Business data and analytics have come a long way. It has proved to be a boon for companies, and not just for their marketing or sales segments, but for business operations, customer support, and various other functional units. It is analytics that has made it possible for them to leverage data collected by data mining experts, to glean insights for making smarter business decisions. Nuances like diagnostic analytics are set to make advanced analytics a superpower.
You as a business have been reaping the benefits of descriptive analytics to empower your decisions with what happened. If you are analyzing data in real-time, it would tell you what’s happening right now. Split testing, for example, is used by digital marketing and branding players to evaluate two different campaigns, two different websites, and two different landing pages with a call to action – by running two different variants at the same time. The number of conversions, page views, page open rates etc. is used to determine which iterations are more effective with the targeted audience.
It’s all old school
However; all this is on the verges on becoming primitive, as it only tells you what and at times even when; but what about why? It also doesn't say anything about what is expected to happen in the future – near or far. Neither does it provide the information required to ensure desired outcomes. Marketers today are trying to not only survive but thrive in this concurrent market dynamic. They want the ability to control the future. Though the science of advanced analytics is yet to be perfected and mastered, it has started edging marketers close to 'we can do it'.
Current analytics models
- Descriptive Analytics is the foundation level of analytics. These are basically to analyze data and provide insights as to what happened, when did it happen and what is happening right now (if real-time analytics is in place)
- Diagnostic Analytics can be said to be an addition to descriptive analytics. It has the characteristic of going a bit deeper to define why an event took place, or why a certain outcome occurred.
- Predictive Analytics is the step further. Diagnostic analytics tells you why, and this gives you insights around 'what will' – what would happen if I opt for A, B, or C? Though it is like a crystal ball, the results are purely based on probabilities. Like those we witness in weather forecasts.
- Prescriptive Analytics has succeeded in retaining its popularity to be one of the most advanced analytical models. It has the ability to highlight or suggest actions that are most likely to conquer desired outcomes. It’s a magician or a genie, as you may call it, bringing your desires to life. Unlike predictive analytics, prescriptive analytic models are not only based on probabilities, but are backed with actionable data and a feedback loop capable of throwing data of actuals, based on specific actions taken.
So does all these make descriptive analytics, irrelevant?
It would be foolish to discard descriptive analytics believing it to be 'simple'. It is the first step towards building an analytical model. It is the stepping stone for transforming or translating large volumes of raw data into information in form of smaller and more digestible numbers. Patterns and trends that can be uncovered with its help provide the foundation to more advanced analytic models.
Combining data and insights from a wide plethora of sources, predictive analytics succeeds in translating descriptive data to predict the most likely outcomes, considering variables like context etc. It is capable of revealing insights such as:
- What are the chances that a particular customer will take advantage of a promotional offer?
- Which all promotional offers, up-selling and cross-selling opportunities that a given customer will take advantage of?
- Leveraging datasets of longevity, customer service requests etc. to narrow down on customers that are expected to churn.
- Predicting borrowers with the odds of defaulting a loan
- Deriving sure shot combinations or mix of products and services, attractive enough to convert a prospect customer
Insights derived from predictive analytics are potent enough to help companies, determine the best course of action to get desired outcomes. Big sharks in the lending or BFSI arena, most of the times use it to assess loan eligibility; and to turn down loan applicants who are likely to default considering their credit score, prior payment history, and debt-to-income ratio etc. As mentioned earlier, prescriptive analytics takes it a step further and goes to the extent of providing data-based recommendations to organizations and enterprises to reach out to business goals and profitable growth.
There has been tremendous advent in data and analytics technology and arena, however, very few companies leverage predictive analytics as a core business process. The number of companies using prescriptive analytics is even lower. Though there has been an increase in data analytics service providers with domain expertise, delivering analytics solutions at cost-effective prices is still a pitiful sight. In other words, we can say that there are many opportunities for enterprises can gain strategic advantage by leveraging advanced analytics. They should certainly hurry and grab them before it becomes a norm.