How collaboration between Product and Customer Support can level up CS data to create Product and Customer Value 

While at Microsoft earlier in my career, execs were doing an internal roadshow in advance of a major Windows release, and someone from Customer Support asked, "Could you make the product team look at our support data before they release something?".  Steve Ballmer sagely, if perhaps controversially, answered "No, I won't force them.  If you have really compelling support data, give it to them in a compelling way that makes them listen". 

He wasn't wrong. Good data can tell a persuasive story.  And Customer Support organizations can create a ton of good data, but how best to leverage it?   

Enter a Mind the Product interview with Heather Williams, Director of Product at Elsevier, in which she talks about the power of user research and ways that product teams can integrate data-driven decision-making through cross-functional collaboration, including roles for data analysts, UX designers, and subject matter experts, all working towards enhancing product experiences. 

I've often evangelized the role that Customer Support should play in any product ecosystem, so clearly I am a champion that one of those subject matter experts should be Customer Support.  

Here are some actionable examples of the data that Customer Support can impactfully feed into the Product decision making: 

Feature requests and impact analysis frequently come through Customer Support in a variety of forms.  Most common are product how-to questions that support fields but end up being a feature request if the “how to” is for functionality the product doesn’t have or doesn’t exist in a particular use case.  This is data that Customer Support teams can easily aggregate as direct inputs to Product or Voice of the Customer endpoints.  With some minor sophistication, datapoints such as trending (requests and volumes over time, by period, etc.), customer types (SMB, Enterprise) and churn risk (via Customer Support ticket sentiment analysis, ticket severity, etc.) can add depth and context to the data as well.

At Athenahealth (SaaS B2B leader in healthcare practice management, mobile applications and EHR solutions with a current base of more than 100K providers serving over 102M patients), Customer Support, Customer Success, Services and Product Management built a robust VOC framework that included feature and enhancement data. In fact, one of the really effective ways we were able to use it was to meet healthcare data interoperability requirements. Data vendors arose and evolved constantly- for mainline API vendors such as Apple Health and AllScripts, Product had well-formed plans and roadmaps.  For others, support was often the first point of contact and was able to quickly capture and quantify requests related to new vendor endpoints being asked about and provide that to Product for evaluation and prioritization.

Feature impact analysis (how many users would benefit from a feature ask or design change), is another proactive datapoint if it’s correlated to support contact volumes.  Also at Athenahealth, our medication management integration was prone to several issues and generated a high level of support volume.  When Product was working on design changes, Customer Support was able to help prioritize efforts based on how many users had what types of issues, how often, and how severe. Within the end-to-end functionality re-design, they were able to prioritize changes that mitigated over 2800 support tickets monthly, impacting over 450 Enterprise and SMB customers.

Bug, defect and escalation prioritization, while not proactive, can heavily benefit by correlation to associated Customer Support ticket volume, severity, and complexity (resolution labor, ticket resolution times, support, engineering and escalation resources required per issue). Many Customer Support teams are well dialed into this- Jira has built-in integrations with major Customer Support CRM’s such as Zendesk and Salesforce that surface associated Customer Support ticket data.  

However, Product teams need to ensure that Engineering/Dev and Customer Support teams have a structured framework, process, and cadence to leverage this data effectively such that regression and maintenance resources can be balanced with feature development priorities. Including Customer Support in Jira triages to provide detail on issue impacts and severities is one way to do so. At Frontline Ed (an EdTech SaaS leader with a customer base of 8,000 districts and 21M students), engineering provided Customer Support with a shared prioritization and impact template, enabling Customer Support to engage in triages with a common perspective.

Churn and retention rates, correlated to issue types handled by support, can similarly provide focused prioritization within a product roadmap.  Top customer pain points measured by support volume and resolution effort (handle time, time to resolve, etc.) alone can shed needed light on more than defects and regression, but analyzed by major feature or component and churned customers, can give valuable inputs to product teams.

CES (Customer Effort Scores) segregated by issue type are another rich dataset around points of friction within the product, or the supportability of the product.   At one point, CS at Athenehealth had a persistent but low volume of tickets related to password resets.  It was easy to assume these were 1-offs that were easily resolved, but in looking to understand why they were coming into support, we happened to notice that CES scores in ticket satisfaction surveys were much higher for these than other low-volume issues. 

Further analysis uncovered that while the fix looked easy, for a specific set of user account profiles, it required user admins to manually update their profiles, then have the user re-request a password reset.  It was a pain for them.  With this new insight, Product was able to drive an architectural change to the password management feature that streamlined it and eliminated these types of user pain points.

Product managers are always looking for quality data to drive effective decisions, a point well articulated by Heather Williams in her MTP interview about Product Manager data sources, whether it’s UX, product telemetry and usage, customer research, A/B usability testing, technical performance, or others.  Don’t overlook Customer Support data as another valuable input to your product teams, as Customer Support engages with customers more often by magnitudes than any other touchpoints- and Customer Support data capabilities have become ever more capable of generating very rich, highly integrated datasets.