According to McKinsey, CPG companies pour 20% of their revenue annually into trade promotion optimization, however, 59% of the investment fails to make any impact.
What causes these trade promotions to fail, despite it being a highly influential element in nudging customers toward specific products?
The answer? Many organizations are still relying on traditional methods which have limited data analysis, are slow, prone to errors, and are out of depth for the complexities of today’s retail with multiple channels and constantly changing customer behaviour, making it a real challenge to craft an effective promotion strategy.
Companies are still lacking in the implementation of AI and analytics
Enters AI. Not only they are better at reacting to market changes and customer preferences, but they can also be predictive. From scenario analysis, forecasting, budget allocation, trend spotting, and consumer sentiment analysis- AI got you covered.
In this article, we’ll dive into the different analysis capabilities that AI enables in developing your Trade Promotion strategy.
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- Demand forecasting and planning
Machine learning models can combine historical data with external data like economic indicators & seasons. Moreover, AI can run causal impact analysis to estimate the effects of different events and trends to create an accurate demand forecast. With accurate demand, your trade promotion can be aligned better with expected customer needs and market changes.
- Dynamic pricing optimization
The current prices can be difficult to predict while optimizing your trade promotions, however, with reinforcement learning, patterns can be identified from past pricing decisions and their outcomes. Price Elasticity Analysis determines how customers react to changes in prices & discounts. You can also develop your dynamic pricing models that stay up-to-date with changing demand, supply, and competitor strategies.
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- Customer segmentation and personalization
Segment your customer using RFM (Recency, Frequency, Monetary) analysis to tailor marketing and promotions strategies specific to their buying behavior.
AI also pinpoints customers who are at risk of leaving through churn analysis, giving you the chance to offer them the right discount at the right time.
On the other hand, Customer lifetime value (CLTV) analysis can determine the long-term value of a customer, so you know exactly which customers are worth that extra treatment!
- Promotion timing and duration optimization
Use cannibalization analysis to anticipate whether the promotion of one product impacts sales of another. Predict when demand surges might hit- like during holidays- with event-based forecasting. Combining it with AI’s predictive and sequential pattern mining technique determines the best window to run your promotions with maximum impact with minimum cannibalization.
- Performance tracking and analysis
For trade promotion optimization in the long run, AI can analyze and learn from what did and what didn’t work in the past, improving its effectiveness over time.
Attribution modeling lets you compare how different promotional strategies work across various channels. While Sentiment Analysis digs into customer feedback using natural language processing (NLP) to gauge the impact of promotions on brand perception.
- Optimizing Promotional Spend Allocation
Budgeting your promotions can be a complex task, but with the various insights from AI, you can accurately forecast the best budget for an effective promotional performance.
Marginal analysis can be used to break down the incremental impact of each additional dollar spent on promotions on profit generation.
Now that you understand AI transformation capabilities, how trade promotion optimization can be achieved with these capabilities?
In the following section, we will take you to the comprehensive steps of creating a Trade promotion strategy.
Trade promotion Optimization steps to follow- A proven framework-
- Data Collection and Integration
The first step involves gathering data from various sources into a centralized platform. These platforms not only help analysis & forecasting later but also help create a synergy between the retailers, wholesalers, traders, and the organization through a common dashboard.
The data collected are internal- Historical, past spending data- and external- market data (trends, consumer behavior, competition), and economic conditions. And integrate third-party data for better decision-making.
Note- Before you proceed further, you must fix your data, i.e., clean it to remove inconsistencies. Create proper APIs for data integration and define data relationships.
- Baseline Forecasting
Before we start creating promotions, we need to establish a baseline forecast of what sales to expect with any promotion.
Modeling Baselines: Use AI forecasting models to split your historical sales into baseline & incremental value. Use casual analysis to quantify the various impacts.
Adjusting Baselines: Consider the current market factor- market changes or distribution shifts to adjust the plan.
- Scenario Planning
Evaluate potential outcomes using AI-driven scenario planning models, accessing pricing strategies, promotional types, and timing. We will also recommend you compare multiple scenarios side-by-side with test groups.
Use various AI models to refine predictions over time, create promotional content, evaluate pricing, and create various mitigation strategies.
Understand partner pain points to create a win-win strategy that will be 0profitable for both. You will then have to allocate the budget that best supports the category’s growth and organizational goals.
You will also need to calculate the cannibalization effect while considering forward buying by retailers and changes in seasonality.
- Promotion Planning and Execution
Based on selected scenarios, create a detailed promotion plan.
Promotion Library: Use successful past promotional templates for informed new strategies.
Collaborative Planning Tools: Keep all the stakeholders in the loop, from teams for planning and executing to third-party retailers.
Adaptive Playbooks- To improve your localization target, continuously update the playbook with real-time insight from AI and analytics
- Performance Tracking and Analytics
Real-Time Reporting: Implement dashboards that give you insights into key performance indicators (KPIs), such as Lift, Incremental sales, Cost per incremental dollar/capital, and ROI.
Post-Promotion Analysis: By evaluating actual results against forecasted performance, AI can identify reasons behind any discrepancies. This learning helps optimize AI and enables it to make better decisions in future campaigns.
Final Thoughts-
With the ability to analyze, massive datasets, spot trends, and create personalized experiences that create everlasting loyalties. When you bring AI into your trade promotion optimization mix, the result can a transformative-insight planning. But the real competitive edge only comes when your model learns from experience and matures. So, the sooner you start, the better! That being said, it’s essential to have a solid data framework that can not only be used to derive insights but is also crucial for training your model.