Hedge funds have a constant, daily need to make informed buy, sell, or hold decisions, creating a clear business problem that data solves. Corporations often lack this frequent, high-stakes decision-making cycle, making the value proposition of external data less immediate and harder to justify.

As ad platforms like Google automate bid management, an agency's value is no longer in manual "button pushing." The new competitive edge is the ability to feed the platform's AI with superior client data and insights. Agencies that cannot access and leverage this data will struggle to demonstrate value.

A common mistake is building a visually impressive data product (like Google Earth) that is interesting but doesn't solve a core, recurring business problem. The most valuable products (like Google Maps) are less about novelty and more about solving a frequent, practical need.

While individual AI companies see slightly lower retention than SaaS, Stripe's data reveals customers often churn from one provider directly to a competitor, and sometimes switch back. This indicates the problem being solved is highly valued, and the churn reflects a rapidly evolving, competitive market, not a lack of product-market fit for the category itself.

Leaders often view brand metrics as 'fuzzy' for two key reasons: marketers suffer from 'learned helplessness' due to a constant churn of new measurement tools, and they often measure brand performance in an absolute vacuum, failing to provide the competitive, longitudinal insights that boardrooms actually need for decision-making.

The company had a significant 'prospecting black box.' For 40% of all opportunities, there was no traceable sales trigger or activity log, such as logged calls. This meant they couldn't measure or optimize a huge portion of their pipeline creation process, particularly SDR outbound efforts.

Companies struggle to get value from AI because their data is fragmented across different systems (ERP, CRM, finance) with poor integrity. The primary challenge isn't the AI models themselves, but integrating these disparate data sets into a unified platform that agents can act upon.

Instead of reacting defensively when a customer mentions a competitor, use it to probe their underlying needs. Asking 'What do you like about it?' helps differentiate between a critical feature gap ('the steak') and a superficial want ('the sizzle'), keeping you focused on solving real problems.

Benchmarking against competitors is dangerous because they may have already made pricing mistakes. Furthermore, you might offer superior value under the same service name, meaning you'd be severely underpricing your more comprehensive offering.