Advanced data is an increasingly overwhelming necessity in marketing. All you need to do is look at the burgeoning growth of competing software, platforms, dashboards, and measurement tools to grasp just how large of a market it’s becoming
It’s a growing industry that’s evolving because of its value to marketers and their clients. Every answer to their questions lies within the numbers. But it’s a matter of extracting the data that matters—the ones below the surface—that requires the help of data mining software.
Data mining, defined as “the practice of examining large databases in order to generate new information”, discovers “patterns in large data sets involving methods at the intersection of machine learning and statistics.”
Basically, it finds patterns and correlations in large datasets you would never be able to fully dissect because the datasets are that massive. Those insights it finds are what end up being used by companies, especially large ones that deal with upwards of millions of customers, in finding patterns.
For example, did you know Amazon is a long-time user of data mining? How else would they know about “Things you might be interested in” when you add a particular item to your cart?
It’s obviously worked. “Amazon has reported a 29% sales increase that has arguably to do with their recommendation systems.” Only their direct mailing efforts can boast a higher percentage increase.
The same rules apply to any store you’ve been to that constantly asks you to sign up for their loyalty program. Do you really think it’s all about signing you up so you can get better deals? Of course not. They want you in their system so they can more easily track your buying habits:
“Grocery stores are well-known users of data mining techniques. Many supermarkets offer free loyalty cards to customers that give them access to reduced prices not available to non-members. The cards make it easy for stores to track who is buying what, when they are buying it and at what price.”
These companies, and digital marketing agencies alike, are intent on making the buying process as cost-effective and fluid as possible. Convenience is the ultimate objective in the buying process, and if a company has to lose a few dollars through loyalty programs to profit more by better understanding buying habits they’ll take that hit.
Data mining comes into play here because of those different factors and variables it can distinguish:
“It ploughs through millions of combinations looking for groups of consumers who share attributes with one another. If it finds enough people whom meet a particular set of attributes, and if that set of attributes is relevant to the question at hand, we deem it a statistically relevant pattern. By studying those patterns, especially those in which a conversion occurs, data scientists can pinpoint key drivers of conversion.”
“Computer algorithms can slice and dice everything from a customer’s age and gender to credit scores and buying history. By carefully mining this information, analytics software can help identify patterns in customer behaviors that can increase sales and reduce customer turnover.”
But it’s not only for large businesses. Even small-to-medium-sized businesses can find value in software doing the dissecting for them.
Grasshopper, a virtual phone system business, observed these factors to stoke a significant drop in their customer attrition:
“Since deploying its analytics engine in February, Grasshopper has reduced customer attrition by more than 25%….
For years, more than 10% of its customers canceled their accounts within the first 30 days. Analyzing things like location, usage rates, type of credit card and email domain has helped keep customers on board.”
Sway, a women’s fashion retailer, saw similar drastic changes:
“Only about 20% of our customers were opening the emails…That all changed when Sway turned to Retention Science’s predictive analytics software in March. Since then, Sway’s email marketing campaign has helped increase online revenue by 300%. Not only that, but now 40% of recipients are opening the emails and the number of click-throughs have tripled.”
The trends they found were able to identify high-risk customers who hadn’t engaged with the brand for an extended period of time:
“We created a ‘We Miss You’ campaign. It lured laggard customers with a 10% or 15% discount or free shipping based on previous buying behavior.
The result: A threefold increase in revenue from past promotional offers.”
Sometimes, data mining doesn’t even have to be limited digital marketing to optimize the physical customer journey:
“The use of Indoor Positioning Systems in large department stores is currently used to gain more insight into the journey of a customer in the department store. For example, IKEA could use this to check whether customers find their showrooms interested enough by measuring the duration of stay in front of a certain showroom.”
In order to understand buying or subscribing habits, businesses first have to understand their buyer. Digital marketing, and data mining as a result of the industry, are ushering in a way to obtain the information necessary to learn more about their customer, even before they buy anything.
It’s gained via data mining software, which then upload the data into data warehouses that are stored in in-house servers or a cloud system. Marketing professionals then determine how they want to organize that data, picking out factors and variables they think would be most important to achieving their goals.
The data is then sorted based on the user’s results, which is then presented through graphs and charts made by the marketer.
Still, while data mining makes it easy to find the answers, it’s still ultimately on the marketer to create an effective marketing strategy that takes these factors into account.