Retailers were hit hard by the COVID-19 crisis. From interrupted supply chains to forced store closures and changed consumer behavior, retailers face a tough climb back to pre-pandemic profitability. They need every advantage they can get right now, and predictive marketing represents a crucial tool in that regard.
What is predictive marketing?
Predictive marketing uses predictive analytics to forecast future needs through an analysis of current and historical data. Predictive marketing employs advanced analytical techniques (like data modeling, statistical algorithms, and machine learning) to uncover real-time insights that can be applied to improve marketing outcomes and ROI. Retailers can use predictive analytics to generate a wide range of insights including – for example – which customers will leave, which products a customer or prospect will need, or which prospects are likely to become new customers and what their lifetime value will be.
Why predictive marketing is crucial for retailers RIGHT NOW
The COVID crisis has not hit all businesses or consumers equally, making it harder than ever to gauge how to serve their needs and understand which offers and messages will resonate with existing and prospective customers. Personalization has always played a role in elevating marketing performance, but now, it’s crucial. Amazon’s hyper-personalized retail offerings have influenced shoppers to the point where a survey of 1,000 US adults by Epsilon and GBH Insights found that the vast majority of respondents (80 percent) want personalization from retailers.1
Retailers must adopt innovative marketing and product strategies to stay ahead. Retailers can leverage predictive analytics as a cost-effective method to drive personalization, as well as prioritize acquisition targets, understand which products and services their customers need now, and identify new opportunities in a shifting landscape.
How retailers can use predictive marketing to improve engagement and boost sales:
When it comes to advanced analytics, retailers need to ensure they have the right data to generate predictive insights. Predictive analytics draws on a clean set of data points to deliver accurate insights – usually requiring both the retailers’ own data on their clients and prospects as well as third-party data to create comprehensive profiles. In addition, with the pandemic prompting rapid changes in consumer needs, brands need real-time, accurate data to understand the needs of consumers.
Brand example: Walmart uses data to understand and engage with their customers
Megaretailer Walmart wanted to not just bring more visitors to its online storefront, but also to meaningfully engage with them. They partnered with a vendor that used predictive analytics to power relevant, personalized experiences with AI-based recommendations on their website and mobile app. The solution used in-session, real-time user behavior as well as historic data to more accurately predict and influence in-session purchases and better cater to their customers.
The results: Walmart saw a 50% redemption rate on personalized offers. They also saw a 10% lift in net-revenue for in-session offers.2
New moms might be directed to this Walmart landing page, featuring products for babies and household essentials.
Every marketer knows that it’s much more expensive to acquire new customers than it is to keep them, particularly in industries with high customer acquisition costs (CAC). Predictive marketing can help retailers reduce CAC by identifying the best prospects to target – accurately predicting the consumers who are most likely to be in market for their products, as well as helping prioritize targets based on demographics that “look like” their most high-value customers. According to Google, companies that use their Similar Audiences feature (Google’s version of lookalikes) generate 60% more impressions, 48% more clicks, and 41% more conversions.3
Brand example: Soft Surroundings targets similar audiences to increase ROI
Women’s apparel and home décor retailer Soft Surroundings, a Data Axle customer, has a very specific target demographic — busy professional women with families and a good amount of disposable income. They decided to use Facebook to engage new prospects and convert them into customers. They leveraged a range of Facebook offerings — including ads in stories (Facebook statuses that only last 24 hours), custom audiences, Instagram sponsored ads, lookalike audiences and Facebook Marketing Partners (Facebooks’ end-to-end campaign service) — to get in front of their target audience.
The results: The full-fledged Facebook campaign worked. The brand was able to lower their cost per new customer by 46%, increase sales by 90% and drive a 78% increase in ROI.4
Predictive marketing can be used to generate greater value from existing accounts and improve customer loyalty by increasing a retailer’s ability to predict the individual needs of their customers and personalize offers based on those needs. For example, a retailer can identify the demographic data of customers that buy certain products and use that to predict which customer segments are likely to be most interested in promotions around those products.
Example: Recommendation engines drive higher customer value
Retail brands can take a page from Amazon’s playbook and employ the same technologies and analytics used in product recommendations to boost profitability. Amazon generates 35% of its revenue from its recommendation engine,5 proving that Amazon’s algorithm knows what products consumers need, often before they do.
There’s no arguing that Amazon’s highly personalized recommendations are a value add to consumers. Other retailers can replicate this strategy using a form of predictive analytics called “next best offer” (aka, “next best action”). Retailers can forecast which services a customer may be interested in based on their purchase history and/or the behaviors of look-alike customers.
Retailers can use predictive marketing to understand which messages resonate with various prospect and customer segments and offer the right content to help them make decisions. Strong customer segmentation is always the foundation for effective personalized messaging. Using predictive marketing and machine learning to improve their segmentation strategies and inform their content, retailers can more accurately deliver the right message to different customer segments.
Example: Lands’ End uses personalized visuals and content to connect with customers
Lands’ End improves email personalization and reduces production times through dynamic content blocks that cater to each subscriber segment and feature the offer of the day. The company’s offer changes daily, but thanks to the dynamic content modules, their production and creative teams do not have to lift a finger to change out the content or links populated in their messages to make them current; it’s all done automatically. This approach has dramatically reduced production and design time.
In addition, these content blocks feature a dynamic recommendations module that enables Lands’ End to personalize email content for more customer segments by populating browsed or previously purchased items, reminding subscribers of what caught their eye and nudging them towards purchase.
Businesses using predictive analytics are twice as likely to identify high-value customers and reach them with the right offer. To thrive in our current economic environment, retailers need to tap into this crucial strategy to improve marketing effectiveness and reduce the costs associated with finding (and keeping) their best customers.
1 https://www.epsilon.com/us/about-us/pressroom/new-epsilon-research-indicates-80-of-consumers-are-more-likely-to-make-a-purchase-when-brands-offer-personalized-experiences 2 https://cdn2.hubspot.net/hubfs/6069939/PDF%20Files/AI-Based%20Personalization%20Provides%2010%25+%20Revenue%20Uplift%20-%20Case%20Study.pdf 3 https://www.thinkwithgoogle.com/future-of-marketing/emerging-technology/similar-audiences/ 4 https://www.data-axle.com/customer-success/case-studies/increasing-marketing-efficiency-through-technology-process/ 5 https://www.forbes.com/sites/blakemorgan/2018/07/16/how-amazon-has-re-organized-around-artificial-intelligence-and-machine-learning/?sh=7c3d21437361
As Content Marketing Manager, Natasia is responsible for helping strategize, produce and execute Data Axle's content. With a passion for writing and an enthusiasm for data management and technology, Natasia creates content that is designed to deliver nuggets of wisdom to help brands and individuals elevate their data governance policies. A native New Yorker, when Natasia is not at work she can be found enjoying New York’s food scene, at one of NYC’s many museums, or at one of the city’s many parks with her two teacup yorkies.