The concept of customer health has been a longstanding practice for businesses to understand and predict customer behaviour. Traditionally, this has been done through complex health scoring systems that combine various data points into a single metric. However, complexity is not always feasible for small or budget-friendly CS teams.
Health Scoring
Customer health scores are typically composed of many factors such as product usage and adoption, communication frequency, sentiment, and support need. These scores are often built by data engineers or analysts who add subjective scoring and weighting to the data by assigning each component with score. The result is a single value representing a customer's health.
Pros:
Comprehensive: Offers a holistic view of customer health by considering various data points.
Customizable: Businesses can choose and weigh data points based on how relevant they are to their product and communication needs.
Automated: Health scores can be leveraged for task automation if used within a CSP or CRM.
Cons:
Resource-Intensive: Requires significant time and effort to develop and maintain, often involving months of iteration. Not to mention, potential costs of third-party software.
Susceptible to Change: As products evolve and customer needs change, the parameters used to calculate health scores may become outdated or irrelevant.
False Sense of Security: Teams can become too trusting of highly-engineered health scores; unidentified false positives and negatives causes oversight of risk.
Tracking Sentiment
Tracking customer sentiment focuses solely on capturing the sentiment of a customer based on their interactions with the business. CS teams leverage data from meetings, emails, and other communication touch points to quickly gauge how customers feel about products or services.
Pros:
Simple: Intuitive to understand, making it easy to implement and adopt internally.
Low Cost: Doesn’t require data expertise to build in-house and there are cost-effective solutions on the market.
Solid Foundation: Sentiment data can eventually be integrated into a more complex health scoring system, once you have the resources to do so.
Cons:
Incomplete Picture: Neglecting additional data points, such as usage data, fails to give teams a complete picture of customer health.
Subjective: Having team members assign sentiment based on communication touch points is subjective, which can lead to inconsistencies.
Depending on the stage of your company and the maturity of your customer success team, your requirements for a solution will vary. Each solution has its strengths and weaknesses, and the decision will likely come down to the unique needs and resources of each business. In many cases, a customer sentiment score serves as a precursor to a health scores and is a good place to start your customer health journey. This approach allows you to gradually build up to a more robust system, learning about what you need and what works along the way. Either way, keeping tabs on customer satisfaction is essential in driving business success.
Interested in learning about a customer sentiment analysis solution? Check out Gauge.