Variable Pricing, Data-Style

Variable pricing is a well-known pricing strategy that changes the price for the same product or service based on factors such as time, date, sale location and level of demand. Implemented properly, variable pricing is a powerful tool to optimize revenue.

The downside to variable pricing is that it has a bad reputation. For example, when prices go up at times of peak demand (which often translates into times of peak need), that’s variable pricing. Generally speaking, when you notice variable pricing, it’s because you’re on the wrong end of the variance.

Variable pricing lends itself nicely to data products. But rather than thinking about a traditional variable pricing strategy, consider pricing based on intensity of usage.

Intensity of usage means tying the price of your data product to how intensely a customer uses it – the greater the use, the greater the price. Intensity pricing is not an attempt to support multiple prices for the same product, but rather an attempt to tie pricing to the value derived from use of the product, with intensity of usage a proxy for value derived from the product.

For data producers, intensity-based pricing can take many forms. Here are just a few examples to fuel your thinking:

1.         Multi-user pricing. Yes, licensing multiple users and seats to large organizations is hardly a new idea. But it’s still a complex, mysterious thing to many data producers who shy away from it, leaving money on the table and probably encouraging widespread password sharing at the same time. The key to multi-user pricing is not to try and extract more from larger organizations simply because “they can afford it,” (a contentious and unsustainable approach), but to tie pricing to actual levels of usage as much as possible.

2.         Modularize data product functionality. Not every user makes use of all your features and functionality. Think about identifying those usage patterns and then re-casting your data product into modules: the more modules you use, the more you pay. We all know the selling power of those grayed-out, extra cost items on the main dashboard!

3.         Limit or meter exports. Many sales-oriented data products command high prices in part because of the contact information that they offer, such as email addresses. Unfortunately, many subscribers still view data products like these as glorified mailing lists to be used for giant email blasts. This is a high intensity use that should be priced at a premium. A growing number of data producers limit the number of records that can be downloaded in list format, charging a premium for additional records to reflect this high-intensity type of usage. It’s similarly possible to limit and then up-charge certain types of high-value reports and other results that provide value beyond the raw data itself.

4.         Modularize the dataset. Just as few users will use all the features available to them in a data product, many will not use all the datamade available to them. For example, it’s not uncommon for data producers to charge more for access to historical data because not everyone will use it, and those who do use it value it highly. Consider whether you have a similar opportunity to segment your dataset.

While your first consideration should be revenue enhancement, also keep in mind that an intensity-based pricing approach helps protect your data from abuse, permits lower entry-level price points, creates up-sell opportunities, and properly positions your data as valuable and important.

There are competitive considerations as well. When you are selling an over-stuffed data product in order to justify a high price, the easiest strategy for a competitor is to build a slimmed-down version of your product at a much lower price – Disruption 101. You simply don’t want to be selling a prix fixe product in an increasingly a la carte world (look at the cable companies and their inability to sustain bundled pricing even with near-monopoly positions).

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! 

Google: Now Organizing the World’s Data

Google's mission is “to organize the world's information and make it universally accessible and useful”. How does its newest foray into data stack up? Early this year, Google officially launched something it calls Dataset Search. It’s been in public beta since 2018 (I still contend that the concept of “public beta” remains Google’s single greatest technological innovation), but now it’s for real and according to Google, already contains information on over 25 million datasets. 

Dataset Search is loosely tied to Google Scholar, a specialized version of the Google search engine intended to make it easier to search for academic papers. Along those lines, Google sees Dataset Search as something most useful to scholars and data journalists.

Improving discovery of datasets is a worthy and important task. Quite likely, 25 million datasets are only a tiny fraction of what exists online. And in this age of open data, Google is tackling a big task at just the right time.

 Anyone can add a database to Dataset Search. Include some metatags on the relevant webpage, and the Google crawler will find it, and automatically inject a record into Dataset Search. Is it worth the effort? Well, it’s free and it’s fairly easy to participate, and it’s Google. Google does note that information in Dataset Search is added to the Google Knowledge Graph, meaning it connects Dataset Search records to all other information it knows about the organization that owns the dataset. Some suspect this may improve your overall Google search ranking, though Google is necessarily playing coy on this point.

What’s in Dataset Search today? I have to say, while it has potential, it’s going through some growing pains. Pro Publica has a very good database of financial data on non-profit organizations. However, rather than list the dataset once, Pro Publica appears to have coded its database so all 800,000 records in its database have become separate records in Database Search. Humorously, for some other organizations, a CEO headshot will be displayed instead of a company logo. This will all be corrected in time. My biggest disappointment, however, is likely to remain: Dataset Search is a database of databases searchable primarily by full text queries. There are very few parameters that can be applied to usefully narrow a search, so much like the primary Google search engine itself, you will still have to manually browse through endless search results to find what you want.

 I do want to stress that Dataset Search is open to commercial data products. It’s an easy, free way to get some additional online exposure for your products and if it bumps up your search result rankings, it’s well worth the effort. And as Dataset Search evolves, it may well become an accepted way to discover and source commercial data products. Why not get in on the ground floor?

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. 

Email: Valuable However You Look at It

The Internet has evolved dramatically in the last 25 years. But one aspect of online interaction has remained largely untouched. I am talking about the humble email address.

Despite the growth of web-based phone and video and the dominance of social media, the importance of the email address has actually increased. Indeed, the primary way to log into these other burgeoning communications channels is most commonly to use your email address as your username.  After all these years, it’s easy to take it for granted. But from a data perspective, it’s worth taking a few minutes to explore some of its hidden value.

First, an email address is a unique identifier. Yes, many people have both a personal and business email address, but those email addresses are role-based, so they generally remain unique within a given role. Moreover, many data aggregators have been busy building databases of individuals and all their known email addresses, making it easy to resolve multiple email addresses back to a single individual. 

Unique identifiers of all kinds are extraordinarily important because they provide disambiguation. That means that you can confidently match datasets based on email address because no two people can have the same email at the same time.

But email addresses aren’t just unique identifiers, they are persistent unique identifiers. That means that people don’t change them often or on a whim. Further, unlike telephone numbers, email addresses tend not to be re-issued. That’s because businesses work hard to avoid re-issuing email addresses and as to personal emails, they are typically very cheap to keep and a big hassle to change, resulting in a lot of stability.

 Let’s go a step further: email addresses are persistent, intelligent unique identifiers. At least for business use, email addresses are not only tied to a particular company, the company name is embedded in the email address. And again, data aggregators have been hard at work mapping domain names to detailed information on the companies behind them. That’s why an increasing number of B2B companies actually prohibit people from signing up for such things as a newsletter using a personal email address. A personal email address (e.g. arty1745@gmail.com) tells them little; a business email address (e.g. jsmith@pfizer.com) readily yields a wealth of company demographics with which to both target promotions and build audience profiles. Indeed, even the structure of email addresses has been monetized. There are, for example, companies that will append inferred email addresses based on the naming convention used by a specific company (e.g. first initial and last name). It’s also interesting that the top level domain can tell you the nature of the organization (e.g. “.org”), the country where it is operating (e.g. “co.uk”), and even the nature of its business (e.g. “.edu”).

 The unique format of the email address also adds to its value. While the length of an email address will vary, the overall pattern with the distinctive @ sign makes it easy to harvest and extract. It also makes it possible to link text documents (perhaps academic papers that include the email address of the author) to data records.

 Sure, email addresses have real value because they can put marketing messages under the noses of prospects, but to a data publisher, email addresses are worth a whole lot more.