4 Ways Predictive Marketing Can Guide Customer Purchases

How marketers can leverage data-driven predictions to improve customer experience and create new sales opportunities

Best Practices
October 21, 2021
Image of Mallory Busch
Mallory Busch
Product Marketer, dbt Labs
Predictive Marketing

The power of predictive marketing has helped fuel Amazon’s meteoric ascent to the top of the ecommerce market. Amazon dominates in its space, owning an unparalleled 40% ecommerce market share that is only expected to increase in the near future. The company’s revenue hit $386 billion in 2020 alone, and it’s estimated thatas much as 35% of product sales are a direct result of Amazon’s prediction-driven recommendation machine.

With over 100 million Amazon Prime subscribers at their disposal, Amazon gathers an unfathomable amount of behavioral data from its customers. Customers volunteer information to the retail giant even by simply browsing products, telling Amazon CEO Andy Jassy and company as much with what they’re not clicking on a page as with the links that they do. The data is then run through predictive analytics software to identify the products customers are most likely to buy—and from there, it’s only a matter of showing those customers the items in question.

One expects a giant like Amazon to have both the data and muscle required to implement a successful predictive marketing program. However, it’s not just the Amazons of the world that stand to benefit from the persuasive power of data-driven predictions. As a marketer, you can and should implement these key predictive marketing tactics to power informed suggestions targeting your high-value customers—all by using insights extrapolated from their own purchasing behaviors. In doing so, you stand to boost conversion rates, fuel sales, and craft a more personalized customer experience.

1. Use Predictive Marketing to Convert “Just Once” Shoppers into Repeat Customers

Predictive marketing can forecast the likelihood of a customer returning to purchase again, allowing marketers to personalize messaging and deals to encourage repeat purchases. Customers are far more likely to become consistent repeat purchasers if they buy from a business at least twice, but it’s estimated that only 20% of customers make the leap from purchasing once to purchasing twice.

Companies often rely on a positive customer experience and satisfaction with the purchased product to drive recurring sales, but a savvy marketer can leverage predictive marketing to their advantage. By taking a company’s existing customer data and running it through predictive analytics software, marketers can group customers based on likely future behaviors, not just past behaviors. A prediction model might suggest that a cohort who received a “thank you” email within an hour of product delivery is statistically more likely to purchase again, allowing businesses to adjust their processes and messaging accordingly.

Finding the likelihood that an individual or cohort is ripe for conversion is only half the battle. In a perfect world, 100% of customers will return to purchase again, but it isn’t realistic or cost effective to strategize for such an incredibly improbable scenario. Instead, predictions can help forecast how much a customer stands to spend with a company and how long they’re likely to stay a customer for—the customer’s lifetime value—and whether it’s worth trying to convert any given customer into a repeat buyer.

By predicting a customer’s lifetime value, marketers can take conversion campaigns into their own hands. A company could run a campaign targeting “just once” shoppers that shows the highest likelihood of converting when presented with a product discount. Instead of making a bet on hoping that a customer converts based on the perceived quality of an offer, marketers can invest their money in strategies guided by insights provided by the behavior of their own customers.

2. Anticipate the Need for Complementary Products, Services and Upgrades

Product marketers can use predictive marketing to target customers with the highest likelihood of requiring a certain product. Individual customers can be targeted by businesses with personalized messaging based on A) a product they’ve already purchased and B) the products predicted to be needed in the near future. Companies can spend money targeting customers with goods their own behaviors have already suggested they will like.

Anticipatory suggestions fall into one of several categories. For instance, a customer orders a toothbrush from an online retailer. Personalized suggestions may include:

More of a particular product

Dental hygiene product manufacturers recommend buying a new toothbrush every three to four months. Knowing this (and understanding the odds of people doing so), a marketer could run predictions on the likelihood of a cohort of customers buying a new toothbrush within the next six months. If the results are desirable, the marketer could target the cohort accordingly.

Similarly themed products

Some products fall under the same category or theme as a purchased item, even if they don’t perform the exact same task. A product marketer might wonder as to whether a customer buying a toothbrush is more likely to desire teeth whitener—and predictions could add a data-based correlation to the line of thought.

Complementary products

Sometimes, a product works better as a part of a whole package. Most customers buying a toothbrush without toothpaste are bound for disappointment. A marketer can help head off these bad experiences by using predictive marketing software to make suggestions based on the likelihood of a customer buying one item to complement another.

While the chief benefit of such suggestions is the increased likelihood of sales lift, customers have begun to expect customization from their shopping experiences. Ninety percent of customers find a responsible level of personalization at least somewhat appealing, making targeted suggestions a win-win for retailers and customers alike.

3. Focus Suggestions on Customer Behaviors and Preferences

Predictive marketing can reveal sales opportunities to product marketers beyond the scope of “just” past purchases. Predictive models process behavioral data to show which products and services a customer is likely to consume despite never having bought them.

To get a sense of how it works, just consider Amazon. Amazon possesses the data of hundreds of millions of customers—data composed of far more than purchase histories and items left in carts or wish lists. The company archives the data of the roads not taken as well, including links hovered over and items browsed but never purchased.

Most companies don’t have Amazon-level data at their disposal, but the data they do possess can be harnessed to make predictions based on interests explored but never purchased. Marketers can forecast the likelihood of a specific customer enjoying a particular product based on the historical behaviors of other similarly behaved members of their cohort. Marketers can even use predictive models to identify if an individual customer prefers working with a particular supplier or type of supplier, allowing for more targeted messaging.

Online retailer Stitch Fix has baked predictions into the heart of its business model. Customers of the retailer build a profile using unique details (for example, a specific size and a range of desired styles), which serves as the basis for Stitch Fix’s predictions. The company’s prediction algorithm runs customer preferences against data provided by clothing suppliers, resulting in clothing selections tailored to what an individual customer is most likely to want. With millions of subscribers and $2 billion worth of revenue within the last fiscal year, Stitch Fix exemplifies the transformative power data-based predictions hold for sales and marketing.

Taking advantage of customer data doesn’t require a Stitch Fix–level adherence to prediction. Marketers can use Amplitude Audiences to segment customers based on how likely they are to behave a certain way in the future. Once the desired cohort is identified, messaging and suggestions can be personalized to fit the products and services predicted to have the highest likelihood of conversion.

4. Set Smarter Price Points Based on Customer Behavior

The last thing a marketer wants to do is offer a discount to a customer willing to purchase a product at full price. Marketers stand to achieve greater success by identifying which customers require more persuasion via discount—and which customers stand to convert with more gentle nudging or even none at all. One prediction could suggest a cohort with high customer lifetime value. Another prediction could show a group of users within that same cohort who would convert with a reasonable, bottom-line-friendly discount, allowing marketers to build a strategy conducive to conversion.

A better understanding of what customers want translates to smarter spending and better sales, and it doesn’t require mind-reading to discover a customer’s preferences. Predictive marketing helps businesses realize the truth about their own data: it already shows what their customers want next. With the aid of predictive analytics, businesses can unlock potential suggestions contained within this data to find countless opportunities for growth. Customers are telling businesses what’s on their minds, and it’s up to product marketers to listen carefully.

Predictive Marketing in a Data-Driven Future

Data is a hot commodity. Businesses have long understood the value of attaining a prospect’s name, address, and email for the purposes of marketing and retention. Now, consumers volunteer their personal data at unprecedented rates, providing companies with even more intimate insights into their preferences and behaviors than ever before. The exact financial worth of customer behavioral data to any given business is difficult to precisely calculate, but it’s valuable enough to have propelled the likes of Netflix, Apple, and Amazon to the summits of their respective industries.

Right now, marketers are sitting on a gold mine’s worth of customer data. Some don’t even realize its worth or have never considered how hard other companies work to collect every possible interaction. Others are already using their data to fuel their predictive marketing engine and are giving customers exactly what they want, before they even realize they want it. There’s no need to predict the impact of predictive marketing—it’s already changing the game, and marketers and customers alike stand to benefit from a data-driven future.

Jumpstart your predictive marketing game today by demoing Amplitude Audiences, the best-in-class solution to fuel your personalized marketing efforts.

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About the Author
Image of Mallory Busch
Mallory Busch
Product Marketer, dbt Labs
Mallory Busch formerly ran the Amplitude blog, frequently named a best blog for product managers. She also created AmpliTour, the live workshop for beginners to product analytics and 6 Clicks, the Amplitude video series. She produced the Flywheels Playbook, wrote The Product Report 2021 and produced The Product Report 2022. A former developer and journalist, Mallory's written work and coding projects have been published by TIME, Chicago Tribune, and The Texas Tribune. She graduated from Northwestern University.

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