CPG-Retail

CPG Retail Execution Analytics Trends for 2025: Key Insights

Ankit Singh
January 7, 2026
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The Consumer Packaged Goods (CPG) industry is evolving faster than ever. In 2025, CPG companies face the dual challenge of meeting increasing consumer expectations while ensuring their products remain visible and compliant in stores.  Poor retail execution often leads to significant sales losses, as products that are missing from the shelf or placed incorrectly cannot be purchased.

Analytics plays a key role by giving CPG teams clearer visibility into what is happening inside physical stores. Recent industry reports show that nearly 60% of CPG executives prioritizing AI and analytics to improve visibility and decision‑making.

By analyzing real-time data, CPG brands can identify stock gaps, track share of shelf, and assess compliance with planograms. Brands that integrate analytics effectively can optimize field operations, close execution gaps, and improve the return on their in-store marketing investments.

At a Glance:

  • Real-Time Shelf Insights: Offers a clear view of stock levels, product placement, and promotions to help teams decide what action to take next.
  • Planogram and Promotion Monitoring: Helps ensure products and campaigns are displayed correctly, keeping consistency and improving impact.
  • AI-Driven Analytics: Uses automation to detect SKUs, prioritizes field actions, and delivers actionable insights quickly.
  • Mobile and SKU-Level Tools: Provides the field team with on-the-go access and detailed SKU data to enhance store performance.

What is CPG Analytics?

CPG analytics refers to the use of data and technology to track, measure, and optimize how products perform in physical stores. It helps CPG companies identify gaps in execution and take corrective actions without relying solely on manual audits.

Here’s why it matters:

  • Real-time Shelf Insights: CPG analytics gives brands immediate visibility into on-shelf stock, product placement, and promotional execution, allowing faster corrective action.
  • Planogram Compliance Tracking: It helps ensure products follow the brand’s planogram, maintaining consistency across all locations.
  • Share of Shelf Monitoring: Brands can track the space their products occupy on the shelf compared to competing SKUs, helping them refine in-store visibility strategies based on accurate shelf data.
  • Actionable Data for Field Teams: Analytics offers clear, prioritized insights for sales and marketing teams, helping them address execution gaps quickly and improve store performance.

7 Key CPG Analytics Trends to Watch in 2025

‍

The CPG analytics space is evolving with new technologies and approaches. Several trends are shaping how companies monitor and improve their in-store execution.

1. Real‑Time Shelf Monitoring

In 2025, CPG brands will increasingly demand instant visibility into in-store shelf conditions instead of relying on delayed, periodic audits. Real-time shelf monitoring helps brands identify gaps, misplacements, or stockouts as they occur.

  • Continuous image capture: Field agents scan stores frequently to provide shelf images, giving brands near-live updates on what’s missing or misplaced.
  • Instant alerts on stockouts: When a product goes out of stock or falls below a target shelf presence, brands get immediate notifications, enabling field teams to act quickly.
  • Visual verification of promotions: Brands verify that promotional displays and price tags are in place right when the campaign starts, not days later.

By using real-time shelf monitoring, CPG companies turn snapshots of store conditions into actionable insights, helping them close the loop between audits and execution.

2. Increased Adoption of AI for Retail Execution

Artificial intelligence is transforming how CPG brands understand and manage in-store execution. AI reduces human dependency, accelerates data analysis, and provides accurate insights for faster corrective action.

  • Automated SKU Detection: AI models quickly identify product placement, missing items, and compliance issues across large numbers of stores.
  • Continuous Learning: AI improves accuracy over time, adapting to new SKUs, layouts, and retail environments.
  • Resource Optimization: By automating shelf data capture and analysis, AI equips field teams with clear, prioritised insights, allowing them to decide where to focus efforts and improve execution quality.

AI adoption enables smarter, more efficient retail execution by converting shelf data into precise, actionable insights at scale.

3. Enhanced Planogram Compliance Tracking

Ensuring that product placement follows the planned layout remains a major challenge for CPG brands in physical stores. In 2025, analytics tools are sharpening their focus on planogram adherence, giving brands clearer visibility into how well store displays follow the approved layouts.

  • Snapshot vs. plan comparison: Shelf images are compared to the approved planogram, enabling detection of missing SKUs, wrong facings, or incorrect positioning.
    • Flagging deviations in real time: If a high-priority SKU is misplaced or a category loses shelf space, brands receive instant alerts. Its crucial as poor planogram compliance directly impacts on-shelf availability and category sales.
  • Prioritised store fixes: Field teams focus on stores where compliance issues are most significant, ensuring efficient use of visits.

With enhanced planogram compliance tracking, CPG brands move beyond “did we do the layout?” to “how closely are we following it?” and ensure shelf execution aligns with strategic planograms.

4. Promotion Monitoring and Validation

Promotions are expensive and rely heavily on in-store execution, yet many fail due to misplacement or missed pricing. In 2025, CPG brands use analytics to monitor promotion setup and validate whether it’s working as intended at the shelf level.

  • Placement check for promotional material: Ensures that POP‑materials, signage, and promo‑tags are present and positioned correctly.
  • Price verification consistency: Brands can spot where discounted price tags are missing or incorrect, helping to avoid lost uplift or compliance penalties.
  • Competitive adjacency tracking: Provides insight into whether competitor SKUs are appearing next to promo displays or taking premium shelf space away during a campaign.

Monitoring and validating promotions ensures that every campaign performs as intended, protecting marketing investments and driving measurable impact.

5. Integration of Mobile Analytics Tools

Field teams are the eyes and ears of in-store execution. In 2025, mobile analytics tools will help these teams respond quickly by delivering shelf insights directly to their smartphones, enabling smarter store visits and faster action.

  • On‑the‑go access to alerts and images: Field agents receive dashboards and alerts on their mobile devices, guiding them to stores with execution gaps.
  • Store‑visit prioritisation: Mobile apps help teams pick the highest-priority store visits based on real-time shelf data, rather than generic route schedules.
  • Instant feedback loops: Actions taken in the store (e.g., correcting a planogram or placing a missing SKU) can be logged and updated immediately, allowing brands to close the feedback loop quickly.

When mobile analytics are integrated into retail execution workflows, CPG brands amplify the value of their shelf data by making sure field teams use it actively, rather than passively reviewing reports later.

6. Focus on SKU‑Level Insights

Detailed insight at the SKU level is becoming non-negotiable for CPG brands. In 2025, analytics platforms are shifting from category-level visibility to SKU-level precision, helping brands see exactly which SKUs are under-performing or mis-placed.

Even a single misplaced or missing SKU can lead to lost share of shelf, reduced visibility for priority products, and revenue leakage that compounds across hundreds of stores.

  • SKU‑specific availability tracking: Brands monitor whether each SKU is present on the shelf, in the right quantity and location, enabling root‑cause correction.
  • SKU shelf‑share measurement: Analytics measure the shelf space each SKU occupies relative to others in the category, enabling brands to protect or expand their share.
  • Performance Tracking: SKU-level tracking supports merchandising and marketing decisions tied directly to execution outcomes.

With SKU‑level insights, CPG brands obtain the precision necessary to address execution gaps and improve overall shelf effectiveness.

7. Expanded Coverage Across Store Formats

Analytics is no longer limited to supermarkets. Brands are extending their monitoring to convenience, discount, and specialty stores to maintain consistent retail execution everywhere.

  • Uniform Oversight: Maintains consistent product visibility and planogram compliance across diverse store types.
  • Optimized Resource Allocation: Helps field teams decide which stores need urgent attention based on coverage data.
  • Market Insights: Identifies format-specific performance trends to refine execution strategies.

Expanding coverage ensures that CPG brands maintain high execution standards across all store formats, maximizing shelf visibility and promotional effectiveness.

These trends collectively indicate a shift toward proactive, data-driven retail execution in 2025.

Key Technologies Driving Analytics in CPG

‍

The rapid growth of analytics in the CPG space is driven by advanced technologies. These technologies help brands collect, process, and use shelf data quickly and accurately.

Here are the key technologies shaping CPG analytics in 2025:

1. Image Recognition and Computer Vision

Image recognition technology helps CPG brands automatically identify products on shelves from photos or video feeds. Computer vision algorithms analyze images to identify missing SKUs, misplaced items, and the share of shelf. This automation reduces manual audits and ensures greater accuracy across multiple store locations.

2. Artificial Intelligence and Machine Learning

AI and machine learning processes large volumes of shelf data at scale. These technologies detect patterns, predict potential execution gaps, and highlight anomalies such as misaligned planograms or missing promotional displays. They enable faster, data-driven decisions by turning raw visual data into actionable insights.

3. Cloud-Based Analytics Platforms

Cloud-based platforms centralize data from multiple stores, giving teams easy access to shelf information in real time. These platforms enable seamless reporting, collaboration across regions, and quick analysis of trends. Cloud infrastructure ensures that insights stay up to date and remain available for decision-making.

4. Mobile and Edge Computing

Mobile devices and edge computing allow field teams to capture and access shelf data on the spot. Field agents can take photos, analyze shelf conditions immediately, and act on insights during store visits. This technology improves efficiency and reduces delays in correcting execution errors.

5. Automated Data Annotation

Automated annotation tools streamline the labeling of images for analytics purposes. These tools reduce manual effort, ensure consistency in data processing, and accelerate the training of machine learning models. Faster annotation means new products or SKUs can be analyzed more quickly, and insights can be generated in less time.

Challenges in CPG Analytics Adoption

While analytics offers tremendous benefits, CPG companies face challenges in implementing these solutions effectively. Traditional manual store audits are time-consuming, inconsistent, and often subjective, whereas automated shelf analytics delivers faster, standardized, and scalable data, highlighting why legacy workflows often fall short.

  • Ensuring Data Accuracy and Consistency: Manual audits often lead to errors, making it hard to compare store performance. Automated image recognition provides standardized, objective data, which helps reduce discrepancies across multiple locations.
  • Integrating Analytics into Workflows: Insights are only useful if they drive action. CPG teams need analytics platforms that deliver real-time data directly to field agents and marketing teams in a format that aligns with daily routines.
  • Resource Limitations: Many brands have limited field staff to cover numerous stores. AI-driven analytics does not replace field teams but supplements their efforts by extending visibility, helping agents prioritize visits and focus on corrective actions.
  • Training and Adoption: New technology adoption can be slow if platforms are complex. User-friendly dashboards, automated insights, and rapid AI model training help teams quickly learn to use data effectively from the start.
  • Setting Realistic Expectations: Brands may expect analytics to solve challenges beyond shelf visibility, such as stock-level or display-related visibility issues.

Understanding the platform’s scope ensures success and accurate measurement of outcomes.

How ParallelDots Can Propel Your CPG Analytics Strategy?

ParallelDots provides visual shelf data solutions that help CPG brands overcome these challenges. By providing accurate, real-time insights, ParallelDots enables brands to take corrective action faster and improve in-store execution efficiency.

Here’s how we can assist you:

  • Real-Time Shelf Visibility: ShelfWatch provides continuous monitoring of in-store product placement, identifying stockouts, misplaced SKUs, or promotional errors as they happen. This enables field teams to act immediately, maintaining consistent shelf availability and presentation.
  • Accurate Planogram Compliance: ParallelDots ensures that SKUs are positioned according to the pre-defined planogram. Deviations are flagged in real-time, allowing sales teams to correct errors and maintain compliance across all stores.
  • Promotional Execution Monitoring: ParallelDots tracks the correct placement of promotional materials and pricing. Any non-compliance can be corrected promptly, ensuring campaigns deliver their intended impact and ROI.
  • Scalable AI Model Training with Saarthi: Through Saarthi, new SKUs can be added to analytics platforms within 48 hours. Automated data annotation and AI model training reduce manual effort, improve accuracy, and deliver actionable shelf KPIs consistently.
  • Actionable Insights for Field Teams: By providing clear, prioritized tasks, ShelfWatch allows field agents to focus on stores and issues that matter most. This increases productivity, reduces audit times, and ensures better execution across the retail network.

Take control of your in-store execution with ParallelDots’ AI-powered shelf analytics and ensure every store visit drives measurable impact. Request a demo today to see ShelfWatch in action.

Frequently Asked Questions

1. How are CPG companies using AI and data science to enhance retail execution?

CPG companies use AI and data science to optimize shelf placement and improve store execution efficiency. Advanced analytics helps identify trends, monitor in-store performance in real time, and make data-driven decisions that improve sales, reduce stockouts, and enhance overall brand visibility.

2. Which new data sources are becoming critical for CPG analytics in 2025?

In 2025, CPG analytics will increasingly rely on data from smart shelves, in-store sensors, and field team apps. These sources provide deeper insights into store-level performance and regional trends, enabling brands to refine assortments and strategies more precisely.

3. What impact do data privacy and compliance regulations have on CPG analytics?

Data privacy and compliance regulations like GDPR and CCPA require CPG companies to manage consumer data responsibly. They affect data collection, storage, and sharing practices, pushing brands to adopt secure, anonymized analytics methods while ensuring transparency, consent management, and adherence to legal standards.

4. What are the emerging best practices for data governance in CPG analytics?

Emerging best practices include establishing clear data ownership, implementing standardized processes for data quality, ensuring regulatory compliance, and adopting centralized data platforms. Companies focus on role-based access, consistent metadata management, and continuous monitoring to improve reliability, trust, and actionable insights across the organization.

The Consumer Packaged Goods (CPG) industry is evolving faster than ever. In 2025, CPG companies face the dual challenge of meeting increasing consumer expectations while ensuring their products remain visible and compliant in stores.  Poor retail execution often leads to significant sales losses, as products that are missing from the shelf or placed incorrectly cannot be purchased.

Analytics plays a key role by giving CPG teams clearer visibility into what is happening inside physical stores. Recent industry reports show that nearly 60% of CPG executives prioritizing AI and analytics to improve visibility and decision‑making.

By analyzing real-time data, CPG brands can identify stock gaps, track share of shelf, and assess compliance with planograms. Brands that integrate analytics effectively can optimize field operations, close execution gaps, and improve the return on their in-store marketing investments.

At a Glance:

  • Real-Time Shelf Insights: Offers a clear view of stock levels, product placement, and promotions to help teams decide what action to take next.
  • Planogram and Promotion Monitoring: Helps ensure products and campaigns are displayed correctly, keeping consistency and improving impact.
  • AI-Driven Analytics: Uses automation to detect SKUs, prioritizes field actions, and delivers actionable insights quickly.
  • Mobile and SKU-Level Tools: Provides the field team with on-the-go access and detailed SKU data to enhance store performance.

What is CPG Analytics?

CPG analytics refers to the use of data and technology to track, measure, and optimize how products perform in physical stores. It helps CPG companies identify gaps in execution and take corrective actions without relying solely on manual audits.

Here’s why it matters:

  • Real-time Shelf Insights: CPG analytics gives brands immediate visibility into on-shelf stock, product placement, and promotional execution, allowing faster corrective action.
  • Planogram Compliance Tracking: It helps ensure products follow the brand’s planogram, maintaining consistency across all locations.
  • Share of Shelf Monitoring: Brands can track the space their products occupy on the shelf compared to competing SKUs, helping them refine in-store visibility strategies based on accurate shelf data.
  • Actionable Data for Field Teams: Analytics offers clear, prioritized insights for sales and marketing teams, helping them address execution gaps quickly and improve store performance.

7 Key CPG Analytics Trends to Watch in 2025

‍

The CPG analytics space is evolving with new technologies and approaches. Several trends are shaping how companies monitor and improve their in-store execution.

1. Real‑Time Shelf Monitoring

In 2025, CPG brands will increasingly demand instant visibility into in-store shelf conditions instead of relying on delayed, periodic audits. Real-time shelf monitoring helps brands identify gaps, misplacements, or stockouts as they occur.

  • Continuous image capture: Field agents scan stores frequently to provide shelf images, giving brands near-live updates on what’s missing or misplaced.
  • Instant alerts on stockouts: When a product goes out of stock or falls below a target shelf presence, brands get immediate notifications, enabling field teams to act quickly.
  • Visual verification of promotions: Brands verify that promotional displays and price tags are in place right when the campaign starts, not days later.

By using real-time shelf monitoring, CPG companies turn snapshots of store conditions into actionable insights, helping them close the loop between audits and execution.

2. Increased Adoption of AI for Retail Execution

Artificial intelligence is transforming how CPG brands understand and manage in-store execution. AI reduces human dependency, accelerates data analysis, and provides accurate insights for faster corrective action.

  • Automated SKU Detection: AI models quickly identify product placement, missing items, and compliance issues across large numbers of stores.
  • Continuous Learning: AI improves accuracy over time, adapting to new SKUs, layouts, and retail environments.
  • Resource Optimization: By automating shelf data capture and analysis, AI equips field teams with clear, prioritised insights, allowing them to decide where to focus efforts and improve execution quality.

AI adoption enables smarter, more efficient retail execution by converting shelf data into precise, actionable insights at scale.

3. Enhanced Planogram Compliance Tracking

Ensuring that product placement follows the planned layout remains a major challenge for CPG brands in physical stores. In 2025, analytics tools are sharpening their focus on planogram adherence, giving brands clearer visibility into how well store displays follow the approved layouts.

  • Snapshot vs. plan comparison: Shelf images are compared to the approved planogram, enabling detection of missing SKUs, wrong facings, or incorrect positioning.
    • Flagging deviations in real time: If a high-priority SKU is misplaced or a category loses shelf space, brands receive instant alerts. Its crucial as poor planogram compliance directly impacts on-shelf availability and category sales.
  • Prioritised store fixes: Field teams focus on stores where compliance issues are most significant, ensuring efficient use of visits.

With enhanced planogram compliance tracking, CPG brands move beyond “did we do the layout?” to “how closely are we following it?” and ensure shelf execution aligns with strategic planograms.

4. Promotion Monitoring and Validation

Promotions are expensive and rely heavily on in-store execution, yet many fail due to misplacement or missed pricing. In 2025, CPG brands use analytics to monitor promotion setup and validate whether it’s working as intended at the shelf level.

  • Placement check for promotional material: Ensures that POP‑materials, signage, and promo‑tags are present and positioned correctly.
  • Price verification consistency: Brands can spot where discounted price tags are missing or incorrect, helping to avoid lost uplift or compliance penalties.
  • Competitive adjacency tracking: Provides insight into whether competitor SKUs are appearing next to promo displays or taking premium shelf space away during a campaign.

Monitoring and validating promotions ensures that every campaign performs as intended, protecting marketing investments and driving measurable impact.

5. Integration of Mobile Analytics Tools

Field teams are the eyes and ears of in-store execution. In 2025, mobile analytics tools will help these teams respond quickly by delivering shelf insights directly to their smartphones, enabling smarter store visits and faster action.

  • On‑the‑go access to alerts and images: Field agents receive dashboards and alerts on their mobile devices, guiding them to stores with execution gaps.
  • Store‑visit prioritisation: Mobile apps help teams pick the highest-priority store visits based on real-time shelf data, rather than generic route schedules.
  • Instant feedback loops: Actions taken in the store (e.g., correcting a planogram or placing a missing SKU) can be logged and updated immediately, allowing brands to close the feedback loop quickly.

When mobile analytics are integrated into retail execution workflows, CPG brands amplify the value of their shelf data by making sure field teams use it actively, rather than passively reviewing reports later.

6. Focus on SKU‑Level Insights

Detailed insight at the SKU level is becoming non-negotiable for CPG brands. In 2025, analytics platforms are shifting from category-level visibility to SKU-level precision, helping brands see exactly which SKUs are under-performing or mis-placed.

Even a single misplaced or missing SKU can lead to lost share of shelf, reduced visibility for priority products, and revenue leakage that compounds across hundreds of stores.

  • SKU‑specific availability tracking: Brands monitor whether each SKU is present on the shelf, in the right quantity and location, enabling root‑cause correction.
  • SKU shelf‑share measurement: Analytics measure the shelf space each SKU occupies relative to others in the category, enabling brands to protect or expand their share.
  • Performance Tracking: SKU-level tracking supports merchandising and marketing decisions tied directly to execution outcomes.

With SKU‑level insights, CPG brands obtain the precision necessary to address execution gaps and improve overall shelf effectiveness.

7. Expanded Coverage Across Store Formats

Analytics is no longer limited to supermarkets. Brands are extending their monitoring to convenience, discount, and specialty stores to maintain consistent retail execution everywhere.

  • Uniform Oversight: Maintains consistent product visibility and planogram compliance across diverse store types.
  • Optimized Resource Allocation: Helps field teams decide which stores need urgent attention based on coverage data.
  • Market Insights: Identifies format-specific performance trends to refine execution strategies.

Expanding coverage ensures that CPG brands maintain high execution standards across all store formats, maximizing shelf visibility and promotional effectiveness.

These trends collectively indicate a shift toward proactive, data-driven retail execution in 2025.

Key Technologies Driving Analytics in CPG

‍

The rapid growth of analytics in the CPG space is driven by advanced technologies. These technologies help brands collect, process, and use shelf data quickly and accurately.

Here are the key technologies shaping CPG analytics in 2025:

1. Image Recognition and Computer Vision

Image recognition technology helps CPG brands automatically identify products on shelves from photos or video feeds. Computer vision algorithms analyze images to identify missing SKUs, misplaced items, and the share of shelf. This automation reduces manual audits and ensures greater accuracy across multiple store locations.

2. Artificial Intelligence and Machine Learning

AI and machine learning processes large volumes of shelf data at scale. These technologies detect patterns, predict potential execution gaps, and highlight anomalies such as misaligned planograms or missing promotional displays. They enable faster, data-driven decisions by turning raw visual data into actionable insights.

3. Cloud-Based Analytics Platforms

Cloud-based platforms centralize data from multiple stores, giving teams easy access to shelf information in real time. These platforms enable seamless reporting, collaboration across regions, and quick analysis of trends. Cloud infrastructure ensures that insights stay up to date and remain available for decision-making.

4. Mobile and Edge Computing

Mobile devices and edge computing allow field teams to capture and access shelf data on the spot. Field agents can take photos, analyze shelf conditions immediately, and act on insights during store visits. This technology improves efficiency and reduces delays in correcting execution errors.

5. Automated Data Annotation

Automated annotation tools streamline the labeling of images for analytics purposes. These tools reduce manual effort, ensure consistency in data processing, and accelerate the training of machine learning models. Faster annotation means new products or SKUs can be analyzed more quickly, and insights can be generated in less time.

Challenges in CPG Analytics Adoption

While analytics offers tremendous benefits, CPG companies face challenges in implementing these solutions effectively. Traditional manual store audits are time-consuming, inconsistent, and often subjective, whereas automated shelf analytics delivers faster, standardized, and scalable data, highlighting why legacy workflows often fall short.

  • Ensuring Data Accuracy and Consistency: Manual audits often lead to errors, making it hard to compare store performance. Automated image recognition provides standardized, objective data, which helps reduce discrepancies across multiple locations.
  • Integrating Analytics into Workflows: Insights are only useful if they drive action. CPG teams need analytics platforms that deliver real-time data directly to field agents and marketing teams in a format that aligns with daily routines.
  • Resource Limitations: Many brands have limited field staff to cover numerous stores. AI-driven analytics does not replace field teams but supplements their efforts by extending visibility, helping agents prioritize visits and focus on corrective actions.
  • Training and Adoption: New technology adoption can be slow if platforms are complex. User-friendly dashboards, automated insights, and rapid AI model training help teams quickly learn to use data effectively from the start.
  • Setting Realistic Expectations: Brands may expect analytics to solve challenges beyond shelf visibility, such as stock-level or display-related visibility issues.

Understanding the platform’s scope ensures success and accurate measurement of outcomes.

How ParallelDots Can Propel Your CPG Analytics Strategy?

ParallelDots provides visual shelf data solutions that help CPG brands overcome these challenges. By providing accurate, real-time insights, ParallelDots enables brands to take corrective action faster and improve in-store execution efficiency.

Here’s how we can assist you:

  • Real-Time Shelf Visibility: ShelfWatch provides continuous monitoring of in-store product placement, identifying stockouts, misplaced SKUs, or promotional errors as they happen. This enables field teams to act immediately, maintaining consistent shelf availability and presentation.
  • Accurate Planogram Compliance: ParallelDots ensures that SKUs are positioned according to the pre-defined planogram. Deviations are flagged in real-time, allowing sales teams to correct errors and maintain compliance across all stores.
  • Promotional Execution Monitoring: ParallelDots tracks the correct placement of promotional materials and pricing. Any non-compliance can be corrected promptly, ensuring campaigns deliver their intended impact and ROI.
  • Scalable AI Model Training with Saarthi: Through Saarthi, new SKUs can be added to analytics platforms within 48 hours. Automated data annotation and AI model training reduce manual effort, improve accuracy, and deliver actionable shelf KPIs consistently.
  • Actionable Insights for Field Teams: By providing clear, prioritized tasks, ShelfWatch allows field agents to focus on stores and issues that matter most. This increases productivity, reduces audit times, and ensures better execution across the retail network.

Take control of your in-store execution with ParallelDots’ AI-powered shelf analytics and ensure every store visit drives measurable impact. Request a demo today to see ShelfWatch in action.

Frequently Asked Questions

1. How are CPG companies using AI and data science to enhance retail execution?

CPG companies use AI and data science to optimize shelf placement and improve store execution efficiency. Advanced analytics helps identify trends, monitor in-store performance in real time, and make data-driven decisions that improve sales, reduce stockouts, and enhance overall brand visibility.

2. Which new data sources are becoming critical for CPG analytics in 2025?

In 2025, CPG analytics will increasingly rely on data from smart shelves, in-store sensors, and field team apps. These sources provide deeper insights into store-level performance and regional trends, enabling brands to refine assortments and strategies more precisely.

3. What impact do data privacy and compliance regulations have on CPG analytics?

Data privacy and compliance regulations like GDPR and CCPA require CPG companies to manage consumer data responsibly. They affect data collection, storage, and sharing practices, pushing brands to adopt secure, anonymized analytics methods while ensuring transparency, consent management, and adherence to legal standards.

4. What are the emerging best practices for data governance in CPG analytics?

Emerging best practices include establishing clear data ownership, implementing standardized processes for data quality, ensuring regulatory compliance, and adopting centralized data platforms. Companies focus on role-based access, consistent metadata management, and continuous monitoring to improve reliability, trust, and actionable insights across the organization.

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