After last week’s post about 5 business trends to lookout for in 2020, Predictive Analytics becomes one (if not the most) important tool companies can start leveraging. But how? 

Let’s just start off by defining what this promising technology really is.

“Predictive Analytics is the practice of extracting information from existing data sets in order to forecast future probabilities. It’s an extension of data mining which refers only to past data. Predictive analytics indicates what might happen in the future with an acceptable level of reliability, including a few alternative scenarios and risk assessment. 

Applied to business, predictive analytics is used to analyze current data and historical facts in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company.”- Sandra Durcevic

Define your problem

If you thought predictive analytics could only be applied to marketing, you are wrong. For example, retailers are now using predictive models to forecast inventory numbers, determine shipping, plan store layouts and more. So begin by asking these three questions:

  1. What are the problems and how are they impacting your business’ goals?
  2. What are your current metrics regarding the problem?
  3. How is your company using technology?

Set a goal

Once your business goal is defined, it is time to make use of predictive analytics models. There are many types of amazing ones out there out there, but to keep it simple, here are some of the most common ones used by organizations:

  1. Reduce customer acquisition costs: pinpoint the right customer for your offering.

  1. Increase customer lifetime value: identify users who are most likely to invest more in services or products.

  1. Increase revenue: combine reaching the right customer with the right message to increase sales or sign ups.

  1. Customer segmentation: group customers based on characteristics and behavior.

  1. Budget allocation: use historical data to determine exact budgets for future initiatives.

  1. Reduce Churn: harness actions of retention when a user or customer stops using a company's products or services.

Acquire existing tools

"You should think about acquiring existing tools/models/vendors from your industry and verify your ROI model as soon as you can" Ye Zhang, CTO of Katabat, an international management consulting firm.

After analyzing various predictive models, teams should not focus on building from scratch. Rather, acquiring existing tools that have proven models and solutions is highly recommended. Building predictive tools from scratch can be a great financial burden for the company. 

"It's okay to first leverage existing technology to build a proof of concept to verify the ROI, then decide whether/how much of the predictive analytics stack you build is worth the effort of keeping it in house,"- Zhang.

Feed the right data

Is your data live data that's collected with a daily recency, like on Facebook and Google, or is it complex insurance data that's needed for claims records? It’s important to understand the types of data being fed to the predictive model since merely throwing loads of it will be setting yourself up for failure. Algorithms find patterns in data in 2 ways:

  1. In the relationships between two columns.

  1. In how a variable changes over time.

So it is more likely to find success when a task is hyper-focused. Mountains of different types of data will have no valuable results. Focus on specific processes, or a specific event, and this will provide the necessary information for the algorithms to find patterns with valuable insights.

Engage teams

In today’s organizations, when there is a shift in numbers, analysts must manually process data, create hypotheses and generate reports to share with the departments in charge. This process is slow, manual and in big corporations, it often does not represent the total amount of data available for processing. 

Although building predictive analytics model is a highly complex endeavor, using acquired tools is not. Team members who have some sort of business analytical knowledge should be engaged in the day to day process of using these tools, since they are the parties interested in specific numbers. 

"Many of today's predictive analytics tools have become little more than drop-down functions in a very friendly GUI interface, so it's becoming easier and easier to train employees on these tools," Andrew Pearson, Managing Director of Intelligencia Consulting Firm.

Predictive analytics makes the future look promising (and brighter) for organizations that are committed to begin a digital transformation. Starting off with a pilot to solve a specific problem is a smart way to dive into it, by keeping costs controlled while getting ready to receive the benefits of using an innovative technology. Read more here