In today’s marketing world, the ability to generate and analyze customer data isn’t simply another advantage over competitors; it’s a necessity to the growth and success of a brand. By being better able to understand the needs, wants, and pain points throughout each step of the customer journey and experience, businesses and brands can create marketing campaigns that are far more personalized using key messaging that drives a greater number of customers towards a purchase—including upsales and cross sales.
As more customer-centric data is generated and made available, however, the ability to do this in a mutually beneficial manner becomes far more complicated. For example, a customer may see an ad for a certain product or good online, subscribe to an email newsletter for a brand or business selling that product, receive a coupon, and then visit a physical storefront to purchase that product. But in order to best understand this data, those businesses need to be able to follow along in each customer’s unique experience at each point throughout the buying process.
This stresses the importance of customer identity unification. By analyzing data from each point (or source) and attributing them to a specific customer, businesses can generate a complete roadmap of their customers’ journey in order to gain valuable insights that can fuel more relevant and meaningful marketing campaigns and activities in the future. And in today’s marketing world, few tools are able to perform this better than through the integration of artificial intelligence (AI) in probabilistic matching for customer identity unification.
The ultimate goal of creating and analyzing various data points of customers in marketing is to use that data effectively. Yet, marketers are often given more data than they can effectively use through human-led labor. This, according to Founder and CEO of FirstHive, Aditya Bhamidipaty, is what makes the integration of AI in customer identity unification so crucial.
“The concept of probabilistic matching utilizes AI to assign key customer identifiers (such as IP address, operating system, as well as device and/or browser type) to a particular customer by being able to calculate the statistic likelihood of a match to data points generated by their online behavior,” says Bhamidipaty. “In having to assemble and convert customer data, as well as build unified customer profiles, analyze optimal customer treatments, then execute upon those analytics and measure them appropriately, the clear bottleneck at each of these points is human labor.”
As Bhamidipaty mentions, simply adding more employees to a marketing team to comb through, analyze, and measure customer behavior and data isn’t a realistic option due to the sheer volume and variety of customer data. Rather, companies should instead integrate technologies such as AI which can improve the productivity of a company’s existing staff, which is exactly what the promise of AI offers.
“AI systems can help eliminate — or at least mitigate — each of these bottlenecks,” Bhamidipaty continues. “It can read data source files, classify their contents, and recommend how they can be mapped into a central storage of customer data. It can also more easily develop advanced processes to handle complex cases, isolate poor quality data inputs, and find matches based on subtle patterns that human analysts might easily miss or that are too complex to build into a conventional rule set. AI can also look for new types of customer data matches as more data appears, amking it much easier to incorporate new sources of data.”
Along with these benefits, AI can also more easily, effectively, accurately and quickly analyze larger and more complex amounts of data, simplify data integration to design data treatment rules, and prepare performance reports to estimate the value of any changes in customer data. Although, in light of these benefits AI poses in customer identity unification, AI in this regard is still not without its own challenges.
As Bhamidipaty explains in a report sponsored by FirstHive and published by the CDP Institute in 2020, the main obstacles that AI faces regarding its success in customer identity unification include the following:
“AI’s performance is only as valuable and accurate as the historical data it is given in order to generate future patterns from customer data,” Bhamidipaty says. “This means the data it is given must be as accurate, relevant, and unbiased as possible to create future inputs. Similarly, AI routinely produces algorithms that are extremely complicated and cumbersome to process in a fast-paced business environment, and often do not explain or showcase the logic behind the results those algorithms produce, which can create another newer bottleneck.”
Additionally, according to Bhamidipaty, an AI’s conversion of results into insights tend to be produced from data options produced in the past, meaning it tends to not recommend newer alternatives regarding future conversions of data results into data insights. Likewise, if an AI is allowed to convert those insights into programs, this poses a risk that the AI will create programs containing mistakes that are obvious to human readers like sending too many messages or ones containing irrelevant information. If those insights are converted into programs by human workers, the process becomes much slower and its nuanced capabilities are limited compared to those of AI.
Thankfully, as Mr. Bhamidipaty tells us, there are ways in which these challenges can be overcome. AI capacitates the marketer to better match customer identities and unitify them.
“When it comes to training data for AI, you may need to collect additional data that would be obvious points to a human, but not to AI,” Mr. Bhamidipaty adds, “such as if a customer is upset at a certain stage of their buying journey. There is also the potential to create artificial training data the AI itself generates.”
Regarding the complexity of AI’s algorithms, Bhamidipaty explains that this complexity can be limited depending on its capabilities and the needs of the business utilizing it. This limit in complexity can also be used to better understand the results that AI program generates, depending on the model’s sensitivity to specific input values that can be flagged if they appear too far outside of expected data ranges.
Lastly, when looking to overcome challenges pertaining to an AI’s conversions of results into insights, and subsequently those insights into programs, human workers can be reskilled to use tools that monitor and explore the values generated by the AI.
“Each solution listed still requires human supervision of a particular AI system,” Bhamidipaty concludes. “AI is evolving much faster than most human teams can keep up, so those workers will need to be trained to better understand the nature of your business, its customers, and their data. If your employees can more clearly understand those points, they will be more adapt at assessing whether your AI is continuing to make reliable, accurate, and sound decisions regarding your customer data and their identity unification.”
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