How To Use Predictive Analytics: 5 Steps To Get Started

Predictive analytics give business leaders a glimpse into future market trends and customer preferences. While this advanced form of data analysis might seem like a tool​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ only large enterprises can use, even small to midsize businesses can implement predictive analytics. 

The only requirement is an adequate amount of business data, the right software, and a solid implementation plan. If you want to know how to use predictive analytics, here’s all the background information you need and five easy steps to get you started.

Predictive analytics is a form of analytics that examines data or content to forecast what is likely to happen in the future. It uses historical data, artificial intelligence, and machine learning to predict future outcomes. 

The foundation of predictive analytics solutions are statistical tools such as regression analysis, data modeling, forecasting, and multivariate statistics, all combined in a complex analysis. Software in a wide range of industries now offers some form of predictive analysis functions, including CRM applications, applicant tracking systems, and accounting applications.

Predictive analytics identifies patterns in data to help business leaders make more informed decisions. For instance, a predictive analysis of customer targeting activities can help your business determine which qualified leads will most likely become paying customers.

Predictive analytics enables you to find solutions to different business challenges. Here are some real-world examples of how some companies turned predictive analysis into a problem-solving tool:

Reduce customer churn through personalized offerings

Coyote, a real-time road information services provider, wanted to strengthen its customer base using an effective loyalty program. The firm wanted to segment its customers, qualify incoming data, and quantify device usage. 

Coyote used data analysis software to implement a predictive behavioral analysis tool to segment customers. The application automatically compiled a variety of data, such as real-time device data, contractual data, and customer details. 

The software then cleaned the data and used machine learning to model user behavior. The results were then used to optimize marketing campaigns. Coyote was able to segment its customer base with high accuracy, increase the performance of outbound call campaigns, and reduce customer churn with personalized marketing campaigns.

Find optimal product prices and increase revenue

Rue La La, a boutique retail firm, often has to predict sales and set prices for products sold in its online store for the first time; therefore, it has no historical sales data. They often found such products either getting sold off within the first few hours after launch or not selling well at all, resulting in lost revenue.

Rue La La developed a set of quantitative product attributes and used historical sales data to predict future demand. They built a demand prediction and price optimization model using statistical and computing technology, such as regression analysis and machine learning. Their automated pricing decision support tool was developed in collaboration with MIT (Massachusetts Institute of Technology).

Implementing the optimized prices, as suggested by the pricing tool, helped to increase revenue by 10 to 13% [1] across different departments.

To get started with predictive data analytics, you must first create a data-driven culture within your business to ensure you generate the data you need to get predictive analytics right.

1. Define the business result you want to achieve

Predictive analytics allows you to visualize future outcomes. Clearly defined objectives help tailor predictive analytics solutions for the best results.

Some examples of business questions to which predictive analytics can provide answers are:

• Which of my customers/customer segments are likely to remain loyal without any incentives? 

• Which product will most likely be in demand during the end-of-year sale? 

• Which of my business-to-business (B2B) customers is likely to default on payments? 

• Which of my suppliers will likely not deliver raw materials on time? 

• Which areas of production might see an increase in costs in the coming quarter?

You may discover that your existing data is insufficient to answer your questions. In these cases, you will either have to work toward collecting relevant data over some time or modify your questions to tackle the same challenge from a different angle.

2. Collect relevant data from all available sources

Predictive analytics models are fed by data. Therefore, identifying the correct data to answer your business questions is essential.

If you store your data in spreadsheets, pulling them into your predictive models can get tedious and may not even be possible in all cases.

Instead, use your CRM applications, point-of-sale software, marketing tools, and other software to store relevant data. These tools allow you to store larger amounts of data (often in the cloud, helping you save IT infrastructure costs) in an orderly fashion.

You can then use data extraction tools to pull data from multiple sources. APIs also allow you to connect multiple apps to collect data.

Database systems, data warehouses, and data lakes are other resources you can use to store large quantities of data.

3. Improve the quality of data using data cleaning techniques

“Garbage in, garbage out” is a computing term referring to the fact that low-quality input generates poor output values.

If your input data is poor, your predictions will be grossly inaccurate. You must ensure that salespeople, marketers, and other employees enter the right data values in the prescribed format. This will help reduce the time spent cleaning and formatting the data.

You’ll also need to prevent and fix duplicate records and normalize data to ensure consistency. Most business intelligence software solutions offer data cleaning features such as elimination, standardization, harmonization, and profiling.

4. Choose predictive analytics solutions or build your own models to test the data

Building your own predictive analytics model requires expertise in data science. You will need the help of data scientists or someone with advanced analytics skills to build predictive models from scratch.

You can outsource this work to a consulting firm that provides analytics services or seek support from university researchers.

But, if cost concerns prevent your small business from engaging experts, many software solutions are available that come embedded with predictive modeling tools.

Though these tools may not offer the advanced knowledge that a skilled data scientist can bring in, they offer built-in predictive models, are easy-to-use, and come at a lower price point. Predictive analytics software can be a good starting point for small businesses trying their hand at forecasting.

5. Evaluate and validate the predictive model to ensure robustness

Evaluating and validating your predictive model with alternate data sets allows you to identify weaknesses and help ensure that it works well under different scenarios.

There are different techniques for validating predictive models, such as cross-validation and regression validation.

Don’t worry: Even if you’re unfamiliar with these techniques, most predictive analytics tools nowadays provide model validation capabilities within the software. You can use these automated features to check the robustness of your model.

Select some of your most critical business problems to test your analytics solution. A 2023 Gartner study of quality leaders [2] found that focusing on the most pressing issues (instead of simple tests for quick wins) is an excellent way to ensure predictive analytics tools are effective and valuable.

Finally, embed the predictive models into your business processes, and use the results to make better business decisions.

Implementing predictive modeling tools is not without its hurdles. Chief among them is navigating the ethical considerations of using large data sets to make business decisions. The potential for misuse of personal data, privacy breaches, and bias could lead to unfair discrimination in the resulting analysis.

You should create or update an organizational data use policy to address these concerns. Seek to exclude personally identifying information from data sets. Examine every predictive analysis for fairness and ensure your actions don’t reflect cultural bias.

Some of the other challenges of implementing predictive analytics include:

Building predictive analytics into your system can take a long time

Predictive analytics cannot be implemented overnight. Building and implementing robust predictive models can take weeks or maybe even months, depending on the level of expertise and knowledge you start with.

Be patient while you constantly test your models and learn the nuances of forecasting. Robust, reusable predictive models will provide you with long-term revenue gains and cost savings.

The costs involved in adopting predictive analytics 

While the cost of predictive analytics software has gone down in the last few years, it is still costly. You’ll also need to invest in training your employees on various predictive analytics concepts. Small businesses​ may have to spend tens of thousands of dollars on a predictive analytics implementation, which may make the project prohibitive.

Machine learning and artificial intelligence are ideally suited for data analysis, so expect to see these technologies at the core of future predictive analytics applications. At the same time, many current generative AI apps are likely to adopt predictive features, since their​ algorithms excel at processing large amounts of historical data.

The combination of AI and data analysis will lead to real-time predictive analytics. Machine learning algorithms will analyze data as it’s generated instead of processing it later with historical context. In industries where decisions must be made quickly and accurately—such as finance and healthcare—real-time analytics will enable businesses to respond faster to changing market conditions.

Industry observers also note a trend of organizations combining predictive and prescriptive analytics to help their decision-making processes. Prescriptive analytics are similar to their predictive counterparts, but instead of showing “what will happen,” they answer the question, “What should our business do?”

A Gartner report [3] found that businesses have started pairing these two analytical models in the past few years. Given the usefulness of future predictions and guidance on the best course of action, you can expect to see more organizations adopt this dual-analytical approach.

Not too long ago, predicting the future was only possible in science fiction. Now, thanks to predictive analytics, it’s becoming a daily reality in businesses everywhere. Advances in machine learning and AI make this a reality even for small businesses. Predictive analytics will only become faster and more accurate as technology improves.