If you ask a slew of companies if they have real-time analytics capabilities surrounding their customer experience, it’s possible that many of them would say yes. However, the odds they’re all using the same definition to get to this answer is slim.
Let’s explore a few different scenarios where businesses may interpret their customer experience as “real time,” but all three prove to be very different.
First, imagine a business that handles its customer interactions through a call center. The caller must provide their identity to the call center employee. Then the employee can look up the customer’s account information. On a rolling basis, this company creates and stores profitability scores for each customer. When a customer calls in to buy a new product or place a compliant, this pre-populated score is used to let the employee know what level of offer or discount they are allowed to give the customer.
Second, imagine that prior to connecting the customer to a call center employee, the customer gets routed through an automated voice response system. In this scenario, the call center is aware of the customer’s identity prior to talking to them. In the time it takes to connect the customer to the call center employee, the company updates the customer’s profitability score in real time, to reflect the most up-to-date information it has. Then the employee makes a recommendation based on this score, which was updated in the last few minutes.
Companies that compress real time analytics down to nanoseconds, with the greatest level of data insight, will be the one that thrives.
In the last scenario, a company captures every pixel of information about a customer — if they were just browsing their website, if their GPS data reveals they are currently standing in a store, if they’re calling for the second time today — and makes a decision about their potential profitability as soon as the customer is connected to an employee. The employee then makes an instant decision based on the information displayed on their screen.
While these are all very different depictions of real time, the third scenario clearly stands apart. For each of these enterprises, “real time” means any computer system that updates information at the same rate it is received.
However, the nature of real time at each business is limited by how its data is stored and interpreted. This all comes down to data architecture. Improving this can lead to an improved customer experience. Chief data officers must prioritize creating an architecture that allows the company to blend its data warehouse, data lake and other systems within the analytic ecosystem to create a scenario where both pre-sorted and raw data can be used in a unified manner.
In this age where there are more data points to analyze along the customer journey than ever before, companies need to make sure they are not limited by their own technological capabilities. The goal should be to change the customer experience
during the experience instead of fixing it after the fact. This leads to a better rate of return for companies, since
62 percent of B2B customers and 42 percent of B2C customers will buy more based entirely on customer experience.
Through a blend of proprietary and open-source machine learning applications, creating choreographed customer experience is squarely more of a data problem than a traditional marketing problem. And whichever companies compress real time down to nanoseconds, with the greatest level of data insight, will be the one that thrives.