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Retailer's Guide to AI-driven Trade Promotion Management
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Retailer's Guide to AI-driven Trade Promotion Management

5 min read
Jane Medwin
Jane MedwinLEAFIO Co-Founder, Retail Optimization Expert
AI driven Trade Promotion management

The Key to Efficient Inventory Optimization

LEAFIO provides cloud solutions that optimize and automate the supply chain for retail companies. With 10 years’ experience, delivering 150 projects in 15 countries with a team of 200 people, LEAFIO stands out as an expert in inventory optimization. 

Inventory optimization is much more than working your supply chain, trade promotion, and management effectively and efficiently. Whether you’re marketing health and beauty, DIY, specialty retail, grocery, electronic, oil & gas, or supermarkets, one consistent truth stands out: every one of these verticals has similar pain points that can be solved the same way.

Supply chain optimization solves inventory and promo challenges that merchandisers face.

However, balanced optimization throughout every stage of the supply chain requires a range of software solutions for accurate forecasting

Leafio products

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 a 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:

  1. Planning
  2. Preparation
  3. Execution
  4. Exit

Promo strategy

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 the 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:

Efficient promo fromula

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.

  1. Strategize. Gather data for each stage of the process. 
  2. Plan. Coordinate with marketing and logistics to negotiate parameters and conditions. 
  3. Prep. The company that uses this application can analyze performance.
  4. Execution. Put the plan into action and collect data.
  5. 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. 

Promo data requirements

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:

  1. Promo will grow.
  2. Efficiency is key.
  3. 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.


Jane Medwin
Jane MedwinLEAFIO Co-Founder, Retail Optimization Expert



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