Every business has to think constantly about ways to reduce the risks for them to survive and thrive in this unpredictable world — especially now that we’re dealing with a global pandemic. The coronavirus is not the first (or last) crisis that your business will face, but it’s a stern reminder on how vulnerable any business is during any crisis. You’ll soon figure out if you’re prepared to get through it.
Crisis or not, the lifeblood of any business is customers and revenue. This makes your user acquisition a mission critical component that can have a huge impact on how you survive and — hopefully — thrive right now. The focus on customer acquisition and growth will become more intense than ever before as we attempt to come out of this economic crisis. A strong performance by your company’s acquisition marketing team now can mean the difference between a company that succeeds or fails.
While acquisition marketing is a huge driver in the growth of all brands, it’s particularly important among the emerging ranks of direct to consumer brands. The days of flowing venture capital are behind us; raising additional rounds will be dependent on how efficient you are at scaling up your revenue. You’ll need to run lean and mean.
Future rounds of business financing will be brutal and basic performance metrics like return on ad spend will separate the haves from the have nots.
Emerging technologies can help you bridge the gap between where your company is now and a nice, healthy growth trajectory. These technologies can help smaller teams scale up their budgets profitably, with less effort, than ever before. At the same time, they reduce the risk inherent in larger teams managing multiple acquisition channels, budgets, and more with a single unified growth goal. Reducing complexity reduces friction and accelerates the rate of experimentation and learning.
Warren Buffett has a saying about the stock market and investing in general: “Be fearful when others are greedy and greedy when others are fearful.” The same wisdom applies to user acquisition (UA). The fear from the coronavirus has led to many advertisers cutting back their advertising budget, resulting in a great opportunity to buy ads more cost-effectively on Facebook, Google and other major platforms. There is a huge cross over between investing properly and successful user acquisition efforts.
The best UA teams are very similar to the best investing teams. They are responsible for taking calculated risks to spend their budgets to generate a positive return on their investment.
Not all UA teams are created equal. In larger, more established companies these teams manage huge budgets with large dedicated teams. While big teams may have been an available luxury in days past, their days are likely numbered and the current crisis is making this reality painfully acute.
We can see first hand the challenges of managing UA with a distributed workforce. Bigger teams aren’t necessarily better in this environment; bigger teams take more coordination, are subject to greater risk of human error, and are more likely to get smacked by a costly lapse in judgement.
That is why so many advertisers are currently panicking right now. Humans are emotional. The easiest, most “safe” thing to do is to cut your acquisition budget because you’re not able to differentiate the signal from the noise with what’s happening in all the different programmatic advertising exchanges like Facebook, Google, DSP, etc.
While there are certainly exceptions, anyone cutting back on their UA budget right now is making a potentially huge mistake. People are sheltering in place and working from home. Data actually show more people are using their phones more now than ever before.
The best way to survive and thrive during the coronavirus is to remove as much of the human emotion from your user acquisition buying process by leveraging artificial intelligence, or an “Intelligent Machine” as I like to call it, to orchestrate your user acquisition efforts.
If you are still manually optimizing paid user acquisition campaigns with humans the same way it was done even just a few years ago, you may find yourself among a quickly disappearing breed in the customer acquisition game — if your business even survives to face the next major crisis.
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.
My new bestselling book Lean AI outlines 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 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.
With Intelligent Machines assisting and guiding your user acquisition efforts, you can focus on the things that matter — which happens to be things humans are uniquely suited to tackle. Humans are great at things like strategy, spotting new opportunities from emerging performance trends, and nurturing the creative process based on real-world performance data.
The data to support AI is critical. But data is nothing without a clearly defined business problem focused on cost reduction, risk reduction, or increasing profit. Before you can prepare a strategy you’ll need to understand what autonomous marketing is capable of — and what is still just hope for the future.
Achieving 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.
In our path to embrace “Lean AI” at IMVU, we started by evaluating our current processes to see how they stacked up. We developed what became the “Lean AI Autonomy Scale” to help us communicate both where we were at the start, where we wanted to go, and set criteria for each stage of our proposed evolution.
We based the scale on the Society of Automotive Engineers work around the industry’s efforts to create driverless cars or “autonomous vehicles” and tweaked it to develop the scale below. Here is where most UA teams stack up — see if you can find your organization’s current level of marketing autonomy on the scale below:
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 shift 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 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.
If your organization is operating at Level 3 or above, you’re in pretty good shape. It means your systems are using AI to observe, learn and improve outcomes across all digital channels. You’re doing so based on advanced statistical analysis empowered by robust marketing automation, so that no intervention is required by the user, apart from setting goals or broad-based parameters such as campaign dates or geographies for digital campaigns.
Scaling UA with a lean marketing team
How can marketers successfully scale customer acquisition and revenue growth with a lean team to get from Level 0 or Level 2 to Level 3, 4 or 5? Out-of-the-box artificial intelligence acquisition solutions from Facebook, Google, and others provide a good start, but with the right software innovative startups that can tailor those solutions to meet their specific needs, objectives, and goals will come out winners.
For example, we worked closely with Nectar9 to bring our vision of autonomous marketing to life at IMVU. The fast-changing nature of the digital marketing industry makes in-house development of AI-powered marketing technologies a particularly risky thing to build themselves. But every situation is different and you need to evaluate your company’s tolerance for risk and openness to embrace new technologies.
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.