The success of every business depends on the quality of its decision-making. While some business decisions can be ground-breaking and far-reaching, and others might seem small and inconsequential — in the aggregate, they all matter a lot.
For years, business leaders have relied on various techniques to make better decisions, from customer surveys to supply chain network audits to business intelligence (BI) apps. Recently, with the rapidly growing importance of data, a new category of tools has emerged from software research labs — predictive analytics.
Let’s see what predictive analytics is, how it works, how it differs from BI, and how it can lead to more accurate decision-making in your organization.
Bonus - see what the future of predictive analytics looks like at the end of this article, and how you can get a head-start on it for a competitive edge.
What Is Predictive Analytics?
Predictive analytics (sometimes also called advanced analytics) funnels historical data through the latest statistical models and algorithms to predict what will happen in the future.
In the business context, predictive analytics answers the question of the likeliest outcome based on your current data (e.g. what are your customers likely to do in a given scenario) and outlines a path to operational changes that can help improve efficiency.
Business analysts can also use predictive analytics to reduce risks, streamline operations, and increase revenue.
Sometimes, predictive analytics gets mixed with business intelligence. While the two might seem similar, they come from completely different foundations.
Business intelligence has traditionally been the end-user product used to build analytics dashboards and integrate various other tools, including predictive analytics.
Predictive analytics, on the other hand, was catered to data scientists and employed the latest predictive models that very few professionals could use.
With time, business intelligence software started to incorporate the more user-friendly aspects of predictive analytics (e.g. user churn) to offer almost everything a business analyst might need in a single product. This change has led to predictive analytics tools becoming more specialized but no less relevant.
How Does Predictive Analytics Work?
For the most part, business intelligence predictive analytics apps are not ready to use right out of the box. They need to be properly set up and trained first. Even more importantly, you need to understand what problems you are trying to solve, what needs to be analyzed, and which models will lead to optimal outcomes.
Broken down into steps, predictive analytics always starts with a question that you want to find the answer for. Then, consider whether you have enough high-quality data to generate predictive patterns (e.g. the number of visitors to your website who complete the checkout process).
Most business intelligence predictive analytics tools have preset learning modules, which need to be trained (and periodically re-trained) on your actual data to identify similar behavioural patterns. Make sure to funnel new data into the learning module to have it continuously adjust its predictions.
Finally, the predictions are only valuable when you leverage them as insights to guide your business decisions.
What Are Some Predictive Analytics Examples?
Some examples of helpful business intelligence predictive analytics include reducing customer churn, improving customer service, and increasing conversion rates through detecting high buyer intent.
For reducing churn, you can set predictive analytics to identify customers who are likely to cancel your service or stop using your product. Once you know that someone is likely to switch away from your offering based on their past behaviour, you can offer them a timely incentive.
For improving customer service, your business intelligence predictive analytics software can show you all the anticipated spikes in demand, so you can plan your resources accordingly.
For detecting high buyer intent, you can use tools, such as Lift AI, that will identify anonymous website visitors who are most likely to buy your product and connect them directly to your sales team.
What Predictive Analytics models should I focus on?
The answer depends on your unique business circumstances and marketplace, but the most low-hanging fruit lies in buyer intent AI.
Why? Because up to 98% of your website visitors are completely anonymous to you. That represents a huge portion of potential, untapped revenue on your website right now.
There’s no real way to “identify” those 98% of visitors (although some tools could possibly tell you the details of a few of your visitors - company name, for example). But, there is a way to predict the buyer or purchase intent of those visitors with over 85% accuracy, personal information aside.
Lift AI is a buyer intent solution based on a proprietary machine-learning model trained on millions of real website visitors 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.
Lift AI is not a traditional predictive analytics tool - it’s a machine-learning solution. The difference is:
- Predictive analytics tools are usually statistical or algorithmic models that focus on using past data to make future predictions. Both the past data and the future predictions are based on data points that the creator (a human) has deemed as ‘related’ to the outcome.
- Machine-learning, on the other hand, is the top-shelf statistical analysis tool in 2021. It doesn't require a human to adjust. Once the model is designed, it can process in real-time to figure out what data is actually related to the outcome, rather than human-input. In this sense, it is far more powerful.
Once Lift AI’s machine-learning model has been installed on your website, it will assign each individual visitor a buyer intent score and connect the highest-scoring ones to your BDRs 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 that typically 9% of your web traffic has high buyer intent and will respond positively with direct, real-time BDR contact.
Within the first 90 days of Lift AI, you could see your conversion rate increase by two to 10 times, since your salespeople will be primarily talking to the most qualified visitors out there.
Using business intelligence predictive analytics and buyer intent solutions like Lift AI to identify anonymous website visitors will significantly improve your decision-making process as well as business outcomes, and be reflected on your bottom line too. It can be a reliable compass you can turn to over and over again, as it's based on both historical and real-time data.