World of DaaS Roundtable Recap: Pricing Strategies

The latest World of DaaS roundtable brought together executives from leading data companies to discuss one of the most complex and evolving challenges in the industry: pricing strategies. The discussion featured leaders from various data-driven organizations and explored the difficulties of balancing revenue predictability with flexibility, competing with free data sources, and adapting pricing models in an AI-driven market.

The AI Pricing Dilemma

One of the first topics tackled was the role of AI in data pricing. Participants debated whether AI-powered insights justified premium pricing or if customers expected AI-driven data to be cheaper due to automation.

Some companies have successfully used AI to lower costs and improve operational efficiency without directly increasing prices. As one participant noted, "We use AI behind the scenes to clean and normalize data, but our customers see it as part of the package rather than an upsell." Others pointed out that AI-generated data is often met with skepticism, with one executive questioning, "Does AI-enhanced data make a dataset more reliable, or does it introduce hallucinations that reduce its perceived value?"

Despite the hype, most agreed that AI is not yet a direct pricing lever but rather a competitive differentiator. Instead of charging a premium for AI-driven insights, companies are using AI to enhance accuracy and efficiency, ensuring customers get the best data possible.

Competing with Free Data

Free and open-source data sources continue to be a major challenge for data vendors. With platforms providing free alternatives, data providers must justify their price tags beyond basic data quality.

For many, the key differentiation lies in methodology. "We incorporate survey research into our audience data — something that is difficult for others to replicate," explained one executive. Others rely on proprietary technology, ensuring their data is cleaner, better structured, and easier to use. "Our value is in accuracy and completeness, not just access to the data," another participant emphasized.

A growing concern is the ability of DSPs and other platforms to use machine learning to create proxies for paid datasets. "We’re seeing DSPs build synthetic datasets that perform ‘well enough’ without paying for real data," one participant shared. This trend forces data companies to double down on their unique signals and prove their data’s impact on customer outcomes.

Lessons from Failed Pricing Models

Pricing strategies in the data industry are far from one-size-fits-all, and some models have fallen flat. Participants shared insights into past mistakes and how they’ve adapted.

A common pitfall was adopting SaaS-style pricing for data. "We initially charged per seat, but that led to enterprises gaming the system by centralizing access," one executive recounted. Moving to value-based pricing, aligned with company size and industry verticals, proved more effective.

Another recurring failure was revenue-sharing models. "Rev-share deals are a nightmare. Relying on partners to report accurately is an exercise in frustration," admitted one participant, with others strongly agreeing. Instead, companies have shifted toward flat-rate licenses with tiered usage rights.

For companies with long-standing customers, adjusting pricing has been particularly challenging. "Getting legacy customers off old contracts is painful. We’ve had to bite the bullet, accept some churn, and focus on bringing in new customers at the right price point," one leader noted.

The Future of Data Pricing

As the discussion wrapped up, participants reflected on where data pricing is headed. A few key trends emerged:

1. Shift Toward Usage-Based Pricing – More companies anticipate being forced into consumption-based models rather than traditional SaaS licenses.

2. Marketplaces as a Lead Gen Tool – Data marketplaces are useful for visibility but are not the primary drivers of direct revenue.

3. Continued Data Commoditization – Over time, all datasets trend toward commoditization. The key to staying ahead is constantly identifying and commercializing new datasets.

As one participant succinctly put it, "Data will always get commoditized. The companies that win will be the ones who keep finding new ways to extract value."

Next Steps

The roundtable concluded with a consensus on the need for ongoing collaboration. Potential next discussions include:

• Best practices for leveraging data marketplaces

• Strategies for protecting high-value datasets from commoditization

• Innovative pricing structures to align with evolving customer expectations

Stay tuned for the next World of DaaS roundtable recap as we continue to navigate the future of data monetization.

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