Unlike sticky workflow software, data products are 'ingredients' that can sit unused. If a new customer doesn't integrate your data into a model, decision engine, or other tangible outcome within the first 12 weeks, the likelihood of renewal drops dramatically.

As its reputation for delivering results grows, Palantir's sales process has flipped. With demand outstripping supply, the company no longer engages in traditional sales cycles. Instead, it requires potential clients to demonstrate their readiness and commitment upfront, making them qualify for Palantir's limited bandwidth.

Vanity metrics like total revenue can be misleading. A startup might acquire many low-priced, low-usage customers without solving a core problem. Deep, consistent user engagement statistics are a much stronger indicator of genuine, 'found' demand than top-line numbers alone.

Early enterprise customers won't invest time to become proficient with a complex data tool. Founders must join their meetings, operate the software for them, and surface insights to demonstrate value. This manual "data monkey" role is crucial for driving initial adoption.

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.

Point-solution SaaS products are at a massive disadvantage in the age of AI because they lack the broad, integrated dataset needed to power effective features. Bundled platforms that 'own the mine' of data are best positioned to win, as AI can perform magic when it has access to a rich, semantic data layer.

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.

Startups challenging Salesforce aren't winning with better UI but with agentic capabilities that replace human SDRs to generate pipeline and bookings. This shifts the CRM from a system of record to an automated revenue engine, making it an easy sell despite market saturation.

The "last mile" difficulty of implementing AI agents makes them economically viable for huge enterprise deals (justifying custom engineering) or mass-market apps. The traditional SaaS sweet spot—the $30k-$50k mid-market contract—is currently a "missing middle" because the cost to deliver the service is too high for the price point.