Increase revenue with on-target recommendations
Unlock new revenue opportunities and better decisions with fresh, accurate data.
Retail/CPG Vertical Webpage
Monte Carlo for Retail and CPG
Improve internal analytics, shopping experiences, and increase revenue with reliable and trustworthy data.
Leading data teams in retail and CPG choose Monte Carlo
Learn how Resident, a leading direct to consumer brand, decreased data quality incidents by 90% with Monte Carlo.
Benefits
90%
Decrease in data quality issues
Dramatic reduction in time-to-detection of data quality issues
“Before Monte Carlo, I was always on the watch and scared that I was missing something. I can’t imagine working without it now. We have 10% of the incidents we had a year ago. Our team is super reliable, and people count on us. I think every data engineer has to have this level of monitoring in order to do this work in an efficient and good way.”
Data Stack
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Missing and duplicative data, complicated queries and inconsistent logic across pipelines causes confusion in analytics
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Missing ingestion pipelines from marketing sources, losing visibility into increase marketing spend efficiency
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Lack of access to fresh, up-to-date data for stakeholders to make decisions
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Custom alerting for known business logic, such as update frequency for 3rd party data
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Field-level lineage graphs to show downstream impact of changes
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Automated thresholding for data quality metrics across key tables
Benefits
90%
Decrease in data quality issues
Dramatic reduction in time-to-detection of data quality issues
-
Missing and duplicative data, complicated queries and inconsistent logic across pipelines causes confusion in analytics
-
Missing ingestion pipelines from marketing sources, losing visibility into increase marketing spend efficiency
-
Lack of access to fresh, up-to-date data for stakeholders to make decisions
-
Custom alerting for known business logic, such as update frequency for 3rd party data
-
Field-level lineage graphs to show downstream impact of changes
-
Automated thresholding for data quality metrics across key tables
Data Stack
“Before Monte Carlo, I was always on the watch and scared that I was missing something. I can’t imagine working without it now. We have 10% of the incidents we had a year ago. Our team is super reliable, and people count on us. I think every data engineer has to have this level of monitoring in order to do this work in an efficient and good way.”
Use cases for e-commerce
Improve inventory management
Prevent excess or insufficient inventory spend by staying on top of critical data issues.
Generate accurate marketing analytics
Acquire more users across your digital channels by using reliable data to analyze your ad spend for the highest performing channels.
Use cases for brick & mortar
Set thousands of reporting thresholds without a single manual test
Every storefront has different thresholds for acceptable levels for their reporting metrics – Establish data quality baselines across business domains
Generate reliable sales data for more accurate forecasting
Inaccurate data leads to missed sales opportunities, wasted spend, and customer frustration that could lead to potential churn when inventory isn’t quickly restocked.
Don’t leave on-prem data behind
With online channels increasingly pointing users towards digital and physical locations, reconcile data between disparate systems to provide a seamless customer experience.
Learn more about our product
Detect
Out-of-the-box coverage across all your data tables, opt-in monitors for key assets, and monitors-as-code.
Resolve
Don’t just sound the alarm when data incidents occur. Empower your data teams to resolve incidents in minutes, not days.
Prevent
Rich insights enable your team to proactively ensure data quality, and make better infrastructure investment decisions.
