Today’s consumer has more freedom of choice than ever before. Merchants and marketers have always wrestled with the challenge of predicting where shoppers will go next to place the things buyers seek in their paths before they reach a comparable product offered by a competitor. The more we know about the customer’s behavior, the more likely we are to make accurate predictions about future buying behavior.
Predictive analytics takes this concept to the next level by applying machine learning and AI. These tools combine disparate data points, (by the millions), and merge them into actionable data sets that can predict the future behavior of crowds, demographics, and individuals. This enables merchants to deliver relevant ads to the right customer, to feature the right products and services in the right place, and to target the availability of audiences to hear or view advertisements at statistically relevant points in time.
Three Predictive Analytics Models
The different model types serve different functions, from leveraging past behavior, categorizing interest types, to evaluating the predictive quality of past purchases.
- Cluster Models: Cluster models (CM) use algorithms to categorize audiences based on previous brand engagement, demographic data, and purchases. These are probability models based on the similarity between previous points of attention and likely future ones.
- Propensity Models: Propensity models (PM) appraise the likelihood that a given a consumer will make certain actions such as purchases, engagements, or disengagements. These are based on stated preferences, social media behavior, onsite behavior metrics and so on.
- Recommendations Filter Models: Recommendations filter models (RFM) evaluate the purchase history of a consumer in order to attempt to anticipate future sales opportunities. These might be called evidence-based filters since they rely on recorded behavior.
The purpose of these models is to reduce the cost of advertising campaigns by providing more accurate marketing intelligence to merchants. In addition to this, the insights provided by these predictive model making tools enable brands to better adjust branding efforts and to improve customer experience and loyalty.
Marketing Insights Through Predictive Analytics
Just having the information provided by predictive analytics isn’t enough. Marketers must also incorporate the information into functional assets. Here are some common ways this is done.
Understanding Consumer Behavior
Obtained insights give marketers a clear picture of the consumer interest of (ideally) an entire demographic. Audiences can be categorized, or segmented, into groups based on demographic data and known interest points. This enables marketers to serve real needs directly by delivering ads at the right time and to the right device. The idea is to break through the background noise of poorly targeted ads and deliver accurate ad targeting.
More accurate user intel enables marketers and merchants to use their resources in a way that is less likely to go unseen or be delivered to uninterested consumers. Predictive data targets the channels and times of day interested buyers will be available to see ads or to be offered engagement opportunities.
Developing and vetting leads has always been an important part of marketing and sales. Predictive analytics streamlines the process through prioritization and probability metrics. These predictive models demonstrate the probability that a given consumer or consumer group is to make a purchase or engage with a given brand.
Customer retention is arguably as or more important than outreach. Predictive models aimed at customer retention evaluate customer satisfaction based on a range of previous and recent behaviors with the aim of improving the customer’s experience with the brand. Currently, cookies are being used almost universally by brands and organizations of all kinds to measure levels of user satisfaction.
Tools to Put all This Information to Good Use
With the mountains of data points and data sets available, brand marketers need advanced tools to organize, analyze, and utilize that data. These tools include:
- Unified Marketing Measurement: Data is correlated and synchronized to develop useful consumer identity models.
- Marketing Analytics Software: Predictive analytics necessitate a multiplicity of inbound metrics to make data usable.
- AI & Machine Learning: The ability of machines to crunch volumes of data and numbers that are impractical for humans and to develop nimble intelligence has finally become a reality.
To stay competitive, marketers require new methods to make their campaigns more accurate and effective, improving marketing ROI, customer experience and retention. Data-driven marketers are leveraging predictive analytics to do just that.