BeatRoute Success Story
"We were pleasantly surprised at how quickly it was possible to create an AI solution"
Accurate forecasting using Neuton models allowed our clients to correctly push the sales of products to retail outlets and reduce lost revenue. Such services open up very wide opportunities for quick completion of their IT products for all companies who want to augment their ML expertise.
Vinay Singh, CEO, BeatRoute
Customer
BeatRoute — Sales Force Automation CRM Software. This easy-to-use & easy-to-deploy system is used by 150+ CPG & BFSI Enterprises to achieve high impact Goal-Driven Sales Transformation.

The Sales Force Automation Software (SFA App) for FMCG & Consumer Goods Companies is one of the company’s primary products. The FMCG Sales Force Automation software is used by companies to manage retailer relationships, distributors, wholesalers, traders, inventory, orders, returns, schemes, etc. as part of their retail distribution execution. The system is also used to enable sales executives to improve their individual performances.
Challenges
For retail distribution to work efficiently, it is important to predict the demand for each product at each point of sale, in advance. Accordingly, there is a need to make predictions for thousands of products, at potentially millions of retail stores. BeatRoute had set a goal for their Sales Force Automation software to provide FMCG companies with an opportunity to make such forecasts in advance & nudge the sales executives to push the product sales accordingly during visits, for all outlets and across a selected range of products, and they opted to utilize Neuton to make this goal a reality, and by the fastest means possible.
Solution
To develop such functionality, a machine learning model that can learn from historical data on sales of certain items of goods, and at certain points of sale, needed to be built. To do this, BeatRoute had collected data over 37 months from more than 22,000 outlets and relating to 127 product names. The data for building the first model was a CSV file with 883 thousand lines. The factors that were used to build the predictive model were:
name of the product
point of sale ID
period of sale
In addition, Neuton generated new factors in automatic mode.

All aspects of building the model took place in Neuton’s fully-automated mode, and the process was relatively simple:
First, historical sales data was uploaded to the Neuton AutoML platform.
The complete model building process then took less than 80 minutes.
Finally, it was necessary to integrate the Sales Force Automation software with the created model. To do this, BeatRoute utilized the option Neuton provides of implementing a model in one click and making requests through the API. Neuton now transmits the data in real-time in the following format: date, point of sale id and product name. Neuton then calculates a prediction and sends it to the BeatRoute’s Sales Force Automation software, where it is displayed to the user.
Vinay Singh, CEO, BeatRoute:
«We were pleasantly surprised at how quickly it was possible to create an AI solution and integrate it into our software.

It took us hours to build the model, not weeks. Thus, we have reduced the time and cost of development and implementation. Accurate forecasting using Neuton models allowed our clients to correctly push the sales of products to retail outlets and reduce lost revenue.

Such services open up very wide opportunities for quick completion of their IT products for all companies who want to augment their ML expertise.»
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