What is predictive marketing?
Predictive technologies use advanced analytical techniques, such as data modeling, statistical algorithms, and machine learning, to learn from past consumer behaviors to predict future needs. Savvy companies are using predictive technologies to change the world: from self-driving cars to smart home devices – like thermostats, TVs, refrigerators – to constantly learning virtual assistants. In today’s predictive world, consumers are demanding more from their devices and services; and they expect brands to deliver.
Predictive marketing uses these technologies and the analytics principles behind them to forecast future consumer needs and behaviors, leading to improved marketing outcomes and ROI. With predictive marketing companies have a wealth of strategy-defining knowledge at their fingertips – they can identify which customers will leave and when, what products/services a customer will want, or which prospects are likely to become high-value new customers and more.
Why marketers need to use predictive marketing now
The rise of predictive technologies means that consumers expect brands to know what they want and when they want it. Luckily, predictive analytics can give sales and marketing teams new ways to identify their audiences’ needs. In fact, a study conducted by the Aberdeen Group found that marketers who use predictive analytics are twice as likely to identify high-value customers and market the right offer.[1] In the past, marketers have put customers and audiences into relatively basic models, now they can more accurately pinpoint individual needs and preferences to deliver a truly personalized experience.
Predictive marketing can be even more important in a COVID-19 world. The pandemic has changed buyer behaviors, possibly forever, and brands are scrambling to understand how consumers behave during this ‘new normal’ way of life. A post-pandemic survey by Dentsu Aegis found that 95% of marketers had either long or short-term changes to their 2020 marketing plans as a result of the pandemic.[2] Predictive analytics gives marketers a quick and efficient method of analyzing real-time and recent data trends to understand these seismic shifts in consumer behaviors and predict the best path forward.
The building blocks of predictive marketing
Data is the bedrock of predictive marketing. Brands need accurate contact and behavioral data on their target audience, in a standardized format in their data platforms (e.g., CRM, DMP, CDP) before they can utilize predictive marketing. Predictive analytics can only provide accurate insights if the data going into it is correct and complete. Polluted or missing data will skew predictions and lead to bad decision-making (and inefficient spending). In order to gain better insights from predictive technologies, brands should consider partnering with a third-party data provider, who can fill in gaps in their data.
Brand Example: MGM Resorts International
Hospitality and entertainment company MGM had deep insights on their repeat customers through their well-designed customer loyalty program. However, MGM wanted to scale their campaign reach, using analytical techniques to help them find and target new prospects similar to their high-value customers.
To achieve this, they partnered with a third-party data provider who was able to consolidate and supplement MGM’s first-party data with insights about consumer interests, shopping behaviors and demographics. This provided MGM with enough high-quality data to paint a clearer picture of what consumers were interacting with their brands across all channels, not just paid media. This also allowed MGM to do high-level prospecting by creating look-alike models based on their most valuable members and their highest converters.[3] These insights helped MGM build the foundations of their predictive marketing approach.
Artificial intelligence (AI) and machine learning (ML) are critical for predictive marketing because most predictive models include ML algorithms. The sheer amount of data companies have to sort through can be daunting, and data scientists need a tool that can help them make sense of it. That’s where ML comes into play. ML algorithms can be used to detect patterns, which lay the groundwork for predictive analytics. Sophisticated ML models can be trained over time to respond to new data or values, giving marketers the insights they need to make decisions about target audiences, messaging, and channels in real-time. During the pandemic, these timely insights are more important than ever. Companies that are effectively using ML right now have a competitive advantage as marketers try to navigate shifting consumer behaviors and spending habits.
Using predictive modeling, a brand can be extremely detailed when segmenting their audiences, allowing them to develop insights from micro-segments that would normally go undetected. When powered by accurate data, predictive marketing helps brands create personalized experiences across the customer journey – from website navigation to the checkout process. Here are a few ways brands use predictive marketing to boost personalization:
Brand Example: Walmart
Megaretailer, Walmart wanted to not just bring more visitors to its online storefront, but 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 12 net-revenue lift in in-session offers.[4]
New moms might be directed to this Walmart landing page, with products for babies and household essentials.
Predictive marketing has the potential to transform the way brands do business. However, in order to reap the benefits, companies need to have a solid foundation of quality data, machine learning technology and personalization strategies.
Watch our webinar to learn more about predictive marketing and how it can be applied to the retail, banking and insurance industries.
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.