In Praise of Small Data

I can't read anything these days without hearing about "big data." Just popped over to Google News today and learned that Cloudera, a company basically distributing an easier-to-use version of open-source Hadoop as I understand it, raised $65 million in a valuation pegging them as a $700 million company. Holy mackerel!

These crazy valuations put me in mind of what I call "small data." If big data means synthesizing meaning from a million different pieces of disparate information coming from a variety of sources, little data means synthesizing meaning from several thousand pieces of information. In the former case, think of my company Vocollect's wearable computers collecting thousands of bits of information about thousands of distribution center picks per day from hundreds of thousands of workers. In the latter case, think of my company's less than five thousand customers.

Of course there are exciting things to be discovered from the millions of interactions we see from the wearable computers. But there are even more valuable things we could learn from our existing customer base, and I have found that most companies--even gigantic, multi-billion dollar ones--are sorely lacking in the ability to aggregate, clean, and take meaning from these existing customers.

Back at one of my last jobs, we found after six months of aggregating and cleaning that 25% of our sales were coming from 300 customers out of 40,000. You might hear people talk about the "80/20" rule, but that's the "25/1" rule for those of you keeping track. As in, "25% of our revenue comes from 1% of our customer base"! You better bet that the sales leadership, marketing department, customer service team, and even the VP now know the names of every single one of those 300 customers and that the company treats them a lot better than they used to.

Little data is about making small investments in technology, process, and people power to get better information that you should already have access to today. The focus requires all three:

1. Technology: This is the area everyone always thinks about when data analysis discussions bubble to the surface. Here, I advocate both investments in technology to store the data like Salesforce.com, but also technology to clean the data so it's not completely worthless. How useful is it to sell your brand new freezer-rated wireless headset to current customers if you don't know which ones have freezers? Acquiring the information that's missing requires the second investment...
2. Process: Great "little data" companies fix the problems of who is responsible for information-gathering, how the information gets into the system in the first place, how you compare it against other systems to ensure links and accuracy, and how it gets cleaned and updated over time. Each of these process fixes ensures that when marketing or sales or finance go to use the information, it gives an accurate and up-to-date picture of the business. That's not possible without...
3. People power: Great companies assign responsibilities and ownership for the information and, yes, pay for it when necessary. The CEB, my first company, was better at this than any company for which I have ever worked. The way they ensured information was retained was to withhold sales commissions unless the information made its way into ELvIS, our Enterprise-Level Information System (precursor to a real CRM). ELvIS was, by the way, built on MS Access but worked just fine for a long time because of the people and process controls in place. Proving that you don't need a top-flight CRM until the body of data gets too large to manage.

Don't get me wrong. I am generally a huge fan of big data. That's one of the reasons I continue to be bullish on Google, the company with more data than possibly any other company in the world (and a company that understands its value). I'm just saying that small- to medium-sized companies can do amazing things with little data if they pay attention to it and manage it well. That's why you need to hire somebody with experience in this kind of "little data" program and then put serious management attention and focus around it.

A little self-promotion here: I have a lot of experience with "little data." If you ever want to get serious about selling to your existing customers and finding more customers that look like your existing ones, give me a call.

Eye Tracking Revisited

A few years ago, I looked at Tobii's cool eye tracking technology as a possible means of evaluating the effectiveness of paint color merchandisers. I ended up getting a new job before I could complete the project, which was a crying shame given the phenomenally stupid metrics the company was using at the time to determine effectiveness of the display, such as number of color chips pulled per year. Like discrete choice research or any of the other "real life" simulation tools gaining in popularity (has anyone seen the growth of Affinova lately?), eye tracking opened the potential for us to figure out what the consumer really wanted to see rather than what we thought we wanted the consumer to see.

So I was excited to see that one of the Next Gen Market Research 2012 award winners was a company I had never heard about called Eye Track Shop. They claim to have perfected the ability to perform eye tracking using a regular Webcam rather than using expensive equipment like Tobii requires. If market researchers on the client side got the tiniest bit creative with this technology and it really worked, this change in cost could offer a revolution in a huge number of businesses.

Even in our business making industrial hardware, the user interface is critical. We now have the potential to borrow a handful of users for short periods of time over the Web to get reactions to early prototypes before we spend millions on tooling for a product that wouldn't otherwise gain user acceptance. We could also easily test iterations of our asset management console to see what improvements made it more user-friendly. We could even present prospects with versions of our trade show displays to determine what grabbed the most attention.

Imagine the possibilities! What about A/B testing on physical packaging without ever having to ship the package? Store display pre-testing for seasonal merchandising? Improved impact testing of direct mail calls to action? All now possible with inexpensive eye tracking.

Makes me want to start a market research firm. Stay tuned.

Simple Modeling

For all you people who thought I was going to talk about supermodels, you can stop reading now.

Today's post is about the kind of model you use to determine your forecasted sales or the effects of a future rebate or the effect of a new product introduction. I have been thinking a lot about this kind of modeling lately because of Nate Silver, the statistics genius who accurately predicted the election results two nights ago. Today, the Guardian had an awesome explanation of the likely content of Nate Silver's model which is worth reading in its entirety.

Although Silver apparently uses an advanced statistical technique called hierarchical modeling to perform his analysis, a manager needn't have a degree in statistics to use something more basic but still useful. I put together a similar but simpler model at Strategic Energy using Crystal Ball, an Excel spreadsheet plug-in now owned by Oracle. The software allowed me to build inputs that had an effect on energy prices and then run a series of simulations describing what would happen to electricity prices if my various inputs fluctuated. I chose how each input would fluctuate (for example, natural gas prices might fluctuate in a normal curve by plus or minus 10%) over a period of time, and the model told me the statistical likelihood that the electricity price would get into the range at which we could compete against the regulated utility price.

It's relatively easy to use this kind of modeling in all sorts of applications. I used it again at PPG to help forecast exterior paint sales, using simple inputs we knew to affect our sales such as temperature, rebates, competitor rebates, advertising, and price competition. This analysis helped to show how unprofitable our existing rebate program was and how dramatically temperature spikes increased our paint sales, both of which led to savings and greater on-shelf inventory at our retail customers.

Amidst all this usefulness, I'm constantly amazed when managers prefer to use experience and judgement rather than data to make decisions. Crystal Ball costs all of $995. Why leave your decisions up to chance when you can get fairly accurate help from a fairly simple model for a fairly cheap price (or free if you're willing to learn the R statistics package)? Alternatively, you could spend hundreds of millions of dollars and just ignore the models like this guy did. Good luck with that.

Read This Now

I was lucky to attend business school with some really smart folks. One is Kerry Edelstein, who founded Research Narrative a year ago today. She has a great post today about interesting questions in media research. It's worth reading particularly because of the emphasis on the business decisions made based on the research. You all know I'm a huge fan of determining the decision you're going to make before doing the research, so I couldn't agree more.

Attention to all full-service market research firms out there: don't forget the message! I always prefer you to come back with a viewpoint. If I don't like what the research said, I can dispute your interpretation with facts, but I (hopefully, if you have done good research) can't dispute the facts themselves. Now, it's up to you to present a story about the facts and help me understand what to do as a result. Then listen to me and help guide my restatement of the story in a way I can tell management.

If Kerry continues to do that for her clients, Research Narrative should go far.

Poll Watchers Beware

Every presidential election year, I find myself re-addicted to an awesome source of polling data, pollingreport.com. These guys aggregate the raw results of various independent polls and post them in a mostly unexpurgated format. I only wish I could do cross-tabs to break down the results further (e.g., by number of Democrats versus Republicans, age, sex, income, and so forth). Frankly, I find the raw data much more enlightening than much of the terrible commentary. [One notable exception to the usual polling pablum was today's excellent Dianne Rehm show with two experts breaking down the polling into the necessary detail.]

Particularly telling is the number of people who are "unsure" or "refused" as reported in some of these polls. The numbers are as high as 8% in some polls, suggesting that a lot of people are either still undecided, are dedicated to the old-fashioned privacy policy about politics, or are just sick of being asked. Nevertheless, one sees that Obama has quite a lead in a number of these polls when voters are given the option to be unsure.

I often find that business executives want to ignore the "don't know" responses in survey data. I believe they think the results are somehow less meaningful if a lot of respondents don't know the answers. On the contrary, I think executives can learn a lot when people are given the "don't know" option.

For example, when I was on the Paint Consumers Research Program board, we changed the survey to allow respondents to say "don't know" when asked what price they paid for paint. Not only did we get much more accurate results, we discovered that almost half of respondents don't know what they paid, even when the purchase was a month ago or less. From this, I learned that price is a lot less important than I think most paint industry executives think it is. In fact, I believe that price point (low, middle or high in the store's assortment) is probably much more critical in paint buyers' decisions than actual real price. This effect could explain in part why consumers are willing to pay $50 per gallon at Sherwin-Williams when they can get decent paint at $35 per gallon at Lowe's or Home Depot.

Some of the most important decisions in new product development fall to market research interpretation, so I believe everyone involved needs to take a closer look at the results. Surprisingly, for example, the products most likely to succeed are often the products with the most positive responses and the most negative responses. When respondents rate new product ideas, the lack of a strong visceral reaction usually indicates disinterest whereas a strong negative reaction can mean that they have a real interest in the product but are not willing to buy it themselves. A number of market research startups have popped up recently to capitalize on this idea by having respondents design products "for other people" instead of making decisions with themselves in mind.

Perhaps this could be good news for Mitt Romney, whose negative ratings have been going through the roof lately. But not if you subscribe to the idea that real money markets can predict presidential elections. If that is true, our next four years will be Obama's second term.

The Globalization Dilemma

My present job includes "Pricing Manager" among the various job descriptions. Facing a challenge in getting IT time to fix our quoting tool (let's face it -- who hasn't had this problem at a company that is not Google?), I turned again as I have in the past to outsourcing. I have successfully used Guru.com in the past to find someone to do the work, but this time I turned to oDesk due to the nature of the work. Within days, I had found a Ukrainian developer with an amazing command of English and 20 years of experience in Java and Visual Basic including extensive work on Excel applications.

As I symbolic analyst, I often find this kind of experience troubling. When it comes down to it, most of my job could be performed anywhere in the world. I often suspect that most of the companies that hire me could find someone in India with my exact qualifications plus a Ph.D. and a background in computer science for 70% of my salary. George, my new Ukranian developer, earns $25 an hour for doing work for which I would probably pay $45 an hour at a minimum in the U.S. His English is so good that he knew the idiomatic phrase, "The devil is in the details." [Funny note of the day: in Ukranian, the literal translation of their equivalent phrase would be, "If your head is stupid on details, your legs go this way and that."]

On the bright side, this kind of internationalization means that local understanding and specialized skills can be in demand anywhere. For the market research expert in me, I find the outsourcing experience liberating because I know that some of my expertise and specialization in the U.S. consumer and B2B research market cannot be matched by someone else. Moreover, the internationalization gives me the opportunity to apply these skills to companies interested in selling into the U.S.

As a sidebar, I am in love with oDesk's awesome contractor time tracking tool called "Work Diary." It takes snapshots of your contractor's work periodically to show what they have been doing with their time. From the client's perspective, this approach gives me confidence that the contractor is working on my job when he says he is working. From the contractor's perspective, Work Diary makes it easy to track billable hours to your client and provides proof that you are billing for legitimate work if the client questions what is taking so long on an hourly project.

I foresee a future in which the percentage of work done on this kind of contract basis goes up dramatically. I can imagine that a number of companies interested in entering the U.S. market would not want to hire a market research professional full-time to do the market entry due diligence and might not have the money (or knowledge or project management abilities) to employ a full-service market research firm. These firms might turn to someone like me for a time-limited engagement that would expand their knowledge as much as they need to take the first steps in the U.S.

Overall, I think I am looking forward to this future, working on varied engaging projects for a range of interesting companies. I just have to get over my natural fear of being replaced by someone less expensive.

Breaking the Sound Barrier

Felix Baumgartner recently broke the world record for the highest skydive at 128,000 feet. The Guardian had an excellent story today about the partnership between Red Bull and Baumgartner. What I love about this idea is breaking the sound barrier... for the brand.

The "sound barrier" I'm talking about is the clutter of noise in today's multi-channel, multi-media environment. I was writing about this problem back in 1994 when I interned at advertising agency Ingalls, Quinn & Johnson in Boston before Facebook was even a twinkle in Zuckerberg's eye (I think he would have been getting his first pimple around that time). Media clutter has gotten so much worse in so many ways since then.

Breaking through the clutter often requires doing something that has never been done before. For Red Bull, it means an outlandish partnership that could have landed the brand in some trouble if Felix Baumgartner had been injured or killed. But for your brand, the partnership doesn't have to be so outlandish. For example, Barack Obama in 2008 created the world's first true nationwide, cloud-based expert system for elections that targeted voters at the individual level with grass-roots (read: millions of volunteers) targeting. This effort was a huge risk although not to the brand itself. Rather, Obama risked misusing millions of campaign dollars that had traditionally been spent on TV.

I have spoken before about one of my favorite marketing books: Mark Stevens' Your Marketing Sucks. Underneath the unpleasant title are many great tales of how to create breakthrough marketing, like Red Bull's stunt, that push the limit of marketing. His premise, with which I heartily agree, is that if you're not making a spectacle of yourself for the sake of the brand, you're probably wasting your money. If nobody sees the marketing and nobody responds, you wasted the money. Period.