4 tips to upgrade your programmatic advertising with Machine Learning – Customer Think

4 tips to upgrade your programmatic advertising with Machine Learning – Customer Think

Lomit Patel, VP of growth at IMVU and best-selling author of Lean AI, shares lessons learned and practical advice for app marketers to unlock open budgets and sustainable growth with machine learning.

Diego Meller interviews Lomit Patel in App Marketers Unplugged

The Lean AI Autonomy Scale

The first step in the automation journey is to identify where you and your team stand. In his book “Lean AI: How Innovative Startups Use Artificial Intelligence to Grow“, Lomit introduces the Lean AI Autonomy Scale, which ranks companies from 0 to 5 based on their level of AI & automation adoption.

The Lean AI Autonomy Scale

A lot of companies aren’t fully relying on AI and automation to power their growth strategies. In fact, on a Lean AI Autonomy Scale from 0 to 5, most companies are at stage 2 or 3, where they rely on the AI of some of their partners without fully garnering the potential of these tools.

Here’s how app marketers can start working their way up to level 5:

#1 Avoid vanity metrics and measure success with the right KPIs

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Put your performance strategy to the test by setting the right indicators. Marketers’ KPIs should be geared towards measuring growth. Identify the metrics that show what’s driving more user quality conversions and revenue, such as:

  • Cost per acquisition (CPA) to measure how much it costs to make a user play your game and track the value of conversions.
  • Return On Ad Spend (ROAS) to track the gross revenue generated from your ad campaign.
  • Payback period to assess the time it takes to earn back the invested money, optimizing results to further drive incremental performance.

#2 Collect the right data, (and clearly communicate why you’re collecting it)

Analyzing data is a critical step towards measuring success through the right KPIs. When getting data ready to be automated and processed with AI, marketers should make sure:

  1. to be collecting high-quality data, and
  2. that all their data is aggregated in one place.

The better the data, the more effective decisions it will allow you to take. By aggregating data, marketers gain a comprehensive view of their efforts, which in turn leads to a better understanding of success metrics.

“You’ve got to make sure that you’re giving them [partners] the right data so that their algorithms can optimize towards your outcomes and clearly define what success is.” — Lomit Patel.

#3 Identify which tasks you can, and should, automate

The role of AI is not to replace jobs or people, but to replace tasks that people do, letting them focus on the things they are good at.

With Lean AI, the machine does a lot of the heavy lifting, allowing marketers to process data and surface insights in a way that wasn’t possible before—and with more data, the accuracy rate continues to go up.

It can be used to:

  • Find the right audience to target, and then automate the way in which that information is passed out to your different partners, in real-time.
  • Automate how much you want to bid in the different exchanges for a certain user, based on the predicted lifetime value of your users.
  • Optimize and extract granular insights from creative testing: app marketers like Lomit build HTML templates or “shells of creatives”, where they can seamlessly swap images, taglines, call to actions, and copies to understand which elements and which combinations of elements work best.

“With our AI machine, we’re constantly testing different audiences, creatives, bids, budgets, and moving all of those different dials. On average, we’re generally running about ten thousand experiments at scale. A majority of those are based on creatives, it’s become a much bigger lever for us.” — Lomit Patel.

#4 Choose the right performance partners

There’s a reason why growth partners have been around for a long time. For a lot of companies, the hassle of taking all marketing operations in-house doesn’t make sense. At first, building a huge in-house data science team might seem like a great way to start leveraging AI—but:

  • Data science is constantly evolving. In-house teams who fail to keep up with technology advancements risk getting dated. This is especially hard for companies with a core business that’s not closely related to AI.
  • In-house data teams lose access to insights outside their own ecosystem. Making data an in-house project means losing key learnings from other companies in the same industry.

Performance partners bring experience from working with multiple players across a number of verticals, making it easier to identify and implement the most effective automation strategy for each marketer. Their knowledge about industry benchmarks and best practices goes a long way in helping marketers outscore their competitors.

Last but not least, once you find the right partners, set them up for success by sharing the right data.

Wrapping up

These recommendations are the takeaways from the first episode of App Marketers Unplugged. Created by Jampp, this video podcast series connects industry leaders and influencers to discuss challenges and trends with their peers.

Watch the full App Marketers Unplugged session with Lomit Patel to learn more about how Lean AI can help you gain users’ insights more efficiently and what marketers need to sail through the automation journey.

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