Today’s consumer-driven world has changed how we must work with customers. Companies find themselves engaged in online activities ranging from monitoring a consumer’s purchase habits to a customer sentiment via social networking sites. Toss in the development of new programs and offerings based on device data, and you have the ingredients for next generation customer engagement, as long as you’re truly listening to the customer. If your organization is not using consumer data yet, check out three examples that could provide inspiration.
Are you looking out for your customers?
You or someone you know may have fallen victim to the increasing number of data breaches. Unfortunately, data breaches and the subsequent financial fraud have become commonplace in today’s online shopping and credit card intensive world.
Roughly 18 months ago I shopped at a large retailer where I paid with a credit card. Unfortunately, there was a data breach. My financial institution was looking out for me. I was contacted shortly after the breach and provided a new card containing new pieces of information. I never had to initiate contact with the institution, although I did monitor our account on a consistent basis.
The story could have easily ended here, but it didn’t. The bank continued to monitor the account (the account number did not change) to make sure that purchases were not made with the stolen information. The bank was using machine-learning techniques to look for inconsistencies in purchases on the card.
It was not a surprise when I received a call one day from the bank asking if I made a purchase in West Virginia at a hotel for $8.64, which I had not. At that point, the bank enacted a second-level security program, which included a change in the account number and a new card. This is a great example of how a streaming data solution partnered with a machine learning solution can be implemented as a fraud detection system to manage risk for the organization’s customers.
Are you listening to your customers?
A couple of years ago, I found a copy of the book entitled Empowered: Unleash Your Employees, Energize Your Customers, and Transform Your Business. The authors, Josh Bernoff and Ted Schadler, provide examples of customer service done right and done not so right.
In the book Empowered, the authors focus on two situations where the response and the follow-up action are at polar ends of the spectrum. The first focuses on an organization not taking the proper approach with social listening, which proved to be disastrous. In this instance, the consumer had a very strong social following. When she posted something people saw it, so the situation took several iterations to resolve. It ultimately cost the company multiple customers.
The second scenario could have been substantially more disastrous had it not been for an empowered employee who worked to resolve the challenge before it became an issue. In this case, the brand was strengthened by the employee’s positive actions as the client posted the experience within his social network.
Are you making products easy for your customers?
Leading organizations have found a combination of real-time analytics and machine learning can provide new innovation opportunities, such as new service offerings.
A manufacturer that includes sensors on its devices gets a plethora of data. The challenge tends to be how to access it and learn from it. We’ve all experienced the electronics-set-up-error-code frustration. You might see something like this: “Error 48 – If you receive Error 48, you need to hold the start button until the panel flashes. Release, then push this button, three times.” The fix may or may not work.
Companies build these lists during testing, and over time, as the company collects more data about the product, they add to the lists. Error 48 may not be a critical error, but it is an annoying one that may make you think twice about buying the brand again.
What would happen if manufacturers continued to collect data from the device through the machine? By finding the root cause of Error 48, they could pass an update to the device and automatically solve the issue without the owner needing to visit a product FAQ or website. The customer would certainly be happier and consider another purchase.
These examples provide an interesting conundrum that various companies must address. Is the collection and analysis of data a goodwill/branding exercise or is the value derived from the innovation consumers are willing to pay for to make them more satisfied? As your organization continues to evaluate the business case, you can start by exploring how streaming analytics, as well as machine learning, can be leveraged to protect, listen and satisfy consumers.
Andy Brockett, Director, Business Innovation