The advent of new algorithms, faster processing, and massive, cloud-based data sets makes it possible for all major digital media providers. They sell advertising to experiment with artificial intelligence to help drive better performance for their advertisers. And while all areas of marketing are particularly ripe for transformation, I will focus on the areas of new customer acquisition and revenue growth because that is where most startups usually spend the most discretionary money. These areas—collectively called Customer Acquisition 3.0—have the most significant impact on scaling growth in your business and the power to unlock future rounds of funding. New Dimensions for Scale and Learning Let’s first quickly define Customer Acquisition 1.0 as the phase of siloed customer data living in different physical servers that resulted in paid user acquisition efforts with poor data without complete confidence in how well it performed. Customer Acquisition 2.0 is the ability to leverage cloud and data processing capabilities to integrate all your customer data from multiple sources into one unified customer data platform. With this, you can share good data to leverage the individual AI capabilities and automation of major advertising partners running in silos like Facebook, Google, Snapchat, and others to help you better optimize your budget to hit your performance goals. This brings us to what I call the world of Customer Acquisition 3.0; no longer will scale represent only the traditional value of achieving cost leadership and optimizing the provision of a stable offering. Instead, the scale will create value in new ways across multiple dimensions: scale in the amount of relevant data companies can generate and access, scale in the quantity of learning that can be extracted from this data, scale to diminish the risks of experimentation, scale in the size and value of collaborative ecosystems, scale in the number of new ideas they can generate as a result of these factors, and scale in buffering the risks of unanticipated shocks. Learning has always been important in business. As , companies can generally reduce their marginal production costs at a predictable rate as their cumulative experience grows. But in traditional learning models, the knowledge that matters—learning how to make one product or execute one process more efficiently—is static and enduring. Building organizational capabilities for dynamic learning will be necessary—learning how to do new things and “learning how to learn” leveraging new technology and vast data sets. Bruce Henderson observed more than 50 years ago Today, artificial intelligence, sensors, and digital platforms have already increased the opportunity for learning more effectively—but according to BCG, competing on the rate of learning will become a necessity in the 2020s. The dynamic, uncertain business environment will require companies to focus more on discovery and adaptation rather than only forecasting and planning. Companies will increasingly adopt and expand their use of AI, raising the competitive bar for learning. And the benefits will generate a “data flywheel” effect—companies that learn faster will have better offerings, attracting more customers and data, further increasing their ability to learn. However, there is an enormous gap between the traditional challenge of learning to improve a static process and the new imperative to learn new things throughout the organization continuously. Therefore, successfully competing in learning will require more than simply plugging AI into today’s processes and structures. Instead, companies will need to: Pursue a digital agenda that embraces all modes of technology relevant to learning—including sensors, platforms, algorithms, data, and automated decision making Connect them in integrated learning architectures that can learn at the speed of data, rather than being gated by slower hierarchical decision making Develop business models that can create and act on dynamic, personalized customer insights Never before have marketers had access to more customer data. The first-party data companies collect with user profiles can go beyond basic name and demographic data and might include downstream rich data points on engagement, retention, monetization, and much more; companies can use this to build significant user segments for prospecting and retargeting campaigns for growth teams. Ingesting and processing all this first-party data from brands layered on top of the rich user data enables these media partners to perform sophisticated modeling and analysis with machine learning that wasn’t possible even a few years ago. This results in better targeting with new insights and data analysis. If you are still manually optimizing campaigns the same way it was done half a decade ago, you may find yourself among a quickly disappearing breed in the customer acquisition game. Any manual process is likely much less effective and far more prone to human error than the new solutions quickly emerging to attack inefficiencies. The future of Customer Acquisition 3.0 rests on the shoulder of intelligent machines, orchestrating complex campaigns across and among key marketing platforms—dynamically allocating budgets, pruning creatives, surfacing insights, and taking actions autonomously. These machines hold the potential to drive outstanding performance with a far more efficient Lean team, hands-off management approach powered by artificial intelligence. About the Author is a forward-thinking leader with 20 years of experience helping startups grow into successful businesses. Lomit has played a critical role in scaling growth at startups, including Roku (IPO), TrustedID (acquired by Equifax), Texture (acquired. by Apple), and IMVU (#2 top-grossing gaming app). Lomit is a public speaker, author, and advisor, with numerous accolades and awards throughout his career, including being recognized as a Mobile Hero by Liftoff. Lomit's book is part of Eric Ries' best-selling "The Lean Startup" series. Lomit Patel Lean AI

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