It’s time for marketers to harness AI when defining and targeting audiences
The customer acquisition challenge
It’s becoming increasingly difficult for marketers to attract and retain customers – and businesses are suffering financially. Identifying future high-value customers requires an intelligent approach. Marketers cannot simply select the parameters they assume will define a receptive audience. Instead, it is much more effective to analyze the top customers in their existing base using a look-alike modeling (LAM) approach.
Why? Well, look-alike modeling allows marketers to identify potential new audiences using precise, micro-segmented targeting. With this data, they can continue to engage their customers with personalized interactions and ultimately benefit from improved conversion rates and higher customer lifetime value. AI-led audience discovery holds great potential, and it’s time for businesses to take advantage.
Establishing audiences has traditionally been the realm of data scientists. Still, their limited availability means businesses must look elsewhere for a flexible and more self-reliant way to select their target customers. This is where artificial intelligence plays a role, which affords marketers a more experimental way to define new audiences.
Identifying audiences using AI-led criterion
Using look-alike modeling, businesses can target audiences with characteristics, attitudes, and behaviors similar to those of their highest-value customers. By analyzing a broad selection of metrics, look-alike models create consistently evolving profiles that help businesses predict customers most likely to be receptive to a product or service.
They combine a customer’s propensity to purchase a product with their intent to do so, creating an opportunity index that allows businesses to develop super-targeted interactions from which customers can truly benefit.
We can look at pension saving plans as an example of how look-alike modeling helps marketers identify potential customers using their existing data sets and provide contextually relevant experiences at the most impactful time. I’ve found that pension plan providers are more likely to see conversions when they target potential customers in households where a member already has a plan, compared to when they define audiences based solely on broader demographics, such as age. Brands can benefit from this kind of intelligence by building AI-led micro-segmentation strategies instead of relying on rigid macro-segmentation to guide their audience discovery.
Combining data with decision-making for truly individualized experiences
The potential for businesses using look-alike modeling is to attract high-value audiences that will continue to appreciate relevant experiences over time.
Of course, this potential can only be unlocked by businesses that recognize the importance of personalization. It’s not enough to build audiences based solely on similar customer characteristics – marketers must also tailor their interactions to create genuinely individualized journeys.
Businesses must recognize that audience exploration is only part of delivering personalized experiences. Once you have used LAM to create audience segments based on your existing customer base, creating a hybrid solution that combines data insights with intelligent decision-making to offer a seamless customer experience is key.
Look-alike models may pinpoint potential customers based on the traits they share with your top audiences. However, putting their relationship with your brand into context is crucial to ensure a cohesive journey. For instance, customers are likely to become frustrated if they continue to receive offers and interactions after opening a complaint that hasn’t yet been resolved.
With this in mind, marketers should use look-alike modeling as they use a car’s cruise control, allowing it to empower rather than overpower their marketing strategies. Businesses should trust AI to support the audience creation process without relinquishing control altogether. After all, some processes cannot be programmed into an algorithm.
A sophisticated solution to audience creation
Nevertheless, the more sophisticated the insights analysis, the better-positioned marketers are to make intelligent decisions when defining audiences and targeting customers with personalized interactions.
Businesses can benefit from advanced insights and propensity scoring using a sophisticated CDP with look-alike modeling capabilities, such as NGDATA’s Intelligent Engagement Platform. The Intelligent Engagement Platform’s Customer DNA also allows marketers to identify customers with a high opportunity index and build audiences based on these real-time selections.
The Intelligent Engagement Platform combines responsive scoring with decision-making functionalities and uses look-alike modeling and clustering algorithms for a more precise audience exploration process. Using intuitive AI solutions to analyze probabilistic and predictive data, the platform also enables businesses to target audiences with the most relevant interactions at exactly the right time.