Customers looking to embed Birst into their own product offering often ask, “How can I make money with my data?” It’s a reasonable question for organizations who have invested time and money in a business intelligence platform and now want to know how to leverage their new capabilities to increase revenue. However, the monetization of analytics doesn’t just happen—it takes a strategic approach to create an analytical product that clients are willing to pay for.

In the course of running my own analytic projects and working with clients over the years, I’ve made many mistakes, but through my own trial and error, I’ve learned how to build a true analytics product that people look at and say, “I can’t live without this.” In this blog, I’ll share my three keys to creating a money-making analytical product.

1. Solve Pain Points

The first key to creating an analytical product that your customers will actually use is to understand the pain they are experiencing due to their current lack of data. What can’t they do because analytics in your product don’t yet exist? Start by selecting two or three personas which will serve as the target audience for your analytics functionality. Once you have defined the personas, put yourself in their situation—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? What kind of return am I getting for my expenditures?” Understanding the pain points the user experiences will help 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 the personas. 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.

2. Create a Workflow

You’ve got the right analytics picked to solve user pain points, now you just plot 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 than any other product or vendor on the market. Randomly scattering analytics on the page leaves the user thinking you’re not invested in their business and are leaving them on your own to figure things out for themselves. 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 a workflow helps them understand how best to use the data and show them that they can benefit from your experience in the field.

3. 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 can 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 to accomplish this is by layering data sources together to provide insights that otherwise might have been overlooked.

For example, one of our clients had a lot of data on web traffic. You could see huge amounts of information about who visited the site, when, from where, etc. The temptation was to take this data and put it into several 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 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.

Now when I hear the 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.