Leasey.AI eliminated pricing improvisation and deal friction by uncovering hidden natural buying patterns and standardizing offer tiers with DecidUp.
Increase in Sales Conversion Rate
Pricing improvisation needed by sales reps on calls
Reduction in demos lost to price resistance
Leasey.AI, a fast-scaling B2B PropTech leasing automation platform, was facing deal friction and lost demos due to an unstandardized, "one-size-fits-all" pricing model. Sales reps were often forced to package custom deals and negotiate pricing on live calls, resulting in inconsistent deals and resistance from prospects.
By utilizing DecidUp to analyze historical deal data and uncover natural buying patterns, the company established clear, data-driven offer tiers, resulting in a 72% increase in sales conversion rates and eliminating rep guesswork.
As the company expanded across multiple client segments (from small property managers to large developers across Canada and the US), their sales process began to stall:
Without standardized offer structures, sales reps were often negotiating custom pricing on live calls to try to win deals.
Every client ended up paying a different rate, creating confusion across the pipeline and making revenue unpredictable.
Smaller prospects interested in specific platform modules choked on full-suite pricing, leading to objections, dead-end deals, or unnecessary discounting.
"We were essentially playing trial and error. Every single client was paying a different price, and sales reps were improvising on calls trying to come up with the most suitable offer based on clients' needs and expected platform usage."— Juan, Co-Founder at Leasey.AI
DecidUp partnered with leadership to turn historical closed-deal data into a structured, predictable sales framework.
DecidUp conducted a deep dive into past deal data to uncover hidden patterns across previous wins: analyzing geography, client size, module usage, and actual closed contract values.
Instead of guessing how to structure packages, DecidUp extracted clear behavioral segments directly from historical sales data. The analysis revealed natural buying clusters:
Segment A: Consistently bought Module Set A+B at Price X.
Segment B: Consistently bought Module Set A+B+C at Price X+Y.
By mapping offers directly to what clients were already naturally buying, the team eliminated forced fits.
DecidUp replaced rep guesswork with a clear, validated decision framework. Sales reps no longer had to improvise pricing during discovery; instead, they could instantly qualify a prospect into a standardized tier based on their firmographics and needs.
By standardizing packages based on actual deal history, the company transformed its sales pipeline execution:
Prospects were immediately presented with a bucket that fit their actual requirements, removing "too expensive" objections early.
Reps stopped negotiating on the fly and began presenting confident, structured pricing backed by proven deal data.
Year-over-year pipeline metrics showed a 72% increase in sales close rates.
"By having a tiered system based on client size and module usage, we eliminated friction and resistance on calls. The impact was immediate: our conversion rates jumped by 72%."
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