Investing in the aggregation and analysis of customer data can have a huge positive impact on Customer Success. A customer’s digital footprint is scattered across many varied data sources, and if analyzed cohesively, they have a complex story to tell, along with right prescriptive actions to take. In 2017, Customer Success teams are now more data savvy than ever.
Every CSM or a CS executive will tell you how critical the customer’s data is for operating efficiently day to day. On the other hand, we are in the age of data deluge, in which it’s easy to get overwhelmed by data. We need to understand which data is useful, which metrics we need to track, and which KPIs to present. Here are the seven key data sources for a successful CS program:
Product Usage Data: In SaaS, usage data is extremely vital for assessing the health of a customer. Deeper usage data, instead of aggregated data, is more valuable because it will provide insights into what’s happening with a particular customer right now, and how to guide the customer in leveraging the product further.
CRM Data: The information in CRM systems regarding the sales process and contract term are good benchmarks to note in the customer relationship. Key data points are the length of contract, main stakeholders, influencers, and their sales process. You can encourage Sales to collect as much data about the customer during the sales process as possible, and record salient observations like decision makers, their characteristics, and organizational culture in the CRM.
Content Consumption Data: The importance of content in the customer lifecycle is poorly understood. Collateral on how to use the product should be an essential piece of the onboarding process, while articles on how get ROI from the product should be a focal point during the adoption phase.
Billing Data: If a customer was previously paying on time, but has recently been inconsistent, it’s better to check if it’s a customer satisfaction problem, whether the value of the product is being questioned, a company viability problem, or something else that you haven’t foreseen.
Website Data: A customer visiting your website to view other products, the FAQs, or search for “how to cancel” provides a direct clue on their current sentiment. Alerts on certain website activity could result in direct prescriptive actions from CSMs.
Customer Interaction Data: It’s important to have a healthy, regular interaction with all customers, but especially those who are high-value. It’s also good way to find which customers are proving to be expensive to support.
Help Desk Data: This data provides insight into customer sentiment, product feedback, and adoption issues. This can also be combined with the spend data to figure out which customers need additional attention.
BONUS! Community Data: Many organizations are now building customer communities for clients to help and answer each other’s questions during various part of the customer journey. A few have even replaced help desks with a community forum and knowledgebase. A customer’s participation and sentiment expressed can be used to discover product issues or determine the commitment of a customer.
The analysis each separate data source is limited by the variables in each data set. To get complete insight, a 360 degree view of your customers, tt is necessary to connect the dots across data repositories. By building a comprehensive integrated dashboard, key trends across customer segments will surface. Most Customer Success programs are not able to extract value from data due to lack of focus on critical metrics. In our Dashboard blog post, we covered the important metrics and KPIs to use, including the Customer Health Score, Adoption Rate, Customer Onboarding Costs, Customer Retention Rate, Customer Lifetime Value, and Customer Feedback.
Data collection technologies have made tremendous advancement due to APIs and data exchange protocols. Not only should you collect and aggregate customer data, but you should also make sure to extract useful action items from the data. CSMs should able to leverage large amounts of customer data to help them better understand their customers’ needs to decrease customer churn and increase up/cross-selling opportunities. It’s all about ensuring customers and users receive value from your solutions. By leveraging data in Customer Success programs, organizations can improve their effectiveness by adopting a quantitative mindset.