Put Big Data to Work: How to Get From Raw Data to Hidden Insight
You keep hearing that big data is it—the holy grail of wisdom that’s going to provide all the answers for your company in 2015. Yes, big data is powerful, and yes, it really can give you the answers you seek to increase customer engagement. The problem is that big data doesn’t automatically lead to big understanding and big success. Gathering the raw data is one thing, but siphoning out the insight and putting it to work for your company is another ballgame entirely.
So, what is big data exactly?
Big data is vast, rolling hills of raw data—petabytes and even exabytes of age, gender, employment and other demographic data, purchase histories, CRM-system data, and online and social-media visits—all about your customers.
This is all wildly important data to have, but raw data is just that—raw. In fact, up to 85 percent of new data is unstructured, meaning it hasn’t been refined, massaged, or processed to be presentable enough to tell you what the customer did and why they did it.
From raw data to insight gold
You can’t unleash the value in big data without big data analytics to turn that raw data into patterns, trends, and correlations—information that connects the dots between the piles of data to help you drive the customer experience along a scale from non-contextual to contextual, on to personalized, then predictive, and finally to proactive.
With analytics you can connect info from all customer touch points to unfurl the bigger customer picture. Transactional data alone will not improve the customer experience, but combine it with website clickstreams, social media content, survey responses, and—increasingly—data from smart connected devices in the Internet of things, and you’ve now got a detailed customer profile that can help you power data-driven actions that will enhance customer engagement and enable better customer service through knowledge. When you begin to augment your raw data with context about your customers, then you can drive truly personalize customer engagement.
A little less reactive, a lot more proactive
Perhaps you want to better predict inventory trends for a more successful holiday season. You comb through big data analytics to predict the hit toy six months in advance. You watch customer browsing patterns, listen to social sentiment, and analyze the toy industry’s media buys to predict this year’s best seller. You then combine that info with your own set of customer data—CRM system data, purchase history, geolocation, social media content—to determine who will buy the toy and where they’ll be buying it. Now you can stock up the right stores with the right amount of inventory to fulfill customer demand and gain a competitive edge over your retail counterparts.
Or maybe you want to improve the self-service experience. Rather than making customers peruse a general knowledge base built for the common denominator, you can now surface custom support content based on the individual customer’s purchase history, past returns, past customer service calls, location, weather, time of day—you get the idea. The customer quickly finds the exact answer needed, and you save time and resources by cutting down on customer support calls.
The best part about all of this is that you’ve now got one giant feedback loop. Customer service interactions are now another relevant data source that continues to further improve the actions you take to keep customers satisfied.
The trick to making big data work for you is knowing the difference between raw data and actionable information. With the right analytics tools, you’ll have the information at your fingertips, helping you stay proactive at fine tuning your now well-oiled customer experience machine.
