Revenue Growth Management: Leveraging it Right to Manage Inflationary, Pandemic Environments
Global trade was significantly disrupted by the COVID outbreak and the ensuing global lockdown, and most industries entered a state of crisis. The CPG sector, however, saw a completely different reaction to these outside influences.
In the USA alone, online CPG retail shot up from 36% to 53%, from 2019 to 2020, changing the industry altogether.
Pricing Optimization and Promotion Management were two traditional methodologies for CPG revenue management, but they were reactive and poorly adapted for the rapidly changing e-commerce landscape of today. There was no room for growing the activity, manual analytics, and insights were only made available after a campaign had already begun.
Success in omnichannel business today requires a certain level of scalability and flexibility in analytics and the accessibility of real-time information that may inform efficient pricing and promotion plans for both short- and long-term success.
The Need for Advanced Analytics in RGM Initiatives
Here’s a quick look at some of the drawbacks of traditional RGM, and why it’s being phased out.
- Being dependent on historical data, traditional Revenue Growth Management (RGM) doesn’t offer a 360o view of data, that can account for the shift in consumer behavior and purchase patterns being witnessed by the CPG industry.
- Added to this, is the fact that there is low cash flow because of a selective focus on high-growth products.
- Then there is a disconnect between field staff and the back office where consumer planning is still siloed and based on limited business intelligence.
- As a result, long-term goals are ignored for quick wins, and unsustainable business plans don’t contribute to revenue growth.
CPG companies required to adopt a data-based business model in order to expand their operations. Whereas their analytics-based RGM methods provide prescriptive insights to address supply chain disruptions, the rising popularity of direct-to-customer (D2C) e-commerce, and take into account new customers and competition.
Following a survey, the Boston Consulting Group came to the conclusion that 95% of businesses who had completed a digital transformation prior to the pandemic were using at least one solution for their efforts to increase sales. In addition, they stated that 77% of these businesses credited AI-driven RGM solutions with producing around 50% of their revenue. This underlines the necessity for RGM plans to be at the core of any CPG company’s business strategies, regardless of the company’s industry.
AI-Based RGM – The Way Forward
Organizations may now take use of the vast amounts of data that are available to key decision-makers by utilising cutting-edge AI and analytics capabilities along with powerful computing capacity provided by the digital transformation. Due to these developments, analytics for RGM projects may now be automated to provide actionable insights, pinpoint issues, identify opportunities, and provide prescriptive advice for business improvement and revenue growth.
Without human interaction, an AI-based RGM solution can assist a CPG company in finding possibilities to optimise trade, pack price, SKU mix, channel mix, and the profit pool. The firm intervenes to match its various business operations with the prospects being offered so that they might be put into action at the consumer level.
The automated analytics tool is still monitoring how the choices have affected the situation.
real-time feedback and ROI estimation, as well as the decisions made and the actions carried out. The machine learning capabilities of the AI and the feedback loop help to improve business decisions and maximise business growth.
The RGM solution also assists CPG firms with enhancing enterprise reporting, enhancing data governance, uncovering hidden value, optimizing KPIs at a fundamental level, eliminating superfluous marketing expenditure, and creating long-term value by redesigning business processes.
This integrated approach to RGM covers all the bases for a CPG company engaged in an omnichannel business model.
The RGM initiatives help to optimize pricing, marketing activities, distribution strategies, and product assortment at both the enterprise and the consumer front, across all channels. This in turn drives a profitable top line and helps grow revenue.
Strategic Revenue Management with Code Ekte
Code ekte has been an analytics partner for multiple Fortune 100 companies from the CPG industry and has been helping to develop effective revenue growth strategies. We understand your business problems and align our analytics solutions with your objectives for revenue growth.
Pricing Analysis and Promotion Optimization are two key elements of RGM, and Code ekte delivers on client requirements with our holistic solution based on Advanced Analytics. The four pillars of our analytics solution include Order Value Pricing, Competitor Intelligence, Price Elasticity, and Consumer Promotions.
Quantity-based Price Optimization Models
Price Optimization models are based on the variety of parameters considered, such as client tiers, periodic purchases, and business trends. We analyze market conditions, trends, profit goals, etc. to conduct intelligent tier selection and maximize revenue.
Price Position vs. Competitors
Leverage competitors’ pricing and understand price position with competitors’ pricing to understand competitive effects.
Promotions Impact Analysis
It considers holidays, promotions, seasonality, media, and coupons in analyzing the impact on volumes, and uplift due to promotions.
Price Elasticity
We compute this metric for each product line, season, and key customer segment, if possible. This helps to understand the effects of price change and Price Band forecasting.
The RGM solution from Code ekte follows an incremental process for execution and insight generation. The process is as follows –
- Business Understanding and Situation Analysis – Understand historical trends for the brand and category. Understand the business problems and objectives from the current pricing exercise.
- Data Collection and Structuring – Support data collection from the client’s site and convert the data to a format compatible with modeling.
- Exploratory Analysis – Discuss with the business teams exploratory analysis, trends, and initial hypotheses.
- Modeling – Model discussions and reviews with the business teams to further crystallize the hypothesis and related findings.
- Analysis, Simulation, and Reporting – Workshop to run various scenarios using our proprietary Price Promotion Simulator. Deliver PowerPoint report on Recommendations and actions for price mix changes, estimated impact on the net revenue and volume, opportunities, and risks associated with each scenario.
Last but not the least, having done the above, the important aspect is to drive the solution at scale. It should empower decision-makers to be able to access the insights in an easy-to-consume recommendation manner. 75% of the organizations that are able to develop sophisticated RGM models are restricted in their decision-making due to the lack of adoption of applied AI.
