Last week, I had the opportunity to speak with Adrian Mendes, Global Director for Customer Success at QuestionPro, a leading provider of survey and research solutions. Adrian's insights into using data to drive customer experience improvements offer valuable lessons for CX leaders across industries. Here's what we discussed:
Last week, I had the opportunity to speak with Adrian Mendes, Global Director for Customer Success at QuestionPro, a leading provider of survey and research solutions. Adrian's insights into using data to drive customer experience improvements offer valuable lessons for CX leaders across industries. Here's what we discussed:
"I'm the Global Director for Customer Success at QuestionPro. We have three main product lines: a research suite catering to market research organisations and agencies, a customer experience (CX) platform for measuring customer metrics and managing customer journeys, and an employee experience (EX) platform for workforce insights. I primarily oversee the research and CX product lines.
We are truly a global team with physical presence in the US, UK, Mexico, Germany, Middle East, APAC and Australia, with a majority of the customer success team located in India. This structure was intentional, as we wanted the customer success team in close proximity to our tech team. We pride ourselves on our human touch approach with customers, which has been a key part of our approach from the beginning."
"For a long time, we didn't actually collect a lot of data around our customer interactions. We just knew that our unique proposition of having human beings deal with other humans worked. It wasn't until around 2019 that we started a small data driven initiative, initially focused on our enterprise customers.
We began by looking at usage data, trying to understand if our product was causing any issues in the customer experience. We looked at metrics like ramp-up time, adoption rates, and the time it takes for a user to be fully onboarded and using what they've signed up for.
We've always done NPS (Net Promoter Score) surveys, and our scores have consistently been above 80. If it ever dropped below that, the indications were usually around feature needs. So we started focusing on experience via product lines, collecting usage data, adoption data, and exit metrics to understand churn.
A key aspect of our approach is that we don't like our data leaving our platforms or databases. We made a conscious decision not to use external CSM tools or CRMs, instead building everything in-house. This took quite some time to get right and is still a work in progress but rapidly improving."
"We do annual NPS surveys, which is the standard norm. However, in 2021, during the pandemic, we noticed a discrepancy between our NPS data and other usage metrics. We had high NPS scores and satisfaction scores, but our churn statistics weren't showing corresponding improvements. This led us to conduct our first comprehensive customer journey map study. Yes we used our platform to conduct and map the entire process.
We took a three-pronged approach:
1. Analysing all the system data we had internally for statistical segmentation
2. Conducting a Voice of the Customer (VoC) survey, focusing on the effort customers had to put in at various stages of their journey
3. Running an internal Voice of the Employee (VoE) survey,
We also leveraged our Customer Advisory Board for insights. What we found was eye-opening. It wasn't a service problem or an adoption problem. Customers wanted to help, but they didn't feel that their opinions mattered enough.
We also discovered that due to the nature of our business, customers often have periods of high activity followed by downtime. When they returned after a break, they found it difficult to re-engage with the platform due to our rapid pace of innovation. This made us realize that we don't have just one onboarding stage, but multiple onboarding stages throughout the customer lifecycle."
"Once we have the data and analysis, the toughest part is having conversations with leadership to drive changes. We start with the easiest changes we can make, which is usually at the frontline with our support, CSM and account management teams .
For example, if the data shows that customers are frustrated with slow initial support during their ramp-up, we focus on improving response times. One significant change we made based on this data was reducing our IVR and chatbot system interaction, which our data suggested were major sources of frustration. This required convincing leadership to invest in more human resources to handle the increased direct customer interactions.
For product-related issues, we bring the customer advisory board, product teams, and engineering heads together. We present our findings and let customers explain their pain points directly to the engineering team. This helps avoid misinterpretation and ensures everyone understands the real issues.
For instance, when customers expressed frustration with our rapid pace of innovation, we shifted from big leaps to incremental changes in our product releases. Instead of one big product release, we now do phased releases, which allows customers to learn and adapt more easily."
"We always go back to our core objective, which for the past two years has been focused on reducing churn. If we have multiple findings or suggestions, we prioritize the one that most closely moves the needle on this key objective.
We map out how each potential change would impact our churn flagging system. Whichever suggestion gives us the highest impact on reducing churn is the one we prioritize, even if it might take more time or resources to implement."
"We're cautious about how and how much we use AI. Currently, the only places we've incorporated AI is in analyzing open-ended qualitative assessments and AI based survey builder. We use AI for sentiment analysis, which is much more efficient than having a person manually go through comments.
We're exploring how to develop proper learning from all the data we've collected to train AI technology effectively. However, we've realized that we can't benchmark ourselves to regular industry standards. Our NPS and CSAT scores have never suffered, yet we still experience churn. We want to understand why someone would say we're perfect but then not renew their subscription.
In terms of using external AI tools, we're cautious. We build all our tools in-house and don't use external CRMs or other systems. However, we're always interested in learning about new technologies, especially as we offer CX systems ourselves and often get inquiries from customers about additional layers of analysis."
Adrian's insights highlight several key principles for leveraging data to improve customer experience:
1. Go beyond surface metrics: High NPS or satisfaction scores don't tell the whole story. Dig deeper to understand the real customer experience.
2. Map the entire customer journey: Understand that there may be multiple onboarding or engagement points throughout the customer lifecycle.
3. Combine multiple data sources: Use a mix of system data, customer feedback, and employee insights to get a comprehensive view.
4. Involve customers directly: Bring customers into direct conversations with product and engineering teams to ensure clear communication of needs and pain points.
5. Start with clear objectives: Have a clear goal (like reducing churn) to guide your prioritization of improvements.
6. Be willing to make significant changes: Sometimes, improving customer experience requires major shifts, like removing automated systems in favor of more human interactions.
7. Consider the pace of innovation: Rapid changes can be challenging for customers. Consider phased rollouts to allow for easier adoption.
8. Use AI judiciously: While AI can be powerful for analysis, be thoughtful about how and where you implement it in customer interactions.
By applying these principles, companies can create a more data-driven, customer-centric approach to experience management. As Adrian put it, "The objective that was set for the CX team is to identify potential reasons for churn, flags for churn, and what we can do to upscale it." This focus on using data to drive tangible improvements in customer retention and satisfaction is key to long-term business success.