When it comes to big data, some online retailers believe the more data collected the better the return. However, studies have shown that it’s not the size of the data you collect that makes you successful, it’s what type.
According to a recent report by Monetate, “Mastering Big Data: Best Practices, Dos & Don’ts,” while most companies out there are collecting and storing plenty of data, leveraging that data and uncovering the true meaning behind that data can be tough to decipher. In fact, according to the report, “companies often collect so much data that it’s easy to feel overwhelmed, and for data projects to stall as a result.”
So, how to not only start the journey of big data but make sure what you’re doing is worthwhile to the company? Here are five tips from Monetate on how to leverage that data and possibly increase customer satisfaction, sales, and productivity.
It’s all about the customer, not the data. The road to the big data success, according to Monetate, is not about shifting through piles and piles of data. For retailers especially, the focus should be on returning customers, the frequency of their purchases and their behavior patterns. Once that is set, Monetate says, to look at that data to uncover new insights about these specific visitors. This can help you generate ideas that will increase engagement about your brand, the products you sell, and your ecommerce website as a whole.
Starting with a customer centric approach, according to Monetate, will also “help you create remarkable experiences, new opportunities, and improved metrics.”
Manage the data that matters. When diving the enormous data that is coming your way, the best thing to do is figure out what matters most to your organization. For retailers, according to Monetate, a merchandising system would likely have the best inventory data. For a content website, onsite analytics should give you the best look into website behavior.
With all that information, Monetate recommends, creating an aggregate data source that is fed by your best analytics. But, Monetate cautions, this will be the moment where you need to make a smart business decision and figure out if it’s better to use technology to compile all these data sets or is it better to assign one person to be in charge of the effort. Both techniques, Monetate says, have pros and cons, and the approach your company should take all depends on the resources available at that time.
No matter who is in charge of the data, Monetate recommends storing it all in one place. This will not only make it easier to use, but “it will become the one true data source in the organization that everyone can turn to when they need analytics.”
Failure is not an option. When diving into collecting big data, many companies can feel frustrated that they are not getting the immediate results that they are looking for. This should not stop you or your organization from moving ahead with big data collection, according to Monetate. It is important to change the company focus away from failure if you do not get the results you wanted into learning why that campaign or test didn’t succeed. No matter what campaign or test you have created, there is still quality and important collected.
The first step in refusing failure, according to Monetate, is to shirt the company mindset away from calling things tests or campaigns. “Instead, understand that when a feature, campaign, or test still doesn’t’ succeed, it has still uncovered a new learning.” No matter what you do, the data you capture is still giving you insight on your visitors and customers.
Get specific. One of the best ways to not get overwhelmed about the data you collect is to focus on a small piece of the pie. Monetate suggest looking into something your organization would like to improve (such as cart abandonment rates or social shares), find out what the measurement criteria is, what the data points are, and then work from there.
As an example, Monetate stated that your data could reveal that for some reason visitors in a specific region could be more prone to abandon their cart. But by examining the data further, you could learn that it could be because they live in a closer proximity to a traditional brick-and-mortar store than the rest of your visitors, or they prefer different payment options, or generally do not hit the free shipping threshold offered.
The western wear retailer, Sheplers, according to Monetate, had sale figures that revealed that certain states were converting at lower than average rates and they needed to find out why. Sheplers, Montate said, thought it was because there were primary retail competitors in those areas. To combat their theory, Shepler’s starting targeting those underperforming states with free shipping offers to see if they could life their conversion.
Shepler’s efforts, according to Monetate, were successful, especially in Texas, where it saw a 20% increase in net contribution and a 48% increase in new customer satisfaction.
Test, test, and then test some more. Once you feel you are successfully collecting data that is working, doesn’t mean it’s end game for big data in general. “Test messages, creative, and placement, and then use data to understand how customers respond to those tweaks in order to iterate based on those responses,” Monetate said.
No matter what the results are, Monetate said, running additional campaigns and more tests will boost your knowledge about your customer base which will lead to more and more revenue.