Predictive Analytics: Using Data Science for Informed Decision-making

In today’s word, data is created on a scale far beyond the human mind’s ability to process it. According to Peter Drucker, information is “data endowed with relevance and purpose.” Raw data, such as customer retention rates, sales data, marketing cost and purchase costs, is of limited value until it’s linked with other data sets and transformed into valuable information that can help with better decision making. A company’s data architecture describes how data is collected, stored, transformed, distributed, and consumed. Information architecture governs the processes and rules that convert data into useful information. For example, Sales Numbers put into a historical or a market context suddenly have meaning, their movement upward or downward will start relating to benchmarks or in response to a specific strategy or advertising / promotional campaign.

In order for a business to have a holistic view of the market, you need to have a robust analytic environment which includes:

  • Descriptive Analytics, which use data aggregation and data mining to provide insight into the past and answer: “What has happened?”
  • Predictive Analytics, which use statistical models and forecasts techniques to understand the future and answer: “What could happen?”
  • Prescriptive Analytics, which use optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?”

Most businesses these days use some kind of MIS or reporting systems that provides basic information about what has happened i.e. a partial Descriptive Analytics. So the next logical step is to unlock the power of Predictive Analytics. Initially, you can start with Customer-centric data sets that can be directly linked to revenue, which is the most critical component for your business. Using Descriptive Analytics, you can anticipate customer behaviors and estimate a customer’s potential value. Understanding the likely next steps of various buyers helps companies improve their customer experience, increase loyalty and build value for the organization. Companies can use predictive analytics for:

  • Demand prediction – Accurate demand prediction can decrease inventory costs and improve stock availability, enhancing short-term revenues and long-term customer experience.
  • Product recommendations – If companies—particularly online retailers—know the products and services their customers are likely to buy next, they can provide relevant recommendations to help speed decision making.
  • Offer and product customization –Companies can boost their sales and improve customer loyalty by offering the products and services that are most relevant to a specific customer.
  • Calculating customer lifetime value –The ability to forecast a customer’s total spending over time can help companies prioritize customer segments and evaluate potential investments and changes.

So, the real question is: how do you start a journey towards creating an effective Data Analytics Framework?

  1. Start small –When you kick-off your Data Analytics project, aim for a single, key area of the business where predictive analytics can have a valuable and immediate impact. Once you get the desired result from applying Data Analytics to the select area of business, then you can extend the scope of work to other areas of business with much more confidence and enthusiasm.
  2. Keep it commercial –It’s paramount for any business to keep track of how money is flowing in and out of business. You must tie analytics to the commercial and operational heart of your organization. Don’t forget, if Data Analytics doesn’t address a core business need, it can be a hindrance more than a help. While you’re at it, place the analytical capability as close as possible to those doing the commercial thinking.

Just remember, Data analytics is ultimately about making good decisions. Business owners need Data Analytics not just to reduce costs but also to achieve growth. It doesn’t matter what business you are in, you need to make smart, informed, evidence-based decisions.

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