4 Ways Predictive Marketing Can Guide Customer Purchases

Predictive marketing is the practice of using data-informed forecasts to optimize a company’s marketing efforts.

Consumers share their personal data at unprecedented rates, providing companies with more intimate insights into their preferences and behaviors than ever before. The financial worth of this behavioral data to an organization is challenging to calculate, but its value has propelled the likes of Netflix, Apple, and Amazon to the summits of their industries.

Organizations of all sizes stand to benefit from the persuasive power of predictions. With predictive marketing technology, businesses can use insights from buyers' purchasing behaviors to target high-value customers with a higher likelihood of conversion. Any company with existing customer data should leverage predictive marketing to boost conversion rates, fuel sales, and craft a more personalized customer experience.

What is predictive marketing?

Data-informed predictions can take the guesswork out of marketing efforts. Direct customer feedback from market research is essential to building and streamlining marketing campaigns. However, predictive marketing takes customer feedback a step further by suggesting how customers are likely to behave in the future based on how similar customers are currently behaving. These predictions enable marketers to spend on the strategies forecasted to have the highest likelihood of success.

Predictions use existing customer behaviors to identify:

• Which audience to target
• The best incentive to offer
• The types of content customers prefer to see

Predictive marketing enables businesses to cater to unique customers by showing them what they want to see when they want to see it. Customers now expect personalization as more companies embrace predictions. 73% of consumers expect businesses to understand their unique needs, making targeted suggestions a win-win for retailers and customers alike.

How predictive marketing works

Predictive marketing is in the field of predictive analytics. Predictive analytics uses data science to forecast future outcomes. Companies create predictions using a combination of statistical modeling, machine learning, and data analysis.

There’s a reason “big data” companies like Amazon are often associated with prediction-based recommendations. Higher volumes of datasets lead to more accurate predictions.

Companies looking to use predictions in their marketing efforts must first invest in their own data management infrastructure to adequately collect and analyze their data from its many potential sources. The types of data used in predictions often include marketing, product, historical, and demographic data.

The advanced nature of these calculations requires companies interested in predictive marketing to invest in powerful analytics tools like Amplitude Audiences. Companies can integrate these analytics tools with compatible customer relationship management (CRM) and marketing systems to craft customized campaigns.

Four predictive marketing strategies

The ability to forecast the likelihood of future events allows marketers to build cost-effective, high-performing campaigns. These predictive marketing campaigns usually focus on one of the following strategies:

1. Use predictive marketing to convert “just once” shoppers into repeat customers

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

Predictive analytics software lets you group buyers by likely future behaviors based on your existing customers’ behavioral data. A prediction model might indicate that a cohort that received a “thank you” email within an hour of product delivery is statistically more likely to purchase again. Insights like these enable businesses to adjust their processes and messaging accordingly.

Predictions can also forecast a customer’s lifetime value, suggesting how much they might spend with a company and how long they’re likely to stay a customer. Similarly, predictive analysis can help determine whether it’s worth trying to convert a given customer into a repeat buyer.

2. Anticipate the need for complementary products, services, and upgrades

With predictive analytics, marketers can anticipate a buyer’s needs based on current purchases and behaviors. They can then personalize their experience with specific messages and offers to drive repeat purchases of the same products or cross-sell and upsell similar or complementary products.

Imagine a customer orders a toothbrush from your e-commerce site. Their personalized suggestions may include:

More of the same product

Dental hygiene product manufacturers recommend buying a new toothbrush every three to four months. Using predictive analytics, you can determine the likelihood of a cohort of customers buying a new toothbrush within the next six months and remind them to do so.

Similarly themed products

Businesses typically group similar products with related characteristics or solve similar customer needs into the same product category. Predictive marketing enables businesses to target customers with similarly-themed products using data-based correlations. For example, toothbrushes, whiteners, and dental floss are all in the oral hygiene category. In this case, a customer buying a toothbrush is likelier to desire whiter teeth, so marketers can personalize their shopping recommendations accordingly.

Complementary products

Often, a product works better as part of a package. A customer buying a toothbrush without toothpaste could be bound for disappointment. With predictive analytics software, a marketer can mitigate these negative customer experiences by anticipating the customer’s need and suggesting a complementary product–in this case, toothpaste–at the time of purchase.

3. Focus suggestions on customer behaviors and preferences

Analytic models can also predict products and services a customer will likely consume despite never buying them before. Amazon is an excellent example of this strategy in action. The retailer possesses data on hundreds of millions of customers. In addition, to purchase histories, wish lists, and items left in carts, Amazon also archives data for links hovered over and items browsed but not purchased.

Marketers can harness their data to make predictions based on interests their customers explore but have yet to act on. Predictive forecasts can calculate the likelihood of a customer enjoying a particular product based on the historical behaviors of other members in their cohort. Marketers even use predictive models to identify customer preferences related to particular brands or suppliers, enabling more targeted messaging.

Businesses that use analytics-powered audience segmentation can drive marketing campaigns and product personalization based on how likely customers are to behave a certain way in the future. Once marketers identify the desired cohort, they can personalize messaging and suggestions to fit the products and services predicted to have the highest likelihood of conversion.

4. Set smarter price points based on customer behavior

Marketers want to avoid offering discounts when a customer is willing to purchase a product at full price. That’s why businesses use predictive analytics to determine a customer’s likelihood to purchase with or without a discount. A better understanding of what customers want and which customers might require persuasion to purchase enables smarter marketing spend and better sales.

Predictions could suggest a cohort with high customer lifetime value and a subset of customers who would convert with a reasonable discount, enabling a marketer to build an effective and fiscally responsible conversion strategy.

Examples of predictive marketing successes

Businesses around the world have implemented predictive marketing strategies to great success. Several more prominent examples include:

Jumbo

The marketing team at top Australian game producer Jumbo used to cast a wide net. They had a massive cohort of customers and frequently blasted them with emails. Some of these customers would end up buying more, but conversion rates were low. For on-site placements, their conversion rate sat at 1.57%. Jumbo knew marketing efforts would be more effective if they automated the discovery process and recommended content tailored to customer habits.

With Amplitude Audiences, Jumbo began to create cohorts based on customers’ on-site behaviors. Audiences enables them to generate personalized recommendations and messages encouraging customers to act further. For example, its machine learning algorithms can predict what Oz Lotteries customers will buy next and steer them in the right direction to purchase. These personalized efforts have paid off in a big way. One checkout page saw an incredible 158% lift in conversions, an increase with the potential to raise revenue by $500,000 year-over-year.

Square

Square believes that all businesses should be able to participate and thrive in the economy. What started with a single point-of-sale card reader has evolved into an entire ecosystem of products that shop owners can take advantage of. To help their customers benefit from the entire Square product portfolio, the product and marketing teams needed data insights to understand customer needs and how they were using existing products. It used to take multiple hours of analysis to make sense of their data and understand things like click-through rates on a specific navigation item.

But with Amplitude Audiences, Square has quick access to this data through intuitive dashboards. Now, marketers at Square leverage these insights to fuel their predictive marketing efforts. Amplitude enables them to recommend products based on customer insights and reach out to them with relevant information.

SoFi

SoFi is on a mission to help people reach financial independence to realize their ambitions and help their members get their money right. While consumers have access to a lot of financial data, it’s not personalized or easy to understand. SoFi tailors their offerings and resources to members so they can understand their financials and make the right decision for themselves. Since everyone is different, SoFi’s challenge is personalizing that sea of data and all the different options to every member at scale to help them get their money right.

Amplitude Audiences helps SoFi better understand their members and their preferences, behaviors, and needs. While the marketing team used to experiment with different offers and campaigns, they could not make data-based decisions. With Amplitude, they stopped talking about opinions and started talking about data.

They’ve improved their predictive marketing strategy, iterating with data to make the right decisions every day to better anticipate and meet their members’ needs and improve their products.

Improve your predictive marketing with Amplitude

With Amplitude, you can leverage customer data insights to personalize and improve your marketing efforts.