Your customers don’t care about your Org chart, so Why does your data?

Every department is making decisions in a vacuum.

📢 Marketing says: “We need more visitors + a bigger budget.”

📰 Product says: “We need stickier features.”

💰 Sales says: “We need better leads.”

📉 Customer Success says: “We need better onboarding.”

Who’s right? Maybe all of them. Maybe none.

The real problem? Your data is just as fragmented as your teams.

Why are Companies Struggling with Customer Analytics?

Companies invest millions in customer analytics, yet their customers still feel misunderstood and ignored.

• 86 Percent of Consumers Will Leave a Brand They Trusted After Only Two Poor Customer Experiences (Source - by Emplifi Inc )
• Only 15% of organizations say they truly understand their customers.
• Marketing, Sales, Product, and CS all use different tools that don’t talk to each other.
• 42% of organizations experience revenue leakage, losing between 1% to 5% of their annual revenue due to misaligned strategies and lack of data visibility (Salesforce Ben).
• Business and IT teams waste over 654 hours annually reconciling siloed data, costing $1.7 million per year for each of the companies (Talkspirit).
• Despite widespread data collection, 67% of business leaders report fragmented or disorganized use of customer data, says Zendesk. 

Your business is structured in silos. But your customers experience you as one brand.

Here’s how chaos unfolds...

Stage 1: Need Usage: Start with a Product Analytics Tool

→ Focus on User Behavior

• A company launches their SaaS or e-commerce product and installs Mixpanel, Amplitude, or Heap (and 200 other tools) to track product usage and user interactions.
• They see what features users click on, but don’t know where those users came from or whether they actually convert into paying customers.

Blind Spot: 
Product teams: track clicks (pages, sessions, errors etc), but cannot answer if the signups and activation drop-offs are because of wrong users (marketing?) or monetization problems (Sales, Revops?).        

Stage 2: Low Acquisition? Buy a Marketing Analytics Tool

→ Focus on Attribution

• They add Google Analytics, HubSpot, or Segment to track where users are coming from (ads, organic search, social media, etc.).
• Now, they see traffic sources, but they can’t tie it to product usage or revenue.

Blind Spot: 
Marketing teams optimize for traffic but don’t know if these users actually engage with product or convert into revenue.        

Stage 3: Low Revenue? Buy a Revenue Analytics Tool

→ Focus on Monetization

• They bring in ProfitWell, Baremetrics, or Chargebee to track subscriptions, churn, and revenue forecasting.
• Now, they know how much money they’re making, but still don’t know why customers are churning or what actions lead to more revenue.

Blind Spot: They see that churn is 8%, but they don’t know which behaviors predict churn—or how to prevent it.        

Stage 4: High Churn? Buy a Retention Analytics Tool

→ Focus on Customer Success

• They add Gainsight, Intercom, or Customer.io to analyze customer engagement, health scores, and retention campaigns.
• Now, they have another layer of data, but no unified way to connect product engagement, marketing attribution, revenue insights, and retention efforts.

Blind Spot: 
They automate customer success workflows but miss early warning signs from product engagement data (e.g. did not fully onboard, stopped using product)        

References

1. Data-driven growth - KPMG US
2. The Surprising Link between a Company’s Growth and its Data Analytics Strategy - @Abhijeet Sarkar
3. Your B2B product-led motion is more of a data problem than a product problem - by Elena Verna
4. Product-led growth is, by nature, also data-led growth - by Hila Qu 曲卉
5. How to build a data-led organization - by Wes Bush
6. Data Model adapted from Bow Tie Revenue Architecture - by Winning by Design , Jacco van der Kooij , Dave Boyce

Invitation to Solve This Together

Is your data stuck in disconnected systems as well?

At ThriveStack , we’re working on a better way to break down data silos and create a unified customer experience analytics.

Let’s build the solution together,

Want to be part of the conversation? Drop a comment or DM us.

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