New mobile apps to keep an eye on
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What new social media mobile apps are available in 2023?
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Use new social media apps as marketing funnels
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Try out Twitter Spaces or Clubhouse on iPhone
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What app are you currently experimenting on?
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We’ve all heard the phrase “data is your most valuable asset.”
But in most SaaS companies, data is more like scattered treasure: valuable, sure — but buried in different systems, hard to piece together, and rarely used at full potential.
If you lead GTM strategy or own enterprise data infrastructure, this isn’t just a technical inconvenience.‍
Siloed data is actively slowing down revenue — and it’s doing more damage than you think.
The Modern GTM Stack Wasn’t Built for Predictive Performance‍
In today’s average SaaS org:
- Sales lives in Salesforce.
- Marketing campaigns and scoring rules are in HubSpot.
- Product usage data is stored in Snowflake or Redshift.
- Support and CS tools track renewals, tickets, and health scores elsewhere.
- Finance is managing billing data in Netsuite or Stripe.
Each of these tools has its own schema, its own access rules, and its own owner.
And when RevOps tries to connect the dots — for forecasts, for lead scoring, for churn risk — they’re left wrestling with exports, manual joins, or slow internal projects.
The result? Disconnected insights, inconsistent reports, and lagging decisions.‍
What Siloed Data Actually Costs You‍
This isn’t just a workflow issue — it’s a performance drag.
Here’s how it shows up:‍
1. Leads Are Scored on Incomplete Context‍
Marketing might hand off “hot” leads based on form fills or ad engagement — but without product usage or firmographic data, sales wastes cycles on the wrong accounts.‍
2. Pipeline Forecasts Miss Real Signals‍
If a rep’s Salesforce stage hasn’t changed but product usage has dropped 90%, is that deal really healthy? Most forecasting models can’t see that — because the data isn’t connected.‍
3. Churn Signals Go Unnoticed‍
Support tickets are spiking. Usage is down. Payment is late. But no one team sees the full picture. By the time it’s flagged, the customer is already halfway out the door.
According to McKinsey, companies that break down GTM data silos and adopt predictive operations improve forecasting accuracy by up to 25% and reduce churn by 10–15%.‍
Why It's So Hard to Fix‍
Siloed data isn’t a new problem — but in the AI era, it’s become a critical one.
The root issue? Fixing it has always meant major lift:
- Centralizing disparate data into a warehouse
- Standardizing across tools with conflicting taxonomies
- Building custom pipelines to feed models
- Hoping teams will wait while all that gets built
So it’s no surprise many organizations try — and stall.‍
There’s a Better Way: Unify Without Rebuilding‍
Here’s what modern SaaS leaders are doing instead:
- Virtualizing access across systems like Salesforce, Snowflake, HubSpot, and more
- Stitching together signal-rich views without copying or relocating data
- Training models on unified views that reflect real customer behavior
- Embedding predictions where teams already work (like CRM and support tools)
This means faster onboarding, lower risk, and predictive power without structural overhaul.‍
Final Thought: Silos Aren’t Just a Data Problem. They’re a Performance Problem.‍
It’s not that your teams don’t have the right tools.
It’s that your systems can’t talk — and your models can’t learn.
The longer data stays siloed, the longer decisions rely on gut feel and partial views.
But when the walls come down?
That’s when AI can finally work — and GTM performance catches up to GTM potential.
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