The advent of new algorithms, faster processing, and massive, cloud-based data sets is making it possible for all the major digital media providers who sell advertising to experiment with artificial intelligence (AI) to help drive better performance for their advertisers. And while all areas of marketing are particularly ripe for transformation, it’s a great time to focus on the areas of new customer acquisition and revenue growth.
That’s where most companies usually spend the most discretionary money. These areas—which collectively we will call Customer Acquisition 3.0—have the biggest impact on scaling your business.
Let’s first quickly define Customer Acquisition 1.0 as the phase of siloed customer data living in different physical servers. As a result, companies running paid user acquisition efforts were hamstrung with poor data, and had less than full confidence into how well their campaigns were performing.
Customer Acquisition 2.0 is the ability to leverage cloud-based data processioning capabilities to integrate all your customer data from multiple sources into one unified customer data platform.
With the Customer Acquisition 2.0 infrastructure in place, you’re in good shape to leverage the individual AI capabilities and automation of major advertising partners running in silos like Facebook, Google, 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, where no longer will scale represent only the traditional value of achieving cost leadership and optimizing the provision of a stable offering.
Instead, 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 quantity 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 Bruce Henderson observed more than 50 years ago, companies can generally reduce their marginal production costs at a predictable rate as their cumulative experience grows.
But in traditional models of learning, the knowledge that matters—learning how to make one product or execute one process more efficiently—is static and enduring.
Going forward, it will instead be necessary to build organizational capabilities for dynamic learning—learning how to do new things, and “learning how to learn” leveraging new technology and vast data sets.
Today, AI, 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 by the 2020s.
The dynamic, uncertain business environment will require companies to focus more on discovery and adaptation rather than only on forecasting and planning.
Companies will therefore 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 more 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 continuously learn new things throughout the organization.
Therefore, successfully competing on learning will require more than simply plugging AI into today’s processes and structures. Instead, companies will need to:
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 great user segments for running prospecting and retargeting campaigns for growth teams.
Ingesting and processing all this first-party data from brands layered on top of the existing 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 accelerated adoption of AI for customer acquisition by major media platforms like Google, Facebook, programmatic ad networks, and many others represents a fundamental and pivotal transition in the way that marketing dollars are invested in mobile marketing campaigns.
No longer do growth marketers have the ability to choose where or how their ads are shown to users—instead, algorithms decide these logistics, guided by few inputs, such as bids and budget.
While that may be good for most growth teams, some of the most intelligent growth marketers in the industry are looking beyond the obvious ways AI can improve results to focus on the cutting edge “out of the box” ways AI can turbocharge their paid user acquisition performance.
At the end of the day, the best way to evaluate any emerging technology is to figure out its practical use in your business or industry. Just like good user experiences are personalized for an individual’s needs, the future of scaling customer acquisition will be won by companies who can adapt each platform’s out-of-the-box artificial intelligence solutions to fit their needs, objectives, and goals.
Successful companies have learned the importance of focusing on the right metrics and key performance indicators (KPIs), which are measurable value that demonstrates how effectively a company is achieving critical business objectives.
Examples of KPIs are customer acquisition costs (CAC), return on ad spend (ROAS), daily active users (DAU), monthly active users (MAU), retention, churn rate, and so on.
AI-powered machines can help orchestrate acquisition campaigns that more efficiently move toward these goals compared to the relatively brittle process of manual campaign intervention.
This requires a holistic cross-channel approach, which massively increases operational complexity—from data-driven targeting to creative proliferation to attribution and performance optimization. And with complexity comes exactly what you don’t want: risk and uncertainty.
Sooner rather than later, your customer acquisition efforts will rely on artificial intelligence, machine learning, and automation to adapt, customize, and personalize cross-channel user journeys and deliver optimal results in ways that would be impossible using last generation business intelligence and dashboards.
Managing complex, cross-channel campaigns with multiple targets, creatives, and sequences to accelerate your rate of learning will require an intelligent machine operational layer above the out-of-the-box solutions to deliver great results—or you may have to settle for being average.
Lomit Patel is the Vice President of Growth at IMVU. Prior to IMVU, Lomit managed growth at early-stage startups including Roku (IPO), TrustedID (acquired by Equifax), Texture (acquired. by Apple) and EarthLink. Lomit is a public speaker, author, advisor, and recognized as a Mobile Hero by Liftoff. Lomit’s new book Lean AI, which is part of Eric Ries’ best-selling “The Lean Startup” series, is now available at Amazon.Reblogged 1 year ago from www.clickz.com