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Predictive Analytics

How to Price Your Predictive Application

By Sriram Parthasarathy | March 6, 2019
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In a recent survey of 500 application teams, predictive analytics was the number one feature being added to product roadmaps. It’s clear why: Predictive analytics solves critical business challenges and adds tremendous value to applications.

>> Related: How to Package and Price Embedded Analytics <<

As application vendors begin to add predictive insights into their applications, the question becomes: How should they price predictive capabilities? Let’s look at the best (and worst) ways to price and package predictive analytics with your product.

The Wrong Way to Price Predictive Analytics

Say you have a cloud-based customer churn reporting application, and your customers pay a $500 per month subscription to use it. Then you add predictive features as a new module. How will you price that module?

A common predictive analytics pricing strategy is to choose a price point—such as 30 to 50 percent—and add that on top of the current monthly license. If we pick 50 percent, then the new module will be priced at $250 per month. But is that a good way to price your product?

Your price should be based on supply and demand as well as the value your product offers. A typical dashboard application is already a commodity, so it is difficult to charge a premium since many vendors offer similar application functionality. On the other hand, predictive analytics is a hot technology. It provides unique insights into the future, and few vendors have incorporated it in their products. Those who roll out predictive analytics features first will have a head start in capturing the market.

So, even though it may seem like a 50 percent markup is reasonable, that doesn’t get to the heart of the value of predictive analytics. The answer to our earlier question is no: Basing the price of a new premium feature (predictive analytics) off of your commodity features (embedded analytics) is not a good way to price your product.

The Right Way to Price Predictive

If the traditional pricing strategy is out, how do we price your predictive application? Since our new module can predict who will churn, let’s identify what value the module offers for a customer. Say a customer is losing one million dollars annually due to churn and is looking to reduce that by 20 percent a year. By identifying who is likely to churn and taking a proactive approach, the predictive module can help your customer save $200,000 a year—that’s a $200,000 value!

So, how much can you charge a customer for that type of value? Fifteen to 30 percent is reasonable, but let’s be very conservative and say you only charge 10 percent. That’s a price of $20,000 per year (or $1,667 per month) for the predictive module.

Remember, your current churn dashboard costs customers $500 per month. We’ve given the new predict module a starting price of $1,667 per month—that’s three times higher than your current commodity dashboard pricing. This may seem like a lot, but it’s your marketing department’s job to create a strong sales pitch that clearly conveys the value to your customers.

The Best Way to Price Predictive: Think Outside the Box

Is there something we can do to make the predictive analytics pricing (and the total application package) more appealing to customers? Yes, there is!

Since the new predict module is adding much more value than your dashboard reporting application, your product manager should lead all sales with it. It doesn’t make sense to sell your customers on the old application and then mention the optional predictive module later on. The new predictive application is going to be the most valuable to customers, so make it the focus of the application. The best way to price predictive analytics is to create a new package for, say, $2,000 a month. This should include everything: your commodity dashboard application and your new predictive capabilities.

In summary: Push yourself to calculate the value your product offers. Challenge your marketing team to figure out innovative ways to articulate that value. And finally, come up with forward-thinking packages that lead with your predictive functionality.

See how Logi can help with your next predictive analytics project. Watch a free demo of Logi Predict today >

 

About the Author

Sriram Parthasarathy is the Senior Director of Predictive Analytics at Logi Analytics. Prior to working at Logi, Sriram was a practicing data scientist, implementing and advising companies in healthcare and financial services for their use of Predictive Analytics. Prior to that, Sriram was with MicroStrategy for over a decade, where he led and launched several product modules/offerings to the market.

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