Better Promotion Management in 4 Steps!

Ask managers what causes them the most problems in the consumer product supply chain – choosing from any product, product group or area of operations – and the most common answer you’ll get is “promotion management”.

Ask managers what causes them the most problems in the consumer product supply chain – choosing from any product, product group or area of operations – and the most common answer you’ll get is “promotion management”.

Promotion management is a step by step process and bringing together the right tools and the right approach should put an end to most, if not all, of your worries.

1. Separate promotion-related sales from normal demand

Promotions still exist in an Excel-jungle, even though other key business processes have been moved to ERP systems. The first step towards making your promotion management more efficient is to give promotion-related sales their own defined category in the ERP system.

Separating promotional sales from normal sales increases demand forecasting accuracy very significantly! Demand spikes generated by campaigns skew forecasts for standard demand when the promotion has run its course. So configuring your system to separate out campaign data automatically allows you to forecast regular sales using regular sales data and to do it painlessly. It should be obvious, but not every inventory management system allows businesses to do this. This simple step can make a big difference.

In practice this first step only needs you to do two things:

  • Divide sales into two categories: ‘normal’ and ‘promotional’
  • Generate an adjusted demand history, from which campaign periods have been filtered out, for regular forecasting.

You can often find the information you need to categorise sales in historical data from your old system. If your campaign included price changes, those changes will have created its own data set that can be pulled from the ERP or cash register system. Now you just need to know how to use this information for campaign forecasting.

2. Track the impact of promotions on demand

When promotion-related demand is separated from normal demand, campaign tracking becomes much easier. If normal demand is used to calculate forecasts, changes in campaign-led demand are easy to identify – just examine the difference between actual demand and the computed base forecast.

Using that differential you can identify promotion-related absolute added sales or the percentage of added sales. Promotion forecast accuracy can also be tracked by comparing forecasts prepared for the campaign with actual demand.

Data from systematic tracking helps to increase the demand forecasting system’s accuracy. The more information there is available from earlier promotions, the easier forecasting becomes for future campaigns. Tracking the accuracy of forecasts accelerates learning – the more feedback there is received from campaign forecasts, the quicker it is to eliminate recurring problems, such as over-optimistic projections.

If the reporting tools you are using support this, you should track the promotion’s effects on demand for a whole product group, as well as its margins. This helps to identify campaigns that really improve the profitability of the business, and also those that decrease its profitability.

3. Utilize quantitative models for campaign forecasting

Creating one campaign forecast manually is fine, but what about when demand forecasts are needed for tens, or even hundreds of stores?

Generating centralised store-specific campaign forecasts manually is labour intensive and impractical. The typical solutions are either:

  1. Replenishment for all stores is based on a single centralised campaign forecast, or
  2. The problem is passed on to each store, with the demand that they pre-order promotional products.

Both ‘solutions’ clearly produce poor results. A single demand forecast cannot reflect the individual profile of each store. Demographics, local competition, and a host of other factors mean that a campaign’s effect on demand may vary significantly from store to store. Even though the purchasing managers may have information on local conditions, they still lack the skill and time required to create accurate forecasts tailored for each outlet. This leads to orders being placed based on ‘gut feeling.’ Some stores even forget to order.

By using quantitative forecasting, it’s possible to achieve great results with promotions. Above all, quantitative forecasting models allow store specific campaign forecasts to be generated efficiently!

4. Analyze factors bearing on demand and use them for planning promotions

When the data you need is available, use it for planning promotions.

Demand forecasting models can be used for selecting products to promote and types of promotion. When we understand how different campaign types work for different products or different product groups, we have a better chance of calculating in advance how best to execute promotions to achieve the desired end result.

The richer and more diverse the data, the more accurate the analysis we can perform. One key area of interest is the effect of price on demand. The impact of price changes can be created quantitatively within a model, for example, with regression analysis. In practice the available output data imposes certain restrictions. The actual application of regression analysis requires data from several executed price changes. If in addition to price research, we also want to research other contributing factors, such as the effect of presentation of available products, then the number of previous campaigns from which data will need to be drawn increases. The more variables you include, the greater the pool of historic data you need to draw on.

The benefit of analysing and forecasting retail sales is that the factors affecting demand, such as price, shelf space and store and media advertising, are known in advance. With regression models the effects of different factors on campaign sales can be identified. Regression models should be built carefully. When standardising the use of, for example, price changes or marketing effects between different products and promotion channels, the models generally use significantly more data elements. This helps create well-functioning models. When calculations are forced to be made on insufficient data on a more granular lever, it is possible to make poor choices that reduce the clarity of the model and the accuracy of the forecast. This is why you need to put aside sufficient time to develop your model and improve demand forecasting accuracy. Moreover, as the amount of data grows, and the market and competitive situation changes, you should check your existing models.

-

Read the full whitepaper with illustrative examples, a step-by-step guide and more in-debt discussions here: Better Promotions Management in 4 Steps

Mere fra...

RELEX Solutions køber Optimity for at tilbyde up-stream supply chain planlægning og -optimering

08.01.2024RELEX Solutions

Sponseret

11.12.2023RELEX Solutions

Sponseret

RELEX recognized as a Leader in 2023

16.08.2023RELEX Solutions

Sponseret

Reitan Convenience Sverige udvider deres partnerskab med RELEX for at transformere kampagneplanlægning og -optimering

REMA 1000 Norge vælger RELEX Solutions til at optimere forecasting, replenishment og space i deres butikker

02.02.2023RELEX Solutions

Sponseret

12.01.2023RELEX Solutions

Sponseret

3 fundamentals to maintaining control of demand in the consumer goods supply chain

13.12.2022RELEX Solutions

Sponseret

JYSK vælger RELEX Solutions til at understøtte ambitiøse globale vækstplaner med AI-drevet demand forecasting