How insights agencies will benefit from their clients’ internalisation

13 March

The various underlying factors behind the phenomena revealed in ESOMAR’s Global Users & Buyers of Insights report.

5 min read
how insights agencies will benefit from their clients internalisation

There may be various underlying factors behind the phenomena revealed in ESOMAR’s Global Users & Buyers of Insights report, including internalisation, but from my perspective, I would like to highlight the following three:

1. Business challenges faced by client’s insight teams: Market instability and business agility are increasing. These drive fundamental changes in the work of insight teams. To deliver results quickly, a portion of the research process is skipped or expedited. The execution of research, business planning, and implementation, based on its results, becomes simultaneous and parallel rather than sequential. As a result, research tasks become integral to business operations. The demand for research outputs that directly contribute to the business and enable their diverse utilisation increases, emphasising the importance of acquiring impactful insights.

2. Available data and tools for a client’s insight teams: With self-serve tools, simple standalone research projects can be conducted quickly and inexpensively. However, this is only a part of the factors driving internalisation. Different suppliers provide diverse data, and a client's insight teams need to integrate them to derive insights. This task is often exclusive to the client's insight team because the offering by each provider is, in most cases, limited to the data they own. The fact that the outsourcing of options for integration tasks is not more common is a significant factor that drives internalisation.

3. Changes brought by AI: AI capabilities, including Generative Pre-trained Transformer (GPT), is rapidly advancing. Tasks that can be accomplished by AI do not necessarily require outsourcing, thus promoting internalisation. However, there is still a lack of expertise in determining in which areas and how AI should be utilised.

All of the above are external factors and irreversible changes for insight teams, making it inevitable for any insight team to address these challenges. In other words, they are business challenges for all clients, creating a significant and long-term demand for support in these areas.

Herein lies significant business opportunities for providers, as discussed below, but realising them would require constructing a slightly different business model than before.

Integrating into the client's insight team:

Insight teams are engaging more than ever before in trial-and-error processes, transforming unstructured issues into research, developing and implementing processes to provide agile insights, and generating impactful insights that contribute to the business. There would be a demand for supporting these trial-and-error processes.

In this context, identifying and outsourcing research tasks could slow down the client's business. Functioning as a part of the client's insight team would lead to practical value offering to clients without time-consuming protocols. By doing so, deeper involvement with the client business would make the insight team more dependent on the provider, raising their switching costs.

Further impact on business contribution could be driven by the provider’s experiences and cases in industries different from that of the client. Providers with cross-category knowledge and experience would work as the competitive edge.

Generating insights using diverse data, including data from other suppliers:

The notion of a single research project for a single problem is a relic of the past century. Providing insights for problem-solving using existing diverse data without conducting new primary research is a valuable proposition and is increasingly carried out by client’s insight teams.

Legal issues may arise depending on the approach, but there should be ways to address them.

Supporting AI utilisation:

No one would disagree with the forecast that AI applications are expanding their scope on an ongoing basis. Then, no one would also disagree with the deduction that more business opportunities will be created by the need for new expertise as new applications of AI emerge. Except for a few clients with deep pockets and actively tackling advanced challenges, it should be fair to say that nobody wants to invest extra time and effort into trial and error. Each new trial-and-error creates a business opportunity calling for support. Therefore, I would suggest it would make the provider’s business model portfolio richer to anticipate and take the trial-and-error before clients do, and struggle with, in order to offer support for their client’s utilisation of AI by accumulating the knowledge from that trial-and-error experience.

Since AI can handle many operational tasks in research and analysis, it will drive a decrease in research costs in those areas and lead to revenue reduction for providers. What providers can charge would be limited to the value only humans can provide.

I do not expect AI to gain the ability to discover insights, at least in the near future. It does a good job of summarising information, but insight discovery requires not just a summary of what is explicitly expressed in data and mentions. It needs an understanding of what lies beneath them and what is not told. It may take time for AI to acquire the capabilities.

As discussed, while internalisation within clients may lead to a decrease in the market size for providers, there are new business opportunities by providing services that address the background and factors driving internalisation. Like many in the client's insight teams, I am really looking forward to seeing providers take the opportunity to work with us closer together.

Discover more about where research is done in the Global Users & Buyers of Insights study.