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2018 Trend 2: 3 Ways Analytics Tools Drive CX Transformation

By Yashwinee GK, Chief Information Officer, HGS

HGS recently released a white paper on this year’s top 10 trends in customer service. Over the course of the year, we’ll dedicate a blog post to each of these CX game changers. Here, we dissect CX Trend No.2: Investments in Advanced Analytics Tools, highlighting strategies designed to drive the right answer, fast, for your customers—to ultimately result in a higher CSAT and NPS score for your business.

Advanced data analytics tools can help businesses uncover business-critical insights and gain a competitive edge. According to Gartner, “By 2018, 50% of agent interactions will be influenced by real-time analytics.” As customer experience analytics matures, it is becoming increasingly predictive and focused on personalization. A sophisticated data analytics tool can make predictions, or generate recommendations based on information gathered, thus giving contact center agents and bots more background and context on each individual customer they are servicing. In addition, analytics plays a significant role in increasing productivity. As one case in point, speech analytics can detect non-value adds, such as long conversational pauses or points in the conversation when the customer has to repeat responses. For these types of opportunities, managers and team leaders can provide agents coaching to educate them on where they have opportunities to be clearer in questions they ask customers. In 2018, expect to see customer care professionals making big investments in advanced data analytics to transform experiences and improve customer satisfaction.

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Investments in analytics tools can reap significant benefits in numerous areas. Here are three major advantages of applying analytics in driving customer transformation:

  • Analytics use Artificial Intelligence (AI) and Natural Language Processing (NLP) to help understand customer behavior and improve quality of customer interaction.

Traditional business intelligence helps us convert data into information to understand patterns. New advancements like AI and NLP have stepped up customer experience. Today, talking to a bot is more and more akin to connecting with a human. Bots employ NLP for more colloquial verbiage and appropriate tone, recognition and understanding, to increasingly replicate a human interaction. Culture and customization can be applied to mimic character and persona of human agents. AI and bots also enable real-time customization of customer experience using their complex self-learning capability. They evolve with each customer interaction becoming smarter and more intuitive about customer preferences. With this AI and NLP innovation, interactive analytics are used to analyze customer conversation tone and context. At key focus is the interaction between the customer and agent by channel, whether it’s phone, email, chat, or social media. Analytics create better understanding of the quality of the interaction between customer and agent. As a feedback mechanism, analytics can relay to brands the effectiveness of their CX talent.

  • Predictive and prescriptive analytics can drive CX transformation through product recommendations.

Predictive analytics study customer history and behavior patterns. Based on customer behavior data points, predictive analytics shed light on patterns and possible occurrences in customers’ browsing and shopping behavior. What’s more, these analytics also help take preemptive measures required to take on upcoming challenges. For example, brands have increasingly been using consumer classification, which ensures customers receive product recommendations based on their buying choices, preferences, and habits. Similarly, healthcare entities have started to apply data analytics to classify patients in different personality types, such as “follower” or “health-nut.” This can help healthcare entities to personalize the level of interaction that can be provided to patients and thus improve patient engagement. Purchasing behavior prediction can be as simple as understanding that a customer who has recently bought a car will eventually be looking to buy accessories. In this case, a brand can recommend accessories and services, possibly at a lower cost, for a minimal effort, yet major boost to sales, revenue, and customer satisfaction. Driven by good predictive business insights, an intelligent recommendation engine can spike both sales and CSAT increases. Used proactively, analytics can set in place the steps and remedies required to handle future business or product challenges. Analytics, for instance, are essential to planning for stock inventory, transport logistics, and customer support—all key contributors to better CSAT. For example, if your company is planning to offer a high-end Bluetooth speaker at a very low price during a sale, ‘prescriptive’ analytics will tell give you the probable number of customers who have purchase interest in premium Bluetooth speakers but haven’t made the purchase. You can plan your stock inventory with the probable number in mind avoiding procurement of unnecessary surplus or shortage of stock. In healthcare, hospitals are trying to predict key aspects like claims denials due to medical necessity or hospital readmissions in 30 days. Identifying high-risk patients can help hospitals to mitigate the risk by various measures. Leveraging information, hospitals also forecast bed census and other operational and financial parameters. Advanced analytics can be deployed to map a customer call to the work queue of a customer service representative (CSR), who can handle the request in the best possible manner and within the shortest time. The algorithm that determines this match is based on historical data on the CSR’s interactions on various service lines and the customer’s past interactions on various product lines, creating a matrix of probable scenarios, a few of them being the most optimal.

  • For optimized CX, analytics ensure better agent coaching and skills reinforcement.

Analytics helps reinforce agents with good interactions. Conversely, analytics can be used to correct agents in areas where they need improvement. Interactive analytics provides better customer-agent interactions as a result of positive reinforcement and highly personalized agent development. Another advantage of interaction analytics is that conversations are analyzed by channel, which helps sort agent performances by the specialized set of skills and knowledge required by that channel. Every interaction channel has its own set of requirements, and agents need to be coached on those parameters while enhancing or correcting their behavior on that channel. Additionally, interaction analytics provide information on the overall team’s performance. This helps ensure more comprehensives coaching and skills training, that is focused on the entire team’s performance (positive and negative). Benchmarking is an especially useful training technique, as it will help guide an agent on the areas in which he or she needs to improve. A call driver pattern analysis, especially in high call volume scenarios, can be used to discover call trends by day/week and accordingly do a skill mapping. On the training front, advanced learning platforms apply analytics and data sciences to analyze past training records of associates, data on previous engagements, job roles, span, and behavior—all to arrive at custom training programs for agents. This training helps agents hone their competencies and skills, to equip them to perform their roles better. There are more ways in which analytical tools can power customer experience transformation. Every industry and company has its unique set of challenges and requirements. The scale of analytics investment can vary, based on business objective. For example, the focus could be solving a specific business problem, making information available for better decision making, or strategizing the creation of an efficient and competitive enterprise. The investments are also a function of the maturity of the enterprise’s information architecture, and the management’s top-down focus on the programs, which can take time to be truly transformational. On the other hand, in the case of some solutions, less investment and strategy could translate to more ROI. Finally, the cornerstone of an analytics engine is data. Data plays an important role helping us to explain, predict, and prescribe. We create data with each interaction we make today. While some of our interactions are recorded, most are not, and can be considered in the context of related events. It’s still early stages for how analytics can affect customer transformation. We have yet to perfect the science of interpreting data, to understand its underlying message and explain it simply. The field has high potential and will be home to many innovations in the years to come. What will be critical is our ability to apply the information in a meaningful and sustainable fashion. Physicist, Albert Einstein aptly summarizes some key thoughts on these changes: “Not everything that can be counted counts, and not everything that counts can be counted.” and “If you can’t explain it simply, you don’t understand it well enough.”  

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