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5 Ways To Improve Customer Service With Real-Time Data and Real-Time Responses

This article is more than 8 years old.

If you’re leading a startup or new business and want to deliver the kinds of exceptional customer experiences that drive revenue and build loyalty, I’ve got just two words for you.

Real time.

Yes, I know you’ve heard it before, in observations by everyone from Forrester to Gartner, but I believe it can’t be emphasized enough. The ability to collect data about customers in real time and respond to it in real time is increasingly what separates successful brands from also-rans. I’ll show you some examples in this article. My main purpose, though, is to share practical advice that I hope will help budding companies that are looking at rolling out a real-time strategy.

Success in real time means collecting, assessing and acting on real-time customer data. You can collect volumes and volumes of real-time data, but they won’t do you any good if you can’t act on it just as immediately. Conversely, you can be fully prepared to act on data, with decision management and other technology-based tools arrayed at your disposal, but they’re useless without the right data coming in.

Keeping that in mind, here are the five things I recommend both new and established companies do to make real-time data and real-time response part of their operations, particularly in marketing and customer service.

1. Collect and consider real-time customer data in context.

Real time is the important thing, but it’s not the only thing. When you know not only what a customer is doing right now, but also the historical context for what they’re doing — i.e., what they did yesterday, or the day before — you have a lot more data with which to make informed decisions about your next step with them. This is why it’s so important to collect data from a variety of sources that tell you different things about customers. For example, a customer service agent’s response to a customer who is contacting the company to complain at this very moment about a product failure will be — or should be — different if she also knows that the customer has called about the very same issue five times in the last month. To simplify collecting and assessing real-time and other data from multiple sources, the big-data experts at Blue Yonder recommend using a customer data platform that provides a unified view of all the information and interaction data you collect.

2. Look for context beyond the customer.

It’s not just real-time data about the customer that creates context for business decisions; it’s also real-time data about other aspects of company operations. One of my favorite examples of this comes from the Dickeys Barbecue Pit chain. They describe responding to real-time data showing both lower-than-anticipated sales at a location and, at the same time, a surfeit of ribs at that very location by texting offers for a ribs special to customers in and around that location. The customers got a deal, the location got rid of the ribs and everybody ended up happy. This points to the importance of a customer data platform that supports integration with other business systems so that you can assess real-time customer data in combination with, and in the context of, real-time data about other areas.

3. Give mobile and social the central roles they deserve.

When it comes to knowing what customers are doing in real time, nothing beats mobile and social as sources of data. Tapping into that data is like being able to follow customers around personally to find out where they are and what they want — and make decisions accordingly. Just last week, I saw a report about Revive Vending, which operates a chain of self-service coffee shops, using IBM’s Watson Analytics to check customers’ tweets to get a sense of what they’d like to buy. This way, the company can make decisions about everything from product inventory to special promotions based on real-time information about what people are thinking and doing in the moment, rather than just on what they’ve done in the past.

4. Act fast. Real fast.

Collecting real-time data about your customers and your company from a variety of sources is, as I suggested at the beginning of this post, only the first step. You need to be able to analyze and act on the data in real time, too. After all, we’re living in a world where, according to Social Media Research, 75% of people who contact a company for customer support via social media expect a response within an hour. That may sound fast, but in the real-time world of marketing and customer service, it seems like quite a lot of time to me. After all, United Airlines is reportedly taking all of 200 milliseconds to generate personalized offers in response to real-time customer insights these days. The ability to seize the moment before it’s gone is critical to making the most of marketing and customer service opportunities these days. Here, again, Blue Yonder counsels about the data platform you choose, recommending an integrated solution that unifies data and also provides the analytics to transform the data into meaningful insights and the ability to recommend actions to take in response.

5. Anticipate your customer’s next move.

As you embark on a real-time strategy for customer data, I think it’s important to start looking beyond real time, too. By that I mean that you should be considering predictive analytics that enable you to not only seize the moment but also to anticipate the next moment — even before your customer does — and seize it as well. According to the consultants at Walker, by 2020, responding immediately to customers won’t be fast enough. Rather, people will expect companies to know how to address their future needs, too. One thing that’s going to impact the ability to do this is the growth in self-learning marketing technology that McKinsey described back in 2012 and that’s becoming ever more sophisticated today. As the name suggests, it’s marketing technology that learns from what it does, getting smarter and smarter – and better able to make predictions and decisions – over time.