The unprecedented amount of data available today, coupled with new technologies, means there is a greater opportunity to use data to drive value for your customers and business. But as marketers, it’s no longer enough to simply leverage data to understand what has happened. Sophisticated marketers know that the true value lies in being able to predict what is likely to happen and then respond to what does happen to influence the outcome.
This advanced technique is known as predictive analytics, and according to a study conducted by Forrester, predictive marketers are 2.9 times more likely to report revenue growth at rates higher than the industry average. Why? Because predictive marketers are able to deliver a greater impact across the entire customer lifecycle. Armed with more insight, they can deliver the right message to the right customer at the right time, and on the right channel.
Predictive analytics moves beyond traditional Business Intelligence and visualisation tools to leverage advanced statistical models and machine learning, giving insights that traditional tools are unlikely to discover. Contrary to its name, predictive analytics isn’t purely about using lagging indicators to predict what will happen in the future, but being responsive to events happening in real-time. It answers the question: how can we serve our customer something relevant, personalised and compelling when triggered by a live event?
The surge in predictive analytics stems from various factors such as a growing focus on digital transformation and the rise in adoption of big data and artificial intelligence (AI) and machine learning (ML) technologies. COVID-19 has also shaken up a lot of regular planning as incremental year-on-year trends need to be re-imagined. These factors, together with the need to forecast future financial scenarios to answer specific business questions, are expected to drive the adoption of the predictive analytics market.
The global predictive analytics market size is expected to grow from USD 7.2 billion in 2020 to USD 21.5 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 24.5% during the forecast period. APAC is expected to grow at the highest CAGR. The increasing investments by tech companies in major APAC countries, such as China and Japan, is further escalating the adoption of artificial intelligence (AI) and deep learning algorithms. This is expected to be the market driver in the APAC region. Executives in Asia are increasingly aware of the tremendous impact that advanced analytics could have on their organisation. Now they must take concrete steps to adopt these technologies.
Whether looking to optimise your advertising spend or prove the effectiveness of your campaigns, predictive analytics is a powerful tool in your marketing arsenal. Here are five practical uses.
Churn or early abandonment
Along with driving new business, keeping existing customers is a key marketing objective. But how can you spot when a customer is likely to churn? Using predictive analytics, customer churn models can be created that analyse behavioural, transactional and other data to determine what factors – be it timing, interactions or other reasons – make customers prone to leave. By identifying high-risk consumers and detecting abandonment in real-time, businesses are empowered to retain them in the small window.
Retargeting & Pre-targeting
In a world proliferated by advertising, it’s critical for marketers to create cut-through by providing customers with relevant, delightful information. Predictive analytics can prevent marketers from serving consumers an experience incongruous to what they want or need by building a rich customer profile to form the basis for retargeting. If a customer has recently bought a television, for example, they don’t want to be served an offer for another television or, god forbid, the same television. Not only does it reflect poorly on the brand, but it’s an unnecessary interruption to the consumer. Instead, sending them an offer on a home audio system – which would complement their recent purchase – would prove a better customer experience.
Preferred method of communication
Marketers know the importance of communicating with the customer, when and how they prefer. Do they like emails, SMS, phone or direct? Would they rather be contacted at 7 pm or 2 pm? And what impact does this have on their behaviour?
From our experience working with a company in the energy sector, we found that it was possible to predict the likelihood of a customer taking the desired action (such as paying a bill) based on their predicted communication preferences. For example, if contacted via SMS at 2 pm with a reminder about that bill, were they likely to act? Or did they prefer that reminder at 7 pm via email? While it may seem like trivial insight, it’s not to be underestimated.
Interpurchase interval and next best action
Calculating when a person is next likely to repeat purchase can help personalise offers and increase sales. For example, a brand that works across sports equipment may be able to see that customers typically repurchase running shoes with every 100km tracked on their sports watch. When customers fit this segment, the brand could then serve them an advertisement when within range of their bricks and mortar store. But would the customer prefer an offer on the same shoe previously purchased or an advertisement for a new alternative?
Predictive analytics can be configured to determine the next best action, all in real-time.
Other stages of the customer lifecycle – such as warranty expiration or onboarding – offer opportunities for businesses to use predictive analytics for cross-selling or to up-sell.
Profit, price and value optimisation
Predicting not only the likelihood that a prospect will buy but also the likely value of that purchase, is a powerful insight. Not only does it allow businesses to prioritise high-value customers most likely to convert, but it enables improved forecasting of demand and profit resulting in a better allocation of resources, improved operational efficiency and, typically, greater return on investment. Businesses need to consider bolstering their data and analytics capability with predictive analytics if they hope to remain competitive in the data-driven future.