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How to Design an AI Marketing Strategy

Mark Schaefer

AI marketing strategy

Today I’d like to move beyond the tips and tricks phase of AI and marketing (although that is still a lot of fun!) and provide something more advanced — how to design an AI marketing strategy. Let’s step back from the hype and take a more strategic view of how AI will be applied to strategy.

The format of this post will be a little different from what you normally see from me. Since this subject is so new and my own perspective is naturally limited, this post will feature a curation of the best ideas from everything I could find (with my insights peppered in!)

I will cover:

Let’s begin!

The two types of AI for marketing

According to an article in Harvard Business Review, it is easiest to break AI down into two main applications for marketing. This helps us ease into a framework.

1. Task automation

These applications perform repetitive, structured tasks that require relatively low levels of intelligence. They’re designed to follow a set of rules or execute a predetermined sequence of operations based on a given input, but they can’t handle complex problems such as nuanced customer requests. An example would be a system that automatically sends a welcome email to each new customer. These applications can’t discern customers’ intent, offer customized responses, or learn from interactions over time. But this focus makes us more productive.

2. Machine learning

These algorithms are trained using large quantities of data to make relatively complex predictions and decisions. Such models can recognize images, decipher text, segment customers, and anticipate how customers will respond to various initiatives, such as promotions.

Machine learning already drives programmatic buying in online advertising, e-commerce recommendation engines, and sales propensity models in CRM systems.

These applications use data to make us more insightful and intelligent in our marketing.

Why is this important? A number of studies show that winning companies—those increasing their market share by at least 10 percent annually—tend to be early adopters of advanced sales and marketing technologies.

Standalone versus integrated solutions

In the early days of digital marketing, my company had to build everything on its own because there weren’t any integrated solutions. Today, a modern ERP like Salesforce has everything under one roof. Likewise, in the early days of social media, we cobbled together standalone apps but today, an AI-powered platform like Sprinklr offers an integrated social media marketing system.

We are in the very same era for AI. We are being deluged by one-off solutions, but the long-term solution will be integration. Keep this in mind as you assess today’s solutions. Be patient and keep an eye on what’s coming next. The future of AI marketing strategy will be a layer of AI applied to everything we do, not just standalone apps.

Before placing your bet on a host of new apps, watch carefully how AI is being implemented into your current systems.

Primary areas of focus for an AI Marketing Strategy

1. Consumer behavior

CMSWire claims the biggest generator of strategic growth lies in leveraging AI for consumer behavior analysis. The world is changing so quickly. AI will help us pinpoint shifts in consumer priorities.

A case in point is offered by Procter & Gamble’s Olay Skin Advisor, which uses deep learning to analyze selfies that customers have taken, assess their age and skin type, and recommend appropriate products. It has improved conversion rates, bounce rates, and average basket sizes in some geographies.

2. Predictive analytics

AI enables the rapid processing of massive amounts of customer data and information. Through these systems, organizations can decipher consumer patterns as they emerge, and further determine how these patterns will evolve into trends and sales patterns.

3. Personal versus personalization

Today’s personalization systems are not necessarily personal. AI will enable granular personalized experiences and customer journeys.

4. Customer experience

The low-hanging fruit of the AI marketing strategy might be customer service, especially as chatbots become more human than humans! Hyper-personalized content and offerings can be based on individual customer behavior, persona, and purchase history.

There are many gen AI use cases after the customer signs on the dotted line, including onboarding and retention. When a new customer joins, gen AI can provide a warm welcome with personalized training content, highlighting relevant best practices. A chatbot functionality can provide immediate answers to customer questions and enhance training materials for future customers.

5. Natural voice and language processing

I am already using AI transcription and translation services heavily. But this is just the beginning. Using AI applications to learn natural language models will enable innovations in customer service, ordering, and personalization.

6. Workflow automation

So much opportunity in content creation and approval, SEO, social media management, research, and team productivity!

AI can optimize marketing strategies through A/B testing of various elements such as page layouts, ad copy, and SEO strategies, leveraging predictive analytics and data-driven recommendations to ensure maximum return on investment. These actions can continue through the customer journey, with gen AI automating lead-nurturing campaigns based on evolving customer patterns.

7. Sales Effectiveness

McKinsey reports that AI can boost sales effectiveness and performance by offloading and automating many mundane sales activities, freeing up capacity to spend more time with customers and prospective customers (while reducing cost to serve). In all these actions, personalization is key. AI coupled with company-specific data and context has enabled consumer insights at the most granular level, allowing B2C lever personalization through targeted marketing and sales offerings. Winning B2B companies go beyond account-based marketing and disproportionately use hyper-personalization in their outreach.

At the top of the funnel, gen AI surpasses traditional AI-driven lead identification and targeting that uses web scraping and simple prioritization. Gen AI’s advanced algorithms can leverage patterns in customer and market data to segment and target relevant audiences. With these capabilities, businesses can efficiently analyze and identify high-quality leads, leading to more effective, tailored lead-activation campaigns.

8. Creative Applications

I am already seeing AI enable a surge in creativity. Once there are systems in place that allow us to generate content and images in a fair way that acknowledges original artists and creators, we’ll go to a whole new level of efficient and inspiring creative output.

These use case are the tip of the iceberg. Here is the result of a McKinsey study anticipating where we might have the biggest impact from an AI marketing strategy

Where to start an AI Marketing Strategy

The authors of the HBR article suggest looking at internal priorities this way:

  1. For firms with limited AI experience, a good way to begin is by building or buying simple rule-based applications. Many firms pursue a “crawl-walk-run” approach, starting with a stand-alone non-customer-facing task-automation app, such as one that guides human service agents who engage with customers.
  2. Once companies acquire basic AI skills and an abundance of customer and market data, they can start moving from task automation to machine learning. A good example of the latter is Stitch Fix’s clothing-selection AI, which helps its stylists curate offers for customers and is based on their self-reported style preferences, the items they keep and return, and their feedback.
  3. Look at where you have the largest amounts of data for obvious applications since most AI applications, particularly machine learning, require vast amounts of high-quality data. You can even tap into public data sources. I was able to do an analysis of marketing hotspots in my home state of Tennessee by pasting public census data into OpenAI.
  4. The biggest gains from AI will come from replacing human systems that rely on repetitive, high-speed decisions, such as those required for programmatic ad buying. In an ideal AI world, human decision-making would be reserved for the most consequential questions, such as whether to continue a campaign or to approve an expensive TV ad. This is where the greatest returns from an AI marketing strategy will be found.

McKinsey points to key indicators of a successful Ai marketing strategy:

  1. There is a clearly defined AI vision and strategy.
  2. More than 20 percent of digital budgets are invested in AI-related technologies.
  3. Teams of data scientists are employed to run algorithms to inform rapid pricing strategy and optimize marketing and sales.
  4. Strategists are looking to the future and outlining simple gen AI use cases.

Many sources describe the importance of constant experimentation, especially when it comes to partnering with startups. The AI landscape is evolving very quickly, and winners today may not be viable tomorrow. Test and iterate with different players, but pursue partnerships strategically based on sales-related innovation, rate of innovation versus time to market, and ability to scale.

Keeping customers first in the AI journey

Almost all of the sources I reviewed emphasized the importance of keeping the focus on the customer, not the technology. It’s easy for the AI enthusiasm to overwhelm the reason we’re here — customer needs.

Truth is, many customers fear how AI applications are collecting, sharing, and using their data, particularly ones that give away their location or are always listening. However, customers have also shown a willingness to let go of some privacy in exchange for the value that AI brings.

Marketers should ensure that AI applications have transparent privacy and security controls and that customers “get what they give” in exchange for their data. Customers should also be given the freedom to choose what data they are willing to share and be in control of how and when it is collected and used.

AI marketing strategy for the small business

Predictably, there is an AI platform gold rush going on. It seems as though there is a new AI service being developed and marketed every day. How do you make sense of what to do and create a competitive advantage through an AI marketing strategy … even if you don’t have an IT department?

The first thing you need to know: Don’t create an AI marketing strategy just to do it. As a small business owner, every decision to reallocate resources is vital. Tread carefully.

A big difference between applying AI to a big business to a small one is the lack of data. A big business needs to turn to the big data sources as a clue on where to start. A small business should look at productivity. Where is the low-hanging fruit to save money and time?

Start by reviewing your current marketing applications for new AI features. Look at current vendors of your marketing software systems to see what’s coming for you within the platforms you already use. As I noted above, it’s going to be much better to have AI integrated into systems you already have compared to new piecemeal solutions.

Here’s an example. Many small businesses use Canva for designing everything from logos to social media posts. Canva has incorporated many amazing new AI functions. This saves you the work of finding your own unique solutions.

Here are the five areas ripe for small businesses to apply an AI marketing strategy.

1. Idea Generation

ChatGPT is a champ for brainstorming. Try prompts like this:

… the possibilities are endless.

2. Customer Experience

Chatbots are becoming increasingly popular and practical for their ability to mimic human-like conversations and perform routine tasks. By using chatbots, small businesses can deliver prompt and seamless customer service, reducing wait times and cutting staffing costs. In addition, chatbots can learn from customer interactions, continuously improving their accuracy and effectiveness.

New tools have emerged for under $100 per month that allows you to upload your own content files to “train” your own chatbot.

Automation is another way to use AI for customer service. With customer service automation, businesses can automate routine tasks such as sending order confirmations and following up on support tickets, saving time and resources while ensuring consistent and timely communication. Some automation tools can enable businesses to integrate with other applications such as customer relationship management (CRM) software, creating a more efficient and streamlined workflow.

3. Customer Research

While some large language models like ChatGPT can’t necessarily create unique customer insights for a specific business, you can “trick” it into helping you define customer wants and needs.

My friend Andy Crestodina of Orbit Media recently provided a free webinar where he explained a very clever way to create a customer persona, save it, and then continuously query it to optimize your content and marketing tactics. You can find the webinar here.

Another trick I use … Ask ChatGPT to be the voice of your customer. Provide a lot of detail about what you know about your customers. Then ask it to tell you their pain points and problems. This could be a source of new product and service ideas.

4. Marketing Strategy (to a point)

I’m a marketing consultant, and I have to admit, I’ve used ChatGPT to help create strategic advice for small businesses! It’s not perfect, and it certainly lacks true insight most of the time, but when I describe a business and ask for a strategic framework, it has a decent response. Certainly a starting point.

Here’s a little secret: The foundational elements of marketing are similar across almost any industry. It’s a pattern, and AI is good at patterns. So if you provide enough detail on your business, you’ll probably get a decent outline.

Then you can call me to make it REALLY great!  : )

5. Content Creation

This is the low-hanging fruit for most businesses! ChatGPT and its AI cousins can create blog posts, images, social media content, videos, and so much more. Experiment with:

Be warned — there are still a lot of legal issues to work out here. Who owns this material? What can be copyrighted? This has to be sorted out over time.

One last warning. A lot has been written about the quality and accuracy of AI content. Honestly, I think there is a place for Ai content, especially for a small business with limited resources.

However.

In the long term, quality wins. Quality is imperative. To win at SEO and establish authority, you have to put some human effort and insight into your content.

So there you have it, a few useful guideposts on the road to an AI marketing strategy. Good luck!

Mark Schaefer is the executive director of Schaefer Marketing Solutions. He is the author of some of the world’s bestselling marketing books and is an acclaimed keynote speaker, college educator, and business consultant. The Marketing Companion podcast is among the top business podcasts in the world. Contact Mark to have him speak at your company event or conference soon.

Follow Mark on Twitter, LinkedIn, YouTube, and Instagram.

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