What is Predictive Marketing & How Does it Work?
Predictive Marketing is a modern marketing approach that makes use of data and advanced analytics deduce which marketing strategies, campaigns, methods, and actions have the highest probability of converting customers and sales.
These days, more businesses – particularly retailers – are utilizing predictive marketing to learn more about their customers, make their data more actionable, and stay ahead of the competition.
The following are 4 critical elements of Predictive marketing that showcase its benefits and range of functions.
1. Predictive Analytics
Predictive analytics is the utilization of data, statistical models and machine learning capabilities in order to pinpoint the probability of future results based on historical, demographic, and other behavioral data.
Through careful examination of what has happened in the past, you can determine who to message, what to offer once you have their attention, and which channel to reach out through.
Predictive models are built to fill in these big data gaps through data mining in order to obtain a cohesive, digestible, and actionable future outlook regarding buyer behavior. These can be used to model your best customer behavior and suggest which actions to take on a personal level. Some applicable modeling techniques include customer segmentation, customer lifetime value models (CLTV), product affinity models, response models, churn prevention models, etc.
This maximizes the power of your data assets and puts your business in the best position to succeed.
One critical function of a predictive model is to summarize and condense convoluted, seemingly disparate data points into simple, easy-to-read “scores.”
A basic, yet exemplary predictive modeling method involves generating a personalized likelihood score for each buyer – for example, on a scale of 1 to 10. In this case, the higher the score, the more likely the chance that person will engage with your business, gravitate to your products, respond to offers, and make a purchase via a specific channel (depending on the nature of the models).
For instance, all retailers deal with loyal and valuable customers as well as one-and-done bargain seekers. The latter are undesirables. Without a doubt, attracting the most loyal and valuable buyers (Most Valuable Buyers, or MVBs) who not only come back but continue to buy again over a long period – is the key factor of true retail success.
2. Machine Learning
Machine Learning (ML) is a subset of Artificial Intelligence (AI) geared to improve data analysis and model building via pattern recognition.
Through ML, computers can get better at identifying patterns over time and make their own informed decisions as they ingest more data.
Predictive marketing utilizes ML to apply algorithms and statistical models that target specific buyer segments. From there, they proactively analyze (and ultimately aim to predict) not just their behavior, but their intentions as well.
In the age of Big Data, this necessitates a degree of speed, aptitude, and accuracy that is simply beyond the scope of human capacity. Thanks to advances in ML, the whole process can be implemented autonomously in real-time more efficiently than ever before.
Rather than sift through immense piles of data to try to get to know your customers better and generate personalized relationships, ML can swiftly cater to a customer’s wants and needs almost instantaneously.
While ML will not magically do everything for you, it is certainly a driving force in the marketing industry that is helping retailers acquire, retain, and satisfy customers at a higher, more consistent rate than ever before.
In addition, ML is helping businesses improve their marketing efforts to drive sales, increase Return on Investment (ROI), and improve the customer shopping experience.
This one is especially important. Even if automation on its own is not a “predictive” method, it is still critical in predictive marketing.
Why? The most important concern users of predictive marketing cite is “actionability.” Automation is the tool that allows you to take some action of economic value when you successfully predict what a customer is most likely to do next – whether it be a response to a form of communication like email, a purchase, or possibly fade into attrition.
Taking action on predictive marketing events (which can occur many times per day across any given customer base) is how marketers turn the critical corner from simply having data and tools to actually achieving predictive marketing success.
Automation allows marketers to synchronize, streamline, and execute omnichannel marketing campaigns from a single, centralized apparatus automatically through machine intelligence.
These days, the process of simply identifying and understanding the past, present, and future browsing/buying habits of your current and potential customer base is insufficient. In order to initiate a sale, that knowledge must transform into insights that are actionable.
Predictive marketing’s automation capacities allow marketers to reach the right customer, at the right time, on the right channel, with the right message. With all of the bases covered, this constitutes full stack automation – where the system is consistently fed new predictive information that has both material and monetary value.
In fact, the system predicts and detects peak changes in either the behavior or lifetime potential value of a customer as they transpire in real time. This enhances customer engagement, maximizes overall Customer Lifetime Value (CLTV) and increases sales.
These days, consumers have come to expect – and even demand – a wholly comprehensive, personalized experience.
When executed properly, personalization becomes a core component of marketing success. Simply put, relevant messages sell better.
Predictive marketing facilitates a fully personalized, relevant customer experience enhances trust, increases loyalty/retention, makes customers feel important, and further drives sales. This is the most critical role of advanced analytics in a time of plentiful, but never complete data.
Of course, these pillars work together elegantly and seamlessly. Without the ability to make a valuable predication and automate the response, omnichannel personalization cannot be achieved across digital and offline communications.