The first word
Artificial intelligence (lovingly referred to AI) seems to be taking advertising by storm, but what are the most impactful ways to leverage it in our world of B2B food? That’s a loaded question, sure. The truth is we’re already using it daily—the opportunity now lies in ways to enhance the technology, so it supports our strategic efforts.
There are quick wins in paid media—in paid, AI refers to technology that utilizes machine learning and analytics to boost return on advertising spend (ROAS) by enhancing the customer journey through target audience insights. In our terms: AI helps us define the right place, the right time and the right message to send our target audience, so they act in a way that we can measure.
Make it make sense for food
In our channel, we have limitations when it comes to AI. The channel’s buying cycles, distribution process and investment in product development cause segmented nuances in the decision-making process. This requires advertisers to take a more traditional approach to communication across the customer journey by applying rich knowledge of how the different audiences feel and act at each stage of the journey. But with that understanding comes opportunity—while there may be current limitations, the future carries exciting potential.
Getting with it
There are three main areas AI is actively being applied across integrated communications in food marketing: paid programmatic ads, paid search ads and analytics.
Programmatic advertising uses AI-powered algorithms to automate the ad-buying process. It makes it more efficient, targeted and cost-effective. As programmatic becomes a more viable tactic for our channel, we’re able to streamline placement and targeting. The result is a more efficient buying process.
For example, programmatic allows for automating and adjusting bid strategies in real-time—resulting in blink-of-the-eye optimizations that improve performance. By pairing this level of optimization with Esrock Partner’s proprietary B2B target segments, we have the ability to cost-efficiently intersect prospects wherever they are. This makes programmatic a sustainable top-of-funnel advertising tactic.
In addition to efficiently delivering ads, we need to efficiently deliver messages based on behavior. As programmatic advances in our industry, personalized ad experiences will be possible. We can display different ads based on past behavior and create dynamic landing page experiences to guide users through the intended actions we strive to achieve.
As we move through the funnel, there is also the need for AI at the lower stages of consideration and conversion. The answer for conversion is paid search. Using Google’s AI-powered algorithms, dynamic search ads use real-time search queries to generate headlines and landing pages. From there, Google tailors ads to users’ specific interests. This increases not only relevancy, but engagement.
AI-supported paid search strategies are great for conversion, but it’s important to keep in mind that paid search is an always-on investment. It’s not for one-off products or campaigns.
AI-powered analytics support the effective execution of paid—an area where it feels like the opportunities for maximizing potential are endless.
Some examples of ways we can use AI to inform analytics right now:
- Automated anomaly detection: artificial intelligence looks for data issues by comparing key metrics such as cost, revenue, clicks and CP. This is something no single person could effectively do based on sheer scale. If an issue is detected, bidding can be paused until the problem is solved.
- Predictive analytics: many ad platforms create accurate performance forecasts using artificial intelligence and the vast data collected across platforms that advertisers don’t have raw access to. This allows advertisers to create informed forecasts and minimize wasted spend. We can split performance by segment, source, medium, KPI and more.
- Sentiment analysis: also known as opinion mining or emotion AI, this analysis uses AI to analyze text data and determine the sentiment behind it. This includes positive, negative or neutral emotions, as well as opinions and attitudes. This has easy applications in social!
Ultimately, AI helps, but it can be detrimental if left unattended. One broad concern is that AI can develop a mind of its own (at least biases of its own). AI systems are trained on data, and if that data is biased, the system will also be biased. This can lead to problems such as discrimination or unfair targeting of ads. It can be challenging to understand how AI systems work, making it difficult to hold them accountable. Therefore, it’s vital data analysts stay on top of optimizations and updates as they’re made.
The last word
Though we’ll still need a hybrid of traditional and AI services for a while, we’re building and preparing for the future. The Integrated Communications Team at Esrock Partners is building proprietary segments, partnering with data companies and exploring new ways to measure the customer journey, so we can maximize return.
The current synopsis: AI will continue to advance and improve the way we communicate with prospects, but there are distinctions in our industry that machines won’t be able to replicate—at least not any time soon. So we’re planning, building, and adapting our arsenal to ensure success as this AI wave continues to crash through our known process.
About the author
Michelle Johnson is the Integrated Communications Director at Esrock Partners, a foodservice marketing agency located near Chicago, Illinois.