With the advancement of big data over the past several years, marketers have more access than ever to consumer data. Having access to even the most microscopic piece of data can make it hard to comb through irrelevant information to build actionable insights.
There four different types of data analytics: descriptive, predictive, and prescriptive.
- Descriptive Analytics tells marketers what previously occurred, reporting on metrics such as the average sale amount or number of clicks that transpired over the past week.
- Predictive Analytics tells marketers what is likely to occur, reporting on the best time to send a customer a coupon for an item they were looking at/left in their cart.
- Prescriptive Analytics tells marketers what action to take, such as creating a pricing strategy that displays optimal pricing at different times of the day depending on the demand.
Although marketers are constantly vying for prescriptive analytics, it’s nearly impossible to gain them without the presence of descriptive analytics. Which is why it’s imperative that companies understand what data is necessary to collect, how to collect it, and most importantly, how to interpret the data, in order to create the right decisions.
On a personal note, just last week I downloaded a new application on my phone called “Flo.” Flo is an application, that well quite frankly, tracks menstrual cycles, ovulation, pregnancy, and much more.
Every single day, Flo collects descriptive data such as your weight, sleep schedule, water consumption, mood, symptoms, and other various feminine activities/events. It then converts that data to create predictive analytics, such as determining the next time you will have your menstrual cycle or ovulate. Finally, Flo uses prescriptive analytics to send personalized Daily Health Insights with relevant information to the consumer at the best time for them. This past week my water consumption wasn’t the best and this morning they sent me an article on water-intake.
At first glance, I just wanted to merely track my cycle but when looking into the application, the marketer within me was just impressed. Ultimately, Flo made great use of all three different types of data analytics that both benefited them as a company and me as a consumer.