We’ve all been watching the world call predictive analytics to action in the wake of the current pandemic; constant data analysis is driving our predictions of how quickly the virus will spread, how long for, how it will manifest, and so on.
This isn’t the first time predictive analytics have dominated media headlines. As we have seen in recent elections, predictive analytics help election campaigners to determine potential voters – especially the undecided, yet receptive voter – by assembling analysts to interpret large-scale big data. Through this, they target their campaigns on the voters who could potentially be a ‘yes’ vote, as opposed to targeting those who would always be a ‘no’ vote.
As predictive analysis becomes evermore present in our lives, it continues to offer huge potential to drive business in the marketing world. With the growth of Big Data and Artificial Intelligence, the use of predictive analytics can help navigate unstable markets, by enabling informed decisions about future marketing and customer trends.
What is Predictive Analytics?
Predictive Analytics is the practice of extracting information from existing data sets in order to predict future outcomes and trends. It is effective in revealing opportunities and solving problems, and is used in cybersecurity, the improvement of operations and services, and in optimising marketing strategies.
In marketing terms, predictive analytics uses customer data to help businesses adapt marketing strategies to future trends, and to attract and retain their most important customers.
How can predictions benefit your marketing strategies?
Predictive analysis is your best tool in prioritising leads and retaining customers. One of our clients, Nectar360, utilises this to remain as the UK’s largest loyalty program; they make sophisticated predictions from customer loyalty cards in order to help some of the UK’s biggest brands, such as Sainsbury’s, to build impactful connections with their customers.
If you want to improve your business sales strategies, you can start with building small testing models with predictive analysis for your digital marketing, such as running a new specific advertising campaign, and examine how the data grows or changes in order to deduce how it is best to adapt your strategy. By consistently collecting and storing data – such as which device is driving the most leads, or which site features are getting the most clicks – you optimise your campaigns for better lead scoring.
You can also use predictive analytics to look at the mix of traffic on your channels, and their respective conversion rates, allowing you to model estimated sales in response to when particular channels are increased or decreased.
For an international client serving multiple markets we have been able to predict sales revenue across multiple markets, using datapoints from the website visitor behaviours to calculate conversions. Being able to see earlier and more accurately how your website is performing as a revenue-generator is invaluable to inform content marketing decisions.
Predictive Advertising
Google Ads and Facebook Ads use Machine Learning to predict which audiences are best to target, and you can set your budget to more/less, depending on how much you want to impact your sales or enquiry levels.
Many of our Google Ads campaigns utilise predictive modelling tools available from Google’s Machine Learning to predict a customer’s chance of purchasing, based on various points including their search terms, their time of day, their previous browsing and buying habits, and much more. These include exploring estimated clicks and sales based on budgets and utilising their data-driven bid strategies to predict how many sales and enquiries you can receive based on the daily budgets you are willing to spend.
Predictions in 2021: more than just guesstimating?
Usually, forecasting the year ahead in terms of sales, visits or enquiries, would be based on prediction from data of previous years and the current trends. As we look to 2021, however, an uncertain 2021 for many, this kind of analysis may not be as reliable as it has been in previous years.
Nevertheless, predictive analysis remains an important way to help businesses adapt to competitive markets, revealing trends within their email systems, advertising data, social media analytics, or website analytics.
