Using Customer Support Data to Improve the Product Experience

Learn how to identify and use customer support data to improve the product experience.
Topics Covered in this Blog
  1. The Product Experience Challenge
  2. How to Collect the Best Data to Improve the Product Experience
    1. Determine the Product Experience Metrics that Matter for Your Business
    2. Invest in a Single Source of Truth
    3. Map Customer Frustration
  3. Acting on Gaps in the Product Experience
  4. Turning Support Data into a Guide for Action

Product Experience Challenges

Many customer experience (CX) teams struggle to understand the entire customer support journey for one of two issues: data isn’t collected deliberately to address specific business needs, or CX teams can’t easily access their data.

Having an analytics dashboard is now the minimum requirement to stay competitive—every customer support platform has one. But this doesn’t always mean the data collected is meaningful. Often, a metric is collected because it’s what everyone else is doing, not because it’s the most important metric for the company.

If a company is capturing specific data, data hygiene is usually the next challenge. Data needs to be organized to be acted on. Here are a few questions to help companies improve their data practices:

  • Are metrics thoughtfully selected and tracked?
  • Does the company have a way to examine their data in one place?
  • Are metrics siloed in various dashboards across the team’s CRM, call center, and other platforms?

How to Collect the Best Data to Improve the Product Experience

Overcoming the inertia of bad data hygiene is hard but possible. If you’re struggling with customer data hygiene, below is an overview of the steps to overcome bad data practices and improve the product experience.

1. Determine the Product Experience Metrics That Matter

First, you need to decide what metrics matter for your business. While customer satisfaction score, resolution time, and tickets solved are important metrics for your team to monitor and understand, they may not be the strongest indicators of product experience satisfaction.

Look at metrics beyond their face value; context matters. If a newly released product sees a spike in ticket reopens, that might mean a rise in frustration with the product experience. Likewise, a drop in first-contact resolution rates might indicate a discrepancy between your support team’s workflows and the needed solution.

Here’s an example from a Mavenoid customer: ABB, an engineering automation company, defines a “time-to-value” metric for each of its products. For a smart doorbell, this metric captures the time between when the product is ordered and when the doorbell captures its first interaction with a visitor. By tracking this metric, ABB can ensure customers are delighted by their purchases quickly.

Identify the metrics that most closely correlate with the product experience you want to offer and focus your attention there.

2. Invest in a Single Source of Truth

Second, consider using a data warehouse to merge sales, marketing, and e-commerce data. This lets you view the entire customer journey from first to repeat purchases and offers a single source of truth for identifying customer friction points.

Here are some questions you should be able to answer after unifying your data:

  • Are customers returning products due to misleading product descriptions?
  • Are customers ordering replacement parts from other vendors?
  • Are customers encountering a common bug that the software team is unaware of?

3. Map Customer Frustration

Knowing the effectiveness of support teams requires knowing how well agents and self-service tools manage customer frustration. Companies often focus on deflection rate as their primary metric — a high deflection rate means most customers are routed away from agents. This might save the company money in the short term, but the result is usually frustrated customers and negative remarks that hurt sales.

Once you have your data warehousing in order, map each metric to an owner:

  • Customer Effort Score reflects how easy or hard it was for a customer to resolve their question. How easy it is to reach a resolution indicates the effectiveness of self-service tools, live agent transfers, and quality of product documentation.
  • Customer Satisfaction (CSAT) Scores reflect the efficacy of live agent support.
  • Self-Service Resolution (SSR) measures efficiency (the cost of a resolution and how many touches are needed). SSR also indicates how well a company understands the support content customers need.

Acting on Gaps in the Product Experience

Once you lay the foundation for tracking and understanding your customer support data, you can begin addressing product experience issues. Here are a few examples:

  • High Customer Effort Score: Customers might be struggling with limited options for troubleshooting. Expand options for contacting support and times your support team is available.
  • Low CSAT Score: Live agents may lack the product knowledge or documentation to address product problems. Build additional resources, so live agents are ready for the most common questions.
  • Low Self-Service Resolution: Consider implementing AI-powered self-service options that can successfully handle complex product support queries.

Turning Support Data into a Guide for Action

Improving the product experience requires intentionality. You can gain insights into your entire customer journey by choosing and tracking metrics with intention, investing in a unified data source, and mapping customer frustration. This allows for targeted actions to address gaps in the product experience and subsequently improve customer satisfaction. By prioritizing these steps, you can turn customer support into a practical tool for improving the product experience.