5 Ways Artificial Intelligence and Machine Learning Will Improve Marketing ROI

By David Ronald

Marketing is going through a profound transformation.

A recent BCG report indicates that 70% of CMOs have already integrated generative AI into their practices, with an additional 19% currently in the testing phase. 

Artificial intelligence and machine learning are already having a transformational impact, and this will only increase in the coming months and years.

This is both exciting and unsettling!

The Benefits Are Almost Limitless

When it comes to better marketing campaigns, increased productivity, and accelerated growth, the potential benefits of leveraging artificial intelligence (AI) and machine learning (ML) are seemingly limitless—and valuable use cases for every stage of the marketing cycle are already available today.

Here are five use cases for AI and ML that can have a positive impact on marketing:

1. Segmentation—Identifying and grouping audiences to tailor campaigns and power faster and attain actionable audience insights.
2. Personalization—Elevating marketing and advertising impact with better Customer 360 perspectives.
3. Lead Scoring—Ranking prospects by value and sales-readiness, and power automation to streamline lead scoring and improve productivity.
4. Forecasting—Predicting the sales pipeline with greater accuracy and optimizing current pipeline in near-real time.
5. Attribution—Measuring the impact of different campaign touchpoints more accurately.

I'll look at each of these in more depth:

1. Segmentation

Segmentation is crucial for helping marketers identify and categorize groups of accounts and people within a broader target audience and then tailor campaigns to each segment’s interests and behaviors. 

Segmentation is foundational to personalization (which is the topic of the next section) since it provides modern marketing departments with an accurate picture of their customer base and equips them to deliver tailor-made messages to various audiences.

Here are some of the advantages that can be derived from using AI/ML for segmentation:

• Speed—Segmentation is exponentially faster when implemented with AI/ML than manual segmentation, because an AI can peer through virtually limitless data points nearly instantly.
• Precision—Marketing teams can analyze a much higher volume of data to segment more precisely segmentation with AI/ML and deep analytics.
• Increased revenue—An AI can boost an organization’s profitability by enabling marketers to identify and target their most valuable customers with the most pertinent marketing messages.

Segmentation can also increase customer loyalty and satisfaction by surfacing segment-specific opportunities or risks that can be addressed more granularly. 

AI/ML segmentation can, therefore, help organizations optimize profitably by not only precisely identify the segments that are at risk of churn, but also automate corrective measures.

2. Personalization

Marketers need to deliver personalized campaigns aligned to an increasingly diverse customer base. To do this requires understanding their customers holistically.

One of the proven ways to do this is by achieving a Customer 360 perspective. This requires aggregating and unifying customer interaction touch points from the full breadth of their enterprise data—encompassing online transactions, social media posts, forum comments, in-store engagements, third-party interactions, customer support and more. 

Marketers now have exciting new ways to personalize customer touch points and create new, hyper-personalized experiences, including the ability to do the following:

• Generate large volumes of creative content, including images, videos and innovative, immersive 3D experiences, for brand advertising and other marketing channel content.
• Micro-target campaign tactics based on individual customer behavior as well as persona and purchase history. 
• Personalize outreach via chatbots, virtual assistants, dynamic content and personalized ads and recommendations.
• Identify “next best action” sales opportunities that are tailored to a particular customer’s needs, at a particular moment in time and on a particular channel.

Marketers to run personalized campaigns more precisely than ever before by applying AI/ML to their data.

3. Lead Scoring

With data from multiple sources, marketers can help build a more objective view of each lead, uncover fresh customer insights and gain a better overall understanding of targets. 

Lead scoring can, however, be time consuming and prone to error, since many methodologies still require manual inputs. 

Scores can be inaccurate due to several issues, all of which revolve around data—ingesting large quantities of data from multiple sources can be particularly challenging and data from different sources often lives in different applications and data repositories, so it isn’t readily available for analysis.

Given these challenges and the ever-growing volume of data involved, ML-powered automation has become critical for lead scoring. With machine learning, marketers can deliver an automated system that learns over time and updates automatically using a constant input of data from multiple sources.

4. Forecasting

Company growth is tied directly to an organization’s ability to maximize its sales pipeline with efficiency. But how do organizations see what’s around the corner and plan resourcing needs to be ready to capitalize on pipeline opportunities with speed and accuracy?

The answer is robust and precise sales pipeline forecasting, and AI/ML makes accurate and objective pipeline forecasting a reality. 

An end-to-end machine learning model can be fully customized to incorporate all relevant organizational and customer data, produce near real-time pipeline predictions, and be accessible to all business teams for better organizational alignment. 

5. Attribution

Marketing attribution allows marketers to track which campaigns and touchpoints customers interact with prior to closing an opportunity. 

Without being able to attribute results to campaign tactics, marketers are operating in the dark, spending time and money creating content or running ads or campaigns without understanding which tactics are driving the best results.

That said, accurate marketing attribution is one of the most difficult use cases in marketing, particularly in an era where customers engage with companies through multiple devices, channels and mediums.

Fortunately, data-driven attribution models powered by AI/ML offer increasingly promising options that create flexibility in their many cutting-edge attribution algorithms. 

Unlike common attribution models that are difficult to scale and often produce inaccurate or simplistic results for certain campaigns or channels, data-driven models allow marketers to experiment with a variety of ML algorithms that produce more accurate results about conversion rates at each touchpoint.

There Are Risks

Up to this point I've focused only on the positive aspects of AI/ML. 

No matter how compelling the potential business advantages are, the unprecedented rate of evolution of AI/ML requires that it be handled with great care—marketers must remain vigilant to mitigate the risks of infringing intellectual property, violating data privacy standards and losing sight of foundational security needs.

Buyer trust isn’t just an everyday issue to manage—it’s a foundational pillar of a modern, data-driven marketing strategy, and even a single AI “hallucination” gone uncaught could have crippling reputational and business impact. 

The risks are significant…but so are the rewards.

Thanks for reading.