Sometimes, Marketing comes across as one of the most tone-deaf, “throw a dart in the dark”, “spray and pray” type of activity – driving its receivers nuts and the executives funding the marketing activity, to exasperation. No wonder response rates tend to stay low, and Sellers are left wondering as to where the promised Marketing air-cover and support is to help them meet quotas.

At other times though, Marketing is so persuasive, personalized and compelling – that the product sells itself, leaving corporate leaders to wonder if they really need a Sales team to close deals.

What makes the difference? Two words – “Customer Context

Understanding this context and tailoring the content and offers to meet the specific needs of that customer, makes all the difference between marketing “noise” and “thank goodness you heard me – please tell me more”.

But how do you get a clear understanding of this Context? The traditional answer has always been, Human Intelligence. If you have someone focused on a particular customer, this person will learn a lot about the customer’s needs and pain points such that all subsequent marketing can be on the mark, all the time.

Needless to say, such an approach simply does not scale.

The next best solution, popular to this day, is to abstract the common pain points across multiple customers and design “journeys” that the customers could be placed on. Email journeys, website journeys, social platform journeys – are all designed through the mindset of these common needs and desires of customer segments. Such journeys can be effective, if created with a lot of care and attention, addressing the most pressing questions on the customers’ mind.


Can AI do one better?

The brilliant aspect about all forms of AI/ML techniques is that they are designed to identify patterns of behavior regarding a customer and identify rules that begin yielding clues about the customer’s context.

For example, applying Cluster Analysis to understand which types of behavior occur most frequently together, and Associative Analytics to unearth rules regarding patterns of experience consumption, can begin highlighting key ‘Moments’ in the customers’ purchase journey. Sequencing out these patterns on their path to purchase can begin giving a general sense for how these ‘Moments’ begin to follow each other on these journeys, allowing us to better understand where the customer is and hence their particular Context. These ‘Moments’ can be called ‘States’ and the model can be powered by the concept of ‘State Machines’. Knowing the State of the Customer gives us immediate clues as to the type of experience they are most likely to respond to – greatly improving Customer Experience and reducing wasted marketing spend.

Readiness to engage in a sales conversation” models, on the other hand, are like a propensity model that take into account Customer’s prior history (recency, frequency, purchase) with the company, Customer’s current needs, Company’s position regarding specific offerings in the industry, and Customer’s recent engagement with the Company. Such propensity models that take the Customer Context heavily into account tend to provide extremely good signals regarding the Customer – and avoid the frustrations of “cold calling” or incorrectly timed calls. The secret ingredient again is the Customer Context.

Propensity to Purchase models, similarly, rely heavily on a thorough 360-degree knowledge about the customer, their prior purchases (and possible approaching replenishment cycle), competitive positioning, active projects at customer sites and more. The richer the Customer Context, the better the model performs.

Now, if the AI-model has been built that is light on Customer Context – the upside delivered is limited as well. For example, if you build an AI-model for selecting target audience for sending emails to – you could simply look at historical “Open Rate” or “Click-through Rate” and find those members of the target audience who are more likely to open the email or click on one of its links. Nothing wrong with such a model – but it is selecting an audience based on a very tactical action, as opposed to putting in enough of the Customer’s Context into the model. The same model will yield superior results if it incorporates an understanding of where the customer is on their purchase journey, if there are active projects at the customer site, when do they typically use email when researching a project and more. The more the model is informed by relevant customer context with regard to using an email, the greater the success the model will have in delivering audiences that want to engage via email right at that time.

As may be becoming clear, the power of applying AI in Marketing is really the power of bringing Context into Marketing, in a scalable and automated way – and that makes all the difference between an indifferent customer reaction and one where the customer says “YES!”


Posts in this series:

  1. Can Marketing be completely AI-driven & Automated? A Genesis
  2. Using AI to identify your Target Customers – automatically!
  3. AI-Marketing is Context-Aware Marketing. Period. (this post)


Sri Srikanth

Advanced Data Analytics & Strategy, Senior Data Scientist, Cisco Digial