In 2010, Google Executive Chairman Eric Schmidt explained the Internet’s magnitude in a way that astonished everyone who listened. Between social media, online transactions, virtual correspondence and sheer workflow, he said, in any two given days humanity was producing as much data as it created, in total, from the advent of computers until 2003.
That was in 2010. By now, we’re probably closer to matching that amount in a single day.
That’s a bewildering expansion, and, unfortunately, that’s what people think about when they hear “big data” — a totally overwhelming volume of intertwined digital information.
If we consider popular applications like Twitter, we see there are also great systems in place for organizing this data. Roughly 4,000 tweets are created every second, by individuals with profile data streaming in from all over the world. Because of the variety of user preferences, locations, and habits, no two users see the same thing on their Twitter home page. And yet, the site is extremely user-friendly.
We don’t follow everyone we know on Twitter, and aren’t faced with that avalanche of 4,000 tweets. We get a few every minute. We glean insight one tweet at a time.
That’s exactly how retailers should approach their own vast databases of consumer information.
Small Bites of Big Bytes
When you first approach big data, never charge in blindly head-first—you’ve got to have a plan, and know what you’re looking for. Break it down and look at it in snippets.
If you’ve never looked at your company’s consumer data before, there are easy places to start. Find a particular customer’s purchase history and you can learn a tremendous amount:
- Demographic data (Who is he/she?)
- Transactional data (How often does he/she make purchases?)
- Product history (What does he/she buy?)
- Response to promotions (What motivates him/her to buy more?)
You can find this out about every customer.
Who You’re Losing, and Who You’re Retaining
You can also divide your big data into big segments to analyze new customer behavior and reasons for attrition.
First you need to create a profile of your customers to better understand them. Who are they? What do they need? You can discern an awful lot by looking at their purchasing habits, including when they’ve experienced major life cycle landmarks, like the birth of a child, or minor ones, like starting a diet.
From there, you can segment your customers into categories, including “best customers” and “lost customers.” For instance, you can look at records of what your best customers are buying.
- How much do they spend on average?
- How often do they respond to promotions?
- Which promotions work, and which ones fail?
- What do you think they’ll buy next? (If they’re buying a cookie jar, you know they’ll be coming back for cookies.)
If you know your best customers are middle-aged women buying home cleaning supplies who, on average, spend $50 every two weeks at your store, you can easily drive sales and customer retention by playing up their preferences. Offer a rewards program on their next purchase: spend $50, save $10.
If you can identify a preferred product, let them know when it goes on sale. Send them a note that say: “Valued customer – you bought this before. Now it’s on sale. Do you need more?”
On the flip side, you can use big data to understand why customers are leaving and work to reverse this trend. Using the insights you already know from your customer’s lifecycle, you can focus on how to make these customers into best customers.
Understand Your Customers Lifecycle Better
Discover who your target consumers are and find out how to target them.
Patch Your Leaky Bucket
Deciphering big data is easy to tackle if you know where to start. The information you need already exists in your big data piles. With the help of an experienced data analytics company, you can crack the code and take your business to the next level.