“We went from fragmented GTM data and outdated reporting to a single view of pipeline performance tied to revenue. That shift didn’t just clean up our systems. It changed how we operate, where we focus, and how fast we move.”
Kassidy Bird, VP of Marketing, AskNicely
At a glance
| Metric | Result |
|---|---|
| Annual platform savings | $84,000 |
| Annual operational overhead reduction | $10,000 – $20,000 |
| Projected 5-year value | $300,000 – $520,000 |
| Cost per opportunity | 30% reduction in one quarter |
The company
AskNicely is a global customer experience platform helping service businesses turn real-time feedback into frontline action. Its AI-native platform serves 1,300+ companies, coaching frontline behaviors and tracking performance across the customer journey.
As AskNicely shifted from transactional lead generation to a sales-led, mid-market motion, its tech stack, fragmented between Salesforce and disconnected marketing tools, could not keep up.
The marketing team was producing pipeline numbers the CRO could not validate. The CRO was making territory and coverage decisions on data marketing had never agreed to. Both functions were right that the numbers were wrong. Neither had the infrastructure to fix it.
The challenge
AskNicely’s GTM data problem was a Jenga tower: each function had built reporting on top of the same fragmented foundation, pulling pieces as needed, and the whole structure had become too unstable to trust. The specific failures:
- Disjointed CRM and marketing systems. Salesforce and marketing automation operating in separate data models, with no unified view of account or pipeline progression. Marketing’s lead data and sales’ opportunity data existed in parallel, not in sequence.
- Fragmented, unreliable reporting. Manual reconciliation between systems produced different pipeline numbers for marketing and sales, undermining executive trust in the data and turning every pipeline review into a negotiation about whose numbers were right.
- No stage-based pipeline tracking. The team could not measure how accounts progressed through funnel stages, making it impossible to identify where the demand engine was stalling versus performing.
- GTM misalignment. Marketing, sales, and customer success operating without shared metrics or shared accountability for revenue outcomes. The CMO and CRO were answering the same questions with different evidence.
The solution
Lative’s Marketing Intelligence platform, which Lative acquired from Mperativ to expand its AI-native GTM capabilities, re-architected AskNicely’s GTM engine from the ground up:
- CRM consolidation. Migrated from Salesforce to a unified HubSpot instance with custom migration tooling that preserved full historical deal data, including a blended timeline maintaining full-funnel reporting continuity despite Salesforce’s migration limitations.
- GTM-first data model. Rebuilt the demand engine to track both leads and accounts through every funnel stage, enabling dual visibility across individual contacts and account-level progression simultaneously. Marketing and sales now measure the same journey.
- AI-native reporting foundation. Implemented Lative’s Marketing Intelligence platform for real-time pipeline visibility, AI-generated executive narratives, and predictive pipeline coverage analysis shared across marketing, sales, and finance.
What changed
Three changes defined AskNicely’s experience after Lative’s Marketing Intelligence went live. Each one addressed a failure mode that had been invisible when pipeline data was fragmented across systems and reporting was done manually. The unifying factor in each: the insight came from the data automatically, not from a RevOps analyst assembling a report.
Full-funnel visibility enabled fast, evidence-based decisions
With unified pipeline reporting in place, the AskNicely marketing team could see which programs were driving account progression and which were not, and act on it immediately.
One data-driven shift in paid search strategy drove a 30% reduction in cost per opportunity within a single quarter. That optimization is not possible when pipeline attribution is manual and quarterly. The CRO could validate the same cost-per-opportunity improvement from the same data source, which changed the nature of the budget conversation entirely.
Data surfaced a strategic blind spot
Lative’s Marketing Intelligence analysis revealed that AskNicely’s buyers were not just searching for NPS solutions. They were looking for broader feedback management capabilities.
That insight triggered a brand and messaging shift that realigned the product narrative with how buyers actually define their problem. The strategic conversation changed because the data changed it, not because of a hypothesis from one function that the other had to accept on faith.
Marketing and sales moved to a shared revenue frame
With real-time, revenue-centric reporting in place, AskNicely’s marketing and sales teams stopped translating metrics for each other and started operating in a shared language.
Pipeline contribution, conversion rates, and revenue influence became the common frame for conversations with customer success, finance, and the CEO. When the CMO and CRO walk into a pipeline review from the same data source, the meeting structure changes: less negotiation about whose numbers are right, more discussion about what to do next.
The AI-native foundation built for what comes next
AskNicely’s platform rebuild was infrastructure built for the operating model the company is scaling into. The data foundation that delivered the 30% cost-per-opportunity improvement is the same foundation that makes predictive pipeline modeling, AI-generated executive narratives, and real-time attribution at scale all possible from the same integration.
OpenView’s 2023 SaaS Benchmarks report, based on 710 operators, found that AI-native companies are 3.3 times more likely to be growth outliers than non-AI-native peers. The distinction is not which AI features a company ships but whether the AI runs on unified, real-time GTM data. AskNicely’s rebuild created that foundation.
With Lative’s Marketing Intelligence in place, the team operates with AI-generated executive narratives that surface complex pipeline data as board-ready stories, predictive pipeline coverage that identifies risks before they impact revenue, and real-time attribution data that traces every dollar of marketing spend to its downstream revenue impact.
CRO Capacity Planning From Shared Demand Data
AskNicely’s CRO now plans sales capacity against the same demand-engine data marketing operates on. The pipeline coverage model, the conversion rate assumptions, and the segment-level opportunity counts that drive headcount and territory decisions all come from the same unified foundation.
AskNicely rebuilt its GTM engine on a foundation of unified data. That shift produced not just better reporting, but decisions made from the same truth.
The 30% cost-per-opportunity reduction came from a data-informed paid search adjustment that was only visible because pipeline attribution was running in real time, not assembled manually at quarter-end. That is the compounding return on a unified data foundation.
Ready to Fix the Architecture?
If your pipeline reviews end with a debate about whose numbers are right rather than a decision about what to do next, that is the architecture problem this case study describes. See how Lative’s Marketing Intelligence rebuilds the GTM data foundation that connects your marketing programs to revenue outcomes.
Lative Team — Lative is the AI-native GTM platform that connects marketing intelligence to sales capacity planning on one shared data foundation.
