Businesses lose more than USD 75 Billion every year due to poor customer service.1 The disruption brought about by digital has only made customer experience more crucial than ever and added layers of complexity in delivering good experiences.
According to KPMG,2 brands that deliver great customer experiences achieve 54 percent higher revenue growth than brands that don’t. Concurrently, it has become increasingly difficult to deliver satisfactory experiences as customers now have many avenues to connect with businesses including online self-service options, social media and Artificial Intelligence (AI)-enabled chatbots.
According to Forrester,3 95 percent of customers use three or more channels in a single customer service interaction. However, more than 57 percent of customers who call a contact center only do so after trying to resolve their issue through a different digital channel, such as the company’s social media accounts or its website.
To cope with change, companies are now leveraging contact center analytics to streamline their customer service offerings. Customer data, speech, text and desktop analytics are helping companies in accurately predicting call volumes and demand. This, in turn, is enabling them to improve planning and scheduling of staffing requirements, and avoid operational overheads while improving efficiency.4
When correctly deployed, analytics enriches the contact center experience. By studying customer and transaction data, organizations are matching customers to the best resources that can help resolve their issues. Analytics is also being used to help contact centers be proactive and anticipate customer problems before they even occur.
While the contact center analytics market is expected to grow to USD 1483.6 Million by 2022,5 a global survey found that voice channel will account for only 47 percent of customer interactions by 2019, as compared to 64 percent in 2016.6 Simultaneously chat and messaging will grow from 6 percent to 16 percent in the same period.
Contact center analytics can be categorized into two layers. The first layer analyzes speech, text and desktop activity data as well as customer surveys. The second layer can be viewed as applied analytics that draw from insights generated in the first layer and other sources. The second layer consists of advanced analytics areas rooted in technologies such as big data and AI.
Figure 1: Analytical Layers in Contact Centers
Let’s take a look at some aspects of both layers.
The global speech analytics market is expected to grow to USD 2175.8 Million by 2022.7 Although the implementation of speech analytics tools is still maturing, its importance is undeniable.8 Speech analytics allows the analysis of voice and word strings to generate insights on call effectiveness and customer experience. These insights can be used to plan training programs or address upstream or downstream customer issues.
Contact center calls are crucial to building relationships with customers. For example, at most banks, contact center calls account for only 10 percent of all interactions, but they account for 30 percent of moment-of-truth encounters that are key to customer loyalty.9 This explains why 72 percent of companies feel that speech analytics can lead to improved customer experience.10
As digital becomes an inseparable part of our lives, chat, e-mail and social media are growing as the preferred channels of customer communication. Text analytics helps extract key insights from such channels by using Natural Language Processing (NLP) to decode the literal meaning of a customer’s speech as well as what is implied.
By tapping this area of analytics, some organizations are already enabling good customer experience on these fast-growing digital channels.
Overall, the global text analytics market is expected to reach USD 8.79 Billion.11
Desktop analytics studies transaction data from contact center agents. It analyzes data points on desktop activity such as key strokes, data entered and application usage.
Desktop analytics tools help monitor service levels, identify training needs and provide suggestions to the contact center agent on the required actions.
Sentiment analytics12 is enabling companies to analyze the cues customers leave behind. It could be the tone of voice on calls, choice of words on e-mails, frequency of calls, or comments left on the company’s social media page.
Sentiment analytics draws from a variety of sources to generate insights on how customers are feeling at both individual and aggregate levels. This helps companies take proactive action to retain customers13 and improve the customer experience.
This area of analytics enables stakeholders to take the best possible action to resolve issues before they surface. The nature of insights could vary from operational-level trends of high call volumes expected due to certain factors to individual customer-level predictions of possible attrition due to poor customer experience.
Companies are employing predictive analytics to plan staffing requirements better, lower operational costs and improve overall efficiency. As predictive analytics can accurately forecast the impact of new policies and practices, it can reduce the time spent in gathering data.
Predictive analytics is particularly useful to improve Net Promoter Score (NPS). One of the challenges in NPS is in determining exactly what the surveyed customer was happy or unhappy with during the contact center interaction. Predictive analytics can pinpoint the main factors that impact NPS during the interaction, as well as understand how patterns in customer data relate to variations in NPS accuracy. It can also be used to build models that forecast the NPS scores of all customers thus helping build a true representation of the entire customer base.
The market for predictive analytics is expected to reach USD 12.41 Billion by 2022.14
Multi-channel analytics draws customer interaction data from various channels to generate insights. These may include trends and patterns of behaviors, unified views of customers with multiple accounts, registered contact details and paths customers take to make purchase decisions. Globally, the multi-channel analytics market is expected to grow to USD 9.89 Billion by 2019.15
Transforming how customer experience is delivered is on the agenda of many businesses. The key imperatives here include diversifying to non-voice channels, increasing digitization with technologies such as chatbots and optimizing the overall cost.
According to McKinsey, the top priority of 57 percent of executives in the next five years is call reduction. Further, 80 percent of executives rated digital solutions as the most important operational call-center technology.16 Each organization will need to tailor the approach based on parameters such as industry, geography, nature of service and customer demographics.
Contact center analytics plays a mission-critical role at every step of the process, such as:
- Identifying channel mix
- Monitoring transition
- Change management
Identifying Channel Mix
Millennials are comfortable using self-services to resolve their queries while demonstrating a preference for personalization. Unlike simple queries, complex queries require talking to a contact center agent. These are just a few of the many permutations a business needs to keep in mind while deciding which channels can complement traditional calls. Cross-channel analytics can help identify customer journeys and decide on an optimum channel mix.
After investing in an alternate channel of customer service, the transition period can be challenging. Customers may need to be prompted to use alternate channels and companies will need to monitor their experience. It is also natural to observe unexpected anomalies in call volumes. For example, customers may call with queries about the new channel. Additionally, as the new channels begin handling the requests, customers may have queries of a different nature. Monitoring this phase using analytics is critical to ensuring a smooth transition for customers without negatively impacting their experience.
As companies move simpler services to digitized platforms, newer and more complex queries that could not be serviced earlier will arise. Analytics will identify evolving patterns and help derive the developmental needs. Also, contact center agents will need to leverage new tools. Hence, change management plans will include aspects such as capability building. In a study of contact centers, 73 percent respondents indicated that investment in talent management programs will be directed towards using analytics to better align their employees and expand training programs.17
One study found that 49 percent of customers purchased something they did not intend to buy due to personalization of services.18 About 44 percent customers also indicated that they will repeat a purchase because of personalized experience. Customer experience in its simplest form is acknowledging and acting on customers’ unique needs, and analytics enables businesses to boost this crucial factor.
Steve Jobs said: “You have got to start with the customer experience and work back towards the technology, not the other way around.” Businesses will need to keep this principle in mind while devising their channel transformation strategy in a multi-channel environment.