In today’s fast-moving FMCG landscape, data is the new currency—but most brands fail to use it effectively. Nearly 80% of field data collected by retail companies goes unused, leading to missed insights and slower decisions. Retail Intelligence changes that. By combining AI in retail with advanced retail intelligence software and retail management systems, businesses can turn raw data into real-time insights that boost sales, improve expense management, and enhance customer experiences. This blog explores how leading brands are using retail intelligence platforms to transform everyday data into smarter strategies and sustainable growth.
What Is Retail Intelligence and Retail Management?
Retail intelligence is the process of collecting and analyzing real-time data from stores, products, and customers to make smarter business decisions. Powered by AI in retail, a modern retail intelligence platform helps brands track sales performance, customer behavior, and promotional impact with precision. When integrated with a retail management system, it ensures smooth operations across inventory, billing, and expense management. Using advanced retail intelligence software, companies can optimize store execution, reduce leakages, and drive profitable growth.
Key Benefits of Retail Intelligence:
- Smarter Decision-Making: Leverage AI in retail to turn raw data into actionable insights that boost sales and efficiency.
- Enhanced Store Performance: A retail intelligence platform helps identify top-performing outlets, SKUs, and regions for better planning.
- Optimized Expense Management: Track spends and ROI through integrated retail management systems and analytics.
- Improved Inventory Control: With retail intelligence software, brands can prevent stockouts and manage shelf availability in real time.
- Data-Driven Promotions: Measure the true impact of campaigns and refine strategies based on performance insights.
Unified Retail Management: Combine sales, operations, and reporting under one intelligent retail management system for seamless execution.
How Businesses Are Using Retail Intelligence to Improve Customer Experiences:
- Personalized Engagement: Brands use retail intelligence to analyze customer behavior and tailor experiences that boost loyalty.
- Smarter Decisions with AI: AI in retail helps predict buying trends, optimize product placement, and enhance promotions.
- Unified Operations: A connected retail intelligence platform and retail management system provide real-time visibility across sales, inventory, and expense management.
- Efficient Store Execution: Retail intelligence software ensures the right SKUs reach the right outlets, improving availability and satisfaction.
- Enhanced Customer Experience: Data-driven insights help deliver seamless, personalized, and memorable shopping journeys.
80% of Field Data Collected by Retail Companies Goes Unused, Here’s How to Translate it into Action
In today’s competitive FMCG world, businesses invest heavily in collecting data on sales, customers, and operations. While data collection is no longer a challenge, the real gap lies in using Retail Intelligence to turn this data into actionable insights. Without a strong retail intelligence platform, companies risk stagnating sales, poor productivity, and delayed decisions.
- The Data Utilization Gap in Retail Companies:
Almost every FMCG brand has embraced Retail Intelligence Software and AI in Retail to collect vast amounts of data — from products, team performance, and outlets to sales and expense tracking. However, most companies still lack the expertise to leverage this information effectively. For instance, metrics like total calls, productive calls, retailing time, and lines cut are readily available in Sales Automation Platforms, yet only a few sales managers use them to improve coverage, range selling, or retail management system strategies.
- Back to Basics with Retail Intelligence:
A harsh truth of today’s market is that businesses need to revisit retailing fundamentals. Retail Intelligence helps ensure salesmen spend sufficient time in the market, add new outlets, and focus on range selling. By analyzing the right metrics, companies can maximize their outlet reach, improve SKU penetration, and streamline expense management at every level.
- Improving Coverage Using Retail Intelligence:
Coverage is the percentage of outlets covered out of your outlet universe. Poor coverage often results from:
Retail Intelligence Platforms can track and improve metrics like First Call Time, Retailing Hours, Strike Rate, Total Calls (TC), Productive Calls (PC), Outlets per Beat, Beat Frequency, Outlets Not Visited, and New Outlets Added. For example, a productive salesman in Delhi/NCR makes 40–45 calls a day. If your team averages below that, AI in Retail can highlight gaps and help improve performance.
- Improving Range Selling Using Retail Intelligence:
If your company has 100 SKUs with lines cut of 5 per outlet, you can use Retail Intelligence Software to identify Focus SKUs, Must-Sell SKUs, and Fast-Moving SKUs Using retail analytics and proper SKU segmentation, companies can train salesmen effectively, ensure scheme communication, and boost range selling outcomes.
- Building an Innovative Incentive Model:
Even with actionable insights, businesses can fail if they simply increase pressure on their teams. Instead, create an innovative incentive model aligned with your KRAs. Retail Management Systems can help you track metrics like Productive Calls, Total Calls, Lines Cut per Outlet, and Retailing Time. Incentivizing the right behavior — for example, extra lines cut per outlet- ensures motivation and continuous improvement.
80% of Field Data Collected by Retail Companies Goes Unused, Here’s How to Translate it into Action
In today’s competitive FMCG world, businesses invest heavily in collecting data on sales, customers, and operations. While data collection is no longer a challenge, the real gap lies in using Retail Intelligence to turn this data into actionable insights. Without a strong retail intelligence platform, companies risk stagnating sales, poor productivity, and delayed decisions.
Back to Basics – Retail Intelligence
A harsh truth of today’s market is that businesses need to revisit the basics of retailing. Your sales won’t improve until your salesmen spend the required amount of time in the market, add new outlets, and do range selling.
How to Improve Coverage using Retail Intelligence?
Coverage is the percentage of outlets covered out of the outlet universe. A few reasons behind poor market coverage are low retailing time, ineffective beat planning, Improper Outlet Segmentation, and poor beat health. And to improve coverage, you might want to target metrics such as First Call Time, Retailing Hours, Strike Rate, Total Calls (TC), Productive Calls(PC) No. of Outlets in a Beat, Beat Frequency as per the Outlet Segmentation, No. of Outlets Not Visited and No. of New Outlets Added.
A productive salesman does 40-45 calls in Delhi/NCR Region. Some salesmen even do around 60 TC. How many calls does your salesman make? If this number is less than 40, it is a red flag. You must immediately react to it and shift your focus to improving productive calls as it will directly impact the coverage. This is how you can leverage retail analytics.
How to Improve Range Selling using retail intelligence?
Let’s suppose that your company has 100 SKUs with lines cut of 5 for each outlet. Now you know that range selling is one of the areas you should target for improvement. One of the reasons behind such a low range selling might be that you have not used data at your hand to find Focus SKUs, Fast Moving SKUs, and Must-Sell SKUs. And even if you figure it out, you still have to evaluate the reason for no sales of those SKUs. Another reason could be non-communication of scheme information to retailers. Retail analytics can help you eliminate all these issues.
However, these issues can be resolved to foster better range selling through Retail Intelligence. The primary thing to do is create proper segmentation of your SKUs and then train your salesmen accordingly. Key metrics such as Lines Cut Per Call, Focus SKU sales, Must Sell SKU Sales, Fast Moving SKU Sales and Reason for No Sale must be targeted.
Innovative Incentive Model
Often organizations struggle even if they have translated the data into actionable insights. The main reason is that they try to do this by just putting extra pressure on the team. What they actually need is an innovative incentive model to keep the team motivated, encouragement rather than compulsion. The incentive model should be based on the KRAs which needs to be improved. If your current lines cut per outlet is 5, then you should incentivize this metric for the extra lines cut per outlet. KRAs such as Productive Calls, Total Calls, Lines Cut per Outlet, Retailing Time should be part of your incentive model.
Conclusion: Turn Data into Decisions with Retail Intelligence
From small brands to global FMCG leaders, those who stick to the basics, analyze data, and act smartly using Retail Intelligence Software consistently outperform competitors. With a powerful retail intelligence platform, integrated retail management system, and AI in Retail, you can:
- Unlock hidden insights from unused data
- Improve coverage and range selling
- Optimize expense management and retail operations
- Boost sales, visibility, and market share
Now is the time to revisit your data, build your retail intelligence strategy, and act. The difference between a successful and a struggling brand is no longer how much data you collect but how intelligently you use it.
Stop collecting data. Start using it.
Discover how FieldAssist’s Retail Intelligence Software turns insights into impact.
About Post Author
