Big Data and Predictive Analytics

Predictive Analytics for Customer Success

In part one, we described the background that has made predictive analytics a necessary and useful technology for customer success.

In this excerpt from Predictive Analytics for Customer Success, we explain how big data and predictive analytics fit into the world of Customer Success.

SaaS businesses have a lot of data about their customers — it's in their CRM system, their billing system, their support ticketing system, not to mention the usage data they can collect from their product. The challenge is making use of this data to identify customers who are not realizing the value they expect from a solution, as well as those that are ripe to convert from trial or buy more.

Exacerbating that challenge is the volume and velocity at which this data is created, and the fact that it’s dispersed across multiple, siloed systems.

Fortunately, advances in big data analytics have made it possible to aggregate and analyze large volumes of customer data.

CSM solutions can capture detailed product usage data, and combine it with other customer data residing in a variety of systems or databases. They use this data to monitor customer behavior and identify accounts that need the attention of customer success managers.

Most CSM solutions require the SaaS vendor to define rules that describe specific customer behaviors that the vendor determines warrant outreach from the customer success team.

For example, a SaaS vendor may create a rule in their CSM system that monitors login frequency and sends a “churn alert” to the customer success manager if a customer hasn’t logged in during the past 2 weeks. Or maybe it sends an “upsell alert” when all of the seats that were sold to a customer are being utilized at a high level.

By actively monitoring the behavior of all customers, CSM solutions can help the customer success team focus on those customers where they can have a meaningful impact.

While defining rules that stipulate which customers should be contacted based on their behavior is a huge improvement over indiscriminate or routine outreach, a technology called predictive analytics promises to dramatically improve the ability of CSM solutions to identify customers who are likely to churn, expand or convert from trial.

Predictive analytics is different from traditional analytics, which is also referred to as descriptive analytics.

Descriptive analytics is the process of summarizing large amounts of data in order to answer questions such as "What were our average revenues by product line last quarter?" Descriptive analytics is focused on understanding “what happened” in the past, whereas predictive analytics attempts to answer the question "what might happen" in the future.

For example, "what will be the impact on sales next year if we double our advertising budget?"

Some industries are mature in their use and implementation of predictive analytics, such as detecting fraud or managing risk at financial institutions. More recently, the use of predictive analytics in sales and marketing platforms has been growing rapidly.

Organizations are adopting a variety of solutions that incorporate predictive analytics to increase sales productivity and opportunity close rates by predicting which leads are most likely to buy.

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In the next installment, we'll explain how machine learning models work in the case of Customer Success.

Want to learn more? Grab our eBook: Predictive Analytics for Customer Success.

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