Responding to Needs First
When face-to-face or on a sales call, we naturally adjust what we want to say according to the caller’s level of interest in the subject and topics.
This is done entirely naturally; it’s justly courteous. It happens to also be obviously in our own best interests.
We’re typically too busy servicing the call to notice how easily we wrap the conversation around ‘them’. In my experience, you need to answer their questions and discuss their interests before steering the conversation.
The Translation Problem
Every website visit is an inbound sales call.
Wrapping conversations around visitors is so obviously the right thing to do. It surprises me how difficult it is to explain the benefits of doing that when written down. Maximising opportunities, developing their interests – why wouldn’t we?
Because we didn’t know how. Because that’s not how we used to do it. But the main reason, I finally realise now, is that we lack a common language to get it done.
The language of marketing and sales.
Why Start a Model from Scratch?
The operative phrase in the opening paragraph regarding callers is “subjects and topics.”
Those things don’t have sufficient meaning for those of us in the marketing and sales community. Until we agree on what those mean, we lack the language to map sales conversations in advance.
How were we supposed to plan and build websites to serve different levels of interest if we couldn’t define those interests and levels in the first place?
As an industry, we have spent far too long being distracted by product keywords. The people who build websites are not listening to visitors. They are coaching clients to express their ego.
We needed a marketing and sales language model that could decode what visitors actually wanted.
The Discovery That Changed Everything
We needed to understand how people conveyed and communicated their commercial intent. Where could we find thousands of examples of people expressing what they wanted, when they wanted it?
Search queries became our laboratory. When people search, they’re already interested – they write down precisely what they want at the moment they want it. This makes search language the most refined source of commercial intent available.
In trying to understand which search terms were most valuable for our clients, we got obsessive.
Over thousands of visits, you learn to recognise value in word patterns, especially if you go to the pain of separating them to measure them.
The difference in commercial value was consistent and enormous.
Ranging from,
at best about 2 leads in 10 clicks,
at worst about 1 in 1000 or none.
Some keywords were consistently worth hundreds of times more than others.
Insight here
It wasn’t ‘keywords’ ..but keyword components that affected the value. We had to categorise types of individual search keyword components to understand why.
The Three-Component Language Model
We discovered you can classify every word into three categories:
Market words – The ‘thing’ they want help with
Journey words – Where are they in their decision process
Audience words – Who they are
The first one determines the visitor’s primary interest – what the conversation should be about.
But the words are not valuable unless combined with the other two types. Inherently ambiguous market words get activated by journey and audience specificity.
This became our new model of buyer intent.
The intent of prospects to want to do things is inherent in the word specificity.
An ontology – a classification system within each word type – allows you to see the relationships. The ontology is what structures and builds the predictive maps.
What This Language Precision Enables
Once decoded and ‘modelled up’, you can predict the needs for continuing any conversation and the questions to ask. The semantic model we developed helps structure what seems to be unstructured data.
When you have prediction, structure and process, you can automate.
Structured marketing and sales conversations can be planned ahead, whether face to face, written down, or via chat window or voice AI. Like a choose-your-own-adventure book, their choices determine the next content.
Conversion is in our future
More importantly, this language precision revealed why most websites fail to convert: they’re structurally too shallow. Why Websites Don’t Support Complete Market Conversations → The content architecture insight that changes everything
The patterns become apparent when you separate words into the three types – something that hadn’t been done before in marketing and sales.
The AI Multiplier Effect
Hopefully, this language model will become the foundation for AI applications that understand commercial contexts.
When AI systems can recognise market, audience, and journey signals in customer language, they can respond with commercial precision rather than generic information.
The same three-component analysis that predicts keyword value can enhance:
- Voice AI conversations that adapt to caller intent
- Chatbot responses that match visitor readiness
- Content automation that serves specific intent combinations
- Sales coaching based on real-time language pattern recognition
What We Learned About Language and Intent
The buyers’ intent has always been encoded in customer language. We were blinded by focusing on individual words rather than word-type patterns.
Building this language model taught us that precision in terminology isn’t academic – it’s the foundation that makes systematic marketing and sales possible. Without agreed-upon definitions for market, audience, and journey concepts, everyone builds on different foundations.
The framework works because it gives us a common language to describe what customers want, who they are, and where they are in their decision-making.
Everything else – better websites, more effective content, predictable conversion, intelligent AI responses – follows from that language precision.
This language invention story explains why we had to create the Market Audiences Framework from first principles rather than adapting existing marketing models.
