How can you optimize renewal pricing and packaging strategies with CRM?

To optimize renewal pricing and packaging strategies using CRM, two pillars are essential; predictive data modeling and feedback loop. These pillars involve collecting valuable data insights such as customer purchase patterns, in addition to renewal and churn behavior trends. By gathering feedback, understanding customer pain points, and preferences, you can tailor renewal packages based on data-driven insights. It is also beneficial to conduct A/B testing across different customer segments and implement dynamic renewal schemes and offerings based on these segments. Also leveraging marketing engagement tools to create personalized targeted workflows can enhance customer engagement and satisfaction by customizing the experience.

Optimizing renewal pricing and packaging strategies with CRM involves leveraging customer data to tailor offerings. Utilize CRM insights to understand customer behavior, preferences, and usage patterns. Implement dynamic pricing based on customer segments, ensuring personalized renewal options. Analyze historical data to identify factors influencing renewals and adjust pricing accordingly. Additionally, use CRM to streamline communication, providing targeted information and incentives to encourage renewals. Regularly update CRM data to stay informed about customer changes and refine strategies over time.

Before going to customer segments, need to divide the catalogue into subscription vs one-time items. Even with one time purchase your customers may exhibit loyalty by repeated purchase, albeit not on a pre-defined frequency. Subscription based products/ services are much easier to put under 'renewal' based analysis, with frequency like 1 month, 1 year type fixed intervals. Historical data is instrumental to do the analysis, which can also be shared with CRM teams in forms of data visualizations. Attributes segmentation can be done further basis functional aspects like Transaction/ Demographical/ Product etc. If multiple items/ services are bought together, that denotes any chances for packaging strategy to sell together in future.

Buying patterns are just one indicator of customer health. Other attributes also collected via a customer lifecycle also may help with doing analysis then against renewal chances and options to package multiple catalogue items together. Since these are kind of future perspective decision making, predictions/ prescriptive power need to be utilized. But before directly making machine learning/ AI powered predictions, enough analysis need to be done for sufficient time to monitor customer health. CRM experts who know certain patterns by their experience, they can validate via data.

As said above, its imperative that hypothesis derived by experience are validated by data. Vice versa also, the hypothesis derived by data need to be tried and tested with select customer base before finalizing a consolidated strategy for say a typical customer segment

For packaging catalogue items together, rather than seller taking that decision; buyer can be given recommendations by way of personalized offers during time of renewal