Predictive Analytics

4 Considerations for Bringing Predictive Capabilities to Market

By Sriram Parthasarathy
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Today’s predictive analytics solutions give application teams an opportunity to set themselves apart from competitors. But how you bring your predictive analytics to market can have a big impact—positive or negative—on the value it provides to you. Go-to-market strategies will differ depending on whether you’re enhancing an existing application or launching a new predictive analytics product.

In order to maximize the benefits for your end users and your business, consider the following factors as you bring your predictive capabilities to market:

#1. Return on Investment (ROI)

In order to determine the ROI of your predictive analytics, you must have a strong value proposition. Your embedded predictive analytics should deliver benefits in at least two quadrants of the framework shown below:

For example: A predictive analytics feature that helps a sales team send targeted, personalized offers to prospects covers three of the four ROI boxes:

  1. Increases revenue by focusing marketing on targeted offers
  2. Decreases costs by preventing resources from being wasted on generalized offers
  3. Makes customers happy by bringing them relevant offers

#2. Business Value

Instead of basing the price of your new premium feature off your initial application, consider the impact your embedded predictive analytics features could have on your customers’ businesses.

Say you have a customer that is losing a million dollars annually to churn. If the predictive module can help your customer understand why churn is so high and take proactive action to reduce it by 20 percent a year, the value is a potential revenue increase of $200,000 a year. If you charge 10 percent of the potential ROI (a conservative estimate), that equals an extra charge of $20,000 per year for the predictive module.

This price—three times that of your current churn dashboard—may be a bargain for some end users, but overpriced for others. That’s why you also have to consider the value to your users (see #3 below) as you package your predictive features.

#3. User Value

For some of your customers, predictive analytics will enable them to perform their jobs at a whole new level. For others, predictive analytics will be a nice-to-have feature, but not central to what they’re trying to accomplish. Depending on where your users fall, use one of the following packaging/pricing strategies:

  • Fold predictive features into your current product at no charge
    Going back to our churn example: Anyone using your analytics dashboard showing current churn rates and historical trends will appreciate the addition of a new panel showing predicted rates for the coming month or quarter. But would that be worth paying more money? For many users, probably not. If you fold basic predictive features into your current product for free, the added value will help you fend off requests for price discounts.
  • Offer an add-on module to your current product or make it part of a multi-tier package
    A subset of users will want to see a ranked list of customers with high churn risk. They may be responsible for reaching out to these customers with retention offers and communications. Providing them with an easy way to identify these targets could improve their work enough to justify an increase in price. Packaging predictive as an add-on module or making it the middle tier of a three-tier package will be the most palatable for these customers. In this case, the price for your analytics should be based on a conservative estimate of the added revenue from reducing churn and the cost savings from working smarter and faster.
  • Offer a new product or top-tier package
    An even smaller subset of users will also want to know the characteristics of customers likely to churn. This gives you the opportunity to demonstrate the clear value proposition of predictive analytics by offering either a new product or a top-tier package to meet their specific needs. Look for an analytics development platform that lets you offer some predictive features to everyone and limit access to others. This way, you can simply turn features off and on. In this case, the price for your new product or tier should be based on a less conservative, more forward-looking estimate of potential top-line and bottom-line improvements.

#4. Minimum Viable Product (MVP)

If you decide to go to market with a new product or add-on module, you’ll need to deliver enough predictive features at initial launch so your customers will feel justified paying an additional price. This is the Minimum Viable Product: The smallest set of predictive features you need in order to make your offering appealing and functional to a critical mass of early customers and validate your value proposition.

When defining your MVP, consider this: Will there be a sufficient number of customers willing to invest? That is, will even a minimum set of features provide welcome, urgently needed relief for the pain points your predictive capabilities aim to solve?

You’ll get better engagement and more substantive feedback if your predictive features play a role in day-to-day workflows. Which end users would not only benefit from the predictive insights you’ll be delivering, but also have a high frequency of use? Your MVP must provide a critical mass of value to these frequent flyers.

Once you’ve scoped out the embedded predictive analytics features you need for your initial go-to-market strategy, you can deploy your application and iterate over time.


Originally published August 27, 2019; updated on July 31st, 2020

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.