What is predictive marketing and how does it boost retail sales?
Everyone wants to stay a step ahead of what’s to come — and for marketers, it’s especially important to predict consumer trends and desires.
Predicting someone’s next move might seem like a job more fit for a wizard than a marketer, but predictive technology provides a framework to help marketers see the future. This foresight isn’t the result of magic. It all comes down to granular data and a retail-specific predictive marketing solution.
Today’s consumers have a lot of needs and even more choices. With predictive technology, marketers can foresee customers’ behaviors, even when those customers don’t know they’re behaving in a predictable fashion. Let’s examine what predictive marketing is and why it’s so important for today’s data-driven marketers.
[ Learn what Retail Marketers Need in their Tech Stack]
What is predictive marketing?
Predictive marketing uses data science to make smarter marketing decisions. It helps marketers understand their customers by outlining actions they may or may not take, determining their lifecycle stage and understanding their future value. This has the potential to significantly transform marketing processes.
In practice, predictive marketing uses first-party data, namely customer, behavior, and product data, to uncover consumer preferences (such as their preferred products or discount affinity) or to determine which action they will take next. Then, a predictive marketing solution will analyze this data to identify patterns that point to future behavior — such as when a customer is most likely to be online, how they engage with certain channels and which products they’re most interested in. Your team can ultimately use this information to determine the best time, location and method to reach your most valuable customers — as well as which products, content and offers to show them.
The power behind predictive marketing can’t be understated. While retailers have a long history of using data to drive decisions, using data and machine learning to predict a customer’s preferred products, messaging, timing, and channels is a much newer capability. As AI and ML become more accessible, predictive technology is becoming a mission-critical piece of retail martech stacks thanks to its ability to help marketers observe, decide, and execute on campaigns quickly to drive seamless marketing interactions across multiple channels.
The benefits of predictive marketing
The primary benefit behind predictive marketing is that it takes the guesswork out of your strategy, helping to create more impactful marketing campaigns. You can use it to find the best product for upselling, calculate the long-term profitability of customers or even determine whether or not a customer needs a discount to make a purchase. All of this is accomplished while finding and accounting for complex relationships between distinct behaviors.
Being able to focus on delivering the best product, content and/or promotional offers for a certain customer on their preferred channel can help you meet specific goals like increasing purchases or driving customer loyalty. Here are a few key benefits to consider:
Encourage replenishment
With predictive models, you can foresee what a customer wants before they know they want it. For instance, if one specific customer usually purchases cat food in a 30-day interval, you can send them an email reminder to buy food on the 29th day. This will give you an opportunity to create content and personalized product recommendations that get in front of a specific customer’s needs.
Enhance personalization
Predictive models help marketers create personalized messaging and recommendations for customers based on their future propensities instead of relying solely on their previous behavior or innate traits. For example, Hammacher Schlemmer combined predictive analytics and machine learning to send relevant, personalized outreach that considered both past and predicted behavioral patterns. This helped Hammacher Schlemmer increase their ROI on email by 28x and realize a 24.6% average increase in email engagement.
Expand profitable audiences
Predictive marketing will help you find customers with an affinity toward certain products, allowing you to expand potential audiences. By targeting based on affinity score, you can maximize your reach beyond people who have previously browsed or purchased from your website — all without sacrificing relevance. This will help you maximize opportunities for new sales.
Prevent churn
Using predictive models to understand a customer’s purchase frequency can help reduce customer attrition. Once a customer stops purchasing within their typical cadence, a predictive model can flag them as “at risk,” giving you a chance to intervene before they turn to a competitor. The value of this cannot be overstated. Many retailers primarily focus on acquiring new customers, which can cause them to neglect the ones they already have. However, since it costs more money to acquire a new customer than retain an existing customer, preventing churn is essential to preserving profits. The best way to do so is to use a predictive model so that you engage customers at the right time as opposed to using a static benchmark that might be too early or too late for certain customers.
Optimize spend based on predicted customer lifetime value
Predictive analytics can identify and account for many small factors that can be used to determine how much revenue a customer is likely to rack up with your brand. This is more effective than calculating lifetime value based on historical insights since someone’s past spending doesn’t always indicate their future spending. Knowing which customers will create the most value over time will help you optimize paid marketing budgets and offers.
Improve marketing ROI
When your team can determine which products an individual is most interested in, predict their preferred channel to maximize marketing impact and send messages when they are most likely to see them, you’ll be able to deliver 1:1 personalization. By using a solution that can personalize marketing outreach on an individualized level, you can instantly sift through all your potential messages and product recommendations to ensure all your outreach is actionable and relevant to the customer. This will help improve loyalty and boost sales, thereby increasing your marketing ROI.
Overall, predictive marketing helps you use the customer data you’re already collecting in a more meaningful way by providing more insight into the decision-making process regarding who to target and what messaging to share with them. Let’s take a look at the underlying processes that help you realize these benefits.
How predictive modeling works
Predictive modeling uses a mixture of data and known results to forecast an outcome. In retail, these outcomes pertain specifically to a customer’s preferences and behavior, which can later be used to inform campaigns. To get these insights, retail predictive models follow a few basic steps.
1. Collect data
Customer, behavior, and product data form the base of a successful predictive modeling strategy. It’s important to get as granular as you can so you can understand everything about the customer — a predictive model should take into account all kinds of data, including the categories shoppers clicked on, how long they lingered on certain pages and which products they carted or purchased.
To get a new predictive model started, you can feed it historical purchase data and continue to refine it with newly generated data. Over time, the technology will learn more about each customer, such as their product preferences and spending habits, as well as the relationships and patterns of interactions between customers and products.
2. Analyze the data
Once your platform has collected and stored plenty of customer, behavior and product data, AI-driven marketing technology will analyze the data to identify patterns among shoppers and connect them to the best next action or purchase. As this ongoing analysis continues, it will start to learn behavioral patterns such as purchase frequency, average order size and popular combinations of products. At the same time, it will account for complicated relationships between various behaviors, allowing it to recognize patterns that might be difficult or impossible for a human to identify.
3. Get predictions
Predictive modeling has a wide range of potential outputs — as long as you give it the right inputs. Oftentimes, the outputs of a predictive model are more relevant and accurate than a similar historical model. Here are four examples of different predictions a retail-specific model can make:
- Personalized product recommendations that predict what a customer might want to buy next. By using predictive technology to power recommendations, you can provide more relevant suggestions to shoppers.
- A customer’s lifetime value, or how much money they will spend with your brand over time. Predicting a customer’s lifetime value will allow you to understand the potential value of a customer that has yet to purchase and accounts for diminishing value from at-risk or lost customers.
- A customer’s predicted lifecycle stage, such as active, at-risk or lost. This can be used to reduce customer churn and target/time messages based on each customer’s unique buying cadence. This is in opposition to strict benchmarks that assume everyone shops with a similar frequency.
- Predicted discount affinity to ensure customers only receive coupons when necessary, protecting your margins and your brand reputation.
4. Refine the model
As you continue to use a predictive model, it will grow increasingly accurate. For example, precisely predicting a customer’s lifetime value requires a wealth of data. At the start, your solution may only be able to identify who spends more than your average customer. Eventually, you will be able to zoom in on narrow segments, such as the top 5% of customers based on predicted lifetime value, and layer that prediction with their affinity to a new product line. The longer your predictive models run, the better their ability to pinpoint a variety of detailed information.
While all predictive models become more powerful over time, some predictions (like category affinity) require less variety and quantity of data than others (like predicted lifetime value and lifecycle stage) to ramp up. The amount of data required depends entirely on the use case.
Predictive marketing use cases
Predictive analytics can be applied to a variety of functions that drive revenue for retailers. Here are some of the most popular ways that modern marketers use predictive analytics.
Predict and target based on customer lifetime value
Predictive marketing solutions can study a customer’s current behavior and reasonably predict how often they will purchase from you, what their purchase typically consists of and how much they spend with each order.
By combining these insights, you can build an audience of customers with the highest potential lifetime value. Then, you can layer in predictive affinity models, such as product or discount affinity, to show them the products and pricing that will compel them to make a purchase. At that point, all that’s left is to allocate spend and deploy outreach that will win over these high-value customers.
[Learn how customer lifetime value can help you set accurate ad budgets.]
Target based on lifecycle stage
Predictive marketing solutions can automatically establish a shopper’s lifecycle stage, whether they’re a non-buyer, an active buyer, an at-risk buyer or a lost buyer. To see how this works in practice, imagine two customers: One active shopper who is loyal to your brand and an at-risk customer who has strayed from their typical active buying cadence.
Both of these customers need different kinds of engagement — for instance, you may want to increase purchase rates for the active buyer while taking steps to reel the at-risk shopper back into your store. With predictive analytics, you’ll be able to immediately intervene with a personalized email that drives each of these customers closer to purchasing based on their unique needs.
[See how customer lifecycle marketing can sharpen your marketing strategy.]
Provide relevant product recommendations
Predictive marketing solutions can use AI to find patterns among shoppers and help connect individual customers to their next best purchase — even if those connections are not intuitive to humans.
For instance, a shopper may purchase a wide variety of products from your diverse catalog, such as a coffee pot, hiking boots, a dog’s leash and non-fiction books. These products may seem to have very little in common at first glance, but a predictive marketing solution could use them to provide perfectly relevant suggestions, such as a foldable dog bowl to give their pet food and water on-the-go. At the same time, if every customer that purchased a leash were recommended a foldable dog bowl, the vast majority would find it completely irrelevant to their needs.
Expand audience size
Every retailer wants to win over more customers — and by expanding your audience size without sacrificing relevance, it’s easy to find the next best customer to engage, retain or win back.
For example, consider the case of one major furniture retailer. They decided to test the value of predictive technology by sending one category promotion email to an audience of 90,000 people with a high predicted affinity toward the category and another untargeted email to over one million customers. When compared to the untargeted audience, the audience with a predicted campaign generated twice as many orders and 1,540% more revenue per email.
[See how Pendleton used predictive analytics to find new, profitable audiences.]
Keep in mind that not every predictive marketing tool can fulfill the use cases outlined above — depending on your industry, different tools may have different functionalities. By investing in a retail-specific solution, you can expect to enjoy all the aforementioned capabilities.
Finding the right predictive marketing tool
Predictive analytics is a fast-growing necessity in a marketing environment where data is instrumental to success. However, collecting and analyzing that data can be complex — so many retailers would benefit from a platform that is simple to deploy, collects data intuitively and provides ongoing analysis with little need for the marketing team to interfere.
The ideal predictive marketing solution will differ depending on your line of business. Retailers, in particular, need predictive marketing capabilities that don’t just provide an in-depth view of customers, but that also takes into account their entire product catalog. This will help meet key retail goals — whether that’s growing customer lifetime value, turning shoppers into lifetime customers or generally improving the bottom line. With a predictive analytics solution designed precisely for the needs of retailers, you’ll be able to craft impactful campaigns that grow revenue and increase loyalty.
