In this article, we look at
- Decision Narrative Overview
- Decision Narrative Anatomy
- Long-Tail Decision Narrative
- Target Decision Narrative (TDN)
- Target Decision Narrative-Product Position (TDNPP)
Decision Narrative Overview
A decision narrative in AIPR is the collection of variables that must be satisfied before a buyer can confidently move forward with a decision.
Humans make decisions differently depending on the objective.
Some are more emotional, some are more rational.
Arguably, it could be said that ultimately, all decisions are tied to emotion.
Something could rationally feel correct, but not “feel” right or compelling.
Something could feel compelling, but rationally right.
The actions humans take are sometimes instinctive – without much thought – and some are highly rational and reflected, long-term decisions.
In the context of AIPR, both are important.
Rational narratives can sometimes become technical in nature. For instance, “how fast a car can accelerate” is signaled by horsepower, torque, and other indicators. A consumer searching for a fast car may examine these details, and AI is likely to know the technical implications of a “fast car”.
Rational narratives can also be experiential and emotional – for instance, one might describe a software to be “easy to use” or “helpful for my project” – these narratives come from the overall experience of using a product for the intended purpose.
So there is some overlap between emotional and rational narratives.
Emotional narratives can include phrases like “it was pretty”, “I like the color”, or “tasted great”.
For this reason, Narr Theory narrows its research to certain product sectors where forming and applying AIPR strategies can have more tangible outcomes rather than purely emotional brands.
It is likely the more rational decision narratives that can see visible changes in outcomes by applying AIPR strategies rather than purely emotional narratives.
Decision Narrative Components
For Narr Theory’s research focus, we set the premise that “every purchase decision begins with a problem or opportunity encounter.”
Constraints
The body of decision narratives consist of two main parts:
- Primary Decision Narrative (PDN)
- The primary reason for product search
- Secondary Decision Narrative(s) (SDN)
- Every other factor(s) that can influence the purchase decision
Primary Decision Narrative (PDN)
When a person encounters a problem, the primary narrative of the solution (product) becomes how well it can solve the problem.
“I need a car for work.”
“I need shoes for running”
“I need a computer for working”
The primary narrative is centerpiece to why a user is seeking a solution.
Like the examples above, it could be said that the primary narrative is almost always what is followed by the “for” in “I need a car for…X”.
Primary Decision Narrative (PDN) Composition
What composes the narrative, “car for work?”
- Everyday usage
- Fuel efficient
- Comfortable
- Easy to maintain
- Drop off kids
- Space for equipment
How about for “shoes for running?”
- Advertised as “Running shoes”
- Comfortable for running
- Breathable for feet
- Good fit for the feet shape
- Durable for long-term
Secondary Decision Narrative (SDN)
SDNs are other narratives that also influence the decision narrative alongside the PDN.
- “I need a car for work” – PDN
- “I also want to use it for camping” – SDN
- “I want it to look good” – SDN
- “I also want to modify and upgrade it” – SDN
- “I might resell it later” – SDN
Weighing
SDN may not logically be as important as the PDN, but can sometimes override by PDN.
For instance, when buying a “car for work”, even though “fuel-efficiency” may be one of the components of the primary decision narrative, a car with “nice exterior” may outweigh the fuel-efficiency part of day-to-day usage.
In AIPR, AI tends to understand the weighing of decision factors not just for product recommendations, but for decision rationale in general.
Depending on the AI model used, this type of reasoning capability can vary.
It is Narr Theory’s hypothesis, however, that LLM creators have the motif to improve their AI’s reasoning capability as one of the core focuses for general AI adoption.
Tradeoff
Tradeoff describe how AI compares how different product options may satisfy one criteria better at the expense of another.
For example, a car may be more “fuel-efficient” at the expense of “higher price”.
This is a tradeoff between fuel-efficiency and price.
Tail of Decision Narrative
How many different factors go into a decision narrative determines the length of the tail.
Short-tail Decision Narrative
“I need a car for work under $60,000”
In this narrative, there is only:
- Car for work – PDN
- Under $60,000 – SDN
There would be many cars that fit this criteria.
Long-tail Decision Narrative
“I need a car for work. I’ll use it everyday, so it should be fuel-efficient. It snows a lot in the winter so I need a larger car that can handle the deep snow. I have to drop off the kids in the morning so I need a spacious backspace like an SUV. I want it to be easy to maintain. Preferably red or white under $80,000.
In this narrative, we can observe
- Car for work – PDN
- Fuel-efficient
- Easy to maintain
- Drive in the snow
- Larger car
- Spacious backseat
- SUV
- Color
- Red or white
- Under $80,000
This is a much longer-tail decision narrative, where available product options will narrow.
Target Decision Narrative (TDN)
Every product should define what their Target Decision Narrative(s) (TDN) are.
Meaning, when “our customers ask AI for a production recommendation using this decision narrative” our product should be the top choice.
In short, we can refer to these as TDN or even “target narratives”.
Target Decision Narrative-Product Position (TDNPP)
TDNPP is where your product’s position in relation to the TDN and AI’s ultimate product recommendation.
It is sometimes referred to as narrative-product position in Narr Theory as well.
When your exact TDN is used by the customer to find a product in AI, is your product being recommended? Considered?
Ideal Position – The Defender
If your product is being recommended by AI (consistently) for your target decision narratives (TDN), your product is at the top AIPR position. There is no visible gap at the moment.
- This would put the product in a defending position, guarding competitor products from being recommended over your product by AI in the future
Recommendation Gap
If your product is mentioned as one of the candidates, but not recommended (consistently) – this would be the first gap in AIPR – the Recommendation Gap.
If your product is being considered, this is a good start. But why would another product be recommended over your product? Is AI seeing stronger corroboration evidence for your competitor’s product for the same narrative? Look at the sources that AI shares upon the final recommendation.
Consideration Gap
If your product is not being mentioned by AI at the Target Decision Narrative (TDN), it may be worth investigating why. Even if your product is not the “top recommendation”, it should at least be considered if it matches the TDN directly.
But how do we know if AI is “seeing” the product but not considering it vs. not seeing it in the first place?
We can ask AI something like “name me 10 products that fit my criteria”. If there are more than 15-20 products in the TDN, personally, I would consider this a crowded market. If there are more than 100, it is a saturated market.
- This is called the Product Naming Question (PNQ). It specifically asks AI to name X number of brands, so it gives you a sense of what products AI is capable of discovering at the TDN when asked directly.
- If your product does not show up at the first 10, try 15, try 20.
- Notice how this is not 10 products in a “target market” – it’s a single “Target Decision Narrative”. It is much narrower than your target market.
- The correct way to use this gap analysis would be to choose specific, realistic long-tail decision narratives that your customers may be using to describe their ideal solution in their natural workflow.
- This way, your product niche can be sectioned into several long-tail narratives and eventually shorten the tail in your TDN search, step by step.
- Contact Narr Theory to learn more on AIPR Gap Analysis (coming soon).
TDN-Product Discovery Gap
If your product is not named from the PNQ, it likely means that your product was not discovered when AI engaged in its usual AIPR workflow using the TDN.
This could mean a number of things:
- AI used its internal knowledge model, where your product was not a part of (possible, but unlikely for each niche product market – there are too many. AI can handle a lot, but this is unlikely)
- AIPR performed a web search for TDN, widen its search to satisfy “X number of products”, but your product still was not mentioned
- This means AI never found came across your product at this step, or
- AI did not see enough corroboration that supports your product does in fact satisfy the TDN.
- (E.g. your product claimed “easy to install” but too many contradictory narratives from customers saying “super difficult to install”)
To accurately place your product in this gap will take a trained eye to ask the right questions, pivot strategies, and pinpoint the exact territory in AIPR where your product seems to be appear consistently in.
Some of these questions may be worth asking to understand why the TDN-Product Discovery Gap exists.
- If AI is performing web search to find knowledge, what is it searching?
- How saturated is the market to begin with?
- Where are your products currently being sold?
- What sources did AI cite to support its recommendation?
Unfulfilled Markets
Narr Theory hypothesizes that AIPR, going forward, will become a useful method for understanding both how saturated certain markets are, as well as potentially unfulfilled and whitespace markets. A future post will discuss this specifically (Coming soon).
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