Real-Time Decision Intelligence Platform

Challenge

The client had strong product-market fit but was leaving revenue on the table. Their customer success team was making cross-sell and upsell recommendations based on gut feel and basic usage metrics. They knew their customers were underutilizing the platform but couldn't systematically identify the right moment or the right offer.

The data existed — product usage, support interactions, billing history — but it lived in silos and nobody was connecting the dots.

Approach

Rather than building a traditional recommendation engine, we focused on decision intelligence: giving the customer success team better information at the right time so they could make better decisions.

The key insight was that timing mattered more than targeting. The team already knew which products to recommend. They didn't know when a customer was most likely to say yes.

I led the technical team through designing and building a real-time scoring system that analyzed:

• Product usage patterns and feature adoption curves
• Support ticket sentiment and resolution outcomes
• Contract and billing cycle timing
• Engagement signals from marketing touchpoints

Implementation

We built iteratively, starting with a single customer segment and expanding based on measured results:

• Weeks 1-4: Data pipeline integration and baseline conversion measurement
• Weeks 5-8: Initial scoring model deployed for the top customer segment, with CS team providing daily feedback on recommendation quality
• Weeks 9-12: Model refinement based on feedback, expansion to two additional segments
• Months 4-5: Full rollout across all customer segments with automated alerting

The system surfaced recommendations directly in the team's existing CRM workflow — no new tools to learn, no behavior change required.

Results

• 12% increase in cross-sell conversion rate
• $800K incremental ARR within the first 6 months
• CS team efficiency improved — reps spent less time researching accounts and more time having conversations
• Model accuracy continued improving through the feedback loop, reaching 78% precision on high-confidence recommendations

The platform paid for itself within 3 months of full deployment and became a core part of the company's revenue operations infrastructure.