The Growth of Marketing AI

Over the last five years, AI’s role within the workforce has expanded significantly. AI is expected to grow at a compound annual growth rate of 20.1% from 2022 to 2029, reaching a market size of $1.4 trillion due to the increasing availability of data and market maturity. And according to Accenture, roughly two-thirds of businesses plan to invest in AI during 2023.

The explosive growth and adoption of AI give some individuals concern. But numerous studies suggest that there is little to be concerned about. For example, the World Economic Forum estimates that AI will create a net total of 97 million new jobs by 2025. 

As the World Economic Forum indicates, the top five job growth opportunities in AI will be in data and analytics, AI and machine learning, big data, and digital marketing. The growth of AI in data, analytics, and machine learning is not a major surprise. However, digital marketing is a newer category, driven by the growth of digital transformation and the availability of transaction data collected by advertisers.

World Economic Forum pie chart of changing job demand due to AI

The Digital Marketing Function

Digital marketing is a blanket term for a wide variety of online marketing activities. Digital marketers use a range of channels to reach consumers, including social media, email, text messages, search engines, display ads, and more. Some of these channels are free, others paid. The marketer has to decide which channel is best for reaching certain audiences and accomplishing specific objectives, and which messages are the best fit for each channel.

Successful digital marketers need exceptional organizational skills, team management capabilities, creativity, and data savvy.  

Today, that data savvy has become especially important as digital marketers develop more robust knowledge of their customers and strive to make data-driven decisions about strategies, campaigns, and messaging.

At the same time, however, digital marketing experts are juggling many data sources and trying to make sense of the information they offer. In fact, according to Salesforce research, marketers will likely use double the number of data sources in 2023 that they used in 2021. 

That’s where AI has become increasingly important. AI-powered automation and analysis can rapidly combine, explore, and reveal actionable information from these large amounts of data. It’s also helping marketers powerfully increase their efficiency across all of their teams’ activities. As a result, various forms of AI are becoming integral to succeeding in marketing — and achieving companies’ goals.

What is Marketing AI?

First, let’s talk more broadly about artificial intelligence (AI). AI is a broad area of innovation that involves developing computerized ways of simulating human intelligence. AI can carry out tasks and complete actions that augment or take the place of humans’ efforts. Data science, machine learning, and predictive analytics are all part of AI, but there’s much more to this field than those technologies.

AI is showing exceptional potential as a way to amplify and enrich marketing and advertising efforts. For example, marketing AI tools can automate tedious and time-consuming tasks, such as audience segmentation, creating targeted ad campaigns, campaign optimization, and even predicting customer behavior. In addition, AI supports marketers by identifying customer needs and trends, personalizing messaging, and improving the customer experience.

AI is often found within three core areas within the marketing department: creative; campaigns (user acquisition, demand generation, performance marketing, etc.); and analytics teams. AI also drives many customer service and customer experience workstreams, including tools like conversational AI, real-time machine learning, chatbots, and other intelligent systems with innovations like natural language processing. In this post, though, we’ll focus on AI’s data and analytics applications in marketing.

Why is AI Needed in Marketing Today?

Some of the main factors contributing to the growing importance of marketing AI are: 

  1. Availability of data: Today, humans collectively have about 80M zettabytes of data at our disposal to guide decisions. We can now leverage that data to shape decisions about the future instead of solely using that data to examine past performance.
  2. Marketplace limitations and restrictions: Over the last few years, many regulatory and platform changes have affected how marketers and advertisers can use and leverage data. Apple’s changes with iOS 14.5 significantly impacted advertisers and platforms alike. For example, Meta declared that Apple’s app tracking and transparency changes created a $10 billion hit to 2022 revenues. And the verdict is still out on how Google’s deprecation of online cookies will affect desktop ad tracking and measurement. Regardless, the writing is on the wall for many advertisers: It’s time to seek an in-house, first-party data solution. Leaving their data future in the tech giants’ hands is not a safe bet. 
  3. Limited resources and scaling needs: As marketing teams continue to operate with less — while still tasked with driving the same or higher return — the use of AI will only intensify. Marketing AI can support all aspects of the marketing organization, from creative development to campaign management and measurement.
  4. Potential boost to revenue and ROI: With greater team efficiencies comes greater marketing ROI, but marketers can also boost their companies’ revenue and ROI more broadly through AI-powered audience creation and targeting, message refinement, campaign optimization, and other applications that directly affect the results of marketing spend.
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How is AI Being Used in Marketing Today?

According to Digiday, as machine learning improves, marketing teams can use these tools to shape “…broader communication strategies at a leadership level.” But AI supports marketing for more than just high-level strategy. Many tactical AI use cases support creative needs and drive campaign results.

How does AI support creative teams?

One of the most interesting developments is how AI marketing tools amplify the work of creative teams. AI has moved beyond automating versions of creative and recommending corrections to typos. Today, it can be used for content creation, including generating text in various forms and original images. 

Specifically, generative AI refers to creating content based on human-provided ideas, called “prompts.” In other words, generative AI isn’t just analyzing current content but creating entirely new ad content, web copy, or even thought leadership articles for a blog. 

These powerful tools are now readily accessible to marketing teams. Platforms like Jasper.ai and generative models like OpenAI’s DALL-E are great examples of how AI can be integrated into developing ad campaigns, copywriting, and even image or graphic design.

On the campaign level, ad agencies like Supernatural are leading the way in shaping creative initiatives with AI. They use these technologies for directing, recommending, and creating content. For example, working on behalf of Kayak, Supernatural used AI to find inspiration and direction for advertising copy and other marketing materials. According to Kayak leadership, the campaign has been one of the company’s most successful in creating favorable attitudes toward its brand.

How does AI support media and campaign teams?

Media and campaign teams are further along in their implementation of AI. While AI is integrated into the tools used by many campaign managers, media planners, and digital marketers to run ad campaigns, AI platforms are also widely available to support campaign management, measurement, and optimization. Some of the more well-known use cases of AI within the media and campaign field are:

Budget allocation 

An ideal method for optimizing the allocation of marketing spend across channels is marketing mix modeling (MMM), also known as media mix modeling. However, this method has traditionally been challenging to execute at all but the largest companies. It is technically complex and requires significant computational power. Today, though, AI has been used to complement traditional MMM methods and dramatically accelerate and simplify its usage. 

MMM is now much more accessible to a wider variety of companies who want to better understand and optimize their budget allocation, even for channels that don’t provide data. AI-enhanced MMM is growing in popularity for marketing teams who seek a solution to the loss of customer-level data from digital platforms and want to build a better omnichannel strategy.

Bid management

Bid management is an essential part of campaign management and optimization. Historically, campaign managers manually adjusted bids on campaigns and keywords based on ad exposure and performance KPIs. Now, Google and Facebook use AI to adjust bids for campaigns and reach objectives quickly and efficiently. AI for bid management takes much of the guesswork out of bid adjustments, allowing campaigns to deliver efficiency.

Campaign management and planning 

Campaign management is a newly developed use case for marketing AI. For digital marketers, managing an ad campaign can takes weeks, if not months, to generate enough data to optimize. Additionally, many marketing teams still use data tools that don’t provide capabilities for agile, fast-paced decision-making with data. That means it’s hard to respond quickly to today’s volatile market and make data-driven decisions about what to do in the near future.

However, AI moves past that retrospective approach to data. AI-powered campaign management and planning tools help campaign managers predict campaign results sooner in the campaign lifecycle. Instead of waiting weeks or months, campaign managers can predict the future results of a campaign as early as day two after its launch. This predictive approach allows managers to invest in the campaign, abandon it, or optimize it earlier, potentially saving significant ad dollars. 

Campaign measurement and optimization 

As more and more platforms become available for marketing, it’s hard to know how each channel influenced a user to purchase a product or fill out a form. AI supports campaign managers with advanced attribution capabilities. AI can connect the dots humans might overlook, resulting in better measurement and faster optimizations.

Campaign predictions 

AI is becoming more popular in marketing for predicting the outcomes of campaigns. Brands can now predict behavioral and transactional results. With the help of AI, you can predict how customers will respond to ad campaigns, when and how much a customer will buy, and the lifetime value they will generate for your company. By understanding customers’ likely behavior when reached with a campaign, you can better determine how to build on those predictions to shape campaigns and long-term outcomes.

How does AI support the marketing analytics team?

Not just the creative and campaign teams use AI. Marketing measurement teams have embraced AI to bring their measurement, analytic frameworks, and data to the next level. 

Predictive analytics isn’t new to many companies, and many marketing teams may already have tried to use predictive models. However, the typical approach to building those models has required the involvement of data scientists and MLOps engineers who handle data infrastructure and hand-coding of models. Marketing teams usually partner with in-house data science teams or external consultants to build predictive models.

Unfortunately, that relationship hasn’t always been straightforward. In recent industry research commissioned by Pecan AI, “The State of Predictive Analytics in Marketing 2022,” 40% of our survey respondents said that the individuals who build predictive models for them didn’t understand marketing goals. Data scientists also didn’t typically ask the right questions about customer behavior, and the finished models received by marketing teams were often built on incomplete or incorrectly selected data. 

In short, taking data science outside the marketing team has too often led to misalignment and poor results — not to mention the time and opportunity cost often involved in waiting for these long-term projects.

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AI is perfectly positioned to help with this challenge. With the help of AI, marketing analysts who know the marketing team’s data best and understand the team’s goals can use their existing data expertise to build predictive models. Automated machine learning can transform their questions into predictions in days, not months, eliminating the communication and data challenges involved in using external data science resources.

With predictive models at their fingertips, marketing analysts can provide the future perspective needed to inform conversations and optimizations. Better decisions about those matters mean marketing goals can be reached on time and within budget.

What Are Popular Uses for Predictive Analytics in Marketing?

In our research, we also asked marketing leaders which AI-powered capabilities they wished they could access to build an AI marketing strategy. According to the study, predicting churn/retention, forecasting customer lifetime value, and identifying cross-sell/upsell opportunities are the top three predictive analytics models respondents would like to access.

Pie chart of survey data of marketers' desire for AI capabilities

How can these models be used in marketing?

  1. Customer Lifetime Value: AI and machine learning make it possible to predict customer lifetime value for new customer relationships. With predictive LTV software, brands can deepen customer relationships and identify VIP customers for exclusive offers and special incentives. 
  2. Customer Churn: Predicting churn helps you stay ahead of your marketing campaign metrics and reduce your churn rate. Predictive churn software can help detect 85% of customer churn from downgrades and cancellations. As a result, you can improve retention rates by as much as 35% and lower revenue lost due to churn. 
  3. Customer Winback Campaigns: Predictive analytics can support more accurate audience segmentation. You can create more specific offers and communication plans with a stronger understanding of each segment’s needs and likely behavior. For example, you could use this approach in email marketing to reactivate lapsed customers. You can win back more customers and increase customer lifetime value through this nuanced approach. 
  4. Cross-Sell/Upsell: Predictive analytics for cross-sell/upsell is one of the best models for AI predictions. Predicting when customers want to increase their service will help you reach your revenue goals with expansion opportunities and drive a deeper understanding of the customer journey. 
  5. AI-Based Behavioral Targeting: Predictive analytics allows you to generate highly accurate lookalike audiences based on the behaviors of your most profitable customers. The potential of this capability extends well beyond the basic tools offered within ad platforms. With rich customer data, benefit from predictions about which customers will behave in the ways you’re most interested in identifying.
  6. Campaign Attribution: Predictive analytics is growing in popularity for campaign attribution. Today, brands seeking to navigate customer privacy changes are using media mix modeling strategies and predicting conversion rates. Maximize your marketing data’s value, improve attribution precision, and better reach customers in their preferred channels.
  7. Lead Scoring: Many businesses have moved away from manual scoring of leads (predicting the likelihood of conversion) to automated lead scoring that applies machine learning and AI. This approach minimizes errors and increases the usefulness of data by highlighting behavior patterns that humans can’t manually identify. Machine learning allows lead scoring models to be continuously updated and improved, keeping your scoring model relevant even as conditions change.

How to Get Started with AI

While AI has become integral to some marketing departments, it’s time for AI to take on more prominent roles within more organizations. HBR points out that CMOs must understand how AI is changing the marketing landscape through the various applications available today. However, they must also stay on top of how innovations expand to include additional use cases. 

To get started with marketing AI, businesses should follow three steps.

First, identify how AI can help support teams as they seek to reach goals and grow revenue. For example, AI can help marketers better segment customers, deliver personalized messages, and target customers with relevant ads.

Next, businesses must develop and implement an AI strategy. This process involves setting a budget, determining which AI tools to use, and creating an implementation plan. Companies should also consider investing in data infrastructure, as AI tools rely heavily on data. Reducing data silos and integrating systems may also be a priority to ensure data projects proceed quickly and offer value across the organization.

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Once businesses create and use an AI strategy, they should track and examine the results. Ongoing and continuous review of AI programs will allow marketers to make informed, proactive campaign decisions while keeping AI projects aligned with the business. 

It might seem that hiring a whole new data science team for marketing is necessary for the organization to succeed with such efforts. But in reality, various software platforms now make it possible for marketing teams to use their existing data expertise to gain AI capabilities. 

Combined with our experts’ support, Pecan’s AI-powered platform offers precisely this streamlined, cost-effective, and rapidly implemented option. Get in touch with us if you’d like to take a closer look.

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