IE 11 is not supported. For an optimal experience visit our site on another browser.

3 Ways to Use Big Data to Drive Repeat Sales

Examine patterns in customers' behavior to score purchases from them once again.
/ Source:

Someone has purchased your product or service. A one-time sale is the first step, but repeat sales are a more meaningful revenue stream. So how can you drive repeat business for your products or services?

The answer is that you already have all the information you need to translate one sale into many. You just need to understand what to do with it. This is very much the theme surrounding Big Data this year: It's not enough to have it; but rather you need to know how to drive revenue from it. We are in the midst of a transition where Big Data is moving over to the marketing suite as chief marketing officers are looking to drive revenue generation from Big Data as quickly as possible.

So how can you look at your customer patterns to increase second-time sales?

Related: Marketing Must: Customer Data Points to Stop Ignoring -- Now

Here are the three critical ways to get started:

1. Know your customer. The basic information is in your transactional history. You have a name, address and specific intel on what this individual wants or needs to buy. Big data analytics blows out a simple transaction by showing not just how much customers have spent, but what they spent it on. That knowledge will likely inform their next purchase if you analyze the first one. Was the purchase an anomaly or part of a behavior pattern? Is this a first-time purchase or is there a history you were unaware of?

All your customers’ interactions with your business -- whether through your website or in person, on the phone or through social media --  provide you with data to learn more about each of them. What tools are they using to learn about your business? What is an individual customer’s preference for how he or she makes a purchase: mobile device or tablet, in the store or through the website?

Once you know everything there is to know about your customers, you’ll better know how to engage with them.

Related: Why Spending on Big Data Isn't a Waste (Infographic) 

2. Build loyalty by listening to your customer. Big data lets you know how to most effectively engage with your customer and build a relationship over time as you learn what he or she likes and dislikes. Social media provides opportunities for web engagement through conversations in which an unlimited number of potential and actual customers can participate. Connecting with your customers via your Twitter feed or Facebook page allows them to tell you what they are thinking -- and allows you to use that information to tweak your product offering or even expand your target market. It’s a chief marketin officer's dream come true.

Each data point tells you something valuable about your customers’ purchasing habits. Leverage client engagement into a source for Big Data through social media interactions. Distributing a coupon or other offer through social media tells you far more than if your customers are clipping newspaper coupons.

3. Deliver the right offer to the right person at the right time. Customers are more likely to buy something that is targeted directly to them right before they need it. For example, before Mother’s Day let Big Data analysis tell you who sends what kinds of gifts to their mothers or mothers-in-law and present those individuals an incentive to buy.   

One of our clients, World Kitchen, deployed this strategy around the time of the Super Bowl. While the Super Bowl is a huge social event, it looks different to a man in his 20s having over some friends from work than to a 40-year-old woman with a family. After we helped World Kitchen capture individual consumer preferences, the company is now better able to drive second-time sales inside their ecommerce and email campaigns in the spring.

Where else can you capture the data you need to drive repeat sales? Sources include web server logs and internet clickstream data, third party data and even information captured by sensors when customers check out in brick-and-mortar stores. 

Related: 10 Questions to Ask When Collecting Customer Data