AI-Trends: Predictive analytics for your subscription strategy & growth

Even though everyone is talking about the new trendy kid “Gen AI”, we want to hype up the tried-and-true method of predictive analytics because it provides actionable insights to measure your subscription success and keep it growing.

A short, easy and very basic explanation of predictive analytics

The easiest way to give a definition of what predictive analytics can and can’t do, is to talk about the weather.

Because weather forecasts are literally based on predictive analytics. They use vast amounts of historical data to predict the weather. The more precise the historical data fits the desired forecast data (e.g. location, weather conditions, seasonality) and the closer the desired forecast is to the present, the more accurate the results. 

That’s why the rain or weather radar that predicts rain showers in your direct neighborhood for the next 30-60 minutes is usually very accurate. So much so, that I can plan when to leave the house and when to arrive before the rain hits me.

However, if a weather announcer predicts on Monday that the weekend might be sunny, that is a fairly broad prediction that could be null and void by the time Friday comes around.

How do you work with predictive analytics?

Predictive analytics uses big amounts of historical data to create probability outcomes. So, when the computer makes a prediction, it chose the outcome that is most likely to happen.

This means that not every prediction will come true. It’s simply more likely based on the data from the past. It’s basically like the instinct of a seasoned professional but with a lot of data to give it more weight.

Obviously, you require a lot of data to generate good predictions. As such, any predictive analytics system will get more precise with time as it collects and learns from your data (or data from similar companies).   

PS: It is also possible to acquire data but make sure that it is closely related to your business, customers, market, etc. Otherwise, you risk comparing apples to oranges (e.g., basing your orange predictions on apple data).

How can you use predictive analytics for your subscription business?

Customer behavior (including churn prediction)

Predict how customers or leads might react to a campaign, email or activity. You can use this to target only customers who will react positively to a campaign. You can even use it to only target customers with a discount campaign who would not have made a purchase without it. That way, you save money by not including customers who would make the purchase with or without the discount.

For most subscription businesses, retention and churn are also very important and predictive analytics can easily be used to forecast trends and even identify churn-risks and automatically trigger counter-measures.

This works by looking at data of past customers that have churned and identify behavior and activities that they all had in common. This data can then be used to predict typical churn behavior. 

Business predictions

You can use analytics to get an estimate on seasonal, market, or even demographic trends to plan your business year. You can also use predictive analytics to regularly check if your goals still match the predicted outcomes based on real-time data. 

It’s safe to say that few companies would have predicted the recession of the past years, however, predictive analytics are processing new data constantly and therefore don’t just look at the last year but also at the last months and weeks and days to make predictions and also change predictions based on the newly available data – just like the weather.

Protection

Cyber-crimes have skyrocketed, especially since almost all achievements in AI technology are also being used by criminals to breach security systems, manipulate people into giving out sensitive information and disrupt infrastructures to blackmail companies.

However, cyber-security tools use the same technology and they learn from past breaches to detect suspicious activities and notify the IT team or automatically take preventive action. In fact, most modern cyber security systems – usually a mixture of AI-based systems and specialized IT personnel – make use of global data bases that document any new type of virus, phishing attempt, and more in real time.

For example, predictive analytics can be used to identify and mark risky payments. If a payment activity has similarities with previous risky payments, the detector will flag it, so it can be dealt with accordingly (e.g., an automated workflow, by notifying the responsible employee, etc.).  

Risk assessment

A safe bet is not always the best way to success and many great businesses are built on high-risk decisions. But there is a fine – and often barely visible – line between a calculated risk and a gamble that can tank an entire company.

Predictive data can provide a safety net by providing possible outcomes depending on the decision.

Bear in mind, that these are usually not as accurate as other forecasts since a risky decision tends to venture into uncharted territory and therefore – as we learned – does not offer the vast historical data needed to create accurate predictions.

But it can still give pointers which decision might have which turnout and therefore help to soften the impact of any decision.

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Want to know more about how Billwerk+ supports not only with subscription management and payment solutions but also provides different options for data analysis and management?

Talk to one of our experts in a casual “meet & greet” and find out how we at Billwerk+ can support your business strategy.