How to Boost Customer Engagement with Predictive Analytics
Dr Christopher Cook
Artificial Intelligence, Machine Learning and Decision Science
Companies always have known that the foundation of customer loyalty is strong relationships. In the early days, building these relationships meant face-to-face interactions and phone calls. But as more aspects of business have been digitized, this sort of engagement has become less common.
In an effort to counter the uniform and impersonal nature of commerce that came along with massive scale and automation, marketers have shifted their emphasis toward personalization, which delivers custom content to highly specific audiences based on their interests and behaviors. Achieving this type of personalized outreach on a massive scale – especially in real time – requires sophisticated machine-learning technologies, more broadly referred to as predictive analytics.
The Growing Popularity and Effectiveness of Personalization
Not too long ago, most companies used “batch-and-blast” email and direct mail campaigns to reach potential buyers. But today, more and more marketers are using advanced segmentation powered by predictive analytics to deliver personalized messages to consumers. These highly targeted campaigns have become a priority for modern businesses for several reasons.
First, the technology necessary to collect, analyze, and act on enormous data sets has only recently become accessible to companies without huge IT budgets. Advances in machine learning automatically surface patterns and actionable insights from vast data sets – and do so in real time, without the help of a data scientist. This has made it easy for businesses to react quickly to a wide range of customer behaviors on a massive scale. By delivering immediate advice on the best way to engage or follow up with a customer, algorithms are powering a new level personalized service.
As customers have become accustomed to more personalized brand experiences, they are increasingly demanding this type of engagement across the board. According to a recent survey, 58 percent of marketers worldwide used real-time personalization, while 80 percent of the remaining segment planned to adopt personalization technologies this year. Research has also highlighted the value of personalized outreach, with a report from McKinsey showing that personalization can deliver five to eight times the ROI on marketing spend and lift sales 10 percent or more. With early adopters of predictive marketing experiencing strong results, more marketers will continue to join the movement.
How Machine Learning Leads to Human Insight
Predictive marketing platforms combine basic customer demographic and purchase data with behavioral cues like email or website interactions, or how discounts affect buying patterns, to offer much deeper insight into consumers’ true intent. The first step to predicting how customers will behave uses a machine-learning algorithm called clustering analysis, which sorts customers and likely buyers into highly distinct groups that have similar behavioral and demographic traits, and are therefore much more likely to take similar actions in the future.
Marketers can add to the predictive power of clustering models with propensity models, which forecast the chances a consumer will take different actions. Propensity models compare the pre-purchase behavior of prospective buyers to the pre-purchase behavior of similar customers (often those in the same cluster) who ended up making a purchase. By comparing attributes like what emails they opened and what products they spent the most time browsing, propensity models can determine how likely different customers are to actually make a purchase at any given time.
Finally, collaborative filtering models can help marketers figure out what products or services customers are most likely to buy based mainly on what other customers (with similar traits) have bought together in the past. Amazon made this type of model ubiquitous with its “people who bought this product also bought…” recommendations, but marketers now have access to even more advanced formulas that use greater context to deliver highly relevant recommendations to shoppers. A teacher and a student could purchase the same book, but for highly different reasons, and would call for completely different product suggestions. Taking into account a person’s demographic profile, past behaviors, and location, for example, can deliver more precise personalization to engage these different customers.
Getting Personal While Respecting Boundaries
While predictive marketing has grown more common, government and consumer-advocacy organizations have started to call for expanded laws to limit data collection and protect individual privacy. The focus has been mainly on large data brokers that vacuum up personally identifiable information and sell it to third parties, but the growing movement to limit what companies do with the data they collect will also affect marketers that use predictive analytics. Businesses always should focus on how to use data to improve the customer’s experience, not just the company’s bottom line, and the new focus on data governance will encourage marketers to take this more seriously.
Brands need to offer benefits that clearly show how sharing data like website visits and purchase history benefits consumers. Companies shouldn’t expect consumers to hand over their information without receiving real value in return. Furthermore, companies need to make sure that customers have full control over the data they share, and offer full transparency into the way that data is being used. Predictive analytics giants like Google and Amazon are already doing this, and many more will follow their lead. To give consumers a sense of trust and control, brands can allow customers to edit the data that the company stores, as well as explain why their algorithms are making specific recommendations and allow customers to give feedback on whether the recommendation was helpful or relevant.
As predictive marketing becomes more widely used, marketers will focus on opening two-way communication rather than simply collecting larger amounts of data to make better guesses about how buyers will react. Predictive marketing presents enormous value to brands and consumers alike, by cutting down on noisy, irrelevant messages, reducing massive marketing outlays, and bringing back the personal aspect of business transactions.
Predictive analytics already has enjoyed widespread adoption – perhaps more so than many people realise. It’s worth noting that Barack Obama used propensity modeling to help him win the presidential election in 2012. And I believe the pace of adoption will increase. As more marketers and consumers recognise the mutual benefit of this technology and embrace it, predictive analytics will play a major role in many more aspects our daily lives.
I hope you found this article interesting. If you would like to discuss better decision-making through Machine Learning or Predictive Analytics, please don’t hesitate to reach out to me at +61 405 753 468 or via InMail