The chance to see into the future and know how much a customer is going to spend with you is an opportunity that no business would ever turn down. Unfortunately, without any time-traveling DeLoreans, crystal balls (or an incredible discovery by a quantum physicist) this isn’t likely to happen any time soon.
The closest thing that we have to a crystal ball in marketing is predictive analytics – a technique that is much older than you might think, with origins that go back as far as the 1940s.
Ok, so it’s true, things have grown more sophisticated than the predictive modeling conducted by governments during WWII, but the basics are essentially the same: predicting the probability of a future outcome based upon historical data.
Today, using data to calculate trends and behavior patterns is used by many industries, such as risk management for financial companies or meteorologists to predict the weather. According to an article in the UK’s The Telegraph, the insurance sector has even gone so far as to develop a death clock – an algorithm designed to predict life expectancy in order to calculate premiums.
Now, more than ever, there is a buzz around the potential of ‘Machine Learning’ and ‘AI’ in the marketing technology market, and marketers are looking seriously at how to blend data and technology to derive potentially game-changing insights in their campaigns and decisions.
Watch on Demand :: Putting Predictive Analytics to use for delivering better customer experiences
Watch this on-demand webinar to understand how a Customer Data Platform provides a foundation of trustworthy data to enable you to build effective and accurate predictive models. We also take you through some examples of predictive analytics that help reinforce customer loyalty and increase purchase probability.
Examples of Predictive Analytics in Marketing
Here are a few examples of how marketers are applying predictive analytics to drive improved marketing and sales results:
- Lower acquisition costs: Predictive analytics can help marketers to identify patterns in behaviors or demographics of customers that spend more and have a higher conversion rate. In doing so Marketers can then use this intelligence to direct their efforts at specifically targeting and attracting them, as well as stop pushing revenue at acquisition channels that do not perform as well.
- Identify loyal customers: Propensity-modeling looks at past transactional behavior alongside customer attributes, transactional and operational history, to predict the likelihood of a customer to continue purchasing your products and services.
- Determine up-sell and cross-sell opportunities: Continuing to target customers with products and services best suited to their needs will keep them coming back, and predictive analytics can look deep into past transactional patterns to reveal what to target them with in the future.
- Increase the success of outbound efforts: Before the use of predictive analytics, marketers would often use a ‘spray and pray’ approach to campaigns that (unsurprisingly) saw low engagement and yielded poor results. Predictive marketing assesses the profiles of prospects to determine who is most likely to convert, improving the efficiency of future campaigns.
- Predict churn: After going to the expense of acquiring customers, nobody likes to lose them. A data-driven predictive churn model will identify the customers you are most at risk of losing before they’re gone, and can give insight into the actions you can take to retain them. Reducing churn can have dramatic effects on the profitability of an organization.
- Predict Customer Lifetime Value: This projects what a new customer is likely to spend with your organization over the course of their relationship with you based on all your historical customer transactions and behaviors. This can help marketing establish how they should market to each customer based on their potential value to you.
Is Predictive Analytics the holy grail for marketing?
Predictive analytics is all about creating a model based on your historical data and using that data to predict what an individual customer is most likely to do next. Of course, for this model to be accurate you need a lot of data and the data also needs to be reliable. So if you don’t have enough data, or the data is inaccurate with duplicates and anomalies, then you are likely to get inaccuracies in your model and predictions.
Similarly, to ensure that you build the right predictive models, understand the insights that you gather and also ensure that you interpret the results in the right way, you also need the right tools and skills in place. Understanding data inconsistencies, the importance of outliers and knowing when to rebuild and retest your models is a significant part of the process.
A Guide to Predictive Analytics eBook
From the importance of good data as a foundation for your strategies, through to building your own predictive models, this 'A Guide to Predictive Analytics' eBook looks at how a range of applications can both reinforce customer loyalty and increase the probability of purchase.
Finally, for predictive analytics to become your holy grail, you also need to be able to act on the insights. There is no point in building models and gaining incredible predictive insights if you can’t do anything with it. Although this might sound simple, it’s actually often very difficult to turn data that is crunched in a silo statistical system into the defined customers that need to enter a campaign.
Ultimately to start using predictive analytics to improve your marketing results you need to consider 3 layers:
- The Data Layer – Ensure you have a single customer view and that you can trust the data that will be used to build your models. Using a Customer Data Platform to unify, cleanse and transform your data is therefore the first step towards building more reliable models. (Our CTO Mark Jameson discusses this in an on-demand Predictive Analytics Webinar.)
- The Decisions Layer – This is both people and tools to be able to extract the right insights and make the right judgments. Often a Data Scientist or analyst is best qualified for this analytical layer, but modern marketing technologies are now making data analytics more accessible for the everyday marketer.
- The Orchestration Layer – This is the ability to take your insights and turn them into improved marketing activities. Namely the ability to use marketing automation that uses predictive insights to optimize the audience, path or next action of a campaign or customer journey.
Predictive marketing may not help you see into the future but it does provide marketers with tools to develop a deeper understanding of their customers, refine their efforts and improve profitability. Arguably not as cool as a time-traveling car, but being on the path to business success isn’t a bad future to look forward to.
Thanks for reading! If you liked this blog then please check out Using Predictive Analytics for More Effective Marketing.
Register for the next BlueVenn Live Demo to see how to utilize Predictive Analytics in your marketing campaigns.
Register for the next presentation to learn how to:
- Create a Single Customer View and trustworthy foundation for analytics with the BlueVenn Customer Data Platform.
- Use predictive analytics to uncover insights and make real-time customer journey decisions.
- Improve audience selections using segmentation and RFV analysis.
- Orchestrate omnichannel customer journeys and real-time personalization.