Marketers love to throw around terms like ‘disruptive’ and ‘step-change’, often to the point that they lose their impact! The rise of AI – and in particular, predictive analytics – however, is worthy of such catchphrases. It boils down to automation and access to more data points than ever before – and machine learning algorithms that can be fed that data to produce spectacular results for sales and marketing teams.
Today the Digiconomy team is cutting through the noise, and providing a clear example of just why this new tech is so special.
So, what is it?
Simple: predictive analytics is the process of taking information from data sets, and using advanced AI learning to accurately predict new patterns and trends. A key thing here, which we touched on in the intro, is machine learning. This is relatively new tech, and essentially it’s an AI algorithm that can be fed data, and over time learn from what it is given. Example? You use machine learning to feed data relating to online browsing behaviour amongst an audience you want to sell to. Over time, the algorithm gets better at predicting where those prospects are in the sales funnel – and when the ideal time to sell to them is.
At its core, it’s about using powerful AI to improve conversion and closure rates without needing excessive man-hours from your marketing team. It uses access to ‘big data’ to learn from enormous sets of data – a resource that is increasingly available to even small businesses.
Have an example
Let’s look at a typical B2B campaign, then, that makes use of predictive analytics. From start to finish.
Define demand: Stage one. Predictive analytics here can make use of available data sets to learn how many prospective targets exist that are accessible to you and suitable to the solution your business provides.
Cut down noise: Machine learning algorithms are clever little things. So long as they have a healthy diet of data, they’re able to pick up what is usually called ‘intent signals’.
By repeatedly absorbing data relating to, for instance, browsing behaviour and general online activity, they can learn to identify who is valuable within a data set, and who isn’t. This easily trims down a target list – without requiring excessive manual man-hours to do it.
Personalising: When engagement is made, it’s important to track how it went. Over time, machine learning algorithms can consume this data and build profiles of usual prospects and targets.
The key? They can begin to understand what type of messaging fits a certain profile, leading to a target list that contains targets with highly qualified demand. Simply put, a target list that you know how to sell to. High conversion rates invariably follow!
Closing the deal: With such a high-quality target list now available, sales and marketing teams can work together better. Marketing can hand over a target list that is so detailed, it’s easy for sales teams to assign estimated values to them.
The sales guys get what they want – a high-value target list that they can easily prioritise. This lets them do their job, and it lets them bring in the best money for the least time invested.
Not bad, right?
It’s an exciting future. We often see ‘big data’ in the news relating to corporate giants like Facebook and Amazon, but there’s real value there too for even an SME. If you’d like to talk about how this might help the growth of your own business, drop us a line. We always love a good chat!
