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Predictive analytics has become a hot topic in recent years.
And while it’s quite obvious at this point that every company should take advantage of it, in reality only few organizations fully realize the potential value this technology has to offer.
And it’s no wonder.
Most companies already have a “state of the art” analytics infrastructure in place, as well as analysts whose job is to retrieve information and surface insights – so why even bother with another change management project?
Well, today we will share our thoughts on how traditional, descriptive analytics differ from predictive analytics, and why we think using predictive analytics can be truly groundbreaking for B2B growth companies.
There are a lot of misconceptions out there about what these things are and aren’t, so let’s explain them and provide some examples.
Descriptive analytics, like its name implies, describes what happened in the past. The most simple analytics will fall under this category, so think of things like totals, percentages, and percent changes when looking at historical data.
Examples of descriptive analytics include KPIs such as revenue per customer and year-over-year sales growth percentage. The output of descriptive analytics is typically displayed in reports, dashboards, and presentations.
Descriptive analytics is useful for retrieving information, but its biggest flaw is that it isn’t forward-looking. And while most companies have a solid analytics infrastructure in place, most times employees would depend on analysts, technical teams, and information systems to generate forward-looking insights and get answers for common questions like:
Having to rely on many stakeholders for answers can significantly slow down our employees’ ability to perform, so let’s see how predictive analytics can help remove barriers so that employees can get answers from data in a fraction of the time.
Predictive analytics is a branch of analytics that provides insight into what will happen in the future. It is specifically formulated for a prediction, like a sales forecasting model, where the end goal is explicitly prediction rather than inference.
Let’s take a look at a specific example.
Let’s assume that I’m a sales development director, and I would like to know “which content pieces are likely to help convert MQLs into opportunities (SQLs).” In this case, I’d need to describe how an opportunity (SQL) is defined, and then correlate all the lead attributes that relate to content consumption and analyze their impact on my SQL objective.
When done manually, the processes of correlation and impact analysis are complicated and not scalable for every business question – ultimately slowing down sales velocity.
Using a predefined set of data science methods, statistical algorithms, and AI - Predictive Analytics can automate the correlation and impact analysis processes between your objective and your enterprise data, and thereby find patterns within your data, to predict future events orders of magnitude faster –– nearly in real-time.
To illustrate the benefits growth startups can gain by leveraging predictive analytics, let’s take a closer look at the example we’ve just mentioned.
If you look at the screenshot below, you will notice at the top left side that we defined our SQL objective and performed an analysis to determine which factors contribute to leads becoming SQLs.
In this case, the most dominant factor is 'asset sent.' It indicates which content pieces our leads engaged with and how these user engagement events are likely to impact future MQL to SQL conversion.
Armed with these forward-looking insights, a sales development director can help team members to better plan and prioritize their outreach activities. For SDRs, it’s vital to know which content pieces are likely to push users over the hump and get them to convert.
The director could either share the insights with SDRs via email notification (as illustrated in the screenshot below), or push them directly into the business apps SDRs use daily, like Salesforce.
If the director wanted to enable employees to draw content-related insights in a self-service fashion, she could share her analysis with team members and thereby allow each team member to view insights within Forwrd whenever they wish.
The analysis in our example can be used as more than just a one-time initiative. The analysis can run continuously and automatically, so Forwrd would constantly sync new data, monitor key metrics, and send alerts to our sales development director and SDRs, when new patterns, trends, and insights are detected.
With the massive amount of data Go-to-Market teams collect, there has never been a better time to take advantage of the efficiencies and potential benefits of predictive analytics and advanced automation, to ultimately better predict the outreach activities that are likely to help us acquire, retain, and delight more customers.
