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:
Define the analytical workflow
Want to build engagement into your analytics? Think about the business problems your users are trying to solve and the process they will go through within your application to solve them. This is accomplished by creating an analytical workflow, the path of least resist users go through to get the data they need and to answer their questions. Organizing analytics into a flow which will guide users both reduces the frustrating searching and head scratching that arises from a less structured layout.
Start building your analytical workflow by determining the order that a user should view your analytics that will drive the greatest insights. For example, rather than automatically grouping analytics by region or by product, it may provide a more optimal experience to begin with an overview, identify outliers, drill into the outlier, identify the root cause, and then assign an action item for follow-up to one of your team members.
Just as each of your user personas have different missions to accomplish, the analytics included in each workflow will differ as well. This means that although the Vp of Operations and a regional manager might both be looking at data across multiple locations, the questions they need answers to are drastically different. Go down additional levels to a site manager or frontline worker, most of their analytics will be focused on shorter term, tactical activities.
Use those personas that you developed in the previous step to identify the specific objectives for the user you’re addressing. Then organize your analytics into a logical flow based on how you — the experts in your product and your data— can help them accomplish their goals most effectively. This will speed up your users time to productivity and at the same time, reinforce your position as an expert in your field. You’ve just increased the value offered to your users and as a result, increased engagement.
Build in triggers and investment mechanisms
The second way to avoid the “lack of engagement” killer mistake is to add in triggers and investment mechanisms. You might ask, and rightfully so, what the heck are triggers and investment mechanisms? (Perhaps visions of Pavlov’s dog come to mind…)
Triggers and investments are techniques based on the work of the author Nir Eyal and are detailed in his book “Hooked: How to Build Habit-Forming Products 1” which you should go read immediately if you haven’t already.
According to Eyal, triggers are one element of a successful model to "hook" or attract users to your product (and keep them coming back). A trigger can be an alert, notification or any other medium that lets the user know "hey, you should come spend some more time with this data product." It's like reloading the itch that the user needs to scratch.
Triggers come in two varieties, internal and external. Internal triggers occur, not surprisingly, in our minds; they come up often in the field of substance abuse but are also used effectively by designers of addictive, feedback loop type products such as Instagram. It could be the fear of missing out, wondering if anyone responded to your post and so on.
External triggers are things like alerts, notifications, emails, or other mechanisms to let the user know that they need to take action. These two types of triggers work hand-in-hand to increase engagement and it’s important that both of these are built into your data product. While you have some control over internal triggers by creating a compelling product that contains must-have insights for users, you have a far greater ability to build external triggers such as alerts and notifications.
Don't leave it to your users to determine when they should come back to your data product to see if anything is changed or if there are any new insights that they should look for. Rather, create an external trigger that reaches out and prompts them to return. It could be an email notification if they haven't viewed their dashboard in 48 hours or some other way of alerting them, but be aware; if you don’t account for triggers, the analytical workflow breaks down. Users can’t follow your recommended steps for data analysis if they don’t visit the application.
Investments allow users to feed information back into your data product. It can be something like a comment, and annotation, or assigning an action item to a team member. Think of it like this: how compelling would a social media platform like Facebook or Twitter be without the burning desire to see if anybody has "liked” your post?
Want to learn more about how to build great data products and avoid other costly pitfalls? Check out our white paper: Five Killer Mistakes Analytics Product Teams Make