Man vs Machine: The Challenges of Synthetic Data in B2B Research

AI has been a topic again and again throughout the market research industry, with lots of conversation around the practical and efficient applications for it (and a few potential pitfalls too).

The Rise of Synthetic Data

More recently, the concept of synthetic data seems to be gaining ground. If you aren’t already familiar with it, synthetic data is artificial data that mimics real-world information. It’s created using algorithms and simulations, and it can be used as a substitute for first-hand data when that data is not available or to supplement first-hand data.

There are a number of applications for this type of data in qualitative and quantitative market research. You can simulate consumer behavior, using hypothetical profiles to understand how different demographics might respond to product offerings. You might develop datasets that represent different consumer segments, so you can analyze preferences. Synthetic data could also be used to A/B test marketing messages or campaigns, so you can predict outcomes without gathering primary data.

The list of uses goes on and on, and it’s clear to see how in some cases, synthetic data can be an effective approach - particularly for companies like those in the CPG space, who have troves of data to tap into to train the models. 

The Limitations of Synthetic Data in B2B Research

But what about B2B market research, where the samples can be harder to find and are often smaller? What about the nuance of experience that many B2B research participants bring to the table? These may not always be as accurately reflected with synthetic data. 

In the B2B space, some of the potential challenges with synthetic data may be heightened. For example, one issue with synthetic data includes a lack of realism, in cases where the data may not accurately reflect real-world behaviors or preferences, as it is dependent on the data used to train the model. This can be especially true if the area of focus is niche, as it often is in B2B.

As another example, if the algorithms used to create the data are biased, they can introduce bias into the results or make it challenging to validate the data and ensure it’s representative. Again, this can be more challenging with a smaller data set or smaller representative set of respondents. And while it can be cost effective and fast, this approach can also present a tough sell to stakeholders, who might prefer insights gained from an actual human in order to avoid these risks altogether - especially when B2B research presents nuanced insights from specialized experts and professionals. 

Human and AI

Of course at Zintro we’re not luddites or against any type of emerging technology. In fact, we use AI across our organization in ways that boost productivity, including using our own proprietary solutions that help us identify ideal respondents from our rich panel for each custom recruiting project.  

Ultimately, every research need is different and will require an approach that best fits the objectives and project parameters. But when it comes to B2B research and synthetic data vs real-life data, for now, it seems important to keep the human element and nuanced experiences firmly in the research.

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