Measurement insights are a key part of optimizing your customer experience. At every step in the journey, it is critical that you understand what consumers are doing and how your channels and touch points are performing. It’s also important that you not only have the ability to see what has happened in the past, but you also have actionable insights to help you make the best decisions moving forward.

The more that companies adopt exponential technologies that garner more personalization and automation, the more difficult it can be to take quick and decisive actions, so analytics becomes increasingly important. Both descriptive and prescriptive analytics play key roles here.

But what do we mean when we use the terms “descriptive” and “prescriptive”? Let’s explore them in detail and discuss how each can help us improve our marketing efforts and create a better customer experience.

Descriptive Analytics

Most marketers are familiar with descriptive analytics because they use tools like Google Analytics or native reporting on platforms like Google AdWords or Facebook. Taking it a step further, you might be feeding this data into a reporting tool like Tableau, Google Data Studio or several others on the market.

Descriptive analytics is the most common form of measurement available, and we use the term “descriptive” because this data shows us what has already happened. For instance, you can choose a time range — such as the last 30 days — and view how users have interacted with your website, or prepare a year-over-year sales report from your CRM.
There’s certainly nothing wrong with looking at the past and analyzing your results. In fact, it’s a critical part of the process of understanding what your customers are doing and how your marketing efforts are performing. Descriptive analytics gives an accurate view of what has transpired and how individual marketing channels are performing, and it provides insights that marketers can use to make better decisions.

For instance, looking at Google Analytics for website traffic information will show you where traffic is coming from, what pages are performing best and whether campaigns are driving the intended conversions.

But there are additional tools to help us look beyond what’s already happened in order to understand how to adjust our efforts to maximize success. This is where prescriptive analytics enters.

Prescriptive Analytics

Prescriptive analytics takes predictive models — based on past activities and trending behaviors — to offer a range of potential actions customers might take. This helps to establish recommended activities that marketers should perform in order to get the biggest return.

While the term “prescriptive analytics” was first coined by IBM in 2010, it has been adopted by many others since and has grown in usage as the adoption of machine learning and artificial intelligence increases.

Prescriptive analytics also goes one step beyond what is referred to as “predictive analytics,” which takes your current analytics models and makes the best guess at what will happen next. One such example is how your credit score is created based on your past history of credit card payments, debt owed and other factors, and a score is assigned based on a prediction of how you will behave in the future.

The difference between predictive and prescriptive analytics is that, while the former makes a guess at what a result may be (such as the credit score example), the latter processes the descriptive numbers and presents a recommended set of options. The options that prescriptive analytics offers then require a few things in order to be most successful.

My agency uses descriptive and predictive analytics tools to create our own customized prescriptive tools using AI/machine learning. But other commonly used prescriptive tools, like IBM Prescriptive Analytics and Ayata, rely on their own proprietary AI platforms to make recommendations and provide decision options.

To make sure you are getting the most out of prescriptive analytics tools, ensure there is a way to see what is being recommended and why. It’s important that you are able to learn why the AI is recommending certain actions and tasks so that you can make better decisions in the future. Remember, machine learning becomes more efficient as you are able to feed it better inputs.

Using Both Types Of Analytics To Optimize The Customer Journey

Both descriptive and prescriptive analytics play a part in optimizing your customer experience. Even though prescriptive analytics takes your marketing efforts a step further because of its ability to advise you on how to get better performance, it can’t stand on its own. After all, you need descriptive analytics in order to feed those recommendations. Working hand in hand, descriptive and prescriptive analytics can provide the guidance and intelligence that enhance the customer journey across all of your marketing and communication channels.

Understanding how to use the different types of analytics is critical to your success. The more descriptive data you have at your disposal, the better. This allows the predictions you feed to your artificial intelligence to be better. Prescriptive analytics needs descriptive data to work, and both combine to enhance the customer journey.

Article written by: Greg Kihlstrom