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5 Ways Artificial Intelligence Is Changing Advertising Sales

AI can help ad sales pros improve both their effectiveness and efficiency.

man looking at an AI smart digital advertising billboard that is suggesting sushi to him
Artificial intelligence powers smart advertising billboards that serve up personalized ads like this lunch suggestion.[Zapp2Photo/Shutterstock]

Artificial intelligence (AI) is getting real in the marketing suite. When asked where they planned to invest this year, marketers ranked AI as their #1 priority, according to our most recent State of Marketing Report. AI adoption is surging: 84% of marketers reported they use AI somewhere in their acquisition and retention engines, up almost three times over just two years ago.

What are these intrepid marketers doing with AI? Reported uses are expanding rapidly, from enhanced personalization to improved segmentation, insight discovery, predictive modeling, and process automation.

Advertising technology also rode the wave of big data-driven AI adoption, as programmatic platforms revolutionized the process of buying and selling digital ads.

Advertising technology also rode the wave of big data-driven AI adoption, as programmatic platforms revolutionized the process of buying and selling digital ads. Programmatic ad sales are soaring, from $60 billion in 2019 to an estimated $97 billion in 2022, according to eMarketer. It may seem from some angles that ad sales would become a kind of self-driving car, requiring the occasional redirect but not much else from the salesperson.

How do these salespeople feel about the impact of AI on their world? Two words: cautiously optimistic. On the one hand, 86% of sales executives see AI as having a positive influence on their future roles, according to our latest research. However, 68% of the same executives voiced concerns — primarily about the ongoing relevance of their current jobs, — and 31% said automation might ultimately hurt the “art” of selling, as human-to-human relationships are replaced by optimized digital touchpoints.

Five reasons to be happy about AI in ad sales

Despite a natural fear of the unknown, there are good reasons to believe AI will make the business and — yes — the current jobs of ad salespeople better. It augments selling while leaving people to do what they do best: be human.

AI can level the playing field for both advertisers and publishers, giving even hard-working salespeople who don’t work for Fortune 100 tech or media companies the tools they wished they’d had in the past. These AI-driven superpowers can help sales professionals improve both their effectiveness (how well their product works) and their efficiency (speed and productivity).

1. Better data unification and harmonization

With the impending deprecation of the third-party cookie in Chrome and Apple’s ongoing privacy changes, marketers are looking to first-party data to power their programs — including media programs. An impressive 88% of marketers said first-party data was a strategic priority for them in 2021, according to Merkle’s 2021 Customer Engagement Report. For publishers, first-party data is key to building audiences that advertisers need; and for advertisers, first-party data is increasingly important as a seed for matching, targeting, and lookalikes.

First-party data is often fragmented and poorly organized, as well as complex.

However, first-party data is often fragmented and poorly organized, as well as complex. For example, 64% of customers start a purchase journey on one device and finish on another, per Salesforce research. And marketers face a daunting average of 12 major sources of customer data, up 20% from 2020, according to our State of Marketing Report. Often the data sitting in these sources contain inconsistent identification information (IDs), out-of-date information, and eccentric taxonomies.

AI can often be combined with a solution such as a Customer Data Platform to greatly improve identity matching. Algorithms can be applied to perform “fuzzy matching” on IDs and resolve discrepancies. And AI can be used to make sure data from different systems is mapped to a common data model to ensure consistency.

2. Stronger segmentation and audience discovery

Customers of today, especially digitally native millennials, say they expect relevant experiences: that is, messages that are useful and timely in the context of their digital lives. Delivering a personalized experience requires both organized data and intelligent algorithms to find segments and uncover needs that are difficult to uncover using manual methods.

AI excels at smart segmentation — uncovering groups of customers and prospects who have attributes in common — at a scale and depth that isn’t possible for human analysts.

AI excels at smart segmentation — uncovering groups of customers and prospects who have attributes in common — at a scale and depth that isn’t possible for human analysts. AI algorithms can traverse billions of rows of customer data looking for patterns that might mean the difference between a valuable publisher audience and the same old “18-to-35 mom.“

For example, imagine a publisher with an established audience of science fiction enthusiasts. Suppose there is a significant subgroup of people who prefer robot-driven stories to human-driven stories, but there is no way to tell that without combining data from content analysis, web analytics, social listening, email, and commerce. Without AI, the publisher would sell a broad “Sci-Fi Lovers” segment. With AI, they can hyper-target “Gear-Head Sci-Fi” to — say — a campaign for a futuristic new vehicle.

By combining customer data from its loyalty, point-of-sale and marketing systems, retailer Casey’s was able to boost sales of its beloved fresh pizza by 16%. The secret? It used the combined data to deliver promotions for the type of pizza the customer bought most often, increasing the relevance of its messages.

3. Natural language interfaces with technology

Few areas of AI have more potential for impact on our lives than natural-language processing (NLP) and image recognition. Already we’ve seen the rapid rise of conversational chatbots in service, and voice interfaces are familiar to any of us who have ever asked Alexa to tell us a joke or told Siri that we’re in the mood for sushi.

AI can reduce these time sinks, as speech recognition approaches 95%+ accuracy and voice navigation becomes commonplace.

Voice is already built into call center software and some analytics tools, but marketing technology is at the beginning of the adoption curve. Imagine an ad salesperson never having to touch a keypad again or — even better — have to click through drop-downs and menus looking for exactly the right command to select.

AI can reduce these time sinks, as speech recognition approaches 95%+ accuracy and voice navigation becomes commonplace. Efficiency will increase (less key clicking), and so will completeness (less reason to procrastinate in updating call records or ideas). The outcome is less time spent searching and updating and more time spent selling.

4. More efficient back-end processes and production

We’ve already mentioned how AI can help automate manual data plumbing and “munging” tasks such as ID mapping and harmonization. It can also help make salespeople more efficient by prioritizing their efforts, sifting through tasks and leads, and focusing their efforts.

There is no better way to increase productivity than by using AI tools because the automation is all there.

Sarah Borrmann, director of sales productivity and operations, Illusive Networks

One of the first areas of the sales process to benefit from AI was lead scoring. Despite initial reluctance from some sales teams — “What can a machine tell me about a good lead? I know one when I see one!” — many now routinely use algorithms to improve their priority lists. The same is true for tasks from summarizing call notes to planning the best way to get from one customer meeting to another, in which order.

“I’ve seen teams waste a lot of time chasing leads that are unqualified or not relevant because the lead scoring systems were not in good shape,” said Sarah Borrmann, director of sales productivity and operations at Illusive Networks. “There is no better way to increase productivity than by using [AI] tools because the automation is all there.”

For ad salespeople, AI can help match available inventory with direct-sales opportunities that are most likely to close, minimizing time spent on frustrating low-likelihood leads.

5. Improved measurement and optimization

Accelerated by pandemic, digital advertising’s rise has been impressive. More than half of all ad spend is now digital, with an increasing proportion flowing through the largest platforms and publishers. AI can help to ingest and analyze all that data in a way that helps both publishers and advertisers estimate the impact of campaigns on desired outcomes, such as sales.

AI is well suited to help ad salespeople separate signal from noise and identify what works and what doesn’t.

Measuring the impact of multi-channel campaigns requires ingesting information from dozens of sources and applying complex models to determine which creative elements, channels, devices, publishers, and tactics (such as time of day or ad size) made an impact. “Noise” can include factors such as competitive moves — Did they run a sale at the same time? Was there a new product launch? — the economy, and even the weather.

AI is well suited to help ad salespeople separate signal from noise and identify what works and what doesn’t. Some leading AI-driven tools already provide automated optimization recommendations based on the historical performance of marketing and ads.

AI excels at automating tasks that are mundane, and also at sifting through daunting amounts of data at dizzying speed. Humans do not. By letting the AI assistants do what they do best, we are already making ad sales jobs less artificial and more intelligent.


Martin Kihn is the Senior Vice President of Market Strategy for Marketing Cloud. In a former life, he was a research vice president at Gartner, where he wrote and spoke widely about marketing technology, and advised numerous Fortune 500 clients on marketing strategy. He’s also authored four books, including “House of Lies,” which was adapted for TV by Showtime, and “Customer Data Platforms: Use People Data to Transform Marketing Engagement,” co-written with Chris O'Hara. Fun fact: Kihn was head writer for the MTV series Pop-Up Video from 1997-1999.

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