Revenue Management​

Despite the upsurge of various business execution tools, it has been observed that many manufacturers of packaged goods targeted at consumers make business decisions related to revenue upscaling and expansions laterally. What is important to remember is that while the tools offered in the market can assist a business to provide the best market data research results based on behavioral patterns and buying capacity of consumers by application of data science and engineering, they lack the assessment of  an essential component i.e each business operates differently and brings in a different level of complexity and context.

Big manufacturers and business need to assess that the requirement of the hour is to adopt a unique solution that involves the integration of data collected from the market research and from the perspective of the retailers and make genuine efforts to applying solution oriented techniques towards complex problems which can in turn amplify the revenue expansion and administration.

The biggest problem currently faced manufacturers worldwide in the food industry is the point to understand the factors that really drive the sales, revenue of the organization and the techniques that can be adopted to further enhance the capacity and provide support to such factors as they are the main contributors of sales and revenue expansion.

They fail to provide substantive answers to the most basic question that affect the expansion of business in the market and they are as follows:

1. What is the right size of budget allocation to the trade affecting the revenue the most?
2. What are the control mechanisms that should be implemented?
3. Which promotional activities and goods to target for revenue maximization?

The approach that should be adopted is the implementation of the tools exclusively owned by our organization i.e OP for mapping the data complexity, OP for hypothesis-driven problem definition. JindalX have developed certain aspects that approach the problem in a systematic manner to assess the problem triggers:

1. Disintegrated Data – The data is placed in many different format in various places and thereby acting as a factor which leads to under-evaluation of the amount spent so far on a particular good due to the lack of a unified structure.
2. Singular Results – Employees interacting with a wide array of retailers follow different approaches and tactics to produce the requisite results and thereby implementing different KPIs

We ensure that we develop a streamlined and a systematic approach of combining the insights obtained from different employees, retailers and consumer packaged goods (CPG) brands to devise a strategic income and profit generation action plan right from the scratch.

This approach of SRM enables any trading organisation to become more data oriented, take realistic decisions about the four most important aspect of Trade promotion:

1. Promotions – What is the right time to initiate product promotions?
2. Strategic Pricing – what is the price or the discount rate that should be highlighted during promotions?
3. Trade structure – whether the promotional activities be supported by advertisements, whether to involve public figures in the entertainment industry for better audience attention or which channels/routes to be adopted to promote?
4. Portfolio mix – in case there are two products are can be co-promoted due to their inter-relation or overlap for the consumers, which product generates more revenue as opposed to the other?

The solution is simple. Adoption of a SRM Framework for prevention of performing work independently without regard to factors that affect the revenue and leading to ineffective revenue expansion.

The Strategic Revenue Management Platform

1. Data Organisation – Data received from multiple sources is organised into promotion-specific cloud-based data pool. This is backed by a system that can again be used and is a viable solution for acquisition, integration, analysis and storage of more than 2 Mn data rows.
2.  Analytical Foundation  – Based on the usage of machine learning based models, this ensures the adoption of the right approach for developing and implementing the right sales aggregator models, optimisation of engines to promote trade and generate a visualization for better insights.
3. Impact – Helps in the generation of values and revenue generation chains