Good Data + Good Analytics = Good Business

Mere weeks ago, I made my predictions of what this decade would bring for the data industry. I said that while the decade we just left behind was largely about collecting and organizing data, the decade in front of us would be about putting these massive datasets to use. Machine learning and artificial intelligence are poised to make data even more powerful and thus valuable … provided of course the underlying data are properly structured and standardized.

Leave it to 2013 Infocommerce Model of Excellence winner Segmint to immediately show us what these predictions mean in practice through its recent acquisition of the Product and Service Taxonomy division of WAND Inc. WAND, by the way, is a 2004 Infocommerce Model of Excellence winner, making us especially proud to report this combination of capabilities.

Segmint is tackling a huge opportunity by helping banks better understand their customers for marketing and other purposes. Banks capture tremendous amounts of transactional activity, much of it in real-time. The banking industry has also invested billions of dollars in building data warehouses to store this information. So far, so good. But if you want to derive insights from all these data, you have to be able to confidently roll it up to get summary data. And that’s where banks came up short. You can’t assess customer spending on home furnishings unless you can identify credit card merchants who sell home furnishings. That’s where Segmint and WAND come in. How many ways can people abbreviate and misspell the name “Home Depot”? Multiply that by billions of transactions and millions of companies, and you start to get the idea of both the problem and the opportunity.

When WAND is done cleaning and standardizing the data, Segmint goes to work with its proprietary segmentation and predictive analytics tools. Segmint helps bank marketers understand the lifestyle characteristics of its customers and target them with appropriate messages both to aid retention and sell new products. These segments are continuously updated via real-time feeds from its bank customers (all fully anonymized). With that level of high quality, real-time and granular data, Segmint can readily move from profiling customers to predicting their needs and interests.

Simply put: this is the future of the data business. It starts with the clean-up work nobody else wants to do (and it’s why data scientists spend more time cleaning data than analyzing it) and then uses advanced software to find actionable, profitable insights from the patterns in that data. This is the magic of the data business that will be realized in this new decade. And we couldn’t be prouder that two Infocommerce Model of Excellence winners are leading the way … together. Congrats to both! 

What Facebook Knows and Doesn’t Know

Privacy concerns have been in the forefront of the news lately, and no article discussing privacy is complete without mentioning Facebook. That’s because Facebook is considered to be the all-knowing machine that’s tirelessly collecting data about us and turning it into insights that can be used to better market things to us with extreme precision. Certainly Facebook isn’t the only online juggernaut with this strategy and sophisticated data collection capabilities, but in many ways it’s the poster child for our collective concerns and anxieties.

I joined Facebook in 2007. At the time, it was becoming the next big thing, and I wanted to see what it was all about. After some initial excitement, I noticed my usage dropping as the years went by. My usage massively dropped in 2019 when I somehow changed my default language settings to German and I didn’t feel any real urgency to figure out how to undo it, all this to say I am certainly not a typical Facebook user.

While not a high intensity Facebook user, I am a high intensity data nerd, so when I read an article that explained how to peek under the hood to see in detail what Facebook knows about you, and what it has learned about me from third parties, I of course could not resist. If your interest is equally high, start your journey here: https://www.facebook.com/off_facebook_activity/

I clicked all the options so that I could see everything Facebook knew about me. While not a heavy user, I was a long-term user, and I imagined Facebook had likely learned a lot about me in 13 years. In due course, Facebook presented me with a downloadable Zip file that contained a number of folders.

The folder “Ads and Businesses” turned out to be the money folder. This is where I learned my personal interests as divined by Facebook – all individual categories that can be selected by marketers. Here are some highlights of my interests:

• Cast iron (who doesn’t love cast iron?)

• Scratching (what can I say?)

• Tesla (Facebook helpfully clarified that my interest was not in the car, but rather the band … the band?)

• Oysters (I don’t eat them)

• Skiing (I don’t ski)

• Star Trek (absolutely true – when I was about 14 years old)

 There were about 50 interest categories in all; not all wrong, but overall far from an accurate picture. What I infer by looking at these interest categories is that they are keywords crudely extracted from various ads I had clicked on over the years. I say “crudely” because these interest tags don’t represent an organized taxonomy; there is no hierarchy, and there is only a lackluster attempt to disambiguate. For example, one of my interests is “online.” Without any context, this is useless information. And if Facebook assesses the recency of my interests, or the intensity of my interest (how many times, for example, did I look at things relating to cast iron?), it is not sharing these data with its users.

If Facebook underwhelmed me with its insights into my interests, the listing of “Advertisers who uploaded a contact list with my information” totally confused me. I was presented with a list of literally hundreds of businesses that ostensibly had my contact information and had tried to match it to my Facebook data. What I saw on this list were probably close to a hundred local car dealerships from all over the country, followed by almost as many local real estate agencies. I feel certain, for example, that I have never visited the website of, much less interacted with, International Honda of Sheboygan, WI. But this car dealership – reportedly – has my contact information and is matching it to Facebook.

There are a few possible explanations for this. The one I find most likely is that in the case of automobiles, some unscrupulous middlemen are selling the same file of “leads” to unsuspecting car dealers nationwide. It could also be inexperienced or bad marketers or marketing agencies. Some free advice to Toledo Edison, Maybelline, The Property Girls of Michigan, Bank Midwest and Choctaw Casinos and Resorts – take a look at your list sources and maybe even your marketing strategies, because something seems broken.

Looking at your own Facebook data gives you a rare opportunity to see and evaluate what’s going on behind the curtain. To me, Facebook’s secret sauce really doesn’t appear to be its technology. Grabbing keywords from ads I have clicked is utterly banal. Offering marketers hundreds of thousands of interest tags does in fact allows for extreme microtargeting, but in the sloppiest, laziest possible way. Capturing all my ad clicks is useful and valuable, but hardly cutting edge. What appears to make Facebook so valuable seems not to be the data it has collected, but the fact it has collected data on a hitherto unknown scale. Knowing that I have an interest in flax (yes, this is really one of my reputed interests!) even if true is pretty useless until you get enough scale to identify thousands of people interested in flax, at which point this obscure data point suddenly acquires monetary value.

What my Facebook  data suggest is that while it may not be good enough to deliver the precision and accuracy many marketers have bought into, what it has done is create “good enough” data at extreme scale. And that is proving to be even better than good enough. 

AI in Action

Two well-known and highly successful data producers, Morningstar and Spiceworks, have both just announced new capabilities built on artificial intelligence (AI) technology. 

Artificial Intelligence is a much-abused umbrella term for a number of distinctive technologies. Speaking very generally, the power of AI initially came from sheer computer processing power. Consider how early AI was applied to the game of chess. The “AI advantage” came from the ability to quickly assess every possible combination of moves and likely responses, as well as having access to a library of all the best moves of the world’s best chess players. It was a brute force approach, and it worked.

Machine learning is a more nuanced approach to AI where the system is fed both large amounts of raw data and examples of desirable outcomes. The software actually learns from these examples and is able to generate successful outcomes of its own using the raw data it is supplied. 

There’s more, much more, to AI, but the power and potential is clear.

So how are data producers using AI? In the case of Morningstar, it has partnered with a company called Mercer to create a huge pool of quantitative and qualitative data, to help investment advisors make smarter decisions for their clients. The application of AI here is to create what is essentially a next generation search engine that moves far beyond keyword searching to make powerful connections between disparate collections of data to identify not only the most relevant results, but to pull meaning out of those search results as well.

 At Spiceworks (a 2010 Model of Excellence), AI is powering two uses. The first is also a supercharged search function, designed to make it easier for IT buyers to more quickly access relevant buying information, something that is particularly important in an industry with so much volatility and change.

Spiceworks is also using AI to power a sell-side application that ingests the billions of data signals created on the Spiceworks platform each day to help marketers better target in-market buyers of specific products and services.

As the data business has evolved from offering fast access to the most data to fast access to the most relevant data, AI looks to play an increasingly important and central role. These two industry innovators, both past Models of Excellence m are blazing the trail for the rest of us, and they are well worth watching to see how their integration of AI into their businesses evolves over time.

For reference:

Spiceworks Model of Excellence profile
Morningstar Model of Excellence Profile

LinkedIn: A D&B For People?

I joined LinkedIn in 2004. I didn’t discover LinkedIn on my own; like many of you, I received an invitation to connect with someone already on LinkedIn, and this required me to create a profile. I did, and became part of what I still believe is one of the most remarkable contributory databases ever created.

Those of you who remember LinkedIn in its early days (it was one of our Models of Excellence in 2004), remember its original premise: making connections – the concept of “six degrees of separation” brought to life. With LinkedIn, you would be able to contact anyone by leveraging “friend of a friend” connections.

It was an original idea, and a nifty piece of programming, but it proved hard to monetize. The key problem is that the people most interested in the idea of contacting someone three hops removed from them were salespeople. People proved remarkably resistant to helping strangers access their friends to make sales pitches. LinkedIn tried all sorts of clever tweaks, but there clearly wasn’t a business opportunity in this approach.

What saved LinkedIn in this early phase was a pivot to selling database access to recruiters. A database this big, deep and current was an obvious winner and it generated significant revenue. But there are ultimately only so many recruiters and large employers to sell to, and that was a problem for LinkedIn, whose ambitions had always been huge.

Where things got off the tracks for LinkedIn was the rise of Facebook, Twitter and the other social networks. Superficially, LinkedIn looked like a B2B social network, and LinkedIn was under tremendous pressure to accept this characterization, because it did wonders for both its profile and its valuation. LinkedIn created a Twitter-like newsfeed (albeit one without character limits), and invested massive resources to promote it. Did it work? My sense is that it didn’t. I never go into LinkedIn with the goal of reading my news feed, and I have the same complaint about it as I have about Twitter: it’s a massive, relentless steam of unorganized content, very little of which is original, and very little of which is useful. 

Today, LinkedIn to me is an endless stream of connection requests from strangers who want to sell me something. LinkedIn today is regular emails reminding me of birthdays of people I barely know because I, like everyone else, have been remarkably undisciplined about accepting new connection requests over the years. LinkedIn is also just one more content dump that I barely glance at, and it’s less and less useful as a database as both its data and search tools are increasingly restricted in order to incent me to become a paid subscriber.

Am I predicting the demise of LinkedIn? Absolutely not! What LinkedIn needs now is another pivot, back to its database roots. It needs to back away from its social media framing, and think of itself more like a Dun & Bradstreet for people. LinkedIn has to use its proven creativity and the resources of its parent to embed itself so deeply into the fabric of business that one’s career is dependent on a current LinkedIn profile. LinkedIn should create tools for HR departments to access and leverage all the structured content in the LinkedIn database so that they will in turn insist on a LinkedIn profile from all candidates and employees. Resurrect the idea of serving as the internal company directory for companies (and deeply integrate it into Microsoft network management tools). Most exciting of all to me is the opportunity to leverage LinkedIn data within Outlook for filtering and prioritizing email – big opportunities that go far beyond the baby steps we’ve seen so far.

I think LinkedIn’s future is bright indeed, but it depends on management focusing on its remarkable data trove, rather than being a Facebook for business. 

Good Ideas Any Publisher Can Use

A recent article in Forbes offers a very thoughtful interview with Marvin Shanken, founder of the eponymous M. Shanken Publications, a company best known for its titles such as Wine Spectator and Cigar Aficionado.

Marvin Shanken is more than a successful publishing entrepreneur. He’s also a true industry innovator. He has started publications that were mocked at launch because nobody thought they had a chance, before they went on to achieve remarkable success. He blends B2B and B2C publishing strategies in ways that few have tried. He’s stayed focused on print more than his peers and continues to profit handsomely from doing so. 

Shanken attributes his success to the quality of his content, and there is no doubt he produces smart, passionate content for smart, passionate audiences. But as the article notes, that alone is not enough these days. So what’s his secret? I think it’s a series of things. Interestingly, many are concepts we’ve held out to data publishers over the years. Let’s review just a few:

First and foremost, Shanken makes his publications central to their markets. His primary technique: rankings and ratings. By offering trusted, independent ratings on a huge number of wines, Wine Spectator in particular began to drive sales because its audience relied on it so heavily. This in turn caused retailers to promote the ratings to drive more sales. That in turn forced wine producers to highlight the ratings, and in many cases, to advertise as well. Wine Spectator is a central player and made itself a real force in the wine business. This drives both readership and advertising.

Secondly, Shanken gets data the way few B2C publishers do. You can’t spend much time on the Wine Spectator website without getting multiple offers to subscribe to the Wine Spectator database – reviews and ratings on a remarkable 378,000 wines. Content never ends up on the floor at M. Shanken Publications – it’s systematically re-used to create not the typical, mediocre searchable archive offered by most publishers, but rather a high-value searchable database. It’s more work but it’s work that yields a lot of revenue opportunity.

Third, Shanken believes in premium pricing because it reinforces the quality of his content. There is something of a universal truth here, provided you don’t go crazy. I can think of few data publishers who charge for their content “by the pound” and are at the same time market leaders.

Finally, Shanken sees the power of what I call crossover markets, where there is an opportunity for a B2B publisher to repurpose its content as B2C.  Indeed, Shanken got into many of his current titles by creating glossy B2C magazines from modest B2B titles.  But he hasn’t exited B2B: he successfully publishes for both business and consumer audiences.

There’s more, much more, but you get the idea. Some of the key success strategies in data publishing work just as well in other forms of publishing because they are so powerful and so fundamental.