Data is valuable because it’s powerful.
On its own, it has the ability to do the job of an army of market researchers, revealing information that helps companies understand and better market to their consumers. And these days, data never stops. It never slows down. In fact, it’s going faster and faster—so fast, in fact, that humans can no longer keep up with it.
This is where data mining comes in to save the day.
Okay. So what is data mining?
At its most basic, data mining is a fancy way of saying “information sorting”.
All this valuable data that companies want (and need) so badly goes into large, complex databases. This data can include such information as what a customer has purchased, their age, where they live, if they are married or have kids, how often they enter the store or visit the website.
Once this information goes into the databases, patterns are created which reveal even more about consumers and how they tend to purchase things. This is obviously very crucial information for any company, as it teaches them how to sell products more effectively to their target demographics.
However, as mentioned before, this data gets very overwhelming. There is simply far too much of it constantly coming in to be humanly digested. Basically every transaction a person makes will leave a digital signature that is being captured and stored—both online and in actual life. With the crazy amount of this data, the patterns within the databases, as well as the relationships between variables, are often too subtle to be observed by simply looking at the data. That’s why data mining is so important.
Data mining automates the process to detect patterns—meaning, it sorts the patterns out so that companies can clearly see the information. It simplifies and summarizes the data in a way that’s easier to understand, which allows companies to take in new information and to then make predictions based on the patterns.
And it’s the gift of being able to predict that data mining really boils down to.
What is data mining mostly used for now?
As stated above, the ultimate goal of data mining is prediction. It allows companies to make educated predictions about what customers may want to purchase next—and the big aim is to make those customers loyal for life.
Once companies have used data mining to sort out patterns, they can read those patterns and say:
“Okay. This group of consumers who purchased a Magic Bullet blender and a Magic Bullet recipe book also tend to buy a lot of smoothie glasses. They’ll probably want our new line of Fitness Dishware that we’re releasing this spring, so let’s make sure they all are targeted for that marketing.”
“Okay. This group of subscribers who love Bruce Lee movies also watch a lot of Jackie Chan and Jet Li films. Now we can predict and recommend similar movies online for them to watch. The more movies they have lined up to watch, the longer they’ll keep paying us for their subscription.”
“Okay. This Facebook user checked in at a Luke Bryan concert, she threw her best friend a ranch-themed baby shower, she went to a Texas-themed BBQ three months ago, and she never misses Country Music Karaoke at her local bar. Clearly she will love our designer cowboy boots. Let’s send her a sale alert right now.”
These are very basic examples. Data mining can reveal far more complex details that allow companies to target consumers in an even more magnified way:
Retail giant Target caused a bit of controversy several years ago when their head of Guest Marketing Analytics, Andrew Pole, talked about being able to market to pregnant women and make them customers for life when they had no way of knowing if the women were actually pregnant. Pole was quoted as saying, “We knew that if we could identify them in their second trimester, there’s a good chance we could capture them for years.”
The prediction power of data mining reaches from the most basic purpose of selling cribs to new mothers, to stock exchange experts using it to better guess how stocks are going to perform based on their historical performance (at a much cheaper rate than having a team of analysts employed). Medical research institutes purchase the information that data mining produces about our DNA to learn more about cancer genome, heart disease, Alzheimer’s, Parkinson’s, birth defects, and more.
This happens in industries of all kinds—from the government to medical researchers, to those who just want to sell funny hats.
The data is coming in so hot and quick that data collectors are just adding more servers for storage because they can’t keep up with it. And companies of all kinds are buying this excess data like a new commodity. A sub-marketplace has emerged because the information is so valuable in order to target and sell to the public.
How can companies make the most of data mining?
Companies can benefit from the information they get from data mining in more ways than one. Here are some beneficial and effective methods.
Customer loyalty can be achieved in the way that Target was brought up before. By “snagging” customers early, you may be able to retain them for years. If companies use data mining to provide consumers with what they want or need before they even realize they want or need it, that consumer will have the company on high alert in their mind—whether it’s subconscious or not.
Affinity analysis or “basket analysis” (referring to a check-out basket online) allows a company to see what a consumer purchased and predict what that consumer may want in the future based on their preferences. This analysis evaluates the use of credit cards and patterns of phone use, and can reveal information such as if a consumer needs constant incentives to stick around, such as upgrades or new features. It can also answer questions like: Do people who buy item A also buy item B? Which one did they buy first, and why? Could we encourage customers to buy A, B, and C, thus boosting point-of-purchase sales?
Sales forecasting observes the product that a customer purchased, and tries to predict when they may buy it again. This helps when companies want to plan to sell to phase a product out, or when trying to determine complimentary products to sell.
Market segmentation is when a company categorizes its market into segments such as age, gender, job, marital status, income, etc. This is an effective strategy on many levels, and it can even help a company understand its competitors in each of these segments. Market segmenting a database can improve a company’s conversion rates and can help companies customize products to further engage their demographics.
Merchandise planning can help a brick-and-mortar business determine stocking and inventory options. By knowing data information of how much of a certain product customers purchase, companies can know how much they need of it. Merchandise planning also assists companies in knowing what to do with inventory that is getting old, intelligence on competitor merchandise, balancing stock, and pricing based on customer sensitivity.
Data mining is also great for product production, wherein a company predicts features and products that their consumers may want. Data mining will reveal customer pain points, leaving it up to the company to provide that customer with the answer to that pain point with a viable product that will solve that problem in a way the customer never imagined.
The more data a company collects from customers, the more value it can deliver to them.
As you can see, data mining can provide quite a bit of power to companies and industries. It can categorize new information about what people buy and think and want, and who they are—and compare it with other information in that same category in order to validate and reveal more. The more information that’s revealed, the more companies can understand how to market.
Who’s buying what, and why? How often, and how much are they willing to spend? What’s causing them to buy certain things? How are they like other consumers who bought the same things, and how are they different?
Knowing these answers is how most successful companies make money—and these answers come from data mining.
Right now, one of the biggest challenges with data is that people are producing more of it than companies know how to analyze, use, or extract the information from it. But data mining is a juggernaut that is getting stronger, and its categorizing and sorting abilities are helping companies thrive.
Computhink is a company that specializes in securing and organizing data. Stay on top of our blog so that you never miss any of our informative posts on the world of data science.