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  1. Home
  2. Technology & Innovation

How TMR Plans to End the Era of Manual Data Analysis in Marketing

The platform aims to eliminate the hours marketers spend deciphering dashboards and instead surface clear, actionable insights the moment they matter

09 December, 2025 | Interaction | By Shraddha Warde|shraddha.warde@mmactiv.com
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In a digital landscape where data volumes are exploding faster than teams can interpret them, marketers are increasingly caught between information overload and the pressure to deliver measurable results. Two Minute Reports (TMR), founded by Paul Jebasingh Emmanuel, is tackling this gap with an ambitious new leap: an AI Data Analyst Agent designed to function like a full-time analyst working around the clock. Built on TMR’s high-performance SpeedBoost data backbone and powered by its real-time AI Watchtower, the platform aims to eliminate the hours marketers spend deciphering dashboards and instead surface clear, actionable insights the moment they matter.

In this interview with AI Spectrum India, Emmanuel discusses the core problem driving the industry’s analytics fatigue, how TMR’s AI stack is redefining decision-making for brands and agencies, and why he believes the future of marketing intelligence lies in predictive automation, conversational analytics, and AI-driven optimisation across every channel.

What core problem in the marketing analytics landscape prompted Two Minute Reports (TMR) to develop the AI Data Analyst Agent, and how does this product transform the way marketers work today?

Two Minute Reports helps marketers automate their data through reports and dashboards. Marketers today are overwhelmed with data but starved of insight. Reports and dashboards automated by Two Minute Reports (TMR) already save them hours - yet research shows that marketers still spend 3.8 hours every week just analysing and explaining that data.

The AI Data Analyst Agent changes this entirely. With the AI Data Analyst Agent, Two Minute Reports helps marketers to quickly analyse past campaigns, figure out new opportunities, strategise future campaigns, identify pitfalls automatically without losing their sleep. Instead of manually figuring out why performance changed, marketers can now simply ask the AI things like:

“Why did conversions drop yesterday?”

“Where are we wasting budget?”

“Which campaign deserves more investment?”

“What hidden opportunities can boost ROI?”

The AI continuously monitors performance, interprets changes, and narrates the insights in plain language. It’s like hiring a full-time data analyst, who is working 24x7 - giving marketers their time back to focus on creativity and strategy.

Can you explain how the SpeedBoost data backbone operates behind the scenes? What makes it different from traditional data integration platforms used by marketing teams?

As marketing data grows, traditional dashboards slow down, break, or require costly data warehousing like BigQuery or Snowflake. Setting those up demands IT skills, database management, sync monitoring, handling API failures, schema design, data blending, adding new metrics/dimensions and constant maintenance - something only large enterprises can afford.

SpeedBoost solves this by giving every business, even the smallest a built-in high-performance data warehouse, automatically managed end-to-end:

Sub-second reports, even with millions of rows

Auto-fixing sync failures

Real-time metric/dimension updates

Zero infrastructure setup

No IT involvement

With SpeedBoost, users simply connect their accounts and the system does the rest. Clean, unified data becomes the foundation for powerful AI-driven insights — without the burden of managing data architecture.

The AI Watchtower seems central to the product. How does it detect opportunities, gaps, and critical issues in real time, and what level of accuracy can marketers expect from its insights?

Without Watchtower, marketers must comb through multiple dashboards to uncover what matters. Watchtower continuously watches all data using domain-trained intelligence across Performance marketing, Ecommerce funnels and SEO signals

It then surfaces only the insights that truly matter, not 1001 meaningless notifications.

Examples:

“Instagram reels outperform stories by 32 per cent — shift budget for 3 days.”

“UTM tracking failed — Meta conversions underreported since Tuesday.”

“Top customer segment stopped engaging — check landing page speed.”

Accuracy has improved significantly through continuous learning. Users can provide feedback, helping the AI refine itself over time, leading to fewer false positives and smarter prioritization every day.

With marketing data growing rapidly, how does your AI ensure reliability and avoid false positives while monitoring dozens of metrics across multiple channels?

It's an important question. We’ve built reliability at two levels: The first one is through a Guardian AI. It reviews every insight before it reaches the user, learning from feedback and automatically reducing false positives over time. The second is through a separate module we call Goals where users define what truly matters: revenue targets, ROAS thresholds, cost limits, inventory limits, etc. This helps the AI steer insights that help the users achieve their goals. This ensures users only see accurate and relevant alerts:

What competitive advantage does this AI Data Analyst Agent offer agencies and brands, especially those struggling with limited bandwidth or data expertise?

Agencies and marketing teams are always short on time and analysts. This product gives them:

A full-time data analyst without hiring one

AI-driven recommendations to improve performance faster

Faster reporting cycles leading to stronger client relationships

Ability to scale more clients without scaling headcount

Better retention through strategic insights, not just data dumps

Instead of spending 70 per cent of their time preparing reports, agencies can now spend that time on strategy, creativity, and client growth - turning analytics from a cost center into a competitive advantage.

Looking ahead, how do you see AI-driven intelligence evolving in the marketing ecosystem? Are there upcoming capabilities or integrations TMR is already exploring?

We believe AI will remove the boring parts in the marketing ecosystem. Looking ahead, we’re building capabilities that go beyond insights:

Predictive intelligence - forecast next week’s revenue, CAC, or churn

Automated optimization - AI can pause poor ads or reallocate budgets instantly

Conversational dashboards - ask any question and get visual answers

Cross-channel attribution intelligence - finally know what’s driving actual revenue

We aim to make decisions fast, insights proactive, and reclaim time for creativity.

 

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