Qlik faced the operational challenge of transforming its support team from reactive to proactive to prevent escalations and better prioritize cases. To achieve this, the company turned to SupportLogic’s AI-powered continuous service experience platform. By leveraging SupportLogic’s customer sentiment and attention metrics, Qlik was able to reduce customer escalations by 30% in just six months for its core analytics product.
Qlik is an AI–fueled business data integration and analytics platform. Its software closes the gaps between data, insights, and action and empowers people of all abilities to easily understand and work with data. Its customer roster includes Airbus, PayPal, Samsung, and Condé Nast. Founded in 1993, the company’s mission is to help enterprises worldwide “move faster, work smarter, and lead the way forward with an end-to-end solution for getting value out of data.” The company has been recognized by Gartner as a Magic Quadrant leader for Analytics and Business Intelligence Platforms for 11 consecutive years.
At the heart of the company is its support organization. As the “unified model for all Qlik customers,” Qlik Enterprise Support spans onboarding, community, knowledge base, support tickets, and more. Qlik’s five support centers handle 55,000 cases each year and are staffed by more than 150 service professionals. They serve over 50,000 customers across 100 countries.
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Like many support organizations, Qlik relied on traditional metrics like CSAT (Customer Satisfaction Score) to measure customer satisfaction and loyalty. The use of this lagging indicator relied on after-the-fact surveys and resulted in missed opportunities to mitigate escalations and provide a better customer experience in real-time, when it counted most.
Ideally, the customer support team at Qlik would be able to take a more proactive approach to service. They knew that waiting for problems to occur was like stocking up on band aids in case someone gets hurt rather than addressing the root cause to avoid injuries altogether. Instead of relying on lagging metrics. Qlik wanted to introduce AI into the customer experience to focus on leading indicators.
As an experienced data company, Qlik considered a homegrown solution. But this strategy came with its own challenges. Building an AI tool in-house would consume precious internal resources without providing the quick return on investment that the company was looking for. They needed AI technology that would be easy to implement, accessible, and provide a short Time-to-Value (TTV). Enter SupportLogic.
In SupportLogic, Qlik found an efficient, scalable SX platform that could extract and analyze the customer signals hidden within day-to-day support interactions and convert them into insights. SupportLogic didn’t just add AI into Qlik’s existing workflows. It also provided real-time customer data to enable Qlik’s support team to transform from reactive to proactive and prioritize a consistently great customer experience above all else.
The implementation was fast and easy. SupportLogic connected to Qlik’s Salesforce system and started streaming signals almost immediately. The SX platform uses AI and natural language processing (NLP) to extract, analyze, and provide a statistical correlation of a customer’s real-time feelings, represented by customer sentiment and attention scores. The platform also provides the full context surrounding each customer: their case history, previous interactions and touchpoints, renewal status, and more.
With all this information at their fingertips, a Qlik support manager can immediately understand what is going on with a customer: what blockers they’re having with the product, their level of confusion or impatience, and how critical the issue is. The manager can then choose the best course of action—now, not later—whether it’s looping in the right support engineer or alerting the product team to a recurring bug that needs to be fixed.
Partnering with SupportLogic enabled Qlik to realize a fast time-to-value. The company reduced customer escalations related to its core analytics platform by 30% in 6 months, a significant win for the team. In the process, the support organization transitioned from lagging (CSAT) to leading indicators (Sentiment Score and Attention Score). And they created an early warning system (EWS) that notifies the support team of what needs to happen before they engage with a customer.
SupportLogic’s sentiment analysis has benefited Qlik in other ways, too. Using proactive AI, Qlik’s support team can provide the “personalized touch” that today’s customers desire and demand. And the information captured in support interactions benefits their entire company, providing a deeper understanding of the customer journey from end to end.
Reduce escalations & better prioritize cases
Leverage SupportLogic to transition from reactive to real-time intelligent support
Qlik reduced customer escalations by 30% in 6 months for its QDA platform
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Reduction in customer escalations
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CSAT
Qlik’s future roadmap includes expanding its partnership with SupportLogic. This year, the company will introduce two new modules for intelligent agent management and intelligent customer management. With sentiment and attention analysis by SupportLogic, Qlik can intelligently route cases to the right group/individual and expand real-time customer insights to other organizations across the company.
Ultimately, Qlik aims to transition from case management to digital touchpoint support. The company is focused on enhancing the ability of customers to answer questions themselves and access the right information across all channels, whether they’re engaging via Twitter, phone, or the website. Expanding AI technology to those digital touchpoints will help Qlik become truly customer-centric. SupportLogic is helping them get there.
“I’m excited about Customer Support delivering a delightful service experience at every stage of the customer journey, in addition to resolving customer issues. SupportLogic helps us achieve our vision.”
VP, Support Service, Qlik
Check out our webinar with Qlik and see how the company is predicting customer escalations in advance using AI.