The Benefits of AI on Marketing

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Hello everyone! Last week, I shared with you that I wrote a little eBook about AI that you can purchase on Amazon Kindle. Just in case you missed last week’s podcast, the name of the book is called The Modern AI Marketer: How to Leverage Artificial Intelligence in Digital Marketing.

The book was written for marketers to understand what AI is and how marketers can apply it in the work they do. Like I said last week, modern marketing is grounded on using technology to reach out to your audience digitally.

You can’t scale, segment or personalize your marketing without technology. That’s why technology plays a critical role in marketing. In this eBook, I touched on many things, from the definition of AI, a bit of history, benefits, and use cases to the expectation that you need to set if you for implement AI.

So for this podcast, I want to pique your interests on the marketing-specific benefits of AI.

When people talk about AI benefits, they tend to mention AI benefits in very generic terms. For example, AI can:

• Enhance data analysis such as predictive analysis
• Offer recommendations and predictions
• Automate your repetitive tasks

But what are the marketing-specific benefit using AI? Well, there are three from my perspective.

Here are the true benefits of using AI in terms of marketing

1) Gain a Deeper Understanding of Your Customers

Say, you have a large dataset on how customers engage your website. If you want to learn what types of content people usually read before they contact your sales team on the website, you can build a model by feeding your existing dataset and see the recommendation from AI. Of course, you can compare your findings with your gut or account-specific data to understand the differences and similarities between the AI’s recommendation and your recommendations.

Use that information to refine your content editorials, in addition to keyword optimization.

2) Personalize engagements with your customers

With digital obviously everything we do is tracked—we are completely tracked—so you can use third-party AI-based tools (there is a lot of software with machine learning or deep learning capabilities built into it), marketers can learn exactly what customers are doing in real-time.

There are 3rd-party tools which can analyze customers’ posts and online conversations to help marketers understand their sentiments, desires, needs or even pains. I mentioned Alyce in the past. Alyce is an AI-based gifting platform which can crawl your target audience’s social profile and come up with specific gift recommendations that you can send to get their attention.

So a lot of stuff moving forward in order to scale, personalize and segment, you need AI to help you and AI is one technology.

3) Improve customer experience through journey mapping

We do AB testing, use heatmaps or even focus group testing to gather information so that we can understand our customers better and improve their customer experience.

What if we can build an AI model by working with your internal data team or work with a data analytic vendor to analyze customer journeys! This obviously will require you to integrate your outbound marketing campaigns to your website analytics. I understand Google Analytics and various third-party tools are doing that. However, you can take the data further by merging with other datasets and also external data to understand, say, the time-lapse from the first visit to the most recent visit on the website, the types of content or pages consumed, the product demo viewed online or sales email exchanges to see the customer journey by industries or by personas.

Then, you can use the information to optimize the touchpoints, maybe use the data to predict or anticipate what the customers will do in the future.

Here is the takeaway:

I’m not here to tell you how to build your AI model or you need to do that. To really look into AI in terms of how AI can help you, the key is to know what questions you want to ask and then find a solution or build a model to answer your questions. Does that make sense? But I’m sure that you can use other approaches to get the answers as well

So here’s a list of questions to think about building an AI model:

• Based on our account history and payment transaction data, what do the top 25 new accounts look like? What is the average transaction size? How does that compare with sales’ own top 25 account list? Are they similar or different, and why?
• With our existing lead scoring, what minimal score do prospects usually reach in order to get them to request a demo? With that score recommendation, what is the average success of closing a deal?
• What marketing channels drive the highest quality MQLs (Marketing Qualified Leads)? How much money can I allocate to that channel to see an improved rate of return?
• How many pieces of content do prospects consume before they contact us? What are they? How are these content pieces different based on different industries or personas?

To me, the biggest benefits of AI for marketers lie in personalization and recommendations. We all know how to do mass communications. But for the next era of marketing, though, AI holds the key to providing insights and recommendations to do even more effective mass customization.

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Before you leave, make sure to check out the previous podcast episodes.

Be well. Let’s talk again next week. Take care!