Analytics helps us to make sense out of things. They give us the support we need to make data-driven decisions, both in our personal and professional lives. And just like the decisions we need to make, analytics also comes in all shapes and sizes: descriptive, diagnostic, predictive, and prescriptive (spoiler: prescriptive is where actionable predictions show up!). Without analytics, businesses would collapse. Luckily that’s not the case and we got plenty to work with. 

When it comes to making business decisions, most marketers tend to get stuck on the 1st or 2nd stage… even though there are 4 stages and they get increasingly important (and yes, order matters): 

  1. What happened?
  2. Why did it happen?
  3. What might happen next?
  4. How can I make it happen?

The first on this list is what we would call descriptive analytics. Like it sounds, descriptive analytics describe, or summarize, the past so we, as humans, can better understand it. It does so by processing vast amounts of raw data and producing the highlighted features and characteristics of said data. 

A marketer may have a very long list of all the products it sold in the past year. With descriptive analytics, the business owner could easily identify which products sold the most each month, the characteristics of the customers who purchased the most of product x, how many people came into the store over a specific time period, etc.    

The second step is called diagnostic analytics and this type of analytics puts the why to the what from the previous step. Although it relies on a business’ past historical data, it often includes external data to help determine why something happened. Let’s take the previous example with the retailer. 

The marketer needs to understand what is affecting the revenue for any product so they will know how to stock the shelves, plan promotions, and forecast sales inventory. The business owner leverages external data to understand that there is a high correlation between time of the year and product revenue. They go ahead and add location in there as well and see if location affects the time of year and revenue, and it does! So they know that next time they plan they need to take these variables into account. 

The third step in the optimized decision-making process is called predictive analytics. Also representative of its name, predictive analytics helps businesses predict what may happen in the future using data from the past. It helps businesses improve their decision-making capabilities by identifying risks and opportunities.   

Someone purchased golf shoes from the business; the transaction is captured by the retailer’s POS and information is logged into the CRM. With predictive analytics, the marketer can instruct their marketing automation platform to send an email to the customer whenever a new piece of golf equipment or apparel appears in stock. Based on past buying behavior, the marketer now knows how to up-sell and cross-sell through personalized recommendations.  

Finally, we hit gold. Prescriptive analytics is the name of the game and that’s the game we’re here to play. Instead of just predicting what’s going to happen, prescriptive analytics enables businesses to understand which actions they need to take to achieve the best possible outcome for their scenario. So in a sense, what you’re getting from prescriptive analytics are actionable predictions.

Actionable predictions do 2 things:

  1. They provide businesses with recommendations on what they should do to get the outcome they want
  2. Provide businesses with “What-If?” scenarios that let them assess various outcomes based on different actions they may take

Let’s go back to our retail example:

Prescriptive analytics can identify that a new trend, let’s say pink beanies, is starting on the West Coast (shoutout to the Bay Area!). So it might provide actionable predictions to our marketer that recommend they start developing marketing campaigns promoting these pink beanies. In fact, they might even need to stock up in inventory as well! 

So you see, it’s not just about the insights. It’s about what you can DO with the insights. But wait. We didn’t address the second definition of actionable predictions which talks about “What-If?” scenarios. 

You see, not all prescriptive or predictive analytics platforms provide you with actionable predictions. Most stop after the prescription is made. 

But certain platforms give businesses the options to play with different variables and see which action will best suit them to fit their needs. This is a “What-If?” scenario. For example, you want to see what if you lower your prices on this specific month with this specific segment or customer, if sales will increase… or what if you send proactive nurture emails on this specific date to this customer, will they still churn or will they stay? “What-If?” scenarios help businesses make true data-driven decisions. 

Actionable predictions are what we, as marketers, have been waiting for. With reduced human assumptions comes reduced error. And with reduced error comes higher profits, ROI, and market share. 

Analytics are the driving force behind today’s successful business. Without analytics, no one would know where to go. But with actionable predictions you don’t just know where to go, you get the finger that points at the sky and tells you how to get there too.