6 Ways Artificial Intelligence is Impacting Advertising Sales in Media Companies

5 min readMar 4, 2019

Artificial Intelligence (AI) is changing the way we run advertising operations. According to Salesforce, 60% of marketing leaders believe AI can help them run more effective programmatic campaigns.

Accenture predicts that in the information and communications industry, AI capabilities can actually “coalesce” with existing systems to generate US$4.7 trillion in gross value added by 2035. When we discuss artificial intelligence for the advertising industry, we think about targeting our advertising inventory but often fail to consider advertising sales in the process.

Salesforce’s State of the Connected Customer report shows that 7 out of 10 business buyers expect vendors to personalize engagement to their needs. However, according to an eMarketer survey:

47% of advertisers are currently using artificial intelligence for audience targeting
Only 39% use it for media spend optimization and personalized offers
One in three advertisers claims they have no plans to use artificial intelligence for offers

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AI for advertising sales can assist publishers, marketers, and agencies with detailed insights on revenue and performance. Machine learning algorithms can use big data gathered from first or third party sources to study their customer’s behavior and use that information to stay ahead of their needs.

How can ad operations make the most of artificial intelligence for advertising sales?

We have identified six ways artificial intelligence had affected the ad sales process for media companies:

1. Advertisers and Agencies’ Buying Patterns

It has been reported that a number of companies are focusing their AI capacities to streamline their sales process, sorting out “hot” leads from “cold” prospects, cutting sales leads considerably, and improving sales productivity.

Salesforce Einstein embraces a set of advanced AI capabilities providing customers with the chance of:

Being more predictive
Deliver personalized customer experiences
Automate repetitive and time-consuming processes

2. Early Warning Alerts Reduce Customer Churn

According to Accenture, companies are monitoring incidents, tracking performance and recording outputs to provide real-time visibility, alert potential problems and propose alternative solutions.

As advertising packages are carefully personalized and customized, we can often fail to see where in our latest media proposal we started to lose customers who were dissatisfied with their return on investment (ROI).

ADvendio recently attended Salesforce’s Dreamforce To You in Santiago where we learned first hand about the new capacities of Einstein. Among their novelties, we learned how heavily retailers are using AI to predict churn, by analyzing customers shopping carts and CRM behavior, and how marketers could use that information to improve their outreach and value proposition to customers potentially considering to leave the company.

3. Boost Media Companies Ad Ops’ Productivity

Many fear that artificial intelligence will lead to people losing their jobs; in fact, it can boost their performance by assisting them in critical tasks and decision making.

Ad operations can delegate time-consuming, low value-added activities to focus on their most critical roles. With machine learning algorithms, computers learn exponentially from their available data.
This helps companies discovering, sourcing, structuring and sharing previously disorganized information from outside organizations along with aiding advertising sales professionals to adjust their course based on campaign performance that remains compliant with data privacy regulations such as GDPR.

This also reduces downtime on logistical efforts and meticulous research, with the assistance of ad exchange and server partners, who are already making the most of AI’s capacities. Examples include:

AppNexus who in 2017 launched a programmable demand-side platform (DSP) to harness machine learning and assist traders in managing and calibrating strategies.
Rubicon Project who uses machine learning to manage the massive amounts of rich data to predict the performance of advertiser campaigns and rank the performance of advertisers depending on different variables, to set the right expectations for pricing and yield.

4. Provide Price Optimization Tools with Big Data

With ad exchanges and dynamic pricing, publishers and advertisers engage in a bidding game for the best possible rates. Publishers can offer significant discounts or commissions depending on the size of the purchase, promotions, and the relationship they have with the customer.

However, knowing what discount to give -if any- is the tricky part, according to the Harvard Business Review.

With machine learning capabilities, artificial intelligence algorithms can help publishers predict what an ideal discount rate could be for a specific proposal. They can ensure that they can win a deal by analyzing the particular features of previous advertising negotiations they may have won or lost.

5. Customers Improve Upselling & Cross-Selling

By using the same prediction tools from the company’s prior relationship history with customers, combined with third party resources, artificial intelligence can help publishers identify which advertiser or agency has a better chance to improve their investment and add more items to their shipping cart.

Sales representatives must dig deep into their client’s mindset and understand their expectations, motivations, and pain points better than them. Big data analytics connects previously unconnected information for that purpose.

AI engines can help create dashboards to understand and learn which channels, messages or content can resonate best with customers, analyzing publishers’ interactions with them via touchpoints. For example, Salesforce Einstein can discover the cause of unexpected business outcomes by helping people dig through their existing data within their CRM or in third-party systems.

6. Key Media Customers with Lead Scoring

With a plethora of various inbound and outbound prospects, looking for qualified leads is like trying to find a needle in a haystack. When publishers have such a rich sales pipeline, it requires cleaning up and prioritizing.

Qualifying leads and scoring them would be a painful task if there weren’t any tools to assist in decision making. Artificial intelligence can help companies in revising historical information, social media performance to aid ranking them on a pipeline.

Artificial Intelligence-assisted Ad Operations: What’s Next?

Artificial intelligence technologies are becoming increasingly available. However, the media industry still needs to take a number of steps to adopt them fully and fully engage in digital transformation.

A report from Lotame asked why US publishers and brand marketers use audience data, where the following results were highlighted:

60% of people consider it to make content or to message more relevant
Only 53% use it to sell more advertising inventory
42% leverage it to win new business or RFPs

The opportunities for digital transformation are just beginning. There is still a long way to go to embrace artificial intelligence and machine learning using big data in full. Media companies need to embrace the opportunities and invest in training advertising operations professionals, to make the most of them to improve their sales and revenue strategy.