Using Predictive Marketing for E-commerce

It is no secret that top companies in today's cutthroat marketplace excel at satisfying consumer needs in a way that is quick, simple, and seamless while providing a unique digital customer experience that sets them apart from the competition.


What is predictive marketing?

In order to determine which marketing initiatives and tactics have the best chance of success, predictive marketing employs data analysis and machine learning techniques to forecast future consumer behavior. With the help of likelihood scores, predictive marketing can forecast a variety of things, including client lifetime value, churn risk, upsell and cross-sell chances, and more.

E-commerce companies find predictive marketing especially useful since it enables them to enhance their marketing strategies by pinpointing customers who are most inclined to make a purchase and providing them with incentives to do so.

By utilizing predictive marketing, organizations can obtain insights into customer behavior, such as the likelihood of a customer making a purchase, their probability of churning, and the optimal timing for sending marketing communications to them. Armed with this information, companies can establish automated marketing campaigns that provide tailored and pertinent marketing messages to individual customers.

What is the rationale behind utilizing predictive marketing?

Employing predictive marketing allows organizations to comprehend their customers' behavior and preferences, allowing them to offer tailored and personalized recommendations and offers. This is particularly crucial for E-commerce companies to guarantee that customers are interested in their products.

For instance, a predictive model can identify the products or product categories that a customer is most likely to be interested in based on their recent behavior and suggest a selection of products to display to that specific customer at a specific time. For example, if a person's recent activity on a sports retailer's website indicates an interest in running, they will be presented with running-related products. If the same person starts searching for tennis equipment, the model will update the suggestions to include tennis gear along with running shoes.

According to Accenture's 2018 report, up to 91% of consumers prefer shopping with brands that provide relevant offers and recommendations based on their previous purchases. By leveraging marketing channels to deliver personalized experiences, businesses can achieve a competitive edge and establish a compelling reason for customers to choose them.

Overall, the objective of predictive marketing and personalization is to provide customers with customized and pertinent experiences that enhance their shopping experience, resulting in higher satisfaction and loyalty.

How can organizations implement predictive marketing?

To harness the benefits of predictive marketing, organizations must follow specific guidelines and prerequisites, all of which begin with data.

Initially, it's essential to collect and examine data on customer behavior. This data may encompass past purchases, browsing behavior on the website, and interactions with marketing campaigns.

The second step involves developing and training a predictive model using the acquired data. This can be accomplished using a predictive marketing platform or working with a data science team to build customized predictive models.

Once the predictive model is constructed, it can be integrated into marketing strategies. For instance, businesses can send personalized product recommendations to customers, aim marketing campaigns at those customers who are most likely to make a purchase, or offer personalized deals and coupons when customers are most engaged.

There are a few rules to follow:

  • You cannot predict, what you cannot measure: first you need enough data to feed and train the model
  • The more you can measure, the more accurate your predictions get as long as the data is relevant and granular enough
  • The value of the prediction lies in what you do with it: leveraging the insights gained from predictive models is what makes them gain value, otherwise it is just data.
  • ALWAYS keep customer privacy and consent top of mind: predictive marketing and analytics needs to be set up within these boundaries. It will backfire if you are not respecting your customers’ privacy or are getting too granular.

When should E-commerce companies contemplate the implementation of predictive marketing?

There are specific instances when predictive marketing can prove to be useful for companies. One such instance is when a company wants to enhance its customer lifetime value. By predicting which customers are more inclined to make repeat purchases, a company can focus its marketing endeavors on those customers, which can lead to higher customer retention rates.

Another situation to contemplate predictive marketing is when a company aims to optimize its marketing expenses. By targeting only those customers who are most likely to make a purchase, a company can save money on marketing campaigns that may not yield positive results.

In general, companies that plan to employ predictive marketing must have enough maturity to utilize the necessary resources such as data, activation, and campaigning to derive value from it.

How can E-commerce businesses leverage predictive marketing?

Here are a few examples of how predictive marketing is used in e-commerce:

  • A clothing retailer uses predictive marketing to send personalized product recommendations to its customers based on their past purchases and browsing behavior.
  • An online grocery store uses predictive marketing to send personalized deals and coupons to its customers based on their past purchases and the time of year.
  • A travel company uses predictive marketing to predict which customers are likely to book a vacation in the near future and targets its marketing efforts towards those customers.


If utilized properly, predictive marketing can be a potent weapon for E-commerce companies seeking to enhance customer lifetime value, streamline marketing expenditure, and offer customized and pertinent marketing communications to their customers. By collecting and scrutinizing data on customer behavior, building and training a predictive model, and integrating the model into your marketing campaigns, you can achieve outcomes and boost customer contentment.

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