Predictive Analytics for SaaS Businesses

Ever wish you could find out what’s about to happen ahead of time? From the market downturn wrecking your expansion plans to the ineffective advertising strategy that’s becoming a black hole on your balance sheet, there’s no shortage of ways that knowing the future could help give you the edge in the competitive software-as-a-service (SaaS) ecosystem. Could predictive analytics – a range of statistical techniques that are used to make predictions about the future – hold the answer? Find out everything you need to know about the predictive analytics solutions available to your SaaS company.

What is predictive analytics?

Predictive analytics is a form of data analytics that uses historical data and analytics techniques (for example, data mining, machine learning, and statistical modelling) to make predictions about potential future outcomes. The goal of predictive analytics, in a nutshell, is to provide your business with a best assessment of what’s likely to happen in the future. It’s a great way to forecast trends and behaviours days, months, or even years into the future.

Although predictive analytics has been around for decades, it has only really come into its own over the past couple of years. That’s down to several factors, but mainly, it’s because the volumes of data have risen, computing power has increased exponentially, and easy-to-use software is far more widely available. Plus, economic conditions are tougher than ever and SaaS companies across the world are looking for a competitive edge, leading them right into the arms of predictive analytics.

What are the most important predictive analytics benefits?

Aside from the fact that Big Data is a veritable treasure-trove of information that’s sure to be invaluable for any businesses smart enough to harness it, there are many SaaS-specific predictive analytics benefits that you should pay attention to.

For example, predictive analytics solutions could allow you to forecast the likely profit that each customer will generate, as well as when they’re likely to churn. This could enable you to focus your attention on customers who are more likely to part ways with your brand, thereby reducing customer churn and boosting your revenues.

Predictive analytics for SaaS has a range of other benefits. For example, it could be an excellent way to build customer personas and forecast which of your users are likely to be most valuable to your business, over time. You could also start predicting in-app purchases or conversion KPIs, helping to optimise your product offerings for maximum impact on your bottom line.

How do predictive analytics solutions work?

There are a range of different predictive analytics techniques – including machine learning, gradient boosting, decision trees, time series data mining, principal component analysis, and so on – that you can use to create a predictive model for forecasting future events. Theoretically, your business could use predictive analytics to focus on anything, but it’s best to settle on a single issue that could be the difference-maker for your company in the market. For example, you could use predictive analytics techniques to explore any of the following topics:

• What is the most effective form of advertising for my business?

• What will the demand for my business’s product/service look like two years from now?

• What will be the impact on our cash flow if we invest in a new piece of equipment?

Once you’ve identified the question that you want to answer, you’ll need to develop a predictive model. Essentially, you’ll import data from a range of different sources, combine the data sources together, remove any outliers (missing data, data spikes, anomalous points, etc.), and produce a predictive model based on the aggregated data. There are many ways that you could produce that predictive model, including neural networks, curve fitting tools, and so on.

Using an iterative process (repeating your analysis multiple times and tweaking with each cycle until you arrive at a workable outcome), you should be able to develop an effective model through which you can run predictive analytics and make data-led predictions about future outcomes.

Predictive analytics solutions for businesses

So, that’s how predictive analytics works, but how accessible is it? Short answer: not very. While issues around insufficient data aren’t insurmountable anymore, predictive analytics still isn’t readily available for all businesses. Simply put, the barriers to entry are just too high – you’ll either need to hire a dedicated team of data scientists to parse the data or invest in proprietary software that’s powerful enough to do the work for you. As such, there are two key predictive analytics solutions that you can pursue:

• Firstly, there’s third-party predictive analytics software. While these platforms can be powerful and effective, they still require a significant level of specialist knowledge to operate, which means that you’ll need at least one data scientist on your team.

• Alternatively, you could outsource your predictive analytics to a vendor. There are plenty of agencies offering bespoke solutions that could give your SaaS business the edge it needs to dominate the market.

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