If there’s one thing that no one is in short supply of these days, it’s data. Every department has its own favorite source of data and we all seem to be trying to solve the same business challenges. But we each come at it from a different direction. The promise of big data is that by combining all of these types of data, we can get better insights. With advanced analytic tools at your disposal, making sure your data is complete becomes more important than ever.
Which begs the question: “What’s missing from your Big Data picture?”
What Problem Are You Trying to Solve?
One starting point in figuring out the data you might need is to examine the kinds of problems you will need to solve. Across your organization there are probably some common themes that are shared by most departments:
How to grow revenue?
How to retain customers?
Ways to be more cost efficient?
Best methods of ensuring compliance with regulations or policy?
How do we do all of this and still satisfy customers?
All of these questions are drowning in data from CRM, Billing or other operational systems. Demographic and market data provides us with information that allows us to segment our customer base a thousand ways. But often all of this data is incomplete. This data typically tells you what happened, but it struggles to tell us why, because it doesn’t account for the customers’ experience.
Most companies rely on customer surveys to fill this void. While survey data can be valuable, it is also largely incomplete. Only the angriest or happiest customers usually bother and the feedback usually leaves more questions about causes of customer sentiment than answers.
Behavioral Data from Customer Interactions
The surprising thing for most company execs to discover is that all of the information about their customers’ experience is sitting right under their noses. Customers tell us everything we need to know every single day. It’s just that this feedback is buried inside of thousands of captured customer interactions and it isn’t easy to extract and organize this data. But it is crucial to do so because it is this data that’s the missing piece of the puzzle. This is the piece that gives predictive models a lift in their accuracy that connects the dots in the customer journey, and breaks down the departmental silos by adding value across the organization.
You see, the customer interactions being captured by a company’s contact center contain information that can’t be found in the traditional data sources currently in use. Customer interactions contain key behavioral events. These events tell you what a customer actually did or experienced during an interaction, such as expressed an emotion, mentioned a competitor, or received a sales offer. When an interaction analytics solution is used to organize them, these events can be quantified and analyzed to uncover correlations, trends and root cause. Thus, the combination of traditional data and customer interaction data is an extremely powerful tool for organizations looking to make significant improvements to the challenges we identified earlier.
How Does it Apply?
Here’s a practical example. Using only traditional data, a company can tell how many times a customer has called into the contact center. They will know what current subscription package that customer has, their age and how long they’ve been a customer and they will know when that customer churns. But do they know why they churned? Could they have better predicted the outcome and proactively stopped it? Well, what they’re unlikely to know is what that customer said during their interactions. Were they unhappy with the service they received? Did they mention a competitor or an increase in their bill? Did the agent make a retention offer every time they called in and expressed dissatisfaction? Also, is marketing confident that the offer the customer did receive was the most targeted and effective one available based on this customer’s circumstances or could a different package have been more appealing and yielded a different result?
Using traditional data, it’s possible that this customer may have ended up on “likely to churn” list that was generated using a predictive model based on the number of calls they placed, their tenure and the agent notes. However, when behavioral events are factored in, such as mentioning a competitor, voicing dissatisfaction about an unresolved technical problem and expressing confusion over the new equipment they were sent, and the propensity for that customer to be tagged as likely to churn increases dramatically. When you think about this example expressed across every interaction, for every customer, suddenly marketing offers become much more refined, predictive models become much more accurate, agent behaviors are better understood, customer satisfaction measures expand and the effect on a company’s bottom line becomes much more profound.
Where Do We Go From Here?
The good news is, companies are collecting the data they need to improve the customer experience and their bottom line. The key is ensuring that you’re taking advantage of the data you have, and not leaving key pieces of it untapped. The rise of big data has brought so many new and innovative ways to use and combine data sources. For companies to take it to the next level, they need to harness a solution that allows them to organize their interaction data in the same way they’re organizing their traditional data so the two can be integrated together. The results that can be achieved when customer behavior is factored into the mix are simply too powerful to be ignored.
{Photo credit: Wikimedia}
