How to build data products that increase user engagement

Think about all the social media platforms out there, which ones do you use the most (and why)? I’m not talking about giving your LinkedIn profile a face lift before you put in a job application or searching for a long lost friend on Facebook; which of these apps are actually driving user engagement? For me, it’s Instagram; the interface is easy to navigate and more than once I’ve found myself re-opening it after I’ve just closed it. Have you thought about why many of these platforms have exploded in user engagement with many people posting to their Twitter or Facebook accounts multiple times per day? According to a recent Gartner blog, adoption rate for some of the BI tools in their Magic Quadrant are at a low but not too surprising 21%. Are people sick and tired of “all that data” or is there something more sinister at work…

We’ve thought a lot about social media platforms (and other apps) that seem to drive such high user engagement and put together a few thoughts on how you can do the same within your data product to ensure you keep users coming back for more. Before we reveal the secret sauce for building engagement in your data products, let’s take a quick look at how many analytics teams approach the problem.

Too often, teams building an analytics product for this customer’s approach the project in the wrong way, the story is oh so familiar. As we covered in a recent blog, this meant taking the reports existing in an Excel spreadsheet and web-ifying them in a cloud BI tool. It’s essentially surfacing the exact same information as before, but now with shiny new charts and graphs, more color choices, and some interactivity. After the initial excitement over the new toy in the room, the latter solution isn’t doing any better than the former at driving engagement; let alone delivering “insights” or creating a new revenue stream.

One of the big reasons customer aren’t lining up to write a check for the latest, greatest data product a vendor has rolled out is that the analytics team failed to make it engaging. Simply put, product teams need to let users know “hey—check this out,” “hey—we’ve got some important information for you”, and “hey—you should come back and see us.” Most teams do the second part, the “we’ve got insights” piece, but they fail to inform users why they need to keep coming back for more. These are essential elements of establishing engagement; not building these in is like skipping the foundation of a new skyscraper. "It's like when you see a skyscraper; you're impressed by the height, but nobody is impressed by the foundation. But make no mistake, it's important," said Akshay Tandon, Head of Strategy & Analytics at LendingTree.

Want to avoid the killer mistakes of failing to build engagement into your data product? Here’s how:

The Best Tool for Your Data Product Journey? A Good Map

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For anyone creating an analytics product, the pressures of engaging customers and generating revenue while protecting your core product and brand can be overwhelming, especially when aiming to hit so many goals on the horizon:

  • Does it target users effectively?

  • Will it guide users to a solution to their business problem?

  • Can it scale to many customers?

  • Will it deliver real results that customers are willing to pay for??

Fortunately, we've been there, done that, and understand what it takes to build a great data product. That's why we've created a map to help you navigate your way to success, built on the experience of countless voyagers who have sailed the same seas before you; the Data Product Readiness Assessment.

Keboola: Data Monetization Series Pt. 2

             

As we examined in part 1 of our Data Monetization blog series, the first step to increasing revenue with data is identifying who the analytics will be surfaced to, what their top priorities are, what questions we need to ask and which data sources we need to include.  For this blog, let’s take a look at what tools we will need to bring it all together.  

With our initial example of a VP of Sales dashboard, fortunately the secondary data sources (NetProspex, Marketo and HubSpot Signals) all integrate fairly seamlessly with the Salesforce CRM.  This should allow for some fairly straightforward analytics built on top of all the data we’ve aggregated.  If we pivot over to our CMO dashboard, things get a bit murkier.

Although our Marketo instance  easily integrates with Salesforce, the sheer volume of data sources that can provide insight to our marketing activity makes this project a much more daunting ask.  What about our social channels, Adobe Omniture, Google Ads, LinkedIn Ads, Facebook Ads, SEO as well as various spreadsheets.  In more and more instances, especially for a team managing multiple brands / channels, this number can easily shoot into the dozens.

Keboola: Data Monetization Series Pt. 1


When a company thinks about monetizing data, the things that come to mind are increasing revenue, identifying operational inefficiencies or creating a new revenue stream.  It’s important to keep in mind that these are the results of an effective strategy but can't be the only goal of the project.  In this blog series, we will exam these avenues with a focus on the added value that ultimately leads to monetization.  For this blog, lets look at it from the perspective of creating executive level dashboards at a B2B software company.

Who will be consuming the data and what do they care about?

Before we jump into the data itself, take a step back and understand who the analytics will be surfaced to and what their challenges are.  Make profiles with their top priorities, pain points and the questions they will be asking.  One way to get started is to make a persona priority matrix listing the top three to five challenges for each (ex. below.)

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Once the matrix is laid out, you can begin mapping specific questions to each priority.  What answers might help a VP of Sales increase the effectiveness of the sales team and ultimately revenue?

  • What do our highest velocity deals look like (vertical, company size, who’s involved)?

  • What do our largest deals look like?

  • Where do our deals typically get stuck in the sales process?

  • What activities and actions are our best reps performing?