NEW CASE STUDY: Implementation of eLeader Mobile Visit at DANONE
In the battle for the shelf it is all about speed, data quality and… a perfect display.
Discover the way to see, know and sell more.
Dispatch artificial intelligence into the field and see your sales strategy execution gets its wings.
DOWNLOAD FREE WHITEPAPER
eLeader Shelf Recognition AI is a solution enabling field representatives to report on product shelf display and price based on intelligent image recognition. The results arrive within the time of the visit. This is why the representative can react instantly using reliable data.
Product and price recognition of the highest quality is now possible thanks to our deep neural network that we are training to become a tireless store shelf analyst, active day and night, 365 days a year.
Owing to the application of the dispersed training method, the system improves quickly and the implementation is performed instantly.
The functionality of eLeader Shelf Recognition AI can be smoothly expanded by any processes used in the field (surveys, promotion audits, route planning, training, perfect store). You need to know that at your fingertips, you have the most functional tool of its class available on Android and iOS mobile platforms.
Shortages will appear in the order form automatically, and the system will request the reasons for the absence of the products.
An immediate check of the fulfilment of a shelf contract means a better position for negotiations with the store manager.
The swiftly calculated share of shelf is an opportunity for a competent discussion with the store manager on how to increase this parameter.
In order to make the work with eLeader Shelf Recognition AI fast and to gain knowledge from it on how products are recognized and what store displays actually look like, we have created a special interactive report for each analyzed product, rack and store. This multi-layer report combines figures, indicators and codes with a visual presentation of the situation in a given store with the accuracy of one product face. It is available both in central and mobile applications.
Among the companies that decided to make a quantum leap towards real reporting and obtaining information valuable to the sales force from the market was DANONE, which decided to support human senses with artificial intelligence closed in a mobile application.
Manual facing audit
The average time of inputting product data from the device’s keyboard.
As much as 70-90% of data is simulated and fraudulent
Shelf Recognition AI
The average time needed to take photos of the display.
Only 1-3% unrecognized product faces
Ensuring free access to our products for consumers calls for constant analysis and maintaining high display standards in stores. The technology for image recognition in the application eLeader Shelf Recognition AI provides effective support to our sales representatives in these activities. Equipped with this technology, they report an unparalleled amount of detailed information about store shelves compared to the period before the system was implemented.
Paweł Kaczyński Business Systems AnalystDANONE
Thanks to eLeader Shelf Recognition, we can manage sales processes much more effectively because we receive high quality data from the field. Work with the application helps us increase the level of key parameters for us: availability of products and the quality of their in-store exposure.
Sebastian ZapałaRegional Operations Manager SalesNUTRICIA Poland
Implementation of eLeader Shelf Recognition caused some user resistance in 2014. Today, former skeptics admit that working with the app gives significant benefits in a shelf competition and optimizes visit reporting.
Rafał PrusSales Support ManagerGTM DepartmentMaspex
Results of a display analysis performed during one visit
Lower costs thanks to artificial intelligence
Reliable and manipulation-resistant data, shorter time of a display analysis
More business decisions during a visit (initiating orders, contracts, promotion audit etc.)
The most extensive functionality in the world
No more inspection visits of supervisors
Artificial intelligence consumes the computing power of machines, but saves human energy
The implementation of the adopted display strategy is gaining momentum due to online verification of product display. KPI – so far calculated based on manual audits – can now be measured automatically and corrected during the same visit.
MORE ABOUT PERFECT STORE
RAO (Retail Activity Optimization)
Display improvement actions, orders for out-of-stocks or contextual surveys can automatically supplement the visit plan right after the shelf has been photographed. The information from the photos on product availability, shelf share or facing contributes to a “ranking” of shops, which determines the frequency and scenarios of visits through automatic planning.
The BI system will happily capture all the parameters of your display, so that the information about the execution of your display strategy is up to date and available on demand. eLeader Shelf Recognition is keen to provide such data.
Verification and building planograms have never been so simple, but the true power of eLeader Shelf Recognition AI is revealed in combination with eLeader mCatMan, which allows relevant Category Management at the level of any store, even… a corner store.
Feel the power of data flowing from eLeader Shelf Recognition AI
Fill in the form – we will provide you with all the details and show how you can benefit from this solution!
I hereby declare my consent to process the data provided via the contact form in order to receive a response from eLeader Sp. z o.o. to the submitted query.
Source: Retail Out-of-Stocks: A Worldwide Examination of Extent, Causes and Consumer Responses, 2002.
Your organization in details
Calculated number of visits per month in the team0
Calculated total length of exposition to measure (km/month) 0
SavingsBadanie ręczne vs eLeader Shelf Recognition
Oszczędność na jednej wizycie (minuty)0 min 0 sek
Oszczędność w dniu pracy 1PH (minuty)0 min 0 sek
Monthly savings per a single sales rep (number of man-days)0
Monthly savings per team (number of FTEs)0
*Assumptions: Time required for manual audit of 1m of shelf 105s, time required for taking a picture of 1m of shelf: 10s, a number of workdays per month: 20, workday duration: 8h.