In today's dynamic retail landscape, effectively managing trade promotions is critical for retailers looking to increase sales, improve customer loyalty and increase profitability. However, traditional approaches to trade promotions often fail to deliver optimal results. Artificial Intelligence (AI) is coming to the rescue, a technology that is revolutionizing the way retailers plan, execute and analyze sales promotions. Using AI-powered trade-share management capabilities, retailers can gain valuable insights, optimize promotional strategies and gain a competitive edge in the marketplace. In this article, we'll explore the challenges and best practices associated with AI-powered trade promotion management, enabling retailers to confidently navigate this transformational environment.
The most common trade promotion management challenges
- Lack of coordination between departments, locations, sales channels when preparing and conducting promotions
Developing effective promotion plans can be challenging for retailers. They need to align promotions with their overall marketing and business strategies while considering factors such as seasonality, consumer demand, and competitive landscape. Managing the execution of promotions across multiple locations or channels requires coordination and efficient logistics to ensure consistent messaging and timely implementation.
- Difficulties in collecting, consolidating and analyzing large data arrays
Trade promotions generate a significant amount of data, including sales figures, pricing information, consumer behavior, and promotional effectiveness. Retailers often struggle with collecting, organizing, and analyzing this data to gain actionable insights. They may face difficulties in integrating data from various sources, ensuring data accuracy, and applying advanced analytics to extract valuable information for decision-making.
- Lack of tools and methodology to measure promotional campaigns effectiveness
Determining the impact and return on investment (ROI) of trade promotions is a complex task for retailers. It can be challenging to isolate the true incremental sales attributable to a specific promotion, considering factors like cannibalization, baseline sales fluctuations, and external market dynamics. Retailers need robust measurement techniques and analytics capabilities to accurately assess the effectiveness of promotions and make data-driven adjustments.
- Budget constraints and conflicting priorities
Allocating financial resources for trade promotions can be a challenge for retailers. They often need to negotiate funding with manufacturers or suppliers while ensuring that promotional expenses align with expected returns. Limited budgets and competing priorities require retailers to make strategic decisions about which promotions to invest in and how to optimize their spending to maximize profitability.
- Lack of communication and conflicts of interest with suppliers and manufacturers
Collaboration between retailers and manufacturers is crucial for successful trade promotion management. However, challenges can arise due to differing objectives, communication gaps, and conflicting interests. Establishing effective collaboration requires open and transparent communication, shared data and insights, and a common understanding of goals and expectations. Building strong partnerships with manufacturers is essential for mutually beneficial trade promotion strategies.
- Consumer fatigue from promotions and frequently changing demand
Continuous promotional activities can lead to promotion fatigue among consumers, causing them to become less responsive to promotions over time. Retailers need to carefully manage the frequency and timing of promotions to maintain customer engagement and avoid diluting the perceived value of their offerings. Additionally, understanding consumer behavior and preferences is critical for targeting promotions effectively and delivering personalized experiences.
- Market pressure and the need to respond quickly to the promotions of competitors
Retailers operate in highly competitive markets where competitors may run aggressive promotions to attract customers. This intensifies the pressure on retailers to develop compelling promotions that stand out and generate results. Retailers must stay vigilant about market trends, monitor competitors' activities, and find innovative ways to differentiate their promotions while maintaining profitability.
Addressing trade promotion management challenges involves a comprehensive approach to optimizing every stage of the supply chain and inventory management, merchandising processes, and assortment planning. For these tasks, today's retailers are increasingly turning to integrated solutions and platforms that combine technology and expertise.
Data forecasting in inventory optimization
Optimization hasn’t always been easy, especially when it comes to shelf space performance. Merchandisers must consider product, position, and profit when determining optimal marketing strategy. We can enhance this strategy by using data to auto-generate planograms that are part of an integrated merchandising solution that covers the process end-to-end, and the result is an effective all-in-one solution.
In third promotion management, we see an increased demand to help retail companies manage forecasts for trade promotions. Forecasting makes data analysis not only helpful but necessary.
Not surprisingly, forecasting is only the tip of the iceberg. Trying to predict product inventory needs and marketing position leads to other challenges.
A vast number of complex processes also require cross-functional coordination between departments and suppliers. Growing trends indicate that companies see an overstock of promo items. Depending on the vertical and the location, the number of items varies from 20%-70%.
Comparing the promo process with the regular sales process reveals a better understanding of the differences and improves development plans.
The dramatic difference between regular sales and promo sales is the speed and frequency at which promo sales operate. Typical sales planning begins with assortment planning, then moves on to negotiation planning with suppliers, merchandising, logistics, finance, etc. Regular sales occur annually, but promo sales happen weekly and sometimes bi-weekly, which puts tremendous pressure on operations.
In response to increased demands, retail companies often follow four steps in developing their promo strategy. The four phases are:
Each phase consists of several stages and coordination between departments. For example, people with diverse roles work simultaneously on hundreds and even thousands of SKUs in parallel promo efforts, creating a vast complexity.
Promotion management processes must be highly efficient, or they will be ineffective.
How retail companies can solve forecasting problems
There are two ways to forecast a promo strategy model.
You can hire a person to do the forecasting for you. This process is simple. Your forecaster compares market situations, applies their intuition, and produces 50% accuracy rates.
There is no way to produce reliable and consistent results. Increased sales create more demand and a lack of stock lessons efficiency. The result is that forecasters overstock while the process continues only to regenerate the same issues.
Your other option is to operate like familiar industry giants. Facebook, Amazon, Google, and Alibaba have learned how to analyze massive amounts of data and use the result for improved efficiency.
Medium-sized retailers can respond just like larger companies. Why not do the same for forecasting your retail?
Ideally, big data would come from similar sources, making artificial intelligence and machine learning a simple process. However, unstructured data comes from different files: email, spreadsheets, documents, etc. We have to determine the type of quality data needed and merge it from these diverse sources.
Effective inventory/promo optimization can be realized with a simple formula:
Having high-quality data does not solve the problem immediately. The AI models need to learn to predict outcomes, which happens only with high-quality big data. To reach this level of efficiency, marketers can use five steps to optimize their sales inventories.
- Strategize. Gather data for each stage of the process.
- Plan. Coordinate with marketing and logistics to negotiate parameters and conditions.
- Prep. The company that uses this application can analyze performance.
- Execution. Put the plan into action and collect data.
- Exit. When the promo ends, survey results and make adjustments in the program for your next promo.
You’ll need at least six months for these steps and 12 months to go through the annual cycle and analyze all trade promotions.
An end-to-end solution like AI + promo management solutions improves a company’s ability to coordinate processes. Internal flow ensures multi-level, multi-step processes for upcoming promos. Every participant in the process completes tasks at the right time.
The AI model predicts current promo efficiency and reduces time spent on forecast generation. The guesswork is gone. You no longer have to rely on a planner predicting your success partly with data and partly with a hunch.
The more trade promotions you have, the faster the models will learn and improve. Ultimately, the data becomes more accurate than promo management results.
Start now for improved positioning.
Unfortunately, more than 80% of retailers do not have the data needed for AI analysis. It can take 18-24 months to collect quality big data. Begin structuring and gathering data now to amass the historical data required for analysis.
AI-processed data must be well-coordinated in end-to-end process management. If necessary, enlist help to determine what data is required and to develop comparisons.
As you undertake this operation, keep these points in mind:
- Promo will grow.
- Efficiency is key.
- There’s no silver bullet solution.
Inventory optimization must be gradual and systematic. You can use AI technology to leverage your position in the market and become more competitive, but only if you amass enough data over time.
The best time to start prepping your data is now. Gather good data that is processed by AI and well-coordinated in end-to-end process management. Combine this strategy with continuous improvement to increase the competitiveness of your business, and you will be on your way toward efficient inventory optimization.