Using zero- and first-party data to personalize digital marketing campaigns across all platforms can boost customer loyalty by guiding potential or existing customers on their buying journey. It starts with collecting data about your customers in a privacy compliant way, which requires their opting into the collection of data. This consent is best attained with the promise of a better customer experience.
Collection of data can be centralized through a brand’s own data warehouse or hosted in a cloud-based warehouse, or businesses can otherwise buy a purpose-built customer data platform.
From the perspective of Tom Strachan, senior vice president of sales of marketing at Lytics, a personalized-marketing technology provider, the key is developing a consistent understanding of who your customers are behaviorally and demographically. “Behavioral data can infer a lot more from customers, but that’s something organizations have to organize,” he said. “That’s where data analysis and machine learning (ML) can come in, to help understand the preferences of your customers.”
Strachen said with the increasing volumes of data businesses must contend with — often across multiple channels — the use of data science and ML tools is critical to successful organization of email clicks, website visits and other metrics.
Nathan Richter, vice president of strategy and insights at DynamicYield, said the gathering of zero-party data can most effectively be used by engaging with customers through digital channels in a conversational way, for example a questionnaire that helps guide purchasing decisions. “This immediate engagement with the customer helps put into focus what you need to know about them, and then you have the opportunity to curate the site focused on what they told you, as well as holding onto that information for the long term,” he said.
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Using AI to Meet Higher Customer Expectations
According to Marylin Montoya, vice president of marketing at AB Tasty, customers today have very high expectations, so the closer you can get to your customer means you can anticipate what they need. “They want experiences tailored to them, and things offered to them when they want,” she said. “It’s about finding that balance between privacy and personalization, getting to know that customer, and the energy you put behind those efforts.”
She recommends starting with anonymous data, which brands can use to try to personalize experiences. “We have invested in some machine learning to analyze anonymous browsing behavior to try and figure out what that customer’s intent in,” she explained. “You don’t have to gather a ton of data but you can make a general personalization attempt — it’s a way to get started.”
Montoya said a lot of companies want to do 1:1 right away before they consider the complexities of data structuring and investment in copy and promotion, which can create problems. “Start simple by using basic data and basic personality, and once you start to understand the customer more, you can invest in more long-term types of strategies,” she said. “The key is to not over-exploit the data just because you can. You need to take into consideration what channel you’re on and the information the customer needs to make that purchase.” The key here, Montoya said, is to make sure personalization is taking the person’s psychology into the equation.
“You have to look at customer behavior and make sure that if you’re personalizing, you’re doing it so it is relevant to that person, like the use of geo-location tools or patterns with shopping behavior, which can change depending on what device they’re using,” she explained.
Richter said AI, ML, and decision engines and modeling can help make the maximum use of customer data by pulling out insights that wouldn’t be seen looking at data in a normal way. “That’s where the industry is most focused from a technology perspective and where lots of brands are looking,” he said. “It’s about creating new opportunities out of that data set. Automation is the golden goose for the industry.”
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Collecting Data and Building Associations
Strachan added graph databases are good for collecting data and associating entities with a primary attribute. “I can take your behavioral data and all the content and product you’re looking at on your favorite brands, and then associate that with you,” he said. “What products are you looking at in real time and creating a product taxonomy so you can understand affinity. This helps people understand relationships between data points and the strength of those relationships.”
Strachan agreed with Montoya‘s perspective that it’s important for brands to understand what channels the individual is active in, something machine learning and data science can also help with — users who tend to click on emails on Mondays, for example. “Most marketing groups are set up by channel — ads, mobile, web, email — and they’re working in their own tool off their own data set,” he said. “That 360-degree customer profile becomes important because they may not be clicking on emails but on the mobile app, so you want to be cognizant of what channels those customers are in. You need to bring all that data into a central location so that you can make sense of it.” From there, brands can send offers when that individual is most active, or based on their knowledge of their behavior, what channel they prefer.
Richter noted ultimately, brands must validate whether or not these 1:1 strategies are more effective to the consumer. “You have to do the A/B testing, figuring out of its instigating purchases, and validating the decisions is something brands struggle with,” he said. “This is where the art of personalized marketing comes in; becoming more intuitive, more personalized, being more valuable to the consumer.”