Objective of the customer, not the whim of a brand – OODA in the world of AIDAA

As a digital marketer, most of my work fits into the 5 stages of AIDAA: Awareness → Interest → Desire → Action → Advocacy.

Our intent as Marketers is simple: influence a favourable decision for the brand — subscribe, buy, like, or share a lead.

Traditionally, we do this by pre-empting what a customer might want to see and hear, then crafting that narrative into a website experience, landing page, microsite, ad, or post.

Here’s the catch: Most marketing systems are still brand-led, not objective-led. Even when we use CRM, web analytics, and behaviour tools, what we often end up doing is:

• creating segments,
• pushing messages,
• and hoping the customer’s real reason aligns with what we assumed.

That’s what I mean by “the whim of a brand”, not randomness, but assumptions and campaign narratives driving the next action, instead of the customer’s actual objective in that moment.

In the last few years, this gap has become more visible.

Because on one side, users are getting used to AI chatbots that ask back and adapt. Users are conditioned to adaptive interfaces.

On the other side, many websites still follow a mostly one-way flow: broadcast first, interpret later.

The measurable consequence is familiar to most D2C teams:

• more browsing but fewer decisions,
• drop-offs around product discovery / comparison,
• higher CAC pressure because conversion rates don’t keep up,
• and often, teams compensate by adding more creatives, more offers, more retargeting.

This is why I don’t think AIDAA is obsolete, but I do think it needs a “control loop” inside each stage.

What does the Control Loop look like? – OODA inside AIDAA

AIDAA explains the stages.

OODA explains the decision-making loop within each stage.

At any point in the journey, the system should continuously ask:

• Observe: What signals is the customer giving right now?
• Orient: What objective might those signals indicate?
• Decide: Should we intervene or stay silent?
• Act: If yes, what is the smallest useful action in this moment?

and then back to ‘Observe’. You keep iterating and improving your strategy.

The key shift is simple but profound: Move from pushing the next message in the funnel to responding to the customer’s real-time objective.

Notice the decision criteria: only intervene when signals suggest confusion or hesitation, and intervene in the lightest way that reduces doubt.

We already use OODA in one part of of the funnel

That is in paid media: we run multiple creatives and let the platform’s AI optimise delivery.

It works, but it mainly learns what gets attention/clicks, not what removes doubt and drives the final decision. Also, most importantly these platforms do not pass all these intent signals back to the brands.

So brand can hit performance goals while still not learning the reason someone didn’t buy once they landed on the site.

Interest + Desire Stage Focus

If I had to pick one place where OODA matters most, it’s the Interest/Desire zone, the “I’m considering” moment.

Because that’s where:

• motivation exists,
• but uncertainty peaks,
• and choice overload kills momentum.

A small improvement here often has an outsized impact on conversion rate (and therefore CAC efficiency).

This is the gap we kept running into ourselves and it’s what led us to build mPrompto.

The idea was straightforward: what if the website could run the same OODA loop a good salesperson runs instinctively? mPrompto sits on the site as a Conversion Co-pilot and does exactly that…

mPrompto sits on the website as a Conversion Co-pilot and runs OODA loops in real time:

• Observes behaviour + inputs
• Orients to the shopper’s objective
• Decides whether to intervene (and how)
• Acts via a nudge, a short question flow, or a focused recommendation with “because” reasoning

and then back to ‘Observing’ other customers and their patterns

The goal isn’t more interaction for its own sake.

It’s fewer drop-offs by helping customers decide faster — based on their objective, not our assumptions.

Author – Ketan Kasabe, Co-founder: mPrompto