By Subramanya C, HGS Chief Technology Officer
Analytics goes hand in hand with today’s optimized customer experience strategy. Analytics is a tool that can be used at every step of the customer journey—from assessing and benchmarking customer support to gauging the voice of your customer for actionable business and product decision making. As predicted by Gartner, this year 50% of agent interactions will be influenced by real-time analytics.
Analytics in customer experience offers many benefits including making data driven, informed decisions that help address a number of business problems.
Customer churn is a common challenge across industries. Business knowledge and domain expertise are critical to countering customer churn. Collaboration across company departments is required to design a strategy for customer retention and to continue generating business from them. This means employing statistics on user browsing history and social profiling, via purchase behavior, from different geographies, as well as understanding the maturity levels of the users in the geo in terms of preparedness towards changes like increased personalization. Then the key is to apply a combination of online and offline initiatives.
To mine and analyze business intelligence, today’s data scientists are best suited to apply a thorough understanding of business problems to address the needs of today’s demanding customers. It is also important to distinguish data relevance from data noise. Relevant data is information (like feedback/ content from genuine purchases/ customers) organized and structured in a manner that aligns with requirements of analytics platforms, while data noise is any irrelevant data/ seasonal spikes of occurrence or unstructured/ disconnected information that cannot be understood and interpreted correctly by machines. Remember that data analysis of relevant feedback has to be done carefully. Listen and analyze existing customer opinions about offerings that are not successful with customers. Based on customer behavior and spend patterns, organizations have the rich data and foundation for critical decision making—or actionable analytics to modify their product or service offerings, keep customers happy, and reduce churn.
For example, in the currently booming e-commerce industry, if someone wants to buy a mobile phone, they are offered a SIM card, phone cover, and a telecom service provider package along with the mobile phone purchase. This can be possible due to collaborated offerings between ecommerce organizations and service providers. Analysis of offerings through usage information would lead to win-win strategy for customers-service providers and retail/e-tail organizations.
Deep levels of customer engagement provide visibility into customer activities, like data usage and billing, buying, product return, and investment patterns due to availability of rich data. For example, banks can offer credit cards, set credit limits, and offer staggered payment options based on their analysis of credit score data and customer buying and spending patterns. Analyze this intelligence to provide customized packages that fulfill individualized needs. Customer engagement can be through various activities like sourcing customer feedback through surveys whenever the customer gets in touch with the contact center (whether it be to raise a complaint, follow up on a request, or trace an order). A more proactive customer engagement can also be with outbound contact to gauge customer satisfaction about your company’s overall services and support.
Competitive market analysis is essential to counter market peer offerings. To do this, marketing campaigns and back-end analytics should be flexible. Customized products and solutions help protect against customer churn because customers can get specifically what they need without expensive add-ons. Intra-organizational communication and data sharing should be robust, with optimal collaboration between internal business departments. In a situation where a new ground breaking player enters the market, it helps to deepen engagement with existing customers who want to move to a competitor. For customers who are looking to switch loyalties from your company to another, analyze the customer’s usage and buying patterns. Provide them with customized offers and solutions that will compel them to continue their relationship adding to your company’s growth. This can be done through a customized survey or an exclusive individual conversation with the customer. Renewed company interest in existing customers increases customer goodwill about the company and reminds customers about the advantages of an enduring connection with your company. This is the catalyst to understand their motivation for change, in order to develop counter offerings to retain or recapture these customers.
In the US insurance industry, insurance providers often reach out pro-actively to help members reduce their health expenditure by making them adopt a healthy lifestyle. With the use of analytics, you can approach members before they develop health problems with tips for developing a healthy routine, preparing them for a certain procedures or alerting them of possible health risks.
Building More Targeted Customer Profiles
Building more targeted customer profiles helps in providing better suited, more personalized experiences. However, this is often an internal challenge in terms of inter-departmental collaboration. For instance, your company’s sales and marketing departments should have information about your online behavior and preferences before the beginning of a customer’s association with your company through online sales and marketing campaigns. Your customer service department will have information about them from the point they became your company’s customer. If sales, marketing, and customer service seamlessly communicate with each other and share crucial customer information, contact center agents will have a more comprehensive picture in front of them when communicating with that customer. This is ideal for social profiling and to more accurately target draw actionable analytics from purchasing behavior. Building targeted customer profiles will also help improve external benchmarking, like CSAT and NPS.
Here’s another example: in the telecom industry, you can analyze back end data like travel patterns during seasonal peaks and provide personalized packages for that duration. This can help in retaining customers and ensuring more business from them in future.
Ensuring Cohesive Experiences across the Company Website
Providing a cohesive experience across your company website requires data readiness and seamless availability of technology integration. The bigger your customer base and higher the volume of interactions, the more comprehensive and inclusive your analytical data and resulting conclusions will be. Ideally, a back end CRM system should be integrated seamlessly across channels. This will optimize data analysis for a cohesive customer experience. Today’s technologies provide end-to-end integration for the most relevant customer interaction information and interaction history available during customer facing conversations. This integration will help live customer service agents pick up right where a previous customer interaction was interrupted.