To stand out from the competition, you need more than just revenue growth management; you need AI-powered revenue growth management initiatives that are tailored to your company’s goals. Find out why.
Revenue Growth Management (RGM) is essential to CPG companies’ ability to balance maintaining top-line revenue growth with sustainable profit margins. In this market, that’s no easy feat; pandemic-influenced market disruptions and shifts in channels and consumer preferences have created new challenges. Add to this the seemingly continual evolution of technological tools and you can see why it’s a complicated environment.
In other words, it’s the ideal time to upgrade your existing RGM with AI. In this article, we’ll look at how AI supercharges revenue growth management and helps CPGs turn their data into actionable analytics.
Now’s the Time to Pair AI and RGM
During the last ten years, many CPG companies have used data initiatives to learn about consumers’ habits: who is buying what, and where, and how. And this progress hasn’t been static; other data sources, studies, and techniques have been added to the arsenal. The upshot is that RGM itself no longer confers a competitive edge – it’s simply a part of standard operations. To really get an impact from RGM, it’s time to add AI to the mix.
Not only does AI open the door to advanced capabilities (think pricing, promotions, trade investments, product optimizations etc.), it allows companies to adjust to the complexities and changing demands of the modern market. It is scalable and flexible, and it enables predictive analysis, hyperpersonalization, and other increasingly essential functions. By integrating multiple data sources and real-time insight, it allows companies to understand what their customers are doing, thinking, and feeling now. And, along with machine learning, AI can learn from and improve its own performance, becoming more effective in its guidance, predictions, and suggestions. And it also tracks how various actions impact KPIs across the entire organization. With this knowledge in place, CPG companies are in a better position to reduce costs, expand their audience, locate new opportunities, and set themselves apart from the competition.
What Does AI Bring to Revenue Growth Management?
Perhaps the key change that AI brings to revenue growth management is moving from insights to recommendations – i.e. from what’s going on to what to do about it. But, much like AI itself, that is a many-faceted thing. So, let’s take a closer look at some of the facets:
- Data unification: Modern data doesn’t just live in spreadsheets and databases; it’s scattered across channels and sources. AI detects, collects, classifies, and harmonizes data from a huge number of sources (point of sale, demographic, social, purchase history, etc.). This data is then made available for analysis, which helps teams create a comprehensive customer view.
- Granularity: This unified data can also be very finely segmented, which helps customize models, predictions, and recommendations to different groups and circumstances.
- Enhanced predictive modeling: Models that incorporate multiple data sources can do large-scale analysis, predict possible outcomes, estimate baselines, and create forecasts combining a bunch of disparate factors. They can also tailor objectives, identify patterns, and recognize complex interactions. And, as mentioned before, they can create plans and segmentations on a very small scale. This makes AI and ML (machine learning) effective for everything from large-scale planning to hyper-individualized marketing campaigns.
- Real-time action recommendations: AI doesn’t depend on last month’s or last quarter’s data; it constantly scans channels, markets, and other company’s actions and gives you real-time insight on threats, possible opportunities, and relationships – including offering guidance on what approach should bring the best results.
- Hyperpersonalization: The ability to personalize recommendations on an individual level is one of today’s leading trends. This applies across sales, marketing, relationship management, customer service, and other areas. It’s interesting to note that AI gauges customer/contact preferences in real time, allowing companies to proverbially strike while the iron is hot.
- Self-learning: Machine learning allows AI systems to monitor their own performance and find ways to improve it. Additionally, it incorporates a feedback loop that enables users to weigh in on the efficacy of recommendations. The result is that performance improves over time. Also, AI’s self-learning capabilities make testing cycles quicker.
- Automation: By taking over repetitive, high-volume (and often tedious) processes, AI reduces the workload on some employees, optimizes workflows, and improves overall efficiency and productivity.
AI-Led RGM: Next Steps
The benefits of using AI to charge up CPG companies’ RGM programs are numerous. But first, ask yourself the following questions. They’ll help you clarify where you need to direct your RGM efforts:
- How does our net revenue compare to inflation?
- How do our capabilities compare to those of our competitors? Are we behind or ahead of the curve?
- Where has RGM historically fit into our overall strategy? How is it implemented across different areas and teams?
- Do we need to change how we integrate RGM?
- Are we approaching RGM from a scalability standpoint, or are we still stuck in silos? Have we taken it online? What is our coverage like on an organizational level? Do we have toolkits for mature and/or developing markets?
- How are we using data analytics to boost our revenue? Is there room for improvement here?
Depending on your answers, you’ll be able to choose the right path to AI RGM. We’ve helped companies create and implement successful AI RGM roadmaps; we know how to tailor them to your objectives using various solutions for strategic and tactical RGM implementations. So, feel free to contact Absolutdata for expert help on your journey to AI-powered revenue growth management.