It happens every time.  Every single time. 

From the moment I meet a customer for that very first session, the clock is ticking. It might be seconds, it might be minutes, it might be hours—but it always happens. They always ask the same question:  "how can I make money with my data?"


It's a reasonable question really, you’ve spent a lot of time and money purchasing a business intelligence platform and now you want to know how to monetize your new capabilities.  Unfortunately the monetization of analytics is often more difficult than it seems.  Everyone starts the analytics journey with dreams of untold riches built upon amazing insights for which customers will fling their cash in your direction but it seldom ends up that way.  

Why don’t the customers come running?  Why aren’t they willing to pay?  Why do so many companies, after spending so much on building analytics, opt to give the functionality away free of charge?

In the course of running my own analytic projects and working with clients over the years, I've made lots of mistakes. Many, many mistakes. But I've also learned a lot, And one of the things I've learned through this trial and error is how you can create an analytical product that people are actually willing to pay for. I'm not talking about analytics that you throw on the page and hope that people use someday, but a true product that people look at and say "I can't live without this."  

Here are three things I've learned:

Solve pain points

The first key to creating an analytical product that people will actually use is to understand the pain they are experiencing due to their current lack of data.  What can't they do because your product doesn't yet exist?  Start by picking a couple of personas which will serve as the target audience for you analytics functionality.  Once you have defined the personas, put yourself in their shoes—what would keep you up at night, what information would you break into a cold sweat at the thought of not knowing?  For a chief marketing officer, it might be "how am I spending my marketing budget, are my campaigns properly targeted, and what kind of return am I getting for my expenditures?"  Understanding the pain points experienced by the user helps you determine which analytics you need in order to alleviate the pain.

Each analytical element on the page should be directly tied to a pain point experienced by your persona.  Don't fall into the trap of placing charts on the screen because you've got the data and you want to it show off.  Everything needs to have a purpose—to solve a problem for the user.

Create a workflow

You've got the right analytics picked to solve user pain points, now you just stick them on the page, right?  Wrong.  In fact, this is both wrong and very, very commonly found in analytical tools.  Using the shotgun method of blasting analytics all over a page is a recipe for disaster for two reasons:  first, it causes the user to have to think "now where do I go next?"  This friction, while it might seem minimal, wears users down and degrades the overall experience of using your analytical product.  The second issue is that you become no different that any other product or vendor out there on the market.  Randomly scattering analytics on the page show the user "hey, we've got no real opinion on the this stuff, you're on your own!"  It's a huge missed opportunity to show that (a) you are a thought leader in your market space and (b) you are a trusted advisor for the user.  

Analytics should be organized in an "analytical workflow —that is, based on your experience with your industry, product, and data and how you recommend the user step though the information to get the most value from their time.  You've done this before.  You know your product and data better than anyone.  Share this insight with users.  Show them how they should step through the data to get the best understanding of what's going on.  This means your dashboards probably shouldn't be organized like this:  

Regions, Teams, Products, Other

but rather like this:

Big Problems, Key Metrics, Comparisons, Predictions, Actions

Let your point of view be apparent to the user.  Arranging the metrics in workflow helps them understand how best to use the data and show them that they can benefit from your experience in the field.

Layer data to create insights  

One argument I hear frequently is "we can't charge for our analytics, customers expect this kind of functionality."  It's a good point—customers do expect analytics and visualizations of data and performance these days.  But, you charge for added value.  If you can't make a case for an increase in price through the addition of analytics, perhaps you aren't adding enough value for your users.  Simply adding pretty charts and graphs to an otherwise text-heavy page isn't adding value.  To create true value for which people will pay an additional fee, you need to show them things they've never seen before.  A good way of accomplishing this is by layering data sources together to provide insights that otherwise might have been overlooked.

Here's an example...  At one client, we had lots of data on web traffic.  You could see huge amounts of information about who visited your site, when, from where, etc.  The temptation was to take this data and put it into a bunch of charts, add a few filters, and call it a day.  But this would have been a missed opportunity to add value through the use of analytics and would have made increasing the price of the product a tough proposition.  What the client did was to layer sales funnel data over top of the web traffic.  Now you could see if traffic was increasing or decreasing around the time a large sales deal was about to close.  Traffic going down as the close date approaches?  That's a sign that the customer has lost interest.  Traffic spiking?  They must be really excited about the deal!

This layer of data from various sources allows users to see relationships that they may have otherwise overlooked—and it provides real value that they couldn't have received without your analytical product.  And you charge for value.

These days when I hear that question "how can I make money with my data," I'm ready with an answer.  Solve pain points, create a workflow that reflects your expertise, and layer data to create new insights.  You've done all the hard work of implementing an analytical platform, why not take the next step and add real value for your users.  Not only does it make for a better user experience, but it transforms your data into an engaging, sticky product that will keep customers coming back.  And, one for which you can charge.