SFA BASICS
All about Image recognition in retail execution
The importance of the presence of goods on the shelf in the store and the prerequisites for the appearance of Image recognition in trade.
The critical point of contact between a brand product and its customer is directly in the store, specifically on the shelf. Therefore, the presence of goods on the shelf, their position on this shelf, the quantity, and correct information about the product affect the final result of sales.
Mistakes in at least one of the product display elements can result in a sales drop from 1% to 5%, depending on how quickly we can identify and eliminate the cause.
Why do brands and retailers need to use Image recognition:
Retail, particularly the number of outlets, is growing yearly by an average of 3-7%, depending on the country. At the same time, the costs of the production of goods, its logistics, and high competition do not allow a significant increase in the budgets for the merchandising team and sales representatives. Retail, in turn, is also being optimized in terms of personnel; as a result, we see a need for more resources for high-quality work on the shelf. In these circumstances, it is essential to serve many outlets with the same high-quality display and work with the shelf, while keeping the same budget. Image recognition is a technology that allows you to find points of project optimization by reducing the time for the sales agents' work and the analysts' team who check reports or collect data on competitors.
The critical point of contact between a brand product and its customer is directly in the store, specifically on the shelf. Therefore, the presence of goods on the shelf, their position on this shelf, the quantity, and correct information about the product affect the final result of sales.
Mistakes in at least one of the product display elements can result in a sales drop from 1% to 5%, depending on how quickly we can identify and eliminate the cause.
Why do brands and retailers need to use Image recognition:
Retail, particularly the number of outlets, is growing yearly by an average of 3-7%, depending on the country. At the same time, the costs of the production of goods, its logistics, and high competition do not allow a significant increase in the budgets for the merchandising team and sales representatives. Retail, in turn, is also being optimized in terms of personnel; as a result, we see a need for more resources for high-quality work on the shelf. In these circumstances, it is essential to serve many outlets with the same high-quality display and work with the shelf, while keeping the same budget. Image recognition is a technology that allows you to find points of project optimization by reducing the time for the sales agents' work and the analysts' team who check reports or collect data on competitors.
Critical problems to solve with IR
1. Human factor. When filling out a questionnaire, sales agents (merchandisers, sales representatives, auditors) often do not carefully evaluate SKUs, prices, quantities, etc. As a result, the questionnaire displays false information about the availability of goods on the shelf.
2. Intentional distortion of reporting by sales agents. Sales personnel enter incorrect OSA and OOS values into the questionnaire, upload previously used photos to SFA, and fill in data on prices from memory. These actions lead to management making decisions based on incorrect data. It is a complex issue, the solution of which lies both in the field of SFA functionality and Image recognition.
3. Non-compliance with the standards of display and work with the shelf. The data filled in by a merchandiser or auditor during a visit to the store requires verification. So needs a photo that is used as proof of the quality of the display. Image recognition automatically checks key performance indicators:
4. Routine in the work of the sales staff. The need to fill in statistical data in the same format day after day can tire and demotivate merchandisers, auditors, and sales representatives. As a result, we see high rotation and a need for additional sources to search for new employees and train them.
5. Inefficient waste of working time of sales agents. To enter all the necessary information on the visit (On-shelf availability, Out of stock, prices, share of shelf, competitor's promo in price, etc.), you must first manually check the SKU on the shelf with your eyes, and then manually enter the data into the application. The built-in Image recognition module in SFA lets you get most of the data automatically while avoiding human error.
6. Additional costs for checking reports and photos by project analysts. Usually, to make sure that the merchandiser visits the store correctly and merchandising standards are met, project analysts randomly check 15-30% of visits. At the same time, the analyst verifies the photo and the entered data. So we spend money on the work hours of an expensive analyst who can still make human errors.
1. Human factor. When filling out a questionnaire, sales agents (merchandisers, sales representatives, auditors) often do not carefully evaluate SKUs, prices, quantities, etc. As a result, the questionnaire displays false information about the availability of goods on the shelf.
2. Intentional distortion of reporting by sales agents. Sales personnel enter incorrect OSA and OOS values into the questionnaire, upload previously used photos to SFA, and fill in data on prices from memory. These actions lead to management making decisions based on incorrect data. It is a complex issue, the solution of which lies both in the field of SFA functionality and Image recognition.
3. Non-compliance with the standards of display and work with the shelf. The data filled in by a merchandiser or auditor during a visit to the store requires verification. So needs a photo that is used as proof of the quality of the display. Image recognition automatically checks key performance indicators:
- On-shelf availability
- Merchandising standards
- Out of stock
- Planogram compliance
- Pricing compliance
- Share of shelf
4. Routine in the work of the sales staff. The need to fill in statistical data in the same format day after day can tire and demotivate merchandisers, auditors, and sales representatives. As a result, we see high rotation and a need for additional sources to search for new employees and train them.
5. Inefficient waste of working time of sales agents. To enter all the necessary information on the visit (On-shelf availability, Out of stock, prices, share of shelf, competitor's promo in price, etc.), you must first manually check the SKU on the shelf with your eyes, and then manually enter the data into the application. The built-in Image recognition module in SFA lets you get most of the data automatically while avoiding human error.
6. Additional costs for checking reports and photos by project analysts. Usually, to make sure that the merchandiser visits the store correctly and merchandising standards are met, project analysts randomly check 15-30% of visits. At the same time, the analyst verifies the photo and the entered data. So we spend money on the work hours of an expensive analyst who can still make human errors.
Tasks for Image recognition
The Image recognition option as part of SFA helps to visit faster while providing accurate and transparent data, including automatically confirmed KPIs. Met KPIs mean confidence for the marketing and sales team that their plans are 100% fulfilled!
The Image recognition option as part of SFA helps to visit faster while providing accurate and transparent data, including automatically confirmed KPIs. Met KPIs mean confidence for the marketing and sales team that their plans are 100% fulfilled!
How does it work?
- Step 1A user takes a photo in a store using the Ailet app
- Step 2Images are instantly processed and recognized
- Step 3Analytics are available in real-time for sales representatives at the point of sale and sales and marketing managers in the office
What are KPIs effectively monitored with Image recognition?
At Key 2 Work, through experience working on projects and in different categories and different formats of stores, we came to the need for a brand to track the following 8 KPIs:
At Key 2 Work, through experience working on projects and in different categories and different formats of stores, we came to the need for a brand to track the following 8 KPIs:
What are KPIs effectively monitored with Image recognition?
At Key 2 Work, through experience working on projects and in different categories and different formats of stores, we came to the need for a brand to track the following 8 KPIs:
- On Shelf Availability (OSA)Compare the planned product range with the actual availability of goods on the shelves and get a percentage of the plan.
- Share on the shelf (SOS)The system compares the manufacturer's assortment with all products of the category or brand and calculates the share percentage on the shelf.
- Out of stock (OOS)The application detects the number of out-of-stock items according to the assortment matrix
- Eye-levelThe presence of goods on the shelves at eye level (eye-level). Order and layout on the shelf (planogram). Layout by blocks (BrandBlock)
- Assortment control in branded coolers and displays
- Planogram Compliance: measurement of the discrepancy between the target and actual placement of goods on the shelves
- Price monitoringMonitoring of promotional activities in trade points: prices availability, correctness, detection of dumping
Competitor price monitoring: price comparison with competitors, price index calculation - POSM detection and classificationPOSM type detection.
Determination of brand belonging to POSM
The results of the implementation of Image recognition in the work of the sales team
#1
Absolute quality control of display and merchandising standards
#2
Accuracy of data and KPIs - up to 96-98%
#3
Release up to 40% of the merchandiser's or auditor's time for a visit.
#4
Invest the freed time in working with additional outlets within the budget or saving money on merchandising.
#5
Quickly collect information about competitors, which means the ability to promptly set tasks for your sales team and not lose sales.
#6
Significantly reduce costs (60-80% of analysts' working time) for checking the quality of the sales team. Keeping a staff of analysts who verify photos and data for each visit is optional.
#7
Reduce the level of rotation of merchandisers and auditors. Due to automation, trade personnel can quickly carry out cyclical actions and spend more time physically laying out the goods, updating the price, and checking the balance in the warehouse. At the same time, there is an opportunity to prove yourself in negotiations with the network manager and prove yourself!
#8
1)The most important result of the introduction of IR is, of course, sales growth. According to the experience of our surveyed clients in 4 countries, the introduction of IR technology has increased sales by an average of 1.5-3%.
Case 1.
Problem: High rotation of merchandisers -28%
Client: major dairy manufacturer
Before: Previously, merchandisers spent 30% of the visit time filling in data about the shelf and prices and comparing the planogram with the actual display. According to team surveys, the reason for the dismissal of 70% of employees was the routine at work and fatigue from the same type of actions.
After: 2 months after the implementation of K2W Image recognition(link), merchandisers spent on the visit 30% less time on and employees received more strategic tasks in negotiating with the store category manager and additional motivation within this task.
Result: Rotation decreased to 16%
Problem: High rotation of merchandisers -28%
Client: major dairy manufacturer
Before: Previously, merchandisers spent 30% of the visit time filling in data about the shelf and prices and comparing the planogram with the actual display. According to team surveys, the reason for the dismissal of 70% of employees was the routine at work and fatigue from the same type of actions.
After: 2 months after the implementation of K2W Image recognition(link), merchandisers spent on the visit 30% less time on and employees received more strategic tasks in negotiating with the store category manager and additional motivation within this task.
Result: Rotation decreased to 16%
Case 2
Problem: High costs of auditing outlets
Client: major soft drink manufacturer
Before: for the audit of 7 regions within one country, a team of 450 people worked, and the time for each visit was 23 minutes. In addition, the company had four analysts who checked the reliability of the auditors' reports.
After: After the introduction of K2W Image recognition (link), auditors spent 5 minutes on a visit, and the company reduced the number of the required personnel to 190 people
Result: The cost of implementing audits, even taking into account the investment in Image recognition, decreased by 36%
Problem: High costs of auditing outlets
Client: major soft drink manufacturer
Before: for the audit of 7 regions within one country, a team of 450 people worked, and the time for each visit was 23 minutes. In addition, the company had four analysts who checked the reliability of the auditors' reports.
After: After the introduction of K2W Image recognition (link), auditors spent 5 minutes on a visit, and the company reduced the number of the required personnel to 190 people
Result: The cost of implementing audits, even taking into account the investment in Image recognition, decreased by 36%
Case 3:
Problem: Poor quality merchandising
Client: confectionery manufacturer
Before: An audit of the quality of merchandising showed systematic errors in compliance with the planogram, the absence of price tags, and often the absence of goods in stock. The average % of KPI completion was 76%
After: By implementing K2W Image recognition, merchandisers take photos before the visit and immediately receive tasks based on the recognition results, and after the visit, a second photo is taken, which confirms the fact of a high-quality calculation.
Result: Average KPI increased to 94%. Rotation also decreased by 11%.
Problem: Poor quality merchandising
Client: confectionery manufacturer
Before: An audit of the quality of merchandising showed systematic errors in compliance with the planogram, the absence of price tags, and often the absence of goods in stock. The average % of KPI completion was 76%
After: By implementing K2W Image recognition, merchandisers take photos before the visit and immediately receive tasks based on the recognition results, and after the visit, a second photo is taken, which confirms the fact of a high-quality calculation.
Result: Average KPI increased to 94%. Rotation also decreased by 11%.
Let it start...
