Interesting fact: We have discovered that Aunalytics Platform Team members are likely to celebrate the joys of an afternoon popcorn snack.
“Would you like to make that a large drink for only a quarter more? How about trying our candy of the month?”
These were a few of the phrases I uttered multiple times a day while working at a movie theater concession stand. It was actually a pretty good job for a high school kid; I was able to see movies before they were released to the public, I had the chance to memorize full movie trailers (since they cycled through the same ones four times per hour in the lobby), and I learned about upselling.
As “concessionists,” we were offered commission based on what we sold. The small commissions (5-25 cents per transaction) added up, especially as teenagers trying to make enough to fill our cars’ gas tanks. In order to maximize our commissions, we were encouraged to upsell.
At first, I was hesitant about hounding people to purchase something they didn’t specifically say they wanted. What if they got offended that I offered the larger size and decided not to buy the popcorn at all?
Fortunately, my fears were unfounded. I discovered that the majority of people did not mind my sales tactics, and in many cases, decided to purchase the item I suggested.
The longer I worked at the theater, the more I noticed trends. People who came to the late night showings really liked nachos and were likely to get some candy, too, if I offered it to them. When an older person considered buying an ice cream cone, their decision was solidified if I mentioned the butter pecan flavor. With this type of knowledge in hand, I was a selling machine!
Simple observations like these worked on an individual scale, but what about a business that has a diverse client base and many more offerings? This is where predictive analytics can give a company an advantage. By looking at seemingly unrelated data sets, data scientists are able to look at the buying habits of past customers and see correlations that were not readily apparent before.
Let’s imagine that a cable company wants to increase revenue by selling current customers a premium channel. Perhaps the data shows that architects are more likely to purchase a premium channel than professors. This is helpful to the cable company because they can focus their sales and marketing efforts on offering the channel to the architects, who are most likely to buy it, instead of wasting time and energy trying to sell to the professors, who just aren’t interested.
This micro-targeting is also good news for the customer, because they can be given customized offers most relevant to their wants and needs, instead of being inundated with ads for products and services they would never buy.
In addition to discovering non-obvious connections between sets of data, there is another benefit of using very large data sets. That is, not all correlations are obvious or even able to be seen at a small scale. It is only when large quantities of data are compared that certain patterns emerge.
Let’s pretend that sales of light bulbs increase in Florida when the temperature dips below 70 degrees in June. This increase may not be noticeable by simply looking at last quarter’s numbers, since a temperature dip like this may happen so infrequently. But when looking at months, or even multiple years worth of data, the trend becomes apparent. A small snapshot of the data doesn’t always give the whole story.
Data analytics is all about getting to know your customers better. When you know your customers well, you can maximize your efficiency and effectiveness. Imagine what insights are hidden in your data. Your company could become a selling machine, too!