Moving from seat-based pricing to AI usage monetization

The Client 

Client is a $35M+ leading software platform provider for customer service that helps empower organizations and customer relationships. Their platform has two primary use cases, customer support and shared inbox, and the offering targets growing businesses (SMBs) across a range of verticals. Most customers purchase the software through a PLG sales-motion.

The client was preparing to launch two new AI based features that effectively eliminated the correlation between seats and value – while the value of the platform was expected to increase, the number of seats would decrease. This would likely have a significant impact on revenue in the current per seat pricing model.

The Context

To mitigate downsell and adapt to their customers’ changing needs, the client had some hypotheses on pricing changes including:

1. New price metrics. Since the existing per-user metric was likely not growth oriented, the client needed to evaluate alternative value-linked and acceptable usage-based metrics that could be used along with or instead of users.
2. Alternative revenue models. Some metrics would be better aligned to revenue models that are different from the existing flat-fee subscription model. For example, there could be a variable pay-as-you-use model or a hybrid (flat plus variable) model.
3. Redesigned architecture. If the price metric is changed from users to be further linked to usage or volume, the metric(s) should scale to optimize customer acceptability and WTP, which is usually driven by predictability of how the metric scales

The Monevation

The Monevate team identified number of contacts as a price metric that could be used along with or instead of the seat-based metric to better capture the growth of customer value over time.

• Analysis of historic customer data showed that the average number of contacts for a customer grew by 10-12% a year, vs users which was close to flat for the last 3 years
• While users was still seen as the most acceptable metric for customers due to its predictability, contacts was also shown through market research to be linked to value – it represented how many end-customers were served through the platform
• Evaluation of two pricing models through impact modeling - one hybrid with users and contacts, and one purely contacts – ultimately guided the strategy to the pure usage-based model due to the rapidly declining number of seats

In market research, the team tested two new AI capabilities through and integrated them into the new pricing strategy with an uplift to the overall price of the platform.

• In the near term, these capabilities were offered as add-ons that were priced based on usage, which helped protect margins with customers who were heavy users of the new features
• However, longer term, it was determined that these AI metrics also had a strong linkage to the contacts metric, and once more broadly adopted, the original platform and new AI capabilities could be priced with a single metric, contacts, to maintain simplicity
• Based on the market research, the team determined that the client could capture ~30% uplift of price on their existing platform based on customer willingness-to-pay for AI capabilities in this space

The Impact

• +$10M in total expected revenue uplift through migration from users to usage metrics
• ~30% uplift in prices based on market research based WTP for new AI features