As Ecommerce companies, we have so much information – sometimes too much information. What can we do with all of this data from our customers to maximize their experience while maintaining their privacy?
Well, today, we’re talking about the challenges that Ecommerce companies face when implementing AI and how AI can unlock the value of your data to increase profitability, lifetime customer value, and return on ad spend.
Richard Harris is the Founder & CEO of Black Crow AI, a no-code, real-time machine-learning-based predictive software that helps companies understand likely customer behavior.
Richard is a veteran entrepreneur and has been involved in tech since the ’90s. He cut his teeth in the world of consulting and was involved with a $10B brand, Travelocity, during the dot com boom. Then, he has continued to be a serial founder, with Black Crow AI being his 3rd VC-funded startup.
In this episode, Richard talks about how we’re at the “cutting edge cusp.” So many great things are happening now that machine learning technologies — commonly referred to nowadays as artificial intelligence (AI) — are being implemented. There are limitless possibilities for how it’s being adapted into their operations.
Given the vast amounts of generative and predictive data, the biggest challenge companies face is understanding the value of your data by figuring out how to make sense of it all. In most cases, the volume of the data is so extreme that it’s impossible for humans to make sense of it.
What is the value of your data? Fortunately, we now have machines to interpret this along with the huge amounts of data. The predictive capabilities of AI will allow you to see the future key drivers of your business.
What’s the value of your data? According to Richard, businesses today often place the customer at the heart of their operations, focusing heavily on the real value of your personal data.
When asked about the key metrics that matter most, brands typically point to customer acquisition cost (CAC) and lifetime value (LTV).
This is where the value of your personal data comes in. Their main goal is to get products in front of potential customers at a value that justifies the investment. Richard explains that his team uses AI to predict LTV with a single click, initially determining whether a user is likely to make a purchase.
In terms of data sources, Richard highlights the challenge of streaming AI, which they’ve successfully addressed.
We all know that companies collect personal data. But to truly understand every user, they capture event data. Their tool monitors how a user interacts with a store, the brand, and the purchase funnel—to analyze behavior in real-time. This includes tracking scrolling depth, image interactions, and repeated product searches. Richard emphasizes that, regardless of the size of the business, big data is available to make precise predictions.
For instance, Richard describes how, within milliseconds of a user action—whether it’s a click or scroll—they can predict the likelihood of a purchase. They can then relay that information to the brand in real-time. For example, if someone is shopping, the AI can indicate whether he falls into a high-likelihood or low-likelihood buyer group. This prediction is not only immediate but can be empirically verified later on.
AI interprets what data is worth by overcoming challenges such as user recognition, which has become more difficult due to increasing privacy restrictions from companies like Apple.
Traditionally, cookies allowed brands such as Google to perform data collection that tracks and identifies users, but with Apple limiting the lifespan of both third-party and first-party cookies, businesses face obstacles in maintaining customer relationships.
According to Richard, AI unlocks the value of your data through infrastructure that allows brands to create their own lifetime identifiers for users at the domain level. This enables businesses to maintain a consistent understanding of who their customers are, even as external tracking methods become less reliable. AI’s ability to handle massive amounts of data in real-time ensures that brands can continue to establish deeper relationships with their customers despite the evolving digital privacy landscape.
The value of your data can unlock the effective use of AI capabilities. To implement AI for data analysis, brands can integrate infrastructure that allows them to set their own permanent user identifiers on the server side, aligning with a company’s privacy requirements. According to Richard, giving Apple as an example, this system acts like a first-party cookie but is stored server-side as part of the brand’s domain infrastructure.
Although technically complex, AI simplifies the process by making this method easy to adopt. Once these permanent identifiers are in place, brands can not only recognize returning customers but also leverage AI to predict their future value.
This capability paves the way for data-driven decisions. For example, a company’s data strategy might involve determining whether to send promotions based on the likelihood of a customer making a purchase. This combination of user recognition and predictive power enables businesses to optimize engagement and customer retention strategies.
Industry-specific applications of AI have evolved from solving complex problems initially tailored for large enterprises, such as Fortune 500 companies. According to Richard, his previous company developed machine learning applications to tackle real-time predictions by processing vast amounts of streaming data at remarkable speeds. These solutions demonstrated immense value for major corporations, but it led to a broader question: how could this technology be adapted for mid-market businesses? Recognizing the potential, Richard’s team saw an opportunity to bring these powerful AI-driven insights to industries beyond the top tier, offering tailored solutions to a wider range of companies so they could also have a competitive advantage.
Measuring the ROI of AI in data analysis revolves around both revenue growth and margin improvement. According to Richard, the cost of AI tools, especially in owned channels like email and SMS, is relatively low, but the revenue generated from these channels is significant. By using AI to better understand customer behavior and predict outcomes, brands can enhance their return on ad spend (ROAS) in paid marketing channels on social media such as Facebook. This leads to more efficient prospecting efforts, allowing businesses to scale without sacrificing profitability.
Many brands have struggled with declining ROAS, particularly after updates like Apple’s iOS changes, which affected companies that heavily relied on platforms like Facebook. In this environment, AI helps businesses focus on sustainable growth by optimizing the use of first-party data. AI-driven tools make it easier for businesses to leverage user recognition and machine learning predictions, even without extensive technical expertise, ensuring that companies can continue growing profitably by making data-driven decisions.
Black Crow is among those harnessing the power of AI to help eCommerce businesses thrive. By connecting and activating shopper data across both paid and owned marketing channels, Black Crow enables brands to pull predictions from their existing data. These insights allow businesses to automate personalized experiences that drive conversions, encourage repurchases, and promote subscriptions, ultimately enhancing customer lifetime value.
Black Crow also provides a comprehensive performance overview, giving brands a holistic view of their marketing efforts across all channels and touchpoints. This leads companies to come up with data-driven decisions that finetunes the customer journey, ensuring sustainable growth and improved profitability. With these AI-powered tools, companies can efficiently manage their marketing strategies, enhancing their ability to achieve measurable ROI in an increasingly competitive market.
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