Launching and growing any new app has always been a challenging endeavor. Beyond having a great app and hiring a great team, one of the keys to success, is to figure out how you can leverage user acquisition to scale up app growth fast. Increasingly, the answer to smart growth looks like it may lie in AI.
If you are still manually optimizing paid user acquisition 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. More than ever, artificial intelligence is being incorporated in one form or another into almost every level of the customer acquisition value chain, from distributing inventory more efficiently to allowing advertisers to scale their decision making. 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 secret to app growth is about taking a rational and methodical approach to iterative learning by continuously running user acquisition experiments across different acquisition and retention channels to better acquire, retain, and monetize customers. This means that learning needs to happen faster because decisions need to be made faster to compete. However, this is a big challenge because there is never an infinite supply of human resources to execute all these experiments. That’s where Lean AI comes in.
Lean AI is an innovative process to scale up app growth significantly faster when you combine a lean team with the judicious use of artificial intelligence and automation. It enables app growth teams to run tens of thousands of simultaneous marketing experiments across all their digital channels with a constant focus on delivering real business value to their organizations—without the overhead of manual processes or intervention—to usher in the new age of Autonomous Marketing. Conducting experiments at scale improves the likelihood of finding successful experiments, some of which you’d never have taken the time to test in a pre-AI world. Incremental experiments that otherwise would have been sidelined for cost or complexity are now valid for observation in the world of autonomous marketing.
The data to support AI is critical. But data is nothing without a clearly defined business problem focused on cost reduction, risk reduction, and profit. Before you can prepare a strategy you’ll need to understand what autonomous marketing is cable of — and what is still just hope for the future.
Cutting through the noise: A framework for assessing the state of Autonomous Marketing
There is no shortage of claims in the world of marketing automation — particularly when it comes to artificial intelligence. How can you systematically assess vendors and their claims, and get an accurate sense of the state of the art? We can take a look at how the automobile industry worked to make sense of all the autonomous vehicle technology coming to the market today and use its guide as a roadmap of sorts for the marketing industry. In 2018, the auto industry trade association SAE (Society of Automobile Engineers) introduced its autonomy scale (see below). It helps the industry determine and classify different levels of autonomous capabilities for vehicles for reference:
SAE Autonomy Scale
|No automation. The driver controls steering, and speed (both acceleration and deceleration) at all times, with no assistance at all. This includes systems that only provide warnings to the driver without taking any action.|
|Limited driver assistance. This includes systems that can control steering and acceleration/deceleration under specific circumstances, but not both at the same time.|
Driver-assist systems that control both steering and acceleration/deceleration. These systems shift some of the workload away from the human driver, but still require that person to be attentive at all times.
Vehicles that can drive themselves in certain situations, such as in traffic on divided highways. When in autonomous mode, human intervention is not needed. But a human driver must be ready to take over when the vehicle encounters a situation that exceeds its limits.
|Vehicles that can drive themselves most of the time, but may need a human driver to take over in certain situations.|
|Fully autonomous. Level 5 vehicles can drive themselves at all times, under all circumstances. They have no need for manual controls.|
I propose a similar scale for the purpose of evaluating autonomous marketing and marketing automation solutions for app growth marketers, taking some cues from the SAE folks:
The Lean AI Autonomy Scale
|No automation. Marketers manage all tasks with basic tools and CRM systems that provide no real automation, but act as storage repositories for marketing data and results reporting (dashboards or “business intelligence” systems).|
|Recommendation automation. Marketers leverage systems capable of following business rules (defined by the marketer) to make business recommendations for optimizing marketing outcomes. Examples include dashboards with recommendation systems for adjusting marketing spend by channel. The user must take the final step of making the recommended adjustments.|
|Rules-based automation. Building on business rules set by marketers in Level 1, Level 2 rules-based automation goes the next step and adjusts marketing campaigns automatically (generally via an application or API) without user intervention or approval. Such systems rely on the user to create the rules; dynamic market conditions shifts on a daily, hourly, or even minute-by-minute basis render rules-based systems brittle or overhanded.|
|Computational autonomy. Systems that use machine learning to observe, learn and improve outcomes based on statistical analysis combined with marketing automation. No intervention is required by the user apart from setting goals or broad-based parameters such as campaign dates or geographies for digital campaigns.|
|Insightful autonomy. Systems that understand the contextual meaning of user interactions, content, behavior, performance data, and more to personalize 1:1 marketing messages across various channels and drive optimal performance for operators.|
|Fully autonomous. Level 5 systems build Insightful Autonomy capabilities but generate their own unsupervised tests, creative variations, targeting parameters, and more with no ongoing intervention from the marketing team.|
Most growth marketing teams are in the process of figuring out how to reach a level of proficiency to move from Level 0 to Level 2. However, the biggest challenge and opportunity for app marketers is to advance from Level 2 to Level 5, to scale up their growth significantly faster with the judicious use of artificial intelligence and automation in the world of Lean AI.
Scaling UA with a lean marketing team
How can app marketers successfully scale customer acquisition and revenue growth with a lean team to get from Level 2 to Level 5? Out-of-the-box artificial intelligence acquisition solutions from Facebook, Google, and others provide a good start, but the innovative startups that can tailor those solutions to meet their specific needs, objectives, and goals will come out winners.
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 targeting, creatives, and sequencing will require an “intelligent machine” operational layer above the out-of-the-box solutions to deliver great results—or settle for being average.
The future of paid user acquisition rests on the shoulder of intelligent machines, orchestrating complex campaigns across key marketing platforms dynamically allocating budgets, pruning creatives, surfacing insights and taking actions autonomously. These machines hold the potential to drive great performance with a far more efficient lean team, hands-off management approach powered by Artificial Intelligence.
This article first appeared on the Mobile Growth Summit blog.