Sign in with Google Our AI Path to Excellence series unpacks results from a global survey of 2,000 marketers to understand how AI leaders drive outsized growth and efficiency. Here, we explore how brands and agencies can achieve marketing excellence through AI and other measurement techniques.
Against the backdrop of today’s complex consumer journey, marketers are under immense pressure to maximize every dollar. That’s easier said than done, but adopting a “modern measurement tool kit” can help.
Today, only 44% of senior marketing analytics professionals use attribution solutions, marketing mix models (MMM), and incrementality experiments together.1 Too many brands are letting opportunities sail by. But today’s marketer is in a position to see themselves as an athletic coach, and each channel, an athlete waiting for strategic tips to transform performance.
Here’s how those marketer-coaches can close the gap and join the ranks of companies driving outsized growth with AI.
A 360° view with marketing mix models
Of all four “pathways” to excellence discussed in our research with Boston Consulting Group (BCG), the one focused on measurement is most notorious among marketers. Measurement and Insights is the pathway with the largest headroom for AI implementation, with only 9% of companies having reached leading capabilities.2
Why? There are still many barriers facing marketers who want to turn what they know about their customers into a plan of action. Just 24% of companies have developed a 360-degree view of the customer, meaning they are leveraging customer data in their marketing.3
If your company already has a strong first-party data strategy, this gap might surprise you. But it makes sense when you consider that many companies lack core testing techniques. For example, 42% do not use A/B testing to assess effectiveness, and 52% do not use brand lift metrics to track perception.4
Think back to the analogy of the star-powered athletic team. Just as a coach must have a comprehensive view of all their players’ efforts, so must a marketer understand their efforts’ relative efficacy. MMMs deliver just that kind of bird’s-eye view of performance, equipping marketers with the knowledge they need to make budget decisions.
Meridian is our open-source MMM, designed for the realities of the modern consumer journey. It provides the clarity marketing leaders need to invest budgets wisely across every channel — online, offline, TV, and everywhere else customers are.
Watch the video
Liquid Death’s Chief Media Officer Benoit Vatere shares how Meridian is helping the brand and its agency build a comprehensive approach to incrementality and measurement.
Faster, better incrementality testing
In our research, we found that early-stage AI adopters lacked the robust testing of their more advanced peers. Unable to integrate AI fully, they apply it intermittently. That makes it much harder to understand which efforts were worth the investment.
Like a postgame analysis, incrementality testing sheds light on which players contributed and how. For a marketer, a star player could be a pivotal ad or a media buy. To fully understand what your MVPs are, you need both the overview of an MMM and the granular view of an incrementality experiment.
In short, when you put MMMs and incrementality testing together, you can supercharge your ROI. Experts agree: 80% of senior marketing analysts in the U.S. say that implementing insights from incrementality experiments has a high impact on revenue growth.5
The most robust testing approaches rely on AI-driven solutions that leave no gaps in data. Case in point: auto manufacturer Nissan’s first-party data strategy. By learning how a certain action influenced a sale, the team could redirect their efforts toward sales, not just showroom visits. Thanks to the team’s reliable, high-quality data, they had the kind of clarity that allowed them to see if their AI solutions were working.
When you put MMMs and incrementality testing together, you can supercharge your ROI.
Like Nissan, U.S.-based hotel chain Westgate Resorts also managed to connect the dots with AI for a better view of demand. The key to its success was the ability to connect hotel guests, renters, and timeshare owners with their next destinations. Eventually, the brand applied the approach to 20 resorts, making always-on testing a key component of its advertising strategy.
Despite these clear gains, just 24% of companies have an always-on testing approach, using AI to derive insights faster.6 That suggests a widespread barrier to entry among companies just starting to implement AI.
The earlier you get these systems and tools in place, the more data you will have to power all future AI integrations.
To help marketers overcome messy, confusing results and get more from their investments, we’ve brought easier, faster incrementality testing to all Google Ads campaign types. We have also reduced spend thresholds, so you can now run incrementality experiments for budgets as low as $5,000. Finally, those using Google Analytics will be able to easily evaluate spending across all channels, using data-driven insights.
Connect the dots with data-driven attribution
Due to a lack of tools that provide actionable insights, too many companies today still give all conversion credit to the last click. As a result, they miss the true value of all the ads that led to an outcome, akin to a football coach who credits the quarterback with all of his team’s success.
With reliable attribution, more companies can move into the leading tier. That’s because they know exactly how much credit to give to each interaction along the consumer journey, from first click to final decision.
To use AI to track attribution with this level of granularity and accuracy, marketers must first manage their data so AI can easily read it. For example, Google Ads uses AI to dynamically assign credit to touchpoints, helping brands adapt to the modern consumer journey.
As an industry, however, marketing has a long way to go. Many companies do not have the tools in place to collect foundational data: 42% do not have a customer relationship management (CRM) system and 57% do not have a customer data platform (CDP).7
They know exactly how much credit to give to each interaction along the consumer journey, from first click to final decision.
If you’re missing important data, you don’t have the full picture of the customer journey. Combining first-party data with your advertising partner’s data can help. In this case, “first-party data” means data from physical stores, CRM, apps, and more. Tools like Data Manager go a step further to help marketers understand, unify, and strengthen their data.
Why level up now?
If you’re not among the top growth drivers adopting AI, you have a lot of company. So why the urgency all of a sudden? It’s simple: The earlier you get these systems and tools in place, the more data you will have to power all future AI integrations.
Companies that implement four or more of the six critical capabilities are 59% more likely to uncover new consumer insights.8 But to achieve critical measurement capabilities, marketers must return to the coach mindset. By gaining a holistic view with MMMs, sharpening analysis with incrementality testing, and giving proper credit with precise data attribution, they can optimize their resources for growth. And that’s a winning strategy.
