The term “analytics” encompasses an entire realm of thought about processing data. There are several different kinds of analytics contained within the overarching term, but all analytics strive to gain some sort of understanding from data. Modern analytical methods tend to be lumped into four groups: descriptive, diagnostic, predictive, and prescriptive.
Descriptive analytics chronicle and parse events that have taken place; we use diagnostic analytics to determine why those events have taken place. Diagnostic analytics identify what elements correlate with an event of interest and the possible causal relationships between variables. We can apply diagnostic analytics to demographic trends, molecular activity, or why specific food products surge in popularity across a population.
Meal kit companies such as HelloFresh have had great success in using their sales data to uncover the relationships between the attributes of its customers and their consumption behavior. For example, observing a spike in sales of pesto-based dishes, the meal kit company could discover strong preferences for the dish in northeast states, by females more than males, and in southern California. After performing diagnostic analysis, they might find that a recent study reported health benefits to women from basil and pine nuts (this example is hypothetical), that their pesto chicken sales rose together with Panko chicken sales, and that their pesto flounder sold well on the Atlantic coast.
Using the diagnostic analysis, the meal kit company might consider offering still more chicken dishes to Californians, to monitor health and nutrient trends for product ideas, and to better accommodate regional preferences tied to traditional tastes, like seasonal northeast Atlantic cuisine.
Descriptive analytics might tell us that chicken increased in popularity. Diagnostic analytics tells us that Boston patrons are attracted to pesto flounder because Bostonians like flounder and halibut. Pesto halibut and curried flounder might do well there.
The previous post discussed traditional descriptive analytics like Return on Invested Capital, lead conversion rates, customer acquisition cost, and web traffic analysis. Continuing with the food sales example, we would call upon diagnostic analytics to understand the impact of dietary trends on profits, a non-trivial calculus when we complicate the subject by including seasonality, geographies, and crop, poultry, and hatchery yield variability stemming from weather, exchange rates, and supply chain problems.
Diagnostic analytics also serve as the core of most large retailers’ Business Intelligence (BI) activities, from automobiles to clothing. Examining sales by color, style, and size, diagnostic analytics might spot regional preferences that have led to inefficient product allocation at retail stores and car dealers. Having this sort of information available in time to act on it is crucial to devising means of addressing allocation variances. When we perform this type of analytics using other historical data to anticipate future product allocation needs, we enter the realm of predictive analytics, which we’ll cover in the next post.
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