October 1, 2023

How Is Predictive Analytics Used in Business? 3 Successful Examples

Don Simpson

Updated May 3, 2023:‌ We updated the article with clear examples of using predictive analytics to improve business performance.

The success of every business depends on the quality of its decision-making. Using the leverage of technology, a few key decisions could catapult a company from the middle of the pack to being an industry leader. 

How do business leaders learn to make better decisions? They use data. For years, companies relied on various techniques, from customer surveys to supply chain audits to business intelligence (BI) apps. Now, a new category of tools has emerged from software research labs — predictive analytics.

What is predictive analytics? How is predictive analytics used in business? Why is predictive analytics more accurate than BI and other tools? 

Let’s explore these and other questions, see what the future of predictive analytics looks like, and discuss how you can get a head start for a competitive edge.

Tip: Get the best of predictive analytics automatically with Lift AI. This solution integrates into your marketing stack and identifies web visitors with the highest buyer intent, streamlining your sales process.

What Is Predictive Analytics? 

Predictive analytics (or advanced analytics) uses historical data in the latest statistical models to predict what might happen in the future.

In the business context, predictive analytics answers the question of the likeliest outcome based on the data at hand (e.g. how does this web design change impact customer behavior?). It can also provide suggestions to improve operational efficiency in marketing and sales.

Business analysts use predictive analytics to reduce risks, streamline operations, and increase revenue

It’s important not to confuse predictive analytics with business intelligence. While similar, their approaches come from different foundations.

Business intelligence, traditionally, has been represented by end-user products used to build custom analytics dashboards, sourcing and analyzing data by integrating with various tools, including predictive analytics.

Predictive analytics was originally used for complex modeling in data science and has only recently become accessible to a wider business audience. 

You can see the more user-friendly aspects of predictive analytics (e.g. user churn) being incorporated into business intelligence tools. The goal is to include everything a business analyst might need in a single product. At the same time, predictive analytics products are pushing innovation forward, becoming more specialized as a result. 

How Does Predictive Analytics Work?

As a rule, business intelligence predictive analytics apps are not ready to use out of the box. They need to be set up and trained on specific datasets first. 

You need to define the problems you’re trying to solve, gather the data that needs to be analyzed, and then pick the right model for an optimal outcome. 

Here’s how to engage with predictive analytics:

  1. Start with the question you want to find the answer for
  2. Check whether you have enough high-quality data to generate predictive patterns (e.g. the number of web visitors who complete the checkout process)
  3. Find a business intelligence predictive analytics tool with preset learning modules
  4. Funnel your data into the learning model and configure it to continuously adjust predictions

When the predictive analytics model gets an update, re-train it on your behavioral data to generate more accurate suggestions. 

Now you’re ready to use analytics predictions to guide your business decisions. 

3 Examples of Predictive Analytics in Action

Here are three things you can do with predictive analytics today: 

  1. Reduce customer churn
  2. Improve customer service
  3. Increase conversion rates (through buyer intent)

To reduce churn, configure your predictive analytics tool to identify customers who are likely to cancel your service or stop using your product. The tool will check usage patterns of your customers over time and detect decreased activity for some accounts. You can then offer them a timely incentive (e.g. a discount or a personalized demo) to make your service more attractive. 

To improve customer service, use predictive analytics to show all the anticipated spikes in demand (e.g. end of fiscal year) and plan to increase customer service resources accordingly. Potential customers will get a faster, better, and more personalized service as a result. 

To increase conversion rates, use a predictive analytics tool like Lift AI to determine buyer intent of all your website visitors, even anonymous ones. Once you know which visitors have the highest probability of buying your product, your sales team can contact them directly.

Which Predictive Analytics Models Should You Use? 

We’ve introduced a few types of predictive analytics tools. While every business will pick the one that fits its circumstances and marketplace demands, the lowest-hanging fruit for most businesses is buyer intent AI

Why? Because up to 98% of your website visitors are anonymous to you. In other words, a lot of untapped revenue is right there on your website right now. 

You can’t “identify” those 98% of visitors with full certainty (although some tools will show you a company name, for example). What you can do instead is predict the buyer or purchase intent of those visitors with over 85% accuracy, without revealing any personal information.

Lift AI is a buyer intent solution based on a proprietary machine-learning model trained on billions of website visitor interactions. As a result, it can identify the buyer intent of your anonymous website visitors in real time — even if they’ve never visited your website before and aren’t recorded in your CRM or any other business system. 

What makes Lift AI unique is that it’s not a traditional predictive analytics tool — it’s a machine-learning solution.

Predictive analytics tools use statistical or algorithmic models that focus on past data to make future predictions. Both the past data and the future predictions are based on data points that you’ve deemed relevant to the outcome. 

Machine-learning is the top-shelf statistical analysis tool. It doesn’t require a human to adjust. Once the model is designed, it can process and select data related to the outcome all on its own. That’s why it’s far more powerful. 

Once Lift AI’s machine-learning model is installed on your website, it will assign each individual visitor a buyer intent score, which can be used by any of your marketing tools. For example, you can connect the highest-scoring visitors to your BDRs directly via any chat platform you currently use. Lower-scoring visitors could then be addressed with a nurturing bot or a self-help chatbot. 

On average, Lift AI finds 9% of your web traffic that has high buyer intent, which can be continuously funnelled into your sales process. 

Within the first 90 days of Lift AI, you could see your conversion rate increase by up to 10 times, especially if you engage your BDRs to follow up with every high-scoring visitor. 

Lift AI’s customers like PointClickCare increased conversions by 400%, adding over $1M of incremental revenue in the first year. Formstack, another customer, increased pipeline sales by 420%

Improve your decision-making and conversions with business intelligence predictive analytics and buyer intent solutions like Lift AI. The future is not in reading historical data, but making the right moves with the data being created right now, in real time. 

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