Lomit Patel, Vice President of Growth at IMVU, provides an overview of how AI fits into the Martech landscape using the Lean AI Autonomy Scale framework.
Today, artificial intelligence (AI), sensors, and digital platforms—and an explosion of data—have already increased the opportunity for learning more effectively; but 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 instead of forecasting and planning.
Artificial intelligence, particularly machine learning, has played an important role in digital advertising for a long time now. In fact, the industry wouldn’t exist at this scale without AI powering the performance of your campaigns.
Cutting through the noise: A framework for assessing the state of martech
There is no shortage of claims in the world of martech — 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?
A good way for marketing leaders to do that is to use the Lean AI Autonomy Scale framework below to help you assess how the different capabilities of vendors stack up with AI in the industry:
The Lean AI Autonomy Scale
Most marketing teams are in the process of figuring out how to reach a level of proficiency to move from, say, Level 0 to Level 2. However, the biggest challenge and greatest opportunity for marketers is to leverage martech to advance from Level 2 to Level 5.
That jump will allow them to scale up their growth significantly faster, aided by the judicious use of artificial intelligence and automation in the world of Lean AI.
Hey, our AI likes your AI
All major ad platforms and exchanges are powered today by a dizzying array of complex calculations with the goal of delivering your message to the right audience at the right time — and encouraging people to take an action, buy a product, register an app, or a host of other outcomes a marketer might be looking for.
These platforms all use AI to negotiate tiny but complex transactions in real time, billions of times per hour.
Platforms have matured (at different rates) over the last 15-20 years but for the most part, these companies have enabled all sorts of effective ways to achieve your marketing goals around acquisition, retention and monetization.
Of course, not one of these platforms will tell you exactly how their AI works — and as advertisers, we’re forced to accept this as part of the bargain. So that’s the deal, and it works out pretty well.
But the big challenge for any marketer working at scale is managing all these AI-powered sell-side platforms in a systematic way. Enter buy-side AI.
This new emerging class of software is capable of taking out as much human bias (and math) as possible to help you gain more control over your cross-channel efforts, manage your campaigns with greater precision, and eliminate wasteful spend by taking things like incrementality into place.
Buy-side platforms use AI to “talk” to the corresponding AI on the platform side of things and work in unison to achieve optimal results.
These buy-side martech platforms can take advantage of the rich APIs made available by the platforms, and take action on your behalf given your overarching goals, based on observing interactions across multiple channels.
There are certainly less sophisticated solutions out there that offer optimization recommendations, also powered by AI — those are classified as Level 2 capabilities.
The reason you want to evolve from there is clear: Level 2 means there is still a human in the loop, so we will exclude business intelligence, reporting, and recommendation platforms for that reason in this discussion.
These buy-side systems can “learn” how to get the most out of each platform in the mix. We see that, for example, Facebook’s AI looks closely at frequency and a proprietary metric they call “relevance”.
These two factors alone have a HUGE impact on how much you pay for each impression, and where and how your ads are displayed.
Frequency, as a distinct campaign metric, works differently on different types of ad platforms. Social platforms like Facebook believe (through lots of research and observation) that frequencies greater than three within a purchase cycle actually have a negative impact on performance.
The higher the frequency, the more likely users are to give Facebook feedback that the ad isn’t relevant to them.
Like it or not, your ad budgets are using AI to evaluate an insanely complex array of variables and data points to drive performance and keep advertisers happy. Well, relatively happy anyway.
Today, we’re at a place where buy-side solutions are emerging that operate a layer above the platforms themselves, working to orchestrate your spend, targeting and even creatives across channels.
The best of these platforms can take you from Level 0 to Level 3 and beyond quickly, and without a massive overhaul to the way you’re currently conducting business.
The evolved AI of today
Automation is the peanut butter to AI’s jelly. A range of emerging vendors are out there today using Artificial intelligence to automatically optimize advertising spend and targeting.
These systems look at your outcomes not just within a campaign, but across a range of “campaigns” both within and across a variety of ad platforms.
Ultimately, we care about results. Why not hand over the orchestration of your cross-channel campaigns to AI?
These platforms vary based on their approach to AI (in terms of both approaches and results) but in general they will outperform an agency or an internal team equipped with a souped-up business intelligence platform with “AI” bolted on.
This is a huge upper hand for those companies that embrace the emergence of these AI-powered buy-side platforms.
When COVID-19 hit, many advertisers suspended their media buys. It’s an understandable, knee-jerk reaction to economic uncertainty. It’s also often the right call.
But because IMVU entrusted intelligent machines (and software from Nectar9, one of the providers of AI-powered marketing automation technologies) we were able advance to Level 5 to let the system we tuned for our needs learn how best to cope with the massive changes impacting each of the individual ad-buying platforms.
AI analyzes a complex array of consumer behavioral data and bidding environments in real-time — running calculations 24/7 with a level of accuracy and at a volume human just can’t compete with.
As a result, we saw our customer acquisition costs drop, allocations shift, and saw our revenue accelerate significantly without making a human decision to either pull back or press on the gas pedal.
A big change is coming to martech in the next five years as more companies race to achieve Level 5. Having a fully connected and open martech and ad tech ecosystem will be the newest must have technology capability.
This agile ecosystem enables companies to be more dependent on strategy and less dependent on humans orchestrating their campaigns.
Sooner rather than later, machines will do the heavy lifting of creating enormous value across the entire customer experience. If you’re at Level 0-2 today, ask yourself, what’s holding you back?
Embrace experiments around AI with an open mind and you’ll be surprised where the journey leads you as teams will be lean in nature as the structure evolves for humans and machines to co-exist.
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.
This article first appeared on ClickZ.
