5 Ways AI Is Changing Advertising Sales

Media companies are losing advertisers at an alarming rate, a trend that’s only expected to continue. According to the Media & Entertainment Industry Insights Report, 39% of media companies expect advertising spend to continue going down over the next 18 months.

Gone are the days when advertising technology rode the wave of soaring programmatic ad sales – from $60 billion in 2019 to $97 billion in 2022 – declining to a projected 74.88 billion dollars by the end of 2023. Still, getting the most out of your no. 1 revenue source should be a priority. That means keeping your advertisers happy and investing in your platform more. 

That’s why the industry is turning to AI for advertising sales.

Five ways AI can help with your ad sales

AI for advertising sales has been around for a while, but the technology now allows you to analyze your audience’s preferences, behaviors, and demographics to target advertising more effectively. The technology now goes beyond merely making predictions or automating manual processes; it can help your teams see scenarios in a different light and come up with new and better solutions. 

Here’s a deeper look into the five ways AI can help with your advertising sales. 

1. Better data unification and harmonization

In the ever-changing world of privacy, marketers are looking to first-party data to power their programs — including media programs. 

For publishers, first-party data – or the data companies collect – is key to building audiences that advertisers need. And for advertisers, first-party data is increasingly important for understanding their audiences and delivering personalized content. 

But, first-party data is often fragmented, poorly organized, and complex.

For example, per Salesforce research, on average, business buyers engage with companies across 10 channels, while consumers typically use eight. And marketers face a daunting average of 18 major sources of customer data, up from 15 last year, according to the 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 Often the data sitting in these sources contain inconsistent identification information (IDs), out-of-date information, and eccentric taxonomies.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 real-time customer data solution to greatly improve identity matching. Algorithms can be applied to perform “fuzzy matching” – identifying similar but not identical data entries – on IDs and resolve discrepancies. And AI for advertising sales can be used to make sure data from different systems is mapped to a common data model to ensure consistency.

2. Creating personalized ad content

Today’s consumers say they expect personalized 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 analyze needs and find relevant segments, which are difficult to uncover using manual methods.

AI excels at smart segmentation — gathering 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 targeted audience and a generic age demographic.

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 purchasing history. Without AI, the publisher would sell a broad “Sci-Fi Lovers” segment. With AI for advertising sales, they can hyper-target “robot-loving post-apocalyptic sci-fi” to — say — a campaign for a futuristic new electric vehicle.

AI can then analyze that historical data to craft personalized advertising content that will best resonate with that niche audience. AI can suggest relevant headlines and personalized messaging, as well as help identify trending topics to create relevant advertising content. 

3. AI for advertising sales provides real-time support and prompts

AI can analyze customer data to give sales teams a deeper understanding of their clients’ needs, and pain points, and offer suggestions on relevant advertising products. Sales teams can also use AI to personalize their pitches and proposals to meet the needs of each client. Following a customized interaction, AI for advertising sales can also help them improve customer relationships by analyzing client feedback and a team member’s approach to selling.

AI can also be used for client retention and customer loyalty. By generating prompts to follow up with clients at the most opportune time, it can suggest playbooks based on real-time data of client interactions and campaign performance.

AI-driven chatbots or virtual assistants can provide immediate responses to inquiries and assist with lead qualification and appointment setting, freeing up teams to focus on more strategic tasks – the customer experience being number one.

4. Optimize back-end processes and production

AI can also help sales teams be more efficient by sifting through tedious tasks, new lead information, and back-end processes that all take up time.

One of the first areas of the sales process to benefit from AI is lead scoring. The same is true for tasks from summarizing call notes to planning the best way to map out the path from one customer meeting to another.

“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.”

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

5. Improve measurement and optimization

Digital now accounts for more than 70% of all ad spend, and that’s expected to eclipse 80% in 2024. AI can help 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 ad sales.

Measuring the impact of multi-channel campaigns requires analyzing 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 or was there a new product launch? — the economy, and even the weather.

AI for advertising sales teams can help separate signal from noise and identify what works and what doesn’t. Some leading AI-driven tools provide automated optimization recommendations – such as messaging, placement, and targeted audiences – based on the historical performance of marketing and ads.

Artificial intelligence in advertising sales may be changing, but if we continue to learn with it, we will continue to benefit. AI excels at automating mundane tasks, and at sifting through daunting amounts of data at dizzying speed. It’s a much-needed boost to the ad-sales process and you can implement it easily today. Let AI work for you.