Big Data and analytics have been a huge boon, not just for marketing but for business operations, customer support, and other functional units. Analytics has made it possible for businesses to leverage the mass volume of data they collect and glean valuable insights that can aid in making smarter business decisions, but ordinary analytics are no match for a newer analytical superpower: diagnostic analytics.
Ordinary analytics, also known as descriptive analytics, tells you what happened and, if you’re analyzing data in real-time, what’s happening right now. Perhaps one of the purest examples is A/B testing or split testing. Often used in digital marketing to evaluate two campaigns “ two website designs, two landing page configurations with different calls to action, two email marketing campaigns “ by simultaneously running the two variants at the same time. By comparing results, such as the number of conversions, page views, or open rates, you can use data to determine which iteration is more effective with your audience.
But the shortcoming with this type of analysis is that while it tells you the what, and even the when, it doesn’t explain the why, it doesn’t offer insight on what is likely to happen in the future, and it doesn’t come close to providing the information needed to ensure desired outcomes. Every marketer, of course, wants the ability to control the future. The science isn’t perfect “ yet “ but advanced analytics models are edging marketers astonishingly close to the realm of it can be done.
Here’s a breakdown of the hierarchy of current analytics models:
- Level 1 – Descriptive Analytics: The foundational level of analytics, descriptive analytics models analyze data to provide insights in what happened, when it happened, and what is currently happening (if you’re analyzing data in real-time).
- Level 2 “ Diagnostic Analytics: A step above descriptive analytics, diagnostic analytics digs a little deeper to determine why an event took place or why the outcome occurred.
- Level 3 “ Predictive Analytics: While diagnostic analytics can tell you why, predictive analytics aims to answer the what will “ what is most likely to happen if I do A, B, or C? You can think of it like a crystal ball of-sorts, but keep in mind that it’s based on probabilities, similar to a weather forecast.
- Level 4 “ Prescriptive Analytics: The most advanced analytics model, prescriptive analytics, as its name suggests, prescribes actions that are most likely to lead to the desired outcome. You can think of prescriptive analytics like a magician, or maybe a genie, who can bring your desires to life. Like predictive analytics, prescriptive analytics is based on probabilities, with the addition of actionable data and a feedback loop that provides data on the actual outcomes based on the specific actions taken.
Descriptive analytics may be the simplest model, but don’t discard it as irrelevant. It’s always the first step in building an analytical model. It’s the first process that translates large volumes of raw data into information you can actually use by compacting it into smaller, more digestible numbers. More importantly, it’s the patterns and trends uncovered through descriptive analytics that provides the foundation for more advanced analytics models.
By analyzing descriptive analytics and combining data and insights from a multitude of sources, predictive analytics translates descriptive data to predict the most likely outcome given the context and other variables. Predictive analytics can reveal insights such as:
- The likelihood that a particular consumer will take advantage of a promotional offer
- Identifying customers that are likely to churn, based on data such as longevity, customer service requests, and other data
- Identifying the promotional offers, up-selling opportunities, and cross-selling opportunities that a given customer is most likely to take advantage of
- Predicting the likelihood that a borrower will default on a loan
- Determining specific combinations or packages of products and services most likely to attract and convert a target consumer
Even with the insights derived from predictive analytics, you can determine the best course of action for getting your desired outcome. The lending industry uses it all the time to determine loan eligibility, for instance, turning down loan applicants who are deemed most likely to default based on factors such as their credit score, prior payment history with the lender, and debt-to-income ratio, among others. Prescriptive analytics takes it a step further, providing data-based recommendations on the best courses of action to achieve desired outcomes or goals.
Analytics technology has become quite advanced, yet surprisingly, less than half of companies are utilizing predictive analytics as a core business process, and even fewer are leveraging prescriptive analytics. Yet, these technologies are becoming increasingly cost-effective and more readily accessible to businesses of all sizes. In other words, there’s a tremendous opportunity for businesses to gain a strategic advantage by levering advanced analytics before it becomes the norm across niche industries.
