For customer success teams, the only constant is change — new business priorities, new processes, new product features, new customer objectives, etc. The best success teams put strategies and processes in place to identify key metrics, prioritize operations and scale effectively. The reward for managing it all correctly is happy, loyal, and profitable customers.
Modern customer success teams are swimming in data: product usage data, account data, user data, billing data, etc. All that information comes from different places and isn’t structured in a way that makes it easily understandable. Organizing it all into a single location is a chance for an opportunistic team to understand the overall customer experience and provide proactive recommendations throughout the relationship. But without the right strategy, that analysis can be overwhelming and distracting.
When customers consider and ultimately purchase a product, they’re expecting to enter into a relationship where they are cared for and supported. The right way to provide that support is to understand their use cases and specific product usage, then translate those insights into actions and recommendations to enhance their experience. When there is a clear strategy to make analysis actionable, data can be the simplest way to nurture those relationships and grow a business.
The bigger purpose of a customer success team involves a lot of personal touches, but there’s still a significant advantage for a team that can manage data properly. If your team hasn’t already ironed out a process to collect and analyze customer data, it’s time to start the conversation. Here are some ideas from our world-class customer success team that will help sort through the flood of information and turn customer data into proactive recommendations.
Today, the rapid pace of incoming data (and changes to that data) is an easy place to get distracted; it’s important to step back and refocus on the top priority — the customers. Ultimately, the best customer success teams will drive positive outcomes by examining all of that shifting data and deciding which pieces of information should be analyzed and which are noise.
Since the industry is moving so rapidly, the winners will be the teams that can identify and prioritize the right data to drive their actions. Though it seems counterintuitive, the best response to the rush of available analytics is actually to narrow the scope of analysis, focusing closely on specific KPIs in order to maintain flexibility.
As a customer success leader, the first priority in surviving the rate of change is to identify which KPIs matter. Some of the classics like retention, adoption, and renewal rates are always going to be important, but organizations can also determine their own unique cornerstone statistics (active users, support interactions, etc.) based on product offerings and industry. A good place to start when determining these crucial KPIs is to ask why the customer success team exists. Are these employees there to maximize retention and minimize churn? To increase upsell? What about cross-sell? Whatever the team’s objectives are, focus on finding the levers that influence those crucial KPIs.
Some might be obvious (such as NPS score), but combining disparate data points can surface deeper insights that move the needle on those valuable top-level KPIs. More data-driven organizations prioritize analysis that learns what will have the greatest impact now and utilize machine learning to systematically predict future customer activities.
Once the crucial KPIs have been identified and paired with likely correlations, customer success managers can prioritize these influential activities. In a landscape that changes so frequently, it’s imperative to have a structure that enables vital data about customers to flow to the success team as quickly as possible. This approach changes the focus of the individual success managers away from a reactive, fire-fighting mindset and empowers them to be more proactive.
A success team that implements this strategy of focusing on important metrics, determining the best ways to influence them, and then scaling those practices across customers are set up for better customer relationships. The entire process is predicated on having accurate, up-to-date customer information, then turning it into insights and executing them. Inaccurate or outdated data can negatively impact customer relationships and result in lost business opportunities. Even if a team has optimized their workflow and tuned processes to achieve maximum efficiency, bad data can turn these well-intentioned communications into spam.
A unified data platform should solve this problem by connecting disparate sources of customer data and ensuring unquestioned data integrity. The team can determine the metrics that are crucial to customer success, create a customer health score or prioritization dashboard for those metrics, and focus their time on high-value opportunities.
Ultimately, a customer success team’s speed is heavily influenced by their tools and processes. At Sisense, we use our own product to optimize customer success. We were delighted by the easy implementation when we had to navigate a change in corporate systems. Not long ago, we went through a period of simultaneously switching billing and ticketing providers, two key parts of our overall customer health score. With another platform, this would require months of reimplementation, but we connected the new solutions into our standard dashboard and had the health scores sorted out in less than a week. We adjusted on the fly, without a need to update schemas or reconfigure our scoring. Better yet, we never lowered the bar for the service that we provided to our customers.
This type of platform means customer success teams can access and analyze data, test a hypothesis, rapidly iterate on ideas, and scale the solution to the rest of the team. This model-as-you-go strategy allows teams to ensure accuracy with customer information, pinpoint retention or upsell opportunities and reduce the effort put into baseline reporting.
With a dynamic data platform like this, successful leaders can measure current customer health, forecast the future growth, segment the base into appropriate cohorts, and manage communications more effectively. The effect is better management of outcomes, both for your customers and your own business.
The only solution to the changing customer success landscape is a strategy that enables a team to evolve at an accelerated rate. It can be easy to get lost in the stream of incoming data, but the best teams will maintain focus on the important customer KPIs. With a unified goal, all that incoming data can be used to experiment and find new avenues to improve relationships. The success team exists to ensure that customers are getting the most out of their experiences so that the team should be set up to measure and deliver exactly that.
If you want to see how your customer success team can use data to improve net retention, set up a free trial of Sisense for Cloud Data Teams. To talk to one of our experts about determining the most important customer metrics, fill in the contact us form and someone from our team will reach out to you soon.
Mondelio Worldwide is an Australian company having commenced operations in 1982. Mondelio's single focus is to provide predictive data modelling and data analytics services to organisations throughout Australia and overseas.
Mondelio has partnered with Stonebridge Consulting, Naveego and Sisense expanding our product offerings to the wider APAC market.