Artificial Intelligence in the marketing funnel

A customer’s journey from becoming aware of your brand to the final action of purchasing is complex, but it can be understood in terms of a hierarchy of stages.

Unless your brand is a household name, you’re likely unknown to prospective customers. Even if you are well known, you might be offering a new product or addressing a new market, so you’re still starting from zero in terms of how your solution solves their problem.

Helping a prospective customer navigate from start to finish through your marketing funnel requires a strategic approach to messaging and how these messages are delivered.

Each stage of your marketing funnel is defined by how a prospective customer views your product and by the type of messaging and interaction that is delivered.

Artificial Intelligence (AI) has reached widespread use up and down the marketing funnel over the last few years. AI-enabled products take advantage of the predictive power of past results, automated language generation, and the quick production of personalized experiences. These techniques can help marketer’s optimize their funnel and solve two main challenges of designing a content strategy:

  • Content is hard to scale: It’s hard to create enough content to address the appropriate customer at the appropriate time. AI language generation, content personalization, and predictive analytics can help segment your users to determine and deliver the correct message at the right time.
  • Creativity becomes stale: All content can start to sound the same, especially from small teams. AI can help kick your writers out of their comfort zones and help them explore perpendicular lines of thought. This is especially useful for creating A/B test variations, trying new messaging strategies, and generating personalized content experiences.

Marketing strategies are often based on the AIDA model, which describes a customer’s journey through the buying cycle in four stages. Each stage can have a unique tone, interaction channel, and messaging cadence that reflects what the customer wants and how to move them to the next stage.

The traditional stages of the AIDA model are Attention, Interest, Desire, and Action.

Communicating with future customers can be drastically different depending on which stage of the marketing funnel they are in, and AI-enabled technology has its place in each of these customer stages.

Among the many categories of AI-enabled technology, marketing applications can be grouped into buckets and a few of them fit naturally into a typical marketing funnel:

The traditional AIDA framework has a few drawbacks and limitations, like any generalized model of complex systems. Not every customer is the same, and not every customer will travel through this traditional customer buying cycle. Some customers wake up one day, find your website, and buy your product. This is great, but most likely an outlier.

Frameworks like AIDA help make sense of the interactions between the average customer and a brand, which is the most important aspect of any marketing strategy. Edge cases are important, but should not drive the fundamentals of a strategy.

#1 Get your customer’s attention

Attracting attention at scale takes a great deal of creativity and output. You need a lot of content, and it needs to be eye-catching. You need to make a customer who has no idea who you are take notice and remember you down the line.

Boost creativity

Improved scalability and creativity helps you here. This stage is all about brand awareness, so creating memorable experiences is critical.

AI language generation can help you turn one piece of content, whether it’s a video, case study, or blog post into 10-15 supporting assets that can be distributed across all channels.

Writers block and stale creative output can throttle this stage because customers are used to seeing the same type of content over and over. The content generated by AI platforms can seem very weird, but marketers can use this to their advantage to create some out-of-the-box messaging strategies to see what leads to efficient engagement.

Example: Automata offers an asset expansion product that helps generate marketing copy at scale based on existing assets like webinars, case studies, blog posts, etc.

Make data-driven predictions

Being able to predict where your customers are and what type of content they respond to is also a task for AI-powered predictive analytics products.

Your past data should be leveraged to inform future plans. Patterns can emerge that are not always easily identified in a manual process. AI tools and machine learning can help identify messaging and channels that work best for a specific customer and product.

Example: DivvyHQ explains how predictive analytics can be used as part of a content marketing strategy.

#2 & #3 Retain your customer’s interest and generate desire

Generating interest for your product or service is a matter of holding your customer’s attention after the initial attention grabbing headline.

This is where a real partnership between AI language models and masterful marketers comes into play. Your messaging needs to educate the user to make them want to learn more about your solution and to leave feeling like they may have found an answer to their problem.

AI-powered content personalization

Personalized content that varies depending on a customer’s profile is a great way to keep your customer’s attention and to make them feel like you have the perfect solution for them.

You prospective customers might vary wildly depending on where they come from. If they found your solution from a Twitter campaign, they might be looking for a different experience than if they came from an outbound cold emailing campaign targeting enterprise executives.

The amount of data you have on anyone that visits your website is growing every year, and it’s important to use that information to deliver personalized experience to maximize your chance at retaining their interest and converting them from “I’m interested” to “I want this.”

Example: Optimizely offers scalable content personalization that reacts to customer profiles in real time.

Conversations at scale

Everyone has had a digital conversation with a chatbot. Sometimes the experience is great, and other times there is something seriously lacking.

However, interactions with real humans can also drive prospective customers away due to various predisposed ideas about sales and marketing tactics.

Conversational AI systems in the form of chatbots are a great way to engage with prospective customers and to help them convert from interest to desire by providing them with the resources they need to understand how their specific problem can be solved with the offered solution.

Example: Drift offers AI-powered chatbots that aim to replicate observed interactions between customers and successful sales reps.

#4 Move your customer to take an action

Every marketing asset has some associated action that you want the consumer to take. You may just want them to remember your brand down the line when they consume another piece of content, or you might want them to actually make a purchase.

The final stage of the funnel is an attempt to get a prospective customer to pull the trigger. You need them to respond to a call to action that results in real revenue.

Figuring out what that call to action should be is a matter of trial and error, and this is where many marketers implement a rigorous set of A/B tests to identify the messaging and format that will minimize the friction between an interested visitor with a desire to purchase and their ability to make that final decision.

The final stage is where marketers should use all the tools at their disposal. Having conversational AI systems like chatbots readily available, delivering personalized experiences and recommendations specific to that customer, and being able to identify the perfect call to action will give you the best shot at closing out the funnel with a successful outcome.

AI-enabled products are a partner, not a replacement

The purpose of AI-enabled products that fit into the marketing funnel is to help make the process more efficient and to make marketers look good. There is no scenario where an AI-enabled product will completely replace a marketer who understands her target market and can spin out engaging and educational content.

Like any complex business process, there are hiccups and places where customer movement through a marketing funnel can improve and many AI tools can help marketers deliver higher performing campaigns.