I imagine consumers of the 1960s would be horrified if they knew what marketers can know about them now. To think that their thoughts can now light up a monitor in the form of brain activation, or that retailers might know that their daughter’s pregnant before they do (et tu, Target?).

50 years later, we have computers, and algorithms that used to take days to run now take seconds. While analysts of ages past were often slaves to Excel and had to build their own bridges, data analysis nowadays is much easier and comes complete with nice intuitive graphic interfaces. This democratization of data has made marketers happy, but has made market researchers somewhat wary.

Why? Because market researchers think it their prerogative to keep tabs on what’s going out to their business partners. When marketers brought home an analytics team, often conveniently embedded in their own function, market research sulked over their loss of control. When the analytics team started brandishing social media listening tools and data mining algorithms, some marketing functions started wondering if market research will go the way of dinosaurs with their surveys and focus groups. This premature tendency to ditch established methods is what one might call the Shiny Object Syndrome in analytics. Read More »

Insurance is one of the few things you buy hoping never to use. Dollar for dollar, you’d rather avoid mayhem than have Allstate (or your insurer of choice) catch you when you fall.

If we want to understand what makes the best insurance company, it pays to understand how consumers define their relationship to their insurer. Shoulder to cry on? Professional advisor? Or just service provider? Given the fluffy nature of sentiments, the only way we could make this more concrete is to have people tradeoff different sets of characteristics because in a perfect world we all want the cheap plan with extensive coverage that pays every cent while also offering timely advice.

Our particular focus is on health insurance, since flood or car insurance is less universally applicable. The way the exercise is conducted consists of us mixing and matching the insurance characteristics in the background via algorithm while respondents go through the survey picking their favorite plan out of each set of choices presented. By the time they’re done, they have implicitly drawn a preference curve and it tells us the things they care about: Read More »

When it comes to small budget items, people usually shop in one of two ways: they buy what they always buy because they’re used to it (shampoo, coffee) or they buy whatever is on sale because they can’t tell the difference (toilet paper, sandwich bag). In fact, our Decision Simplicity work from last year would show that a whopping 51% of consumers only consider one brand when buying items under $10 and 37% are buying more store brand items than they did a year ago. The room for substantive differentiation is often so small that all one can do is changing how consumers perceive your wares – like the hugely successful Miller Lite vortex bottle (> 1.4 million results when I search for “does it work?”). Fluid dynamics doesn’t usually get this exciting.

Recently, we’ve been doing our own bit of probing of what’s on consumers’ minds when making small purchases. We used salad dressing as the stand-in for all things CPG – the assumption being that you buy most of these items with a similar mindset. Through a conjoint exercise, we test consumers’ implicit preferences on several different dimensions, from price to taste, to brand, to who recommended the item to them. Read More »

Sometimes life made simple trumps life made fabulous – when you’d rather come home to a clean sink and kids in bed instead of to a candle light dinner. I suspect the same goes with banking. Personally, I’d prefer a bill with no surprise charges that gives a clear, categorized view of my spending to a new app allowing me to bank via iPad. Stack the frustration of spending an hour getting bounced around customer service against the joys of a new reward program and no wonder “Bank Dumping Day” exists.

What I want in a bank essentially comes down to the good old KISS -“Keep It Simple, Stupid”- and the 1000 or so banking customers we recently surveyed agree. To tease out their real preferences, we had them go through a conjoint exercise in which they are presented sets of banking products with different attributes and asked to choose their favorite one from each set. We then ran market simulation based on that information to see which offerings will sink or swim. Turns out that consumers flock to “straight up”, clean and hassle free banking solutions that make their lives easier. MLC’s Decision Simplicity research from last year would also suggest longer banking relationships and higher recommendation rates as a result.

Yet simple is easier said than done, because the highest form of simplicity is letting others do things their own way, some call it “Natural Pathing”, others call it following “Desire Lines”.  It is the idea that instead of designing paths as you see fit and tell people to stay off the grass, you build paths where the grass is worn by footfall. You’ve got to go with the flow because if you don’t, customers will circumnavigate, or worse yet, quit.

If you look at the chart above, most traditional banks are only scratching the surface when it comes to achieving “simplicity”, sometimes going no deeper than changing the tag line. Bank of America launched a 40 million ad effort in 2009 touting “simple, clear banking”, which Adweek considered to be “rather generic — educational, almost” and could just as easily have the names of other big banks like Citigroup, JP Morgan Chase slapped on it. Bottom line is, customers don’t care how much effort you expanded, they care about how much effort you’ve saved them.

Compare that to the likes of Mint or Simple.com. Mint positions itself as “Your financial life, all in one place” which I personally like for the fact that it gives me nice little pie charts of where I’ve spent my money. On my regular bank accounts I’d have to do that by downloading my transactions into Excel and doing the summation myself which is a drag. Simple.com lets you ask questions in normal English like “How much did I spent on taxis in New York last month?” and have real people (often the same one) answer your call at first try instead of tossing you around making selections. That’s almost Zappos-ish. I’d like to see real banks make customer service the bedrock of their business, but they seem to lack the vision, the will, or both.

Does simplicity make you invincible? No, but getting there will put you a long shot ahead of competitors. The only thing that kills simplicity in our simulation is fees – even if it’s just $3 or $5 a month. Customers said no to debit card fees, I think they mean no to basically any kind of fee.

Marketers are getting more personal. Not only do they anticipate my needs on Amazon, invite me to sign in with Facebook, they also peek at my browsing history and plant “cookies” where I can’t find them. As much as I like being delighted with right-on-target recommendation, I, as do most consumers, remember most clearly the times we’ve been annoyed. I mean all the time spent deleting and junking emails, unsubscribing, getting rid of cookies, adjusting privacy levels, putting certain numbers on the “no-call” list or just giving up.

Usually, when the customer has an issue, customer service is there to help. But in this case, the reps are often as confused as the customer. As a rep at a national retailer recently told me when I called, the personalized ad “is not on our site so it’s Pandora’s ad not ours”. With personalization being a relatively new and under-regulated phenomenon, the chance to be exactly right is often counter balanced by the chance to be completely wrong. Sophisticated algorithms running in the background don’t guarantee success – any financial firm can tell you that.

As marketers rightly understand it, personalization is on their turf. While they are positioned to take the lead in delivering greater relevance to consumers, marketers can’t hope to ace it on their own. Here’s why:

• Personalization calls for inter-departmental coordination. Your interactive marketing vendor isn’t the only party you’ve got to work with. Not letting your left hand know what the right is doing when it comes to targeting customers is inviting trouble. At the very least, sales and customer service need to know what personalization is and be able to give a informed explanation when customers call with questions/comments ranging from “Why am I seeing this?” to “Stop spamming me!” To consumers, anything with your logo on it is your ad and hence your responsibility to explain / fix / make disappear. Having a short, scripted FAQ beforehand on how personalized ads work and how settings can be adjusted could save reps from coming up with their own explanations. For sales, integrating the detailed customer data your use for personalization into the CRM system could help them gain valuable context before each conversation and more willing to track additional consumers metrics for you next time around. The simple fact is, if you don’t talk to other departments beforehand about what’s going on, they’ll come back to you later about what’s going wrong.

• Personalization calls for coordination within marketing itself. In the same vein, marketers involved in personalization shouldn’t be allowed to sit in their own niche while keeping the rest of the department in the dark. Digital and social marketers can tell you who is poking around on brand’s Facebook and campaign pages; product managers can help you zoom in on purchase motivation in a particular segment; and market research analysts have primary research and tracked metrics that would add another layer of do’s and don’ts.

Hippocrates said, “First, do no harm.” Embarrassed or annoyed consumers aren’t likely to be loyal – they said as much in our recently concluded consumer survey on personalization and privacy. The bottom line: consumer data can be bought but consumer trust cannot. We’ll talk more about how you can get personalization done right in your segment so stay tuned for more insight.

Thanksgiving is just around the corner. And if you’re the chef, you know not to overstuff your turkey unless you want to risk a bird explosion in the oven. Ask Rachael Ray or Martha Stewart if you don’t believe me.

I know, it’s tempting to put in just a little bit more deliciousness. If we have it, why not use it?  Be it the extra stuffing or the bunch of variables that haven’t made it into your very comprehensive marketing analysis. Maybe it’s the regret at seeing the extras “go to waste”; maybe it’s the urge to supersize in response to complexity and diminished control.  Cooking up an insightful model from a mountain of data can be just as stressful as cooking a banquet. Read More »

Most of us are perfectly content not knowing how everything works, we push a button, turn a handle, move a mouse or scan a card and voila! The things we want done are done. But there are times that knowing a thing or two about the mechanics saves you great trouble in the future in the forms of mistakes avoided and opportunities salvaged.

In our most recent survey on the future of marketing, 54% of B2B and 71% of B2C marketers tell us they expect marketing analytics to inform their day-to-day business decision making within 3 years (more on this in later posts). While you don’t need to become a quant person overnight, it’s helpful to understand a few key statistical concepts. Here are two things that every marketer should know about data: Read More »

Business decisions didn’t used to be numbers-based, but now the pendulum is swinging to “In God we trust, all others bring data”, for good reason.  One of the best analogies I’ve heard is that a company built on analytics is like an animal while a company with no analytics is like a plant. Analytics enables the former to respond dynamically to opportunities and dangers while the latter can only sit and wait hoping that nothing bad happens. Back when everyone is a plant (i.e. not analytical), you’re probably fine being the neighboring tuft, but the day one of them turns into a cow (i.e. becomes analytical), well, good luck.

This evolution is to a great extent accelerated by the proliferation of consumer data and the many good things you can get out of it. While you can certainly observe customers in store (people still do), it is infinitely more scalable to use web mining to “observe” thousands perusing the net, estimate what stage they are at in their purchase process and tailor your messages accordingly. Few would have heard of Signet Banking Corp but many are familiar with its spin-off Capital One, the 8^th largest bank in the country – and one that was built to a great extent on the strength of analytics.

But just like anything with massive impact, analytics can also go massively wrong and the failure very often has less to do with the tool itself than with the one wielding it. The only thing worse than no data is bad data, and the only thing worse than bad data is bad interpretations. Here’s how you go down that slippery slope and how you can climb back up: Read More »

The PhDs are hired, the software installed, the data collected and the rest of the company waits eagerly for profit to climb – why shouldn’t it, now that we have advanced analytics? Why can’t we hit a homerun just like the Oakland A’s did using sabermetrics? If Billy Beane had one Paul DePodesta, shouldn’t we do better with an entire geek squad?

We all secretly wish for a magic weapon to vanquish competition. Where better to place our faith than in a model churning out intimidating, neat lines of output? A perfect deus ex machina to get out of a sticky situation. However, as with anything complex, we forget that it’s one thing to own analytical infrastructure and another entirely to be able to use it well.  If you’ve ever gotten your parents a smartphone, you know what I mean.

Regardless of whether you’re advanced enough to implement agent based modeling or just taking baby steps beyond bar charts, a few ground rules remain the same:

• Set a target because your analysis is only as good as your data. Whether you’re pulling data from the CRM system or fielding a new survey to gauge consumer loyalty, you should know up front what you’re looking for, starting with a list of hypothesis to prove or disprove. Which metrics, markets and consumer segments are relevant? Gather the quant and non-quant people together at this stage will help you get at what is both interesting and feasible to test. If you’re pulling from existing data, be generous in allotting time to data cleaning, especially if they come from disparate business lines and geographies, something as simple as an erroneous currency or unit conversion could derail your conclusion.
• More/ fancier analytics does not equal higher “analytical maturity”. For many companies, the arrival of analytics elicits two common responses – “yay-for-hard-definitive-proof” and “who-cares-I-use-my-gut”. In their survey of 5,000 employees at 22 global companies,  our sister program for IT executives, the CIO Executive Council, found that 43% of all employees are “unquestioning empiricists”, while another 19% are “visceral decision makers” – overly reliant on or lack appreciation for data.  Only 38% are the “informed skeptics”, people who apply judgment to data, are aware of its limitations, their own biases and are ready to teach. Which group do you belong to? Take their quiz to find out.
• Build your team of “T”s. “T-shaped” talent describes people with deep knowledge in one area (the “I” part) and comfortable operating less deeply in a host of others (the “―” part). For a quant person, that means statistical expertise complemented by a healthy dose of business acumen and communication skills.  Depending on one’s level, the shape of the “T” should vary. An executive “analytical champion” (a la Thomas Davenport) would be something of a “fat T” (broader rather than deeper) whereas a “data scientist” would fit the profile of a “skinny T” (deeper rather than broader). The remaining 80% of the analytical taskforce falls somewhere in the middle.

It is easy to get lost in the numbers and forget all about intuition and judgment. In my daily encounters with data, I’m often reminded of this sobering fact: “The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.” Tip of my hat to John Tukey, the statistician who championed exploratory data analysis.

All right, Netflix has apologized to me, not just once, three times. Reed Hastings told me he “messed up” in an email last week, he also repeated himself in long form on the Netflix blog, then went on to deliver it face-to-face with colleague Andy Rendich on YouTube with a trashcan and service entrance in the background.

Being a customer, I wasn’t impressed, not because I believe Netflix has its strategy all wrong, but because they’ve missed a great opportunity. People pay attention to contrarian messages, but instead of delivering a brave statement about the future of media Netflix made many confusing statements. Saying something confusing in three different places won’t make it clearer.

If only this were simply a Netflix problem. Read More »