In my Linkedin Pulse article here and my blog post here, I explained that the theoretical foundations or the basic systemic model of digital marketing are fairly simple! Marketers have to use multiple tools and channels to inform and educate customers as they go through the three stages of what is called the buyer journey. Marketers must seamlessly nudge customers along the buyer journey from awareness to decision – and provide them with all the relevant information so as to foster affinity with their brands. Integration and Engagement are the cardinal rules here – what marketers do in the awareness stage must be linked to what they do in the consideration stage, which in turn should be linked to what they do in the decision or sales stage.

Here’s the graphic illustration of the buyer journey from the Hubspot.com blog from a post by Lauren Hintz:

buyers-journey-example

(image source: https://blog.hubspot.com/sales/what-is-the-buyers-journey)

A Simple (Simplistic) Example of the Marketing Funnel

Start with Facebook or LinkedIn ads – make customers aware of the salient features of your value proposition. Attract them to your website or blog posts for more information. Write content so compelling that you should be able to gather the customers’ contact info. Now you are ready to use these leads to send your well-crafted sales emails! Ideally you should use a Customer Relationship Management or Leads Management system to gather your prospects’ information.

Here is a simple graphic illustration from the good folks over at the Utah-based Foxtail Marketing from their posts on B2B lead generation:

Sales-Funnel-01

(image source: https://foxtailmarketing.com/category/b2b-lead-generation/)

 

But there’s a HUGE catch … and that’s why you need AI

The marketing funnel above might have you believe that the customer’s buyer journey is straightforward and linear – and you can nudge her gently all the way to the sale.

The buyer journey of the modern customer is anything but simple or linear. And that’s the biggest nugget of knowledge you should take away from this post:

The buyer journey of the modern customer is non-linear. In the absence of some sort of predictive analytics, you can simply not predict what channels or information or in what form will work to nudge customers towards a sale

Will they connect with you on Facebook? Twitter? Pinterest? LinkedIn? Will they like long-form blog posts or shorter videos on your YouTube page? Exactly what path will they take as they go from awareness to consideration to decision and exactly what information will nudge them along?

These are questions that can make or break a business.

Enter Artificial Intelligence, more specifically, Machine Learning, a subset of AI.

Here is a brilliant summary from Think With Google, Google’s platform to offer the latest technological insights for non-tech users, from the post here:

A big part of the opportunity for marketers is how AI will help us fully realize personalization—and relevance—at scale. With platforms like Search and YouTube reaching billions of people everyday, digital ad platforms finally can achieve communication at scale. This scale, combined with customization possible through AI, means we’ll soon be able to tailor campaigns to consumer intent in the moment. It will be like having a million planners in your pocket.

We’re getting closer to a point where campaigns and customer interactions can be made more relevant end-to-end—from planning to creative messaging to media targeting to the retail experience. We will be able to take into account all the signals we have at the customer level, so we can consider not only things like a consumer’s color and tone preferences, but also purchase history and contextual relevance. And all of this will be optimized on the fly in real time.

The key ideas expressed above are:

  • tailor campaigns to consumer intent in the moment – which means instead of trying to nudge customers linearly within your marketing funnel and media channels of your choice, you instead focus on what the customer wants – does he want to watch more videos or read a blog post
  • campaigns and customer interactions can be made more relevant end-to-end – which means that instead of focusing on your campaign, media, and content, you can focus on what the path the customer wants to take to a purchase
  • take into account all signals we have at consumer level – what is the customer telling you by how much time he spends watching a video or how much time he spends on your website – or what his click-through-trail is through your website?
  • optimized on the fly in real time – the marketing funnel will effectively be optimized for every customer in real-time – something virtually impossible without AI and Machine Learning

The last point is the most important one you should take away – optimization of the marketing funnel in real time. AI and Machine Learning can ensure that despite the fact that the customer’s buyer journey is non-linear, we can cater to his needs by having AI systems design the optimal marketing funnel in real time.

The same article emphasizes:

AI and machine learning could get us closer to of one of advertising’s most-sought goals: relevance at scale.

In fact, relevance at scale is the holy grail for marketers in 2018 and beyond, as written about in a Forbes.com article here:

Machine learning techniques are being used to solve many diverse problems, and we stand to benefit as we move towards a world of hyper-converged data, channels, content, and context — having the right conversation at the right time with the right person in the right way. For us marketers, ML is about finding nuggets of “predictive” knowledge in the waves of structured and unstructured data.

The author goes on to highlight the key areas that will benefit from AI and ML:

I believe we will see a focus in four major areas:

Automated data visualization (including ML results) will become more rich, and user-friendly.

Content analysis (textual, lexical, multimedia/rich) will be used to drive better marketing conversations.

Incremental ML techniques will become more prevalent, leading to real-time, not just on-going and automated, changes in marketing execution.

Learning from ML results will accelerate the growth and skills of marketing professionals.

The author, Jeffry Nimeroff is the CIO at Zeta Global, a company whose SaaS-based marketing cloud helps leading brands acquire, retain and grow customer relationships. Zeta Global uses the term “Person-based Marketing”. From their website:

We make it easier for the world’s leading brands to master 1:1 marketing at scale—what we call Person-Based Marketing. Our Marketing Cloud combines individual-level data, marketing automation and AI to identify and engage the right consumers with personalized experiences that are relevant, valuable and inspiring. Our Data Cloud contains billions of unique profiles and signals that seamlessly integrate with our patented AI to help marketers acquire, retain and grow customer relationships that increase revenue, build loyalty and improve marketing ROI. Our Experience Cloud enables content creators like publishers and brands to deepen engagement, extend the conversation and increase lifetime value.

Zeta Global is still much smaller in size compared to two giants other than Google or Facebook – Salesforce and Adobe – both of whom are betting big on AI-enabled marketing. Salesforce has a new product called Einstein, which the company claims makes redundant data prep or model management:

From Salesforce.com page here:

Einstein features are now available across the entire Customer Success Platform. With Al embedded where you work, everyone can now have a data scientist working for them with no data prep or model management required. Your CRM constantly grows smarter, making you more productive and your customers happier.

On their part, Adobe.com writes on this page here:

End-to-End digital marketing

Adobe Marketing Cloud gives you the most complete set of integrated digital marketing solutions available. It provides everything you need to organize, access, and personalize your marketing content. It gives you deep insights into what’s working with your customers and the ability to consistently deliver the best experiences to every customer across every channel.

The key concepts resonate across all these platforms – end-to-end solutions, person-marketing, integrated digital marketing, personalized content, and above all, relevance-at-scale.

Internet and Mobile Technologies (Apps) must present an integrated, customizable, relevant-at-scale buyer journey experience.

What this means is that most standalone martech (marketing technology) and adtech (advertising technology) platforms will now gradually give way to AI and Machine Learning enabled end-to-end platforms that facilitate integration and engagement as customers prefer. That is the beautiful simplicity in this seemingly increasing complexity.

Quotefancy-24139-3840x2160

In the absence of sophisticated simplicity, the martech landscape is crazily populated by vendors. The (legendary) graphic below from (Scott Brinker) Martechtoday.com cramps key martech players by domain area into one slide. You can get a sense of how cluttered and confusing the landscape is. To read the original, very interesting article where you can enlarge this graphic, go here.

marketing_technology_landscape_2018_slide

I will sign off on this post here – because hopefully the snippets from the resource sites would’ve convinced you that as customers get more complex, their buyer journeys get more unpredictable, only the high technology of AI and Machine Learning can ensure that you can still deliver relevance at scale and nudge them across an increasingly complex marketing funnel to the point of purchase.

It’s not difficult to get started with AI. You don’t need a background in data science or coding. Most platforms like Salesforce Einstein offer a free trial. So … get started!

Good luck!

 

 

 

 

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