Big Data vs. Small Data… Why not both?
Big data, small data… do we really have to choose sides? In a world where data is essential to business, and big data is all the rage, one must ask if all data has to be “big”? Can businesses benefit from “small” data?
The answer is sure, businesses can benefit from small data, but there are certain questions that can only be answered by big data. For example, Target uses big data to identify pregnant customers, and Wal-Mart is working on auto-generating shopping lists for its customers. Small data simply doesn’t have the power tackle such big jobs.
On the other hand, while big data can effectively take away the guesswork about who our customers are and what they want, it cannot replace conversation. Brands need “small” data to identify and join conversations—particularly on social media sites—about their company, brand, or product.
If big data tells the numerical story of spending behaviors and how to take advantage of them, small data tells the customer’s thoughts and emotions. Together, the strength of big and small data is a powerful combination that all businesses should be aware of.
Let’s look at how marketers can use both big and small data to simultaneously bring more business and increase customer loyalty by using the following example of the fictional Jane’s Coffee Shop in Manhattan.
Joe, on his way to a business meeting, stops into Jane’s Coffee to order an espresso at 9AM. At 9:15 Joe tweets that his espresso tastes like water and he’s never going back to Jane’s Coffee. Now, let’s look at the two different ways this could play out using big and small data:
Scenario A – big data: Using big data, Jane’s Coffee’s marketing team tracks how often and on what days customers like Joe come in to buy an espresso, so they can target him, and those like him, with fast expiration coupons in hopes of getting Joe in another time during the week. The marketing team, using the big data, automatically tweets Joe with a 50 percent off coupon code that expires in two days.
Now Joe, who hated his espresso, may reconsider returning to Jane’s Coffee to take advantage of the 50 percent off offer. Or he might even pass the coupon code on to a friend or coworker, increasing the chance of a customer returning to Jane’s Coffee in the next couple of days.
Scenario B: Jane’s Coffee tracks its mentions on social media across Facebook, Twitter, Google+, LinkedIn, and Instagram to tap into real-time conversations, both positive and negative, about the shop. One of Jane’s marketers comes across Joe’s tweet about his watered down espresso so he tweets Joe from the shop’s official Twitter account to apologize and promise to look into the issue. He also attaches a coupon for a free espresso as a token the shop’s appreciation for Joe’s business.
After further research, the marketing team concludes that there are more tweets complaining of watered down drinks during the morning shift within the same two block radius of that particular Jane’s Coffee. The team reaches out to the unhappy customers to let them know the shop is listening and working to find the solution. The team also contacts the store manager to identify the problem, which turned out to be a new barista using the wrong roast during the morning shift.
In Case A, big data allows marketers to directly reach the shop’s target audience and provide coupons and discounts with fast expiration dates to entice the customers to come back soon. In case B, small data allows marketers to target specific conversations to quickly pick up a pattern, directly communicate with customers, and find a solution to a current problem.
This example illustrates the key difference between big data and small data. Big data is big… and brings insight on a larger scale. It’s about picking up huge patterns and insights that can’t be gleaned from everyday conversation and interaction with customers so marketers can reach a larger audience more quickly.
Small data, particularly in social media, is for picking up on immediate, actionable patterns. Small data focuses on finding specific conversations to allow a response so brands can interact with their customers directly. Small data catches social cues so marketers can respond and personify the company’s brand.
The trick to data, then, is not making it purely colossal and automated or narrowed down and specific, but instead figuring out what data you need to simultaneously make customers happy and boost revenue. Don’t focus solely on big data or small data. Instead, figure out what combination of large and small data will make your company profitable and likeable.