Summary

Recht Associates sought to increase business visibility with an intelligent platform that enables analytics across commission and sales volume to track missed commission payments and the movement of products across their thousands of customers

The Business

Recht Associates’ legacy began in 1962 in Los Altos CA and has then successfully grown into a multi-million dollar business by leveraging long-term relationships with customers, manufacturers, and distribution channel partners. By leveraging its deep knowledge of customers in the ever-changing marketplace, Recht Associates is consistently able to maximize their principal manufacturers’ revenue while providing best-in-class service to their customers. Recht Associates remains one of the leading manufacturers’ representatives in Northern California and is recognized for its dedicated and disciplined approach to bringing value to its principal manufacturers and customers.

The Challenge

With the massive volume of sales Recht Associates processes, any manual attempt to digest the data will be incomplete. This led to multiple problems: commissions were regularly unpaid by the manufacturers, there was no visibility in the order history of customers, and the overall movement of specific parts across their customers was unknown.

The Solution

The goal was to create a highly customizable user interface that facilitates automated data processing, aggregation, and analytics. Once the data passes through these steps, potential problematic transactions could be identified and exported to an Excel file for an analyst to review.

CodeFutures began with data exploration in order to better understand how to identify missed commissions and holes within Recht Associates’ processed transaction history. A data processing strategy was created based on insight from Recht’s manual procedures and further analysis of raw transaction data:

• Given the specified unique identifiers and aggregation function, automatically pivot raw data into a time-series transaction sheet (per unique buyer/seller/part/etc.)
• Automate period-over-period transaction checking and analysis using machine learning algorithms and transaction comparisons
• Cut relevant transaction data from millions of entries to a few thousand. The remaining data consists of those transactions which have been deemed as potentially erroneous based on the criteria chosen by the user.

The above strategy has been implemented as a desktop business intelligence application. The frontend application, developed using the React and Electron javascript frameworks, is designed to be user-friendly to all across an organization. The user is given free rein to customize the analytics, aggregations, and checks that will be applied to the data. Data processing and backend APIs are run through Python scripts that are deployed onto an AWS EC2 server which also holds the relevant raw transaction data. On the front-end, user-customized analytics are displayed as reports containing statistics and charts, as well as a table with the remaining erroneous transactions. Satisfactory reports are downloadable as Excel spreadsheets for ease of further analysis.

The Result

A successful MVP was launched providing Recht Associates with the necessary visibility and analytics for their business. The increased efficiency of finding missed commission and potential loss of business allows Recht Associates to divert time saved to further support customers and manufacturers.

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