MarTech Rebuild. From Data Blind Spots to Actionable Business Insights

About Tokeet

Tokeet came to me with a property management platform serving thousands of property managers, plus five additional products in their portfolio AdvanceCM, Sympl, Automata, Rategenie, Checklist, and Webready.

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The Challenge

• Six products with minor data visibility
• No consistency on website traffic
• No visibility into signup flow performance
• No channel attribution - how each marketing channel is performing
• No product analytics to understand user behavior and conversion patterns
• No reporting system that will support future decisions

In short, there was no connected funnel journey, their data lived in isolated silos across tools that couldn’t communicate with each other.

The founder's ask was straightforward: "We need to see what's actually happening. Weekly traffic, signup conversions, drop-off points, channel performance, customer acquisition costs, conversion rates in entire funnel.

Beside guiding technical implementation this was consulting engagement focused on three areas:

1. Founder alignment. Weekly KPI reviews, priority setting, and strategic decision making.
2. Team enablement. Working directly with their dev, product, and marketing teams to implement the right stack.
3. Knowledge transfer. Building comprehensive documentation and training so they could own the system long term.

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Approach: Founder + Team, Strategic and Tactical

Most Martech implementation projects fail because they're either too high-level (strategy decks with no execution) or too tactical (hired guns who implement and disappear). I built a process that connects both.

1. Comprehensive audit. Documented every tool they were using, every data point they were trying (and failing) to track, and every gap in visibility.
2. Data architecture design: Sketched out the full stack we'd need, and how it would all connect
3. Detailed implementation plan. Timeline, dependencies, who owns what, and how we'd measure success

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The Core Problem

They were using wrong tools with disconnected data and no customer journey visibility.

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Toolstack gap

❌ No Customer Data Platform to unify data across touchpoints

❌ Marketing automation tool that couldn't support behavioral triggers, special attributes connected to person or company, event and event properties

❌ No product analytics to understand feature adoption or user segments

❌ Analytics that couldn't track their multi-step signup flow

❌ CRM data living in isolation from product and marketing data

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Visibility gap

❌ Zero insight into daily/weekly/monthly traffic - no reporting cadence

❌ No tracking on their multi-step signup process (where were people dropping off?)

❌ No idea which channels drove quality signups vs. junk traffic

❌ No behavioral data to understand how different user segments used the product

❌ No way to identify cross-product users (e.g., someone using both Tokeet and Sympl)

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The Solution

The project was more than new tool stack implementation. It was building comprehensive documentation, outlining clear specifications for product and development and implementation timeline.

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Customer Data Platform (We opted in for Segment)

• Unified data collection across all six products
• Single source of truth for customer journey tracking
• Connected every downstream tool

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**Product Analytics (**Mixpanel + Amplitude)

• Event tracking for user behavior across all products
• Cohort analysis, feature adoption, time-to-value metrics
• User segmentation (freemium, trial, paid customers)

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**Marketing Automation (**Customer.io)

• Behavioral email sequences (not time-based spam)
• User segmentation based on product usage

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**CRM (**HubSpot)

• Sales pipeline tracking
• Lead scoring based on product usage + marketing engagement
• Unified view of customer across marketing, sales, and product

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Website Analytics

• Full funnel tracking from first visit to customer
• Multi-step signup process visibility with drop-off analysis
• Channel attribution and conversion rates

All connected through customer data platform - no more data silos, every tool talking to every other tool. Complete customer journey visibility from first website visit to paid customer to feature adoption.

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The Implementation Reality

I worked directly with Tokeet's dev and product team to:

✅ Instrument tracking across six different products

✅ Build event specifications for 50+ key user actions

✅ Set up data pipelines that wouldn't break every time they shipped new features

✅ Test everything end-to-end before calling it done

Product managers gave me the context I needed: "What are the key moments in your product? What actions predict retention? What should we be tracking?" Then I translated that into a detailed tracking plan that their dev team could execute.

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Reporting Systems That Actually Drive Decisions

Once the data was flowing, I built reporting systems tailored to how different teams actually work and how they make a decisions on the next initiatives.

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Founder and executive team

• Weekly traffic, signup trends, funnel conversion rates
• Channel performance (total spent, conversion rates, CAC, LTV)
• Funnel visibility from website visit → Sign up → PQL/MQL → SQL → Customer
• Customer cohort analysis by acquisition channel

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Product team

• DAU/MAU tracking by user segment
• Feature adoption rates
• Time-to-value analysis
• Onboarding completion rates and drop-off points

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Marketing team

• PPC campaign performance (conversion stats by channel, campaign, country)
• Organic traffic performance
• Trend reports (marketing spent, CAC payback, signup-to-customer time/conversion rates ,etc.)
• Marketing + sales funnel metrics

No vanity metrics, no dashboards that look pretty but don't inform decisions. Just the numbers that mattered - updated automatically, easy to understand, and actionable.

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Knowledge Transfer & Team Training

The final step wasn't about building, it was about handing off.

• Comprehensive Wikis. How the stack works, software capability, how they connect
• Tracking plan specifications. Every event, every property, every data point - clearly documented
• Data dictionaries. What each metric means, how it's calculated, where it comes from, event documentation, people and event properties.

Then I ran training sessions (group and one-on-one), where I walked the team through wow to read each report. where data flows from (source → CDP → destination tools), how to best utilize Mixpanel, Amplitude, HubSpot, and Customer.io. The goal was to make Tokeet team self-sufficient and no ongoing dependency on me.

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The Results: From Blind Spots to Data-Driven Decisions

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1. Data visibility

• Daily/weekly/monthly trends—finally visible
• Complete signup funnel tracking with drop-off analysis at each step
• Channel attribution showing which sources drive quality customers (not just clicks)
• Product usage patterns by customer segment
• Cross-product user identification

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2. Acquisition improvements

• PPC conversion rates by channel, campaign, location, ads, keywords, etc.
• CAC payback periods tracked accurately
• Customer cohort analysis showing retention by acquisition source
• Cost metrics and benchmarks. Sign up cost, MQL cost, SQL cost, etc.
• Funnel reporting from website visit → trial → PQL/MQL → SQL → Customer

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3. Product improvements

• User behavior analysis (what do successful customers do in week 1?)
• Feature adoption rates by segment
• Time-to-value tracking
• Onboarding completion analysis

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4. Marketing automation improvements

21% increase in open rates on onboarding sequences (behavioral triggers vs. time-based)

37% increase in post-trial email engagement

206% increase in demo bookings (better targeting + better timing)

412% increase in survey responses from churned/inactive customers (finally reaching them at the right time)

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What Actually Made This Work

Three things separated this from a typical "implement some tools and disappear" engagement.

1. Founder-level alignment from day one. Weekly standups kept priorities clear, no surprises, no scope creep. Just systematic progress toward the KPIs that mattered.
2. Systematic team collaboration. It’s not just about documentation hand over. The crucial point was working with internal teams ensuring everyone understood the "why" behind every decision.
3. Comprehensive knowledge transfer. Wikis, specs, data dictionaries, training sessions. They didn't just get a working system, they got the ability to maintain and extend it.

Martech consolidation should work not just by  implementing tools, but building systems that drive decisions, and making sure your team can own them long-term.

If your SaaS company is struggling with disconnected data, the wrong tools, or zero visibility into what's actually driving growth, let's talk.

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