Solvoyo Blog

How Smart is your In-Season Pricing Capabilities?

May 24, 2017 11:09:40 AM
Yosun Denizeri
Written by Yosun Denizeri
Yosun is a retail and apparel industry expert focused on supply chain and pricing.

In today’s dynamic and competitive retail landscape, value offering is becoming more relevant to apparel brands competing for the last dollar in a consumer’s wallets. Value offering however is not just about price. It is about the perception of a retailer in the eyes of the consumer in terms of convenience, affordability, trendiness, functionality, quality, and of course price. Smart in-season pricing for a retailer therefore is simply not about a race to the bottom but a well choreographed dance that adapts pricing to the changing marketplace.    

Market dynamics change every day and customers are keeping up with competitive offers

ecommerce.jpgAccording to the latest survey of U.S. consumers by Retail Dive, more than 65% of consumers conduct online product research before stepping foot in a store. In addition to competitive shopping online, consumers are constantly bombarded with new arrival announcements, promotions and discount offers coming through email, texts or on social media. Today, it is safe to assume that consumers are very aware of what is out there in terms of product assortments and prices and that your offering is always competing with other retailers and e-tailers.

When it comes to staying competitive, in-season pricing strategies (promotions and markdown pricing) play a big role in the ability of a retailer to stay relevant in a changing market. Many retailers today use some version of an in-season discount management or optimization tool to plan, evaluate, and forecast the outcomes of the discount offers they are considering. Some of these solutions allow retailers to evaluate different price points or discount levels manually entered by users. More advanced solutions, like Solvoyo’s Markdown Optimization tool, can provide prescriptive price and discount level recommendations for each style, color or location choice based on optimization results to meet different business goals - from maximizing sales, maximizing profitability, or a combination of both. 

Analytics Behind Price Optimization

In each of these markdown management tools, there is a critical component that estimates how demand will vary with a given discount offer. How do these systems know how consumers are going to respond to changes in prices?  This is where the art and science of advanced analytics comes in.

One of the fundamental components of price optimization is simulating how Retail store photo.jpgcustomers are likely to respond to changes in price. In order to do this, historical data from past seasons are collected and analyzed for how demand varied in response to changing prices. In pricing circles, we call this ‘price elasticity.’ This is the mathematical quantification of changes in demand in response to changes in price, which can and should be computed down to as granular a level as sensible. When it comes to price-elasticity modeling, the devil really is in the detail and getting this right will play a big role in how effective your price optimization system is.

If you are retailer looking to build an analytical foundation for price optimizations, a few important questions to explore are:

  • What is the relevant price history for these products in each market?

When price optimization systems were first implemented 15-20 years ago, the rule of thumb was ‘more the merrier’ in terms of how much historical data was used in building the analytical models and calculating price elasticity. Similar to seasonality modeling in forecasting, the minimum requirements was usually around 2 years of transaction data.

Then, the recession came and soon enough pretty much everything we knew about price elasticity, especially in apparel retail, went out the window because the fundamental market dynamics changed. Consumers had less money to spend, retailers had excess stock to sell, so within a few months, how consumers responded to 15% discount was re-defined. At times like this, data from two years ago is less relevant and recent transactions become more telling for predicting what is going to happen in the near future. Therefore, analytical models being used for markdown optimization should be constantly learning from recent trends and self-adjusting based on the latest available data.

  • What is the best way to forecast sales of fashion styles with short life cycle?

The most common approach to forecasting fashion or any short-life cycle product is to link it to a similar product that has sold in the past and assume they will behave similarly. However, for retailers that carry thousands of products on a given day, this process itself becomes quite laborious and error-prone, and relies heavily on the knowledge of the individual planners about past seasons products.

Another approach that has gained momentum over the years is Attribute-Based modeling. This requires that historical styles as well as the styles in the current collection are properly documented in terms of the attributes that influence their buying decisions. These attributes can vary across retailers, requiring each seller to perform their own analytics to help identify the most influential ones from whom reliable inferences can be drawn.

  • Are there regional or store-level differences in the way customers respond to price changes?

Market dynamics can vary across  geographies, and even across neighborhoods sale.pngdepending upon income levels, occupations, age groups, lifestyle, and level of competition. Often, it is not easy to pinpoint the specific factors that influence how customers respond to a 20% discount. It is worthwhile however, to explore whether there are significant differences across locations. To incorporate this type of insight into forecasting, it is critical to do store-level price elasticity analysis to understand exactly how individual stores might behave in their response to price changes.

  • Does price elasticity change over time for your products and markets?

Getting it right at a point in time does not necessarily guarantee that the price elasticity values will remain the same in the months and years to come. Over the last decade, machine learning based or self-calibrating analytical models are becoming more relevant as players in the market keep changing and socio-economic factors are undeniably impacting consumers decisions.

In conclusion, in today’s world, agility or flexibility is the name of the game in retail business, whether in customer service, supply chain management, or price optimization. Having a formulaic 'tried and tested system' just doesn’t cut it anymore especially when it comes to markdown pricing. The question to ask is how agile and smart is the system itself to learn and adapt  to the growing complexities and pace of change of your market.

 

Topics: Retail, Analytics, Markdown Optimization