seasonality

Seasonality can be defined as the repetition of events at fixed time in a time cycle. In the context of retail, seasonality can be defined as the repetition of patterns in consumer behaviour in a given period of time, mostly in a year. Just by intuition, we know that sales peak every year around Christmas and new year. The repeatability of patterns does not need to coincide exactly by day. For example, Indian festivals fall on different dates each year. But you can see their impact on the sales numbers every year.

Why seasonality consideration is important

Seasonality in retail represents events where the sales are usually high or low as compared to the “normal” pattern. This means that to cater to such almost predictable demand patterns, the retailers usually plan well in advance. The seasonal demand patterns provide a great opportunity for retailers to sell their products at an accelerated rate and also find cross-sell and up-sell opportunities.

Challenges to determining seasonal demand

Determining seasonal demands requires consideration for multiple factors. Some for the factors which are listed below would, for example, introduce noise to the data, making it difficult to forecast seasonal demand

  • New products:
    Introduction of new products happens quite frequently for fashion retailers. In the case of new products, it is very difficult to factor in seasonality, because for the new product the reception in the market might not reflect properly immediately.
  • Other factors influencing the sales:
    other factors like promotions, discounts and even geographical factors impact the sales of the products. Which adds noise to the sales data, making it a bit difficult to determine the seasonality.
  • Trends:
    For retail segments like footwear and fast fashion, trends change quite rapidly. Which means the seasonality might not work the same way as it did a year or two back. The fast-fashion seasonality is an art to get right.
  • Market volatility:
    Overall economic sentiment and other factors also add to the noise of the sales data. Some products and stores might be impacted more, some less depending on the segment they serve.

How to overcome these challenges

Basics never change which means the primary methods of overcoming uncertainty is better planning, tight execution and agile configuration for teams and processes.

  • Nimble supply chain -
    ITo take advantage of the seasonality and to circumvent the noise in the data, we need nimble supply chains with short planning periods. This requires optimization at different levels throughout the supply chain.
  • Multi horizon forecasting -
    The forecasts need to be done at multiple horizons with different modelling techniques for different products. Multi horizon and multi-time series forecasts are the best way to better preparation.
  • Fast reacting forecasts -
    Forecasts also need to be able to adapt quickly to recent trends. A fast-reacting forecast recognizes early signs of change in the signals and adapts the forecast accordingly.

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