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Predictive NPS: Scale customer
initiatives to 100% of your customer base

Most CX measurement programs cover only 10-15% of your customers. With Predictive NPS you can predict the satisfaction of silent customers and scale the impact of your CX program to each and every customer.

Benefits for B2C Businesses


Get in touch with predicted unhappy customers and save them before they leave

Relevant upsell and cross-sell campaigns

Fine tune your offers based on predicted satisfaction levels

Get more new

Activate your most happy customers with targeted referral programs

Customer Service Efficiencies

Get less inbound calls and solve issues quickly

Benefits for B2B Businesses

Decrease lost opportunities

Get in touch with predicted unhappy customers and win back deals and tenders

Efficient Sales Interactions

Educate your sales staff and enable them to hit the right note from start

Understand the real impact of issues

Get easier buy-in to solve complex strategic issues

Efficient Services Interactions

Serve customers more effectively and resolve potential issues quickly

How does Predictive NPS work?

Predictive NPS accurately predicts satisfaction scores for the whole of your customer base at any moment of time, not just after a customer completes a satisfaction survey. In the model we combine operational data and customer satisfaction data – NPS survey responses or other satisfaction metrics. The model then identifies which factors in customer behaviour have the highest impact on what the relational NPS of a specific customer would and assigns predicted satisfaction scores to silent customers who have never replied to surveys.

Predictive NPS is available at the Leading CX & VOC Tech Platforms

Predictive NPS integrates seamlessly with leading CX platforms. Predicted scores are displayed in your existing setup, side by side with survey responses.

Frequently Asked Questions

The model can predict any type of satisfaction score – transactional or relationship NPS, CSAT, CES or other, as long as you are already running a survey-based program.  

Our general rule of thumb is the more data, the better. Before the start of each project, we typically have a data discovery session where we evaluate the available data the client can provide. Based on that, we make a final recommendation whether it is feasible to proceed with Predictive NPS.
1. NPS/CSAT data – at least 300 unique cases, customers, accounts that goes back at least 1-2 years depending on survey frequency
2. Additional Data points – at least 10 CRM/operational variables and 2 time-specific behavioural variables (e.g. help call, cases, issue, usage, etc.) 

One thing to be mindful of is that with predictive analytics it is never an "all or nothing" situation. It is always a trade-off. If you aim for perfection, you may never get the insights you need. You need to weigh what your organization needs, what it can handle, and what will get the job done. Predictions will almost never be 100% correct but you still get so much value by being able to save some customers from leaving you, that the investment pays off.

The accuracy of Predictive NPS full rollouts is higher (usually above 80%) since it is based on higher quality and quantity of data as inputs for the model.

Predictive NPS works well for any industry or business model. We have seen great results in telecoms, financial services, insurance, b2b manufacturing, medtech, retail and others. Check out our Success Stories page for more information.

One-time fixed cost for a pilot project and afterwards a monthly subscription that varies depends on number of markets & touchpoints. Contact us for more details or, if you are a client of Medallia, Forsta or InMoment, get in touch with your account manager.