Predictive Marketing for E-commerce

In today’s competitive market, it is no secret that leading organizations are excelling in meeting customer needs in a way that is convenient, easy, and seamless, while offering a distinct digital customer experience that separates them from the rest.

Business leaders must not only understand how to best meet the needs of current customers at a specific point in time, but also how to anticipate what those needs will be in the future to stay ahead of the competition.

This is especially relevant in E-commerce, where competitors are just a click away. Leveraging predictive marketing helps E-commerce organizations do just that. In this blog article, we explain the fundamentals behind predictive marketing: what is it, why is it relevant and how companies can leverage it.

What is predictive marketing?

Predictive marketing is a type of marketing strategy that uses data analysis and machine learning techniques to predict future customer behavior in order to identify which marketing actions and strategies are the most likely to succeed. Predictive marketing can be used to predict customer lifetime value, churn risk, upsell and cross-sell opportunities, and more, often provided in terms of probability score.

Predictive marketing is particularly valuable for E-commerce companies as it helps them optimize their marketing; targeting those customers who are most likely to make a purchase and offer them incentives for it.

Predictive marketing means getting answers to: “How likely is it for this individual customer to make a purchase?”, How likely is it that this customer will churn?” “What is the predicted best time to send marketing communications to this specific customer?”. Once we have these answers, organizations can set up automated marketing campaigns that allow them to deliver personalized, relevant marketing messages to their customers.

Why use predictive marketing?

Leveraging predictive marketing enables organizations to understand the likelihood of the behavior of their customers and therefore provide more relevant and personalized offers and recommendations. For E-commerce this is key to ensuring customers will be interested in what brands offer.

The predictive model can be used, for example to identify the product or categories of products that the customer will most likely be interested in based on recent behavior and create a selection/suggestion of products to show to that specific customer, at a specific point in time. For example, a person, whose recent behavior on a sports retailer’s website implies that they have an affinity for running, will be shown running products. If they, all of a sudden, start researching for tennis material, the model will understand they probably just started playing tennis too, and adjust the suggestion to show tennis accessories along with running sneakers.

Up to 91% of consumers say they are more likely to shop with brands who recognize, remember, and provide relevant offers and recommendations. (Accenture 2018). Having the ability to offer customers these personalized experiences by using marketing channels, can result in a competitive advantage and a significant reason for customers to prefer to buy with you.

Overall, the goal of predictive marketing and personalization is to create more personalized and relevant experiences that make your customers’ shopping experiences more convenient and enjoyable which can lead to increased customer satisfaction and loyalty.

How can organizations implement predictive marketing?

For organizations to be able to leverage predictive marketing, a few requirements and rules must be followed, always starting with data.

First you need to gather and analyze data on customer behavior. This may include data on past purchases, website browsing behavior, and interactions with marketing campaigns.

Next, you will need to develop and train a predictive model using this data. There are several ways to do this, including using a predictive marketing platform or working with a data science team to build custom predictive models.

Once you have developed your predictive model, you can begin implementing it in your marketing efforts. For example, sending personalized product recommendations to customers, targeting marketing campaigns towards those customers who are most likely to make a purchase, or offering personalized deals and coupons at the time the customer is 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 consider predictive marketing?

There are several key times when a company should consider leveraging predictive marketing. One is when a company is looking to increase its customer lifetime value. By predicting which customers are most likely to make repeat purchases, a company can target its marketing efforts towards those customers, leading to higher customer retention rates

Another time to consider predictive marketing is when a company is looking to optimize its marketing spend. By targeting only those customers who are most likely to make a purchase, a company can save money on marketing efforts that are not likely to be successful.

Overall, companies that consider leveraging predictive marketing need to have enough maturity to be able to leverage the required resources: data, activation, and campaigning to gain 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.

When leveraged correctly, predictive marketing is a powerful tool for E-commerce companies looking to increase customer lifetime value, optimize their marketing spend, and deliver personalized, relevant marketing messages to their customers. By gathering and analyzing data on customer behavior, developing, and training a predictive model, and implementing that model in your marketing efforts, you can drive results and improve customer satisfaction.

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