Predictive Analytics Key Component of Customer Experience Management
Statistics from the White House Office of Consumer Affairs indicate it’s 6-7 times more expensive to acquire a new customer than it is to keep an existing customer. Considering that Marketing Metrics predicts the probability of selling to an existing customer 50 percent more likely than selling to a new prospect, it’s fairly clear that paying attention to the expectations and experience of existing clients should be a fundamental part of your strategy. How can the CIO facilitate customer experience management?
Integrate All Data to Reveal Patterns & Develop Contextual Marketing Engines
According to Google, 30 percent of mobile searches are related to location and 76 percent of people who search on their smartphones for something nearby visit a business within a day. This reinforces that it’s critical to look not just at web or digital data, but also data gleaned from in-store visits, sales calls, and the customer service department. Every time a customer interacts with your company, they are telling you about their intent. Tracking and integrating these interactions between online and offline engagements allow you to develop real-time marketing capabilities. Incorporating all sources of data that you can track with what you already know of the customer can yield a fuller picture of the customer’s experience and intent. Map all this information with a customer’s journey to figure out their consideration path, and crucially important: why they buy from you. These insights will reveal ways to encourage repeat business and upselling opportunities.
Customer engagement management is about developing relationships, not transactions
Product reviews, comments from the service department, and user-generated content such as Twitter and Facebook posts can also offer considerable information and insight into customer intent. In a study from the Institute of Management Sciences, 60 percent of commercially successful innovations across nine industries came from customers. Ask yourself: What are people looking to do with your product? What problems do they need to solve with it? What issues do they encounter trying to use it? Tap into their shared data and complaints and the picture becomes clearer.
The key is to look at existing data—both in your own systems and available publicly—to anticipate customer needs. Data isn’t enough. You need people on staff capable of sifting through it to determine patterns, identify cause-and-effect, and to postulate hypotheses as to how specific behaviors are signs of customer pain points or changes in their experience of your product. Develop a culture of experimentation and analysis to test and perfect them. Developing algorithms based on these hypotheses move you closer to real-time marketing capabilities. These patterns of behaviors then become key indicators of customer intent and become a factor in predicting their needs and creating contextual marketing engines. They integrate everything you know about your customer with their real-time interactions which then can trigger nurturing campaigns, customer check-ins from customer service or sales and contextually relevant content. All of these informed interactions can help dramatically increase sales.
Create Contextual Engines around Customer Interactions
With every interaction, customers give you information on where they are in the purchase process. Once the full consideration process becomes clear, creating contextual marketing that nurtures them to the next logical step in the interaction cycle then becomes simpler. This is the key function of IT in analytics: Enabling contextual marketing by bridging the gap between customer expectations and experience. Integrate the programs you have—marketing automation, real-time analytics, and contact databases—with algorithms that determine intent and then personalize the marketing content to be extremely relevant to that particular customer in that moment.
This is why programs such as Nike+ and Fuel Bands are so addictive—they enable and anticipate the customer’s next action. They cheer you on while you’re running. They immediately tell you your personal stats, how they compare to previous runs, and how you’re doing compared to your friends. You can then brag about a good run immediately by posting it on social media, or challenge a friend to beat you. The product becomes part of your running experience and it becomes difficult to train without it.
Imagine what an organization could do if it picked up on behaviors that signal a customer is unhappy or Banks observed that consolidating accounts shifting large sums of money around within accounts often indicates a customer is getting ready to switch banks. Knowing that would certainly spark a personal customer service check in with the customer to try to resolve any discontent. By calling these customers, these banks revealed a deeper insight: these customers were often switching banks because of major life changes. Marriages, divorce, deaths, purchasing a house—these are all shifts that will change the needs of the customer experiencing them. Rather than stopping at customer service recovery calls these became opportunities to upsell current customers into new bank products and services like mortgages, insurance, wealth management, retirement, and estate planning services.
This kind of data mining and predictive analytics is not easy, but it is within every company’s control. You are probably using tools such as web analytics platforms like GUA, tag management like GTM, and A/B testing solutions like Optimizely. The challenge is to inventory and integrate these data sources to get a full view of customer activity, map out the intended customer journeys and identify gaps in tracking.
Develop a Culture of Analytical Thinking Centered on Customer Relationships
Customer engagement management is about developing relationships, not transactions. Customer service and engagement is a skill everyone needs to learn, no matter what their job entails. Developing those relationships and value requires employees to have curiosity, attention to detail, and the willingness to go beyond the immediate request. It’s about anticipating your customer’s needs and being observant enough to know when they’ve reached that point. It requires critical thinking and analytical skills. One of my favorite models is the DIKW (data, information, knowledge, wisdom) projection. You only get to wisdom by climbing the model and by having the human resources that are capable of using the knowledge to gain wisdom. Research from the American Management Association shows that sales acumen, customer service, and analytical skills are among the top skill sets that high-performing organizations excel in. It pays to develop your staff in these areas for needed job skills and to develop an inquisitive culture.
Sometimes even seemingly small changes to policies can change culture and create dramatic results. American Express today interacts with its customers based on the customer’s personality . Amex has empowered customers to resolve problems on the first contact with the customer. There’s no pressure for reps to reduce the call time, or make a specific number of calls. The focus is on the customer and making them happy. And this strategy has paid off for Amex; customer satisfaction rates are year after year exceptional. Research backs this decision up: A recent Gallup study indicates customers are more impressed by customer service that feels “thorough” and friendly than they are by quick service.
Big data continues to challenge most organizations. Introduce the time factor and you have moved from managing Big Data to working with Fast Data. It’s up to the CIO to drive internal change, empower employees to adapt to customer needs and leverage the tools available to gain insight to customer behaviors and intent. Are you leading the charge?