Activating Strategic Assets: How Synthetic Personas Bring Dormant Research to Life

By Market Logic Software

• article

• Knowledge Management
• Artificial Intelligence
• Generative AI
• Research Management
• Competitor Analysis
• Insight Transformation
• Insight Activation
• Machine Learning
• Trend Reports
• Trend Analytics
• Reporting
• Syndicated Reports
• Research Repository
• AI
• Synthetic Data

I was blown away by the demand for synthetic personas. That is something we started on maybe a year ago or so, and whilst it was an opportunity, we were ourselves a little bit sceptical about how well it would work and how much people would really want to use it. Because there is always the fundamental question and reservation about trustworthiness – is that really something you could use?

But factually, it works better than I expected. I understand now why organisations are so strongly focused on that and want to build on it and bring it to life.

Learn more by watching or listening to Olaf on the Founders and Leaders Series podcast here:

The Problem: Strategic Assets That Sit Unused

We have so many customers who have strategic assets, knowledge assets that they work on. They invest in demand spaces or other kinds of research that they continuously do. And they have lovely dashboards, reports, and everything. But it is so difficult to work with it in real life.

This is the core problem that synthetic personas solve. It is not primarily about replacing traditional research or cutting costs, though efficiency is part of it. The real value is that it is such a great instrument for bringing the knowledge organisations already have to life, in a practical way.

Why Personas Work: Natural Interaction with Data

If you use things like bringing personas to life based on that data, you can suddenly begin to talk to your data and to those virtual consumers. And that is why I think it is such a strong pull: to make it useful for people in business, in situations where they have questions, and in a way that is very natural and human to interact with.

Think about it this way: you might have invested significantly in segmentation studies or understanding demand spaces in your category. That research is valuable, but when someone in a product development or brand team has a question, they are not going to dig through a 200-page report or try to interpret a dashboard. The friction is too high.

Now, if you can build personas based on that data, suddenly there is a consumer at the table in their process already, automatically. People can ask questions in natural language, explore different angles, and bring the consumer perspective into their thinking without waiting for a briefing or requesting a study.

It Enables the Leverage of Existing Assets

What we see is that organisations really strongly focus on personas because it helps them activate existing assets. Whatever segmentations you have, consumer knowledge, usage and attitudes studies or demand spaces you have mapped, all of this can be leveraged.

This is particularly important because it shifts the conversation away from “what new research do we need?” to “how can we make better use of what we already know?”. That has always been one of the core challenges: making sure all the lovely assets that organisations have invested in are reused and do not duplicate research spend on things they already know.

The Trust Question

I mentioned earlier that we were initially sceptical. There has been research – I have seen work from Colgate-Palmolive, for example – comparing what personas say versus what real people say, and how accurate that is. The evidence suggests it can work quite well when it is based on solid proprietary data.

Organisations care about this being well-defined and having a system and method. They want to ensure the personas they are building are grounded in real research, not just generic AI outputs. This is why the insights team plays such a crucial role in setting this up properly.

The personas need to be built on vetted data sources. The scope needs to be clear: which questions can this persona reliably answer based on the underlying research, and which require new primary research? These guardrails are essential for building trust with stakeholders.

A Shift in How Innovation Works

The next wave we are seeing – and we are working on this with some customers at the moment – is to build on personas by fully supporting the innovation journey itself. We are working on a product that helps people collaborate with AI, with your consumer at the table from the start, automatically.

This creates opportunities for the insights teams to be closer and more directive in that process. But also, again, a lot of opportunities to activate all the assets you have. As I said, whatever segmentations, consumer knowledge, usage, attitudes, demand spaces, and whatever else you have works.

Also, the assets you’ve been building through your innovation testing can be reused in a targeted way. The system can surface: “We tested something similar 18 months ago with this segment – here is what we learned.”

It Is Not Perfect, but It Is Practical

I want to be clear: this is still evolving. Critical thinking remains important anyway, whether you are working with humans or with AI. Both make mistakes, but what personas do is make it vastly more practical to bring consumer understanding into everyday decisions.

The key is getting the foundation right: high-quality proprietary data, clear scope definition, proper guardrails, and integration into actual workflows rather than being a separate tool people need to remember to use.

When that is done well, organisations finally have a way to activate all those strategic assets they have invested in over the years. And that, in my opinion, is why we are seeing such strong demand.

Learn more by watching or listening to Olaf on the Founders and Leaders Series podcast here:

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