom and Dad (who turns 80 this year!), ask me to take over the day-to-day management of their finances, their money is getting pulled out of your bank, as well. THEN AGAIN you’ll learn who has INFLUENCE.

All because you made the bad decision to reward one group of customers over another.

Bottom line: The purpose of a business metric isn’t just making sense out of chaos — it’s taking action. And unless your customer base is made up of just heavy social media users, then making decisions on what to do based on Klout scores may lead to sub-optimal decisions. 


More Likely To Purchase: Quantipulation In Action

How many times this week have you heard about some research study that found that one consumer segment is XX% more likely to purchase your products than another segment?

These studies and claims come out every day. And every one of them is a shining example of Quantipulation: The art and act of using unverifiable math and statistics to convince people of what you believe to be true.

The problem with these “more likely to purchase” claims is that they’re leading you to make bad marketing decisions.

For example, it’s popular these days to claim that Facebook fans are an important segment of your customer base because they’re “more likely to purchase” than other customers are. DDB (a very reputable advertising and marketing services firm) conducted a study last year and found that:

“Facebook users who like a brand’s page on the site are thirty-three percent more likely to buy a product, and 92 percent more likely to recommend a product to others. “Fan status is indicative of high purchase intent, especially when compared to any traditional form of advertising, and is an even greater predictor of advocacy with over 90% noting that being a fan has a positive impact on recommending a brand to friends,” said Catherine Lautier, Director of Business Intelligence at DDB.”

The implication of this is that: 1) If marketers can drive up their brands’ Facebook fan count, then more customers will become more likely to buy, and 2) Marketers should focus their marketing efforts on Facebook fans because of higher purchase likelihood.

But there are a few problems here:

1. What does “more likely to purchase” mean? If in a survey Customer A (Facebook fan) says he’s “very likely to purchase” and Customer B (non-Facebook fan) says he’s “somewhat likely to purchase”, what does this really tell you? How much more likely is “very likely” than “somewhat likely”? Isn’t timeframe important? Is that very likely to buy in the next 2 weeks or very likely to buy at some point in the future? Even if Customer B says “not likely”, does that mean we should give up on marketing to him? Really? People don’t change their opinions? After all, he’s already a customer — and isn’t the cost of acquisition 5x higher than the cost of retention?

2. The absolute numbers might not be compelling. In the DDB study, only 36% of Facebook fans said that they were very likely to purchase. Which means that 27% of non-Facebook fans were very likely to purchase (you do the math). Assume that your company has 10 million customers, of which 1 million are Facebook fans. That means you’ve got 360,00 Facebook fans who are very likely to purchase, and 2, 430,000 non-Facebook fans that are very likely to purchase. Which group do you want to market to?

3. Causation versus correlation. Do Facebook fans become “more likely to purchase” after becoming Facebook fans, or did the fact that they were already “more likely to purchase” lead them to become Facebook fans? Granted, their act of becoming a Facebook fan helps marketers better identify them out of the pack. But if — as the numbers above indicate — the differences in likelihood to purchase aren’t that compelling, then it’s simply not a very helpful segmentation tool.

Bottom line: Don’t be quantipulated into believing these “more likely to purchase” claims.

Quantipulation: ROI Versus Success

[This is a follow-up post to Quantipulation. I thought I could get away with just floating a few ideas out there, but some comments I’ve seen suggest that there’s a lot more to quantipulation than I wrote about, and those comments are correct.]

Quantipulation — the art and act of using unverifiable math and statistics to convince people of what you believe to be true — is commonplace in the marketing world, but perhaps nowhere more so than in the social media environment. Especially when it comes to everyone’s favorite topic: Social media ROI.

Whenever I use the term ROI in my reports, the editor where I work asks me to spell it out. As she rightly says, there may be people who aren’t familiar with the term. I don’t tell her this, but if you don’t know what ROI is, I don’t want you reading my reports.

There’s another reason why she’s right: There may be people who define ROI differently than I do. I won’t tell her this, either, but those people don’t deserve to read my reports.

ROI = return on investment. It doesn’t mean return on influence or any other “I” word you can dream up. And despite what some quantipulators would have us believe there’s only one formula for ROI: Financial return divided by financial investment. The only “variable” piece to the formula is the timeframe you use to quantify these variables.

That won’t stop some people from trying to redefine the formula, however.

The most egregious example comes from a firm called Digital Royalty. I won’t besmirch my blog by linking to the offending post. Instead, I’ll point you to Anna O’Brien’s brilliant (and very funny) critique of it.

Here’s another example of ROI quantipulation:

My bet is that tthe firm that put this chart together wanted to include other ROI components, but since it would have messed up their inverted hour glass figure, they decided to leave them out.

Then there’s attempt at redefining social media ROI:

This guy has decided that the ROI unit of measure should be “conversation”. He goes on to tell us that we can measure the “value” of conversation by looking at participation, engagement, influence, imagination, energy, and stickiness. But not increased revenue or decreased cost. Sweet.

There are (at least) two things going on with these attempts to redefine ROI. One is bad, the other is good. 

The bad: An annoying attempt to demonstrate thought leadership. Ugh. Not the way to do it. Anna O’Brien said it best in her blog post: “Random metric names and symbols is not an equation.” (Maybe she didn’t say it best, because it should be “are not an equation”).

There is a good aspect to what the ROI quantipulators are doing, however. They’re raising the very valid point that there are other measures of success beyond ROI. 

There’s a formula for that, too. The one I like is from Pat LaPointe who writes a blog called Marketing NPV. Pat’s formula says that success can be measured by dividing the value added by the resources used. And as this formula implies, “value” can take on the form of many of those measures that those other people wanted to use to calculate ROI.

But this isn’t the whole formula.

Pat added something on to this formula that, as far as I’m concerned, qualifies Pat as a marketing genius. Pat’s formula for calculating success is:

(Value Added/Resources Used) * Perception

What Pat recognized was that what you might consider to be “value” might not be viewed as valuable by other people. Other people like, say, your CEO or CFO.

We’re living in an ROI culture. Suggest that your company do something, and somebody will ask “what’s the ROI on that?” If you want to get up in front of your management team and suggest that your company do something because you “feel” it’s the best thing for the company to do, go for it. Just don’t send me your resume when you’re on the street. 

That doesn’t make your feeling wrong. But being right doesn’t make you successful. Persuading others to do the right thing does. 

This is why quantipulation is so important:  Quantipulation is an attempt to influence perception. To be a successful leader, innovator, or change agent, you have to shape, change, and confirm people’s perceptions.

There’s a reason I call quantipulation an art. Successful quantipulators know that it’s about more than just the data – it’s about logic and emotion. And there’s no formula or recipe for figuring out how much logic and emotion to mix in with the data.

The examples of ROI quantipulation shown above fail not because they’re wrong, but because they fail to influence perception. Those formulas simply confirm for the social media believers what they already believe. That’s easy. Converting the heathen is hard.

Had those social media ROI formulas made any attempt to link social media results to the conventional definition of ROI — financial return — they might have been more persuasive.

Last thought: Quantipulation is not inherently bad or evil. Yes, it’s a play on the word manipulative, which doesn’t have positive connotations. But I prefer to take a more realistic view: It is what it is. And it’s a necessary skill for today’s business world.

Twitter Vs. Facebook: Which Is Better For Driving Purchase Activity?

Compete recently published a blog post called Four Things You Might Not Know About Twitter. Based on its consumer data, Compete concluded that:

“Twitter is more effective at driving purchase activity than Facebook. 56% of those who follow a brand on Twitter indicated they are “more likely” to make a purchase of that brand’s products compared to a 47% lift for those who “Like” a brand on Facebook. This is further evidence that marketers can drive ROI with Twitter by engaging followers through compelling content.”

My take: Nonsense.

Compete is off-base concluding that Twitter “drove” purchase behavior simply because a larger percentage of Twitter users are “more likely” to purchase from a brand than Facebook followers do. The only way to conclude that a source is a more effective driver is by comparing actual purchase activity resulting from specific messages or offers.

In addition, without a measure of what consumers’ purchase intention was before following a brand on Twitter or liking it on Facebook, it’s impossible to determine if Twitter or Facebook is having any impact on the customer relationship (Compete’s use of the term “lift” is inappropriate in the context it was used in).

Even if Compete had that benchmark, a change in purchase intention could not be attributed to Twitter or Facebook unless the messages, content, and offers were identical.

Bottom line: This is just one example of many that claim the “superiority” of one social media platform over another. Sadly, all of them are based on flawed data and assumptions, and misses the important point:

Different platforms are better suited for different types of messages/interactions.

It’s blindingly obvious how Facebook and Twitter differ in terms of the types of messages, interactions, and content each are suited to. As a result, the only way to determine which is more “effective” is in terms of an individual company’s objectives and needs regarding engaging with customers and prospects. And that means that “effectiveness” is based on the message or content — not the platform.

In other words, neither Twitter nor Facebook is “better” for driving purchase activity.

p.s. Note to bloggers/researchers/consultants/pundits: When publishing data that purports to claim that one social network is superior to another for driving purchase activity, ROI, or whatever metric you’re talking about, it would be very helpful if you talked about WHY one platform is better than another. I don’t think I’m asking for too much, here.

Web Analytics And Analytical Maturity

I attended the Web Analytic Association’s symposium in Boston not long ago. For a good overview of the event, see Dean Westervelt’s recap on the Metrics Marketing blog. My motivation for attending was two-fold: 1) Picking up some nuggets to add to the Online Marketing Maturity model that I’ve developed, and 2) Seeing Tom Davenport speak.

Tom is the author of Competing on Analytics and a leading light in a number of management innovations over the past 20 years. I first heard of Tom in the early 90s when he wrote a Harvard Business Review article about business process redesign. He later wrote about knowledge management, and now champions the use of analytics for management decision-making. When Tom writes, I read. I can’t say that about a lot of folks in the business world.

The gist of Tom’s presentation is that we’re in a “new quantitative” era. Businesses need new decision approaches, approaches that will be driven by enterprise analytics (which are comprised by web analytics, marketing analytics, supply chain/OR analytics, HR analytics, predictive analytics, etc.).

Tom told the web analytics folks that they need new skills. They need to fix a problem — not just identify it. They need to tell a story with data, help frame decisions, and stand firm when necessary. According to Tom:

“Analytics without plans for decision-making is a waste of time.”

I couldn’t agree more.

So it struck me as a bit off-message when Tom displayed a graphic (shown below) that purported to show analytical intelligence and maturity (other citations I’ve seen of this model label the X axis as “degree of intelligence”).


My take: This model isn’t correct.

A little context on why: I often talk about a “new” competency that marketers need: A sense-and-respond competency.

Specifically, marketers needs to sense where customers and prospects are in the buying cycle and to respond with the most appropriate message.

The problem with my idea is that it’s not a new competency. It’s always what marketing has done. Tried to sense what consumer needs and wants are, and to respond with messages, offers, and actions.

What is different about marketing in the 2000s versus 30 or 40 years ago is that the amount of information available to fuel sensing activities has proliferated. Instead of relying on demographic and (internal) purchase data, the amount of data available to marketers has become overwhelming.

The respond side of the model is also very different. Not only are there many more channels or touchpoints with which a marketer can respond in, but the timing of those messages, offers, and actions can be made much more rapidly than in the past.

So why is Davenport’s model wrong?

Because it ignores the respond side of the sense-and-respond construct. Simply developing optimization or predictive models does not make you a mature marketer if you haven’t figured out how to take the output of those models and respond effectively and in a timely manner.

Ironically, based on the comment I cited above, Tom knows this very well.

Tom also commented, when presenting this model, that web analytics was mostly focused on the bottom half of the chart. I joke-tweeted at that point that Tom had pretty much ensured that he wouldn’t get invited back to next year’s symposium. Another attendee responded that I was wrong, and that web analytics agreed with Tom, and they were “getting there” in terms of moving up the model. 

That’s all well and fine. But it might be the absolutely wrong thing to do.

Analytical maturity isn’t a function of how “intelligent” your analytical approaches and models are. It’s a function of:

1. Alignment. Specifically, the alignment of analytical approach with the type of decision that needs to be made or the issue or problem that the firm is facing. Not every business problem requires an optimization or predictive model .Would you want to take a less-than-intelligent approach to solving a business problem? Of course not. That’s why drill-downs and alerts are not less intelligent than a predictive model. It all depends on what problem is being addressed.

2. Sense-and-respond ability. Queries, drill downs, and alerts might not be high on Davenport’s intelligence scale. But they can often be accomplished more quickly than a firm can develop, test, and implement a predictive or optimization model. Often, it’s the fastest response that wins, not the most elegant.

When I first thought about the sense-and-respond construct, I thought there was a third component: Assessment. I thought “first we sense, then we respond, then we evaluate or assess our actions.” But upon further thought, I realized that wasn’t quite right. Assessment/evaluation is just another form of sensing. It’s a continuous loop — we sense, respond, sense, respond, etc. So a contributor to how mature your analytics capability is depends not just on whether you respond timely, but how well you assess how effective your analytics are, and can recalibrate and adjust your models and approaches.

3. Culture. I know of some large financial services firms with teams of statisticians who develop (and implement) predictive and optimization models, who look down on web analytics efforts as somehow being inferior to their highly statistical efforts. An analytically mature organization doesn’t have this problem.

4. Data usability. In the list of factors determining analytical maturity, this is certainly not the last one that should be mentioned, but the immature use of data often stems from a cultural immaturity. As I mentioned above. plenty of firms hare developed predictive and optimization models to drive their marketing efforts. But many still use a relatively narrow set of data to power those models. Specifically, they often do not incorporate web-based data. An analytically mature marketing department uses a range of data sources, and gathers data for their analytical efforts more effectively and efficiently than a less-mature firm.

Bottom line: I really can’t speak to analytics efforts outside of the financial services world, but among financial institutions, there is an analytics gap. The gap is that highly sophisticated statistical approaches to analytics are being undertaken, but often in a very untimely manner and with a limited set of data. On the other hand, while web analytics efforts are often more timely, and incorporate a more efficiently gathered set of data, these efforts haven’t grown beyond relatively simplistic analytic approaches.

I don’t think the web analytics folks can bridge this gap by themselves.

When my fellow Symposium attendee tweeted that web analysts were “getting there” in moving up the (so-called) intelligence scale, I couldn’t help but wonder: 1) How is that going to happen? and 2) Why hasn’t it happened before?

Here’s the problem, as I see it: Unless you’re a web analyst with a good understanding of statistical approaches AND a good understanding of business problems, decision making, marketing, and organizational politics, then I’m not sure you’re well equipped to move web analytics up the maturity scale.

I’m not saying that aren’t web analysts out there who meet this criteria, but looking at this from the other end of the spectrum, I can’t say I’ve met a lot of statisticians with a good understanding of web analytics AND a good understanding of business problems, etc.

So who’s going to bridge the gap?

My bet (hope?) is on the vendor community. When I look at what IBM is doing with its acquisition of Unica, SPSS, and Coremetrics, or an Adobe with its acquisition of Omniture (who had previously acquired Offermatica and Touch Clarity), I see the pieces coming together. The part of the maturity equation that I’m not sure the vendor community can address is the cultural component.

Whatever happens, it’s an interesting time to be involved in marketing analytics.

It’s Not This Verus That, But How Much Of This And How Much Of That

All the talk about how direct mail has run its course, the superiority of email ROI, and how influential print inserts are (relative to TV ads) is simply not very helpful. What do those who put out these research findings expect us to do? Move our entire marketing investment to the new or better investment vehicle?

If you want to put 100% of your marketing budget in print inserts because it’s better than TV, or shift 100% of your direct mail investment into email because of its higher ROI, that’s fine with me. Especially if you’re one of my competitors. Cuz’ you’re going to fail faster than Asafa Powell runs the 100 meter dash.

The issue isn’t direct mail vs. email or print inserts vs. TV, but how much of each should we do. The firms that succeed will be those that figure out the right mix. If you’re a marketer, there are three questions you need answers to:

1. What’s our current mix?
2. How did we arrive at that mix?
3. What should the mix be?

I know of firms that are deploying statistical models to attempt to answer the last question. I’m not saying that they should stop what they’re doing, but I guarantee you that if they don’t answer #2 (and #1, for that matter), their models won’t be successful.

Here’s why: Because today’s investment mix is often the result of politics, historical spending levels, budget negotiations, outdated market assumptions, ineffective or non-existent testing approaches, and, in general, an absence of strategic thinking.

When they try to implement the model’s results, because they have no handle on how much is actually being spent on what and by whom, the spending mix won’t change the way the model says it should.

In other words: Garbage in, garbage out.

In a white paper titled Competing On Analytics (which ultimately became a book of the same title) Tom Davenport wrote:

Virtually every major company uses some form of statistical or mathematical analysis, but some take analytics much further than others. [One attribute] of firms that compete on analytics: Widespread use of not just descriptive statistics, but predictive modeling and complex optimization techniques.”

Davenport is spot on. But getting there isn’t going to be easy, and I don’t think that a lot of firms really understand the cultural changes needed to get there.

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Four BS BI Trends (And One Good One)

A recent CIO magazine article listed five trends in BI (business intelligence). The article reports that Gartner found BI to be the “number one technology issue for 2007” (which I find incredibly hard to believe). Here’s my take on the trends:

BI Trend #1: There’s so much data, but too little insight.

My take: This isn’t a “trend.” It’s an observation. And an old one at that. This was the case when I started working.

According to a Gartner analyst “organizations are recognizing they don’t have the information they need to manage the business.” Hogwash. They have plenty of information, or more precisely, data. And while it’s true that disparate apps and organizational silos are barriers to accessing the data, the bigger issue is that few firms have figured out which pieces of data is truly the most important to making strategic decisions. As a result, online behavior data sits in one data store, while call center interaction data sits in another data warehouse.

BI Trend #2: Market consolidation means fewer choices for BI users.

My take: Again, not exactly a trend. Even worse, it just states the obvious. Musta took a genius to figure this one out.

BI Trend #3: BI expands from the board room to the front lines.

My take: It’s the other way around. To help explain why, it’s interesting to note the example the article provides to support this trend:

“integrating BI into operational processes could allow companies to react faster to changing business conditions, for example, alerting call center worker to offer a particular promotion or to potential credit card fraud.”

The example embodies an erroneous assumption: That it’s the boardroom that has its finger on the pulse of changing business conditions.

It’s the other way around. It’s the people on the front lines who sniff out the changing conditions — long before it works its way up to the boardroom. If anything, BI will expand from the front line to the board room.

BI Trend #4: The convergence of structured and unstructured data will create better business intelligence.

My take: Huh? This claim basically contradicts trend #1. Structured and unstructured data have been converging for years. If this hasn’t produced more insights to date (the contention of trend #1), then why will it happen next year or the year after?

The reality is that combining structured data with unstructured data does not, in and of itself, “create” better business intelligence. Better business intelligence is created when managers use structured and unstructured data in ways that they haven’t been used before. And this isn’t going to happen for a few years, and here’s why: Because us old guys are too stuck in our decision-making ways. Translation: We don’t know how to incorporate unstructured data into strategic decisions.

BI Trend #5: Applications will provide new views of BI data.

My take: Bingo. According to the article, “the next generation of BI apps is moving beyond the pie charts and bar charts into more visual depictions of data and trends.” According to the analysts quoted in the article, “alternative ways of displaying complex data — to increase interaction and usefulness — is an area that will continue growing in the coming years.”

This is the exciting trend in the BI space — tools that help managers assimilate and digest data in new ways. Tools that go beyond those silly little gas gauge icons in dashboards that BI vendors think help execs monitor trends.

And therein lies my frustration with the article — four BS BI trends, all leading up to the one trend worth expounding on. And the article devotes just three measly sentences to it.

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Global Analytics

Anunay Gupta, COO of startup analytics firm Marketelligent, contributed a great article to American Banker (pw reqd) recently, in which he predicts:

As the globalization of software-related services grows and matures, companies will look for the next area where they can improve their productivity. I predict that will mean adopting the globalization model in analytics operations. [This will require] selective access to sensitive customer information as well as tight data security. In addition, a significant part of analytics involves understanding the local economy and its consumer mindset.

My take: Marketelligent may very well be on the vanguard of a trend towards the globalization of analytics. And Mr. Gupta is certainly right that security and local knowledge is critical for this to happen. But there’s another factor that will determine the extent to which global analytics succeeds: How well it integrates back into core marketing processes.

In many firms today, key analytic functions like planning, sizing, list selection, and auditing account for half the time it takes to execute marketing campaigns. On top of that, post-campaign performance reporting is often an after-thought, and is conducted by people who had little to no involvement in the upfront campaign planning and analysis.

For global analytics — or domestically-performed analytics, for that matter — to succeed in improving productivity, analytic functions need to be better integrated with broader marketing processes.

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Customer Engagement Is Measurable

In a recent post, Avinash claims that engagement is not a metric, and writes:

Engagement is not a metric that anyone understands and even when used it rarely drives the action/improvement on the website….It is nearly impossible to define engagement in a standard way that can be applied across the board.”

My take: I think Avinash is taking an uncharacteristically narrow view of the term engagement, and the ability to measure it.

The biggest issue with the way the term engagement is used in the marketing community is its narrow connection to websites and the online channel. When marketers think of “customer engagement”, they should be thinking about how engaged the customer is with the company, product, or brand. The level of involvement with the website — or with a particular ad (online or offline) — is just one dimension of a customer’s engagement.

Customer engagement encompasses a number of dimensions:

  1. Product involvement. A customer who doesn’t care about the product, is likely to be less committed or emotionally attached to the firm providing the product.
  2. Frequency of purchase. A customer who purchases more frequently may be more engaged than other customers.
  3. Frequency of service interactions. Branding experts like to say that repeated, positive interactions lead to brand affinity. And they’re right to a certain extent, but….
  4. Types of interactions. …not all types of interactions are created equally. Checking account balances is a very different type of interaction than a request to help choose between product or service options.
  5. Online behavior. Time spent on a site might be very important. But, like types of interactions, not all web pages are created equally.
  6. Referral behavior/intention. Customer who are likely to refer a firm to friends/family might be more engaged — a customer who actually does refer the firm, even more engaged.
  7. Velocity. The rate of change in the indicators listed above may be a signal of engagement.

Avinash is on the right track, however when he says that it is nearly impossible to define engagement in a standard way. I would suggest, though, that a standard definition is feasible — but that measuring it in a universally standard way is what’s impossible.

And that’s good.

Who said we need a standard way of measuring engagement? This insistence on a standard definition and approach is to measurement is silly. You don’t hear anyone getting all worked up about the fact that market share can be calculated any number of ways, and that the denominator in that metric is hardly consistent or easily measured.

Measuring engagement needs to be done in the context of a firm’s strategy and it’s own theory of the customer — that is, what behaviors the firm believes constitutes an engaged customer.

Measured correctly, engagement meets one of Avinash’s golden rules — to me instantly useful. Using market research data, I measured customer engagement with their banks using the attributes described above.

I then segmented the respondents into four categories, based on their level of engagement, and the breadth of their relationship with their banks (based on the number of products owned). The result: A metric that is immediately useful in helping marketers address some strategic questions about their marketing and customer strategy.


Marketers need to stop getting their knickers in a knot trying to boil engagement down to a single metric that relates to a web site or the online channel. It’s a descriptor of a customer’s attitudes, not a channel’s performance.

A metric, when used appropriately, can help execs make decisions and manage. But considering the way engagement is being defined and measured today, it’s no wonder Avinash has come to the conclusions that he has.

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Announcing A New NPS Metric

Sufferers from Net Promoter Syndrome share a common symptom with those afflicted with another marketing malady called Simplificosis, the tendency to believe that just because a marketing or management metric is simpler to understand or measure than other metrics, then it must be better.

I understand that companies often make things more difficult than they need to be and that simplification has its benefits.

Example: Applying for a mortgage. The complex way: Filling out a 15 page form and waiting three weeks for an answer. The simple way: Answering three questions (what’s your name, what’s the address of the home you’re looking to buy, and how much money do you make) and getting your answer immediately.

But sometimes, simplification isn’t an improvement.

Example: Getting directions from Boston to New York. The complex way: Take the Mass Pike to the Rte 84 exit, merge onto Rte 15 south towards I-91, go east on …. and so on. The simple way: Go southwest. Correct, simpler, but not exactly an improvement.

This is the trap that Net Promoter Syndrome sufferers fall into. Paul Marsden, writing on his Viral Culture blog, wrote “the simplicity of the model…has made research intelligible at the board level.”

It’s intelligible, however, not because it’s right, but because Reichheld knows how to communicate with senior management. I hate to say it, but many market researchers don’t. Citing margins of error, R-squared scores, etc. doesn’t resonate with a lot of senior execs.

But the reality of the Net Promoter Score is that it’s really not that simple. The common practice is to only consider customers as promoters if they give a 9 or 10 on the 10-point scale. But in comparing NPS between time periods, it’s quite possible that the net score could increase while a significant number of customers shift from 7s and 8s to 1s and 2s. Not so simple, after all.

Bottom line: Simpler doesn’t mean better, nor does it make it “more right” than complex.

But hey, if it’s simpler you want, it’s simpler I’ll give you. Here’s a new metric for you. And since it might be too complex for some people to remember a new acronym, my new metric will keep the NPS moniker. Announcing the new NPS:

Net Purchaser Score — The net difference between the number of people who bought your product and the number of people who returned it.

I haven’t done the “research” yet, but I just know that my NPS will correlate with revenue and profitability growth. And the beauty of my metric is that it:

  1. Measures behavior (not intention)
  2. Encompasses all customers (not just a sample)
  3. Directly impacts the bottom line (not indirectly)
  4. Can be measured in real-time (or at least more often than surveys)
  5. Is simple!

As soon as I finish the “research”, I’ll publish the book (and I’ll even send you a signed copy if you leave a comment here). And I’ll expect all the Net Promoter promoters to drop their support of their metric, since something better — and simpler — will have come along.

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p.s. Check out Adelino’s take on this