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Data Research Analysis

Data Research Analysis

AI-powered marketing analytics for executives

mustafaneguibmustafaneguib·Published on Oct 23, 2026·0 reputationReputation invested into this product to boost its ranking. Anyone can spend the reputation they've earned here — recent investments count for more and lift the product higher on the leaderboard. This is separate from community upvotes.
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Categories
MarketingAnalytics & MonitoringData & Infrastructure
Use cases
AI AnalyticsAnalytics & ReportingData Integration
Target audience
MarketersFounders & CEOsDigital Marketers
Pricing model
Freemium
Platforms
Web

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About Data Research Analysis

Marketing leaders waste months and tens of thousands of dollars on BI tools that still can not answer the CFO's most critical questions: "Which marketing channels actually drive revenue?"

We have developed the Data Research Analysis Marketing Intelligence Platform specifically for B2B marketing executives who are frustrated with traditional analytics.

We help marketing leaders who are struggling with:

  1. No multi-touch attribution: Stuck with last-click attribution from Google Analytics that over simplifies the customer journey and leads to poor budget decisions.
  2. Data analyst dependency: Waiting 2-4 weeks for reports because traditional BI tools require SQL/DAX expertise that marketing teams do not have.
  3. Long implementation timelines: Traditional BI takes 4-6 months to deploy - your competitors are making data driven decisions while you are still waiting on it.
  4. Disconnected marketing data: Exporting CSVs from 5+ platforms (Google Ads, Meta Ads, Google Analytics etc...) and manually combining them in Excel are error prone and unsustainable.
  5. Can't prove marketing ROI: CFO questions every budget request because you can not show which channels deliver returns vs. which waste money.
  6. Expensive tool stack: Paying $8500+/month for platforms including analyst time with costs coming from already stretched marketing budgets.
  7. Wasted resources: Team exhaustion is the result of high data drudgery and inadequate resourcing. Strategic talent is wasted on manual data entry and maintenance.

What we deliver:

  1. 6 attribution models out-of-the-box: First-touch, last-touch, linear, time-decay, Position Based, and Data Driven switch between models with one click to see the complete picture of your customer journey.
  2. AI-powered data modelling: Describe what you need in plain English, for example, "Show me ROI by channel". AI builds the entire data and even dashboard visualisations extremely quickly - no SQL required.
  3. Native marketing integrations: Connect Google Ads, Meta Ads, etc... in 2-3 clicks via OAuth - no data engineer needed.
  4. Automated channel level, campaign level and ad group level comparison: The Marketing Overview provides detailed analysis so that you can instantly know which of your channels are performing and which are not performing.
  5. Self-service platform: Your entire marketing team can build reports, analysis, and more without waiting on IT or data teams.

What makes us different from Tableau, Power BI, Looker, etc...:

  1. Built for marketers, not adapted for marketers: Every feature exists because a CMO requested it. We started with "Which channels drive revenue?" and built backwards.
  2. Attribution is native and not custom: Competitors require 4-8 weeks of custom development for attribution models; ours work out of the box from day 1.
  3. AI does the heavy lifting: While Tableau users wait weeks for analysts to build data models, Data Research Analysis Marketing Intelligence Platform users describe what they want and get it in the fraction of the time.

✦ Good to know

  • Connects Google Ads, Google Analytics, and SQL data into one view of ad spend against revenue.
  • Requires no SQL or data engineer to build reports, according to the product page.
  • A free trial is offered, and demos can be booked through the website.

Taken from the product's own website and re-checked automatically.

DON'T TAKE OUR WORD FOR IT

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