Artificial Intelligence 101 for Marketers and Sales: What To Know and What To Do

AI is everywhere, and everyone is talking about it, trying it out and having a wide range of experiences. Marketers are particularly interested in trying to figure out how to use it, what it’s good for, what it’s not good for and how it can save time while improving performance.

It’s a challenging environment with tons of new products, services and applications. It’s also presenting a lot of challenges for marketers who are leaning into it and finding mixed results.

Now companies like Google are working hard to balance human-created content vs. AI-created content and penalizing organizations that are leaning too far into AI-generated stuff. We’ve seen several clients realize a drop in organic rankings from their AI-generated content.

AI has a place for marketers, but without a strategy and a framework for using it, you might find it hurting more than it’s helping.

Here are some tips from the Square 2 Lab on how to use AI to keep your marketing program moving forward.

AI and Written Content Creation

The key to using AI to create content is not hopping on ChatGPT and asking it to write you a blog article. Instead, it’s applying a framework that helps you know when to use AI, how to use it for what application and, just as importantly, when NOT to use it.

That might sound strange, so let me illustrate how we’ve been thinking about deploying AI in the area of content creation and how it helped us drive efficiencies for both us and our clients.

Think about a pyramid. There are three layers – a large layer that makes up the base, a middle layer that is a little smaller and the top layer.

The Base Layer

This layer requires the most content. Here, quantity is more important than quality. For example, social posts – you need a lot of them across a variety of platforms and they are short.

Email content could fall into this category too. You need a lot of email copy to keep your email campaigns going, so AI content creation can be helpful here.

The Middle Layer

Consider this layer for content that needs to be longer, done less frequently and demand a higher quality. This category might apply a blend of AI-generated content with human intervention and human content creation.

This is important in two specific areas – blogging and website page creation. Google is already excellent at sniffing out AI-generated content and penalizing sites that are publishing too much of it.

This means you can’t have your AI bot create three blog articles a day in the hopes it bumps you up to the top ranking for your pet keywords or phrases. In fact, companies trying to do this often see dramatic drops in organic traffic across their site and pages. Don’t do this!

Instead, you can use AI to get an outline, supplement an article or page with specific filler copy and even develop a draft that a person goes through to turn into human-created content.

You gain time savings here without jeopardizing quality and getting snagged by the Google police.

The Top Layer

Finally, the top of the pyramid is where your industry-specific thought leadership and highly disruptive content finds its home. These articles, whitepapers, e-books, videos, tip guides, studies and podcasts need to be 100% human generated.

While you can use AI for your grammar and spelling, the ideas, the words and any visuals that go with it should be from your brain and used specifically to help tell your story, differentiate you company and disrupt the status quo in your industry to gain attention.

Manage these three different types of applications for AI-generated content and you’ll quickly have a content strategy and plan that drives leads, sales opportunities and new revenue for your company.

AI and Campaign Creation

When it comes to running successful campaigns, there are a couple of challenges that AI might be uniquely suited to help with.

The first is your campaigns need to be omnichannel, which means you must have them running on a variety of channels simultaneously. Consider paid social on multiple platforms, paid search, organic search, email marketing and even referring sites or partner marketing programs. This means juggling all this at the same time.

AI can be used to turn general messaging into platform- or channel-specific messaging by understanding the format requirements and automatically moving content from one platform to the next.

Next, campaigns have to be highly personalized. Leveraging AI to create more personalized elements that are part of your campaign can help them improve effectiveness.

Instead of just changing the name, you can now personalize by company, industry, pain/challenge, imagery, offer and more.

Finally, campaigns rarely run in full optimization mode out of the gate. Most campaigns take tuning, and that requires iterations. AI can recommend A/B tests across a variety of variables. Copy, creative, content and offer testing are all important parts of optimizing campaigns, and the more times you can iterate or cycle tests, the faster you can get your campaigns to optimal performance.

Using AI to recommend alternatives, manage the testing and deploy the different variations can be extremely helpful in most campaign optimization scenarios.

AI and Sales Execution

A Salesforce study found that AI is one of the top sales tools considered significantly more valuable in 2022 compared to 2019. One reason is because sales reps tend to spend most of their time on administrative tasks instead of on selling. HubSpot research shows that reps spend only about 33% of their time selling.

Artificial intelligence presents a compelling opportunity to improve this stat and level up your sales operation.

One of the best ways to use AI for sales is to add data to your existing CRM to provide insights that make your sales teams more effective and more efficient. For example, HubSpot offers a predictive scoring tool that uses AI to identify high-quality leads based on predefined criteria. This software also continues to learn over time, increasing its accuracy.

Several tools “listen” to sales conversations and provide conversational intelligence to help reps uncover insights buried in the dialogue they’re having with prospects. Let’s face it, sometimes reps are doing many things at the same time, like taking notes, recording information in the CRM and maybe even writing an email. Having AI listen to their conversations and highlight key points or takeaways for them to consider can be eye-opening and extremely helpful.

Tools like HubSpot and others like Chorus are good at picking up cues in the conversation, noting competitors that might have been mentioned and helping reps improve their overall skills across a variety of typical sales conversations.

Using AI to set up nurture sequences that can be triggered with the click of a button means no more babysitting the steps required to get someone to a meeting, follow up on a proposal or confirm schedules.

When you apply lead scoring, you can also use AI to help create more accurate forecasts. This takes the task away from the reps, who might be overly optimistic, and applies a model that takes into consideration conversations, data and sales process stages to forecast for them more accurately. This lets reps do more follow-up, have more conversations and uncover more opportunities.

With power comes great responsibility. This saying is very important when it comes to considering AI for anything marketing or sales related. You can’t just bust into a platform and start using it. You should go slowly to test and experiment. Make sure the promise of more or better is actually realized, and then consider weaving it into your operationalized marketing and/or sales execution.