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.
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. If you randomly pick a percentage of your core product as your upsell cost for predictive analytics, you risk undervaluing a promising technology that provides a strong return on investment (ROI).
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 base application is probably not a good way to price your product.
A Better Way to Price Predictive
Another way to price predictive analytics is to determine the impact predictive analytics could potentially have for your customers. 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 pricing. This may seem like a lot, but if your marketing and sales teams focus on the value the new module can bring, your customers should clearly see the value.
How to Price Predictive Analytics: 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 to your product, consider leading 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 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 base application and your new predictive capabilities.
Of course, this may all be dependent on your competitive market. Are you the first company to offer new predictive analytics capabilities, or the 12th? If you’re early to the game, it’s more likely that you can set the competitive pricing without worrying about what others are doing.
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.