Dangers of Treating Embedded Analytics like Enterprise Analytics

Building a data product can be great for your business. It comes with many added benefits such as increased revenue, more users and reduced churn. But not all companies that build a data product are able to capitalize on these. That is because of one fatal assumption. Thinking that embedded analytics is the same as enterprise analytics. 

But making such an assumption, businesses incur added costs and reduced adoption on top of losing the aforementioned benefits. But why does this happen? To find out let's go through the data product life cycle.

Embed vs enterprise

Stage 1: The big idea

Here you are conceptualizing a new product that you could introduce for your customers using the data you have. It seems like a stroke of genius. Some to add value to your users and make you more money. The excitement in the team is at an all-time high. You start looking for an analytics vendor in ernest or decide to build your own added solution. 

Stage 2: Anticipation

The anticipation grows to see your idea come to fruition. You announce the planned features to your users and they seem to be on board. But building an in-house product can be hard. Before you know, you have spent almost a year with not a lot of progress to show. The feature may be implemented by a competitor or your users are just not that interested anymore. 

 

What if you choose the other route and decided to buy an analytics solution vs build one in-house? Then you would be evaluating vendors, seeing who can best fit your needs and deliver a complete data product in the shortest time possible without any technical hassles. With the chosen vendor, finished product and eager users, you plan the launch.

Stage 3: Excitement 

The implement data product is rolled out to users and it's a hit. Everyone loves it. Everyone loves new. You have integrated it with your existing product and offered all basic features free of charge, since it is something you believe your users deserve, reserving a few advanced but not sort after features for a priced tier.

 

After all that handwork you weren’t able to generate a successful additional revenue stream, but it seems like the usage has increased and the likelihood of churn has gone down. 

Stage 4: Sadness

After a few months, the initial excitement has died. It isn’t the shiny new data product anymore and the usage declines. The users were quick to adopt because of excitement rather than usability and need. Slowly the usage rate approaches zero but never hits zero. This makes it worse. 

 

If it was zero usage you could simply cancel the data product and cut down the expense of maintaining it. But a select few users come back to your product just for the analytics. You don’t want to lose these customers so you continue to pay for a service that isn’t providing any substantial profit. 

The failure of your data product and be easily avoided by following the 5 basic steps to bustling a great data app. Watch the webinar to learn more about each step and how you can implement it. 

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