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The Difficulties in Fashion Pricing
Fashion pricing depends on so many factors like brand positioning, the apparel categories, changes in trends, seasonal changes and new styles, and much more. Retailers also have to look at their own internal data in addition to studying external influencers like market trends, competitor pricing etc.
All pricing begins with the markup on the cost price needed to make a profit. The initial markup price decision could be changed – brought up or down – when considering competitor pricing in that segment.
Certain fashion brands have an established reputation as high-end brands. Retailers will not have much flexibility in playing with prices here. Offering too deep a discount will not be in line with the brand image.
Within apparel categories, there are different styles, each of which may have varying levels of popularity with customers. To make things more complicated, within each apparel SKU, there are different sizes, colors, embellishments and so on.
The fashion market is always dynamic, following popular trends. And this can change quickly, influenced by popular movies, subcultures, opinions expressed by fashion gurus, or by the outfits worn by celebrities either on the red carpet or on casual occasions (which social media sites like Instagram have spurred in a big way).
With so many factors to keep track of, price optimization in fashion retail can become an intricate affair.
Data Science And Fashion
Most retailers rely on traditional data analysis and combine this with instinct to make merchandising and pricing decisions. However, in the current market, with competition from online retailers, more detailed data and more complex analytical tools are needed.
Many prominent retailers have begun closing many of their brick & mortar stores, because of diminishing sales, their market share taken away by online retailers. More and more customers are switching over to online shopping because of the convenience of shopping from home and the ability to visit so many different shops to compare a variety of choices and prices.
Fashion retailers now have to work harder to bring customers into their shops and keep them coming back. Customers now demand more choices in styles, fabrics, designs, brands and prices.
Data science and complex data analytics can help make some sense of all this chaos for price optimization. Besides gathering information about market trends and competitor pricing, Big Data takes into consideration unstructured data from various sources like social media shares and likes, reviews posted on different sites about retailers, brands, and different apparels. Every bit of information collected from various sources are then collated and analyzed to spot trends and patterns.
This is then combined with internal data. Data about sales during regular days, festive seasons, discount periods, customer buying patterns, repeat purchases, the particular retailer’s own business constraints are all factored in – and with this, predictive analysis can project future trends in merchandising and pricing.
You can even crunch data on prices adopted by competitors and competing brands to come up with an effective fashion pricing strategy. Even among the high-end customers, brand loyalty isn’t as hard-wired as in previous decades. If another brand offers similar quality and style at a lower prices, many customers are willing to move away from their favorite brand.
These patterns, trends and predictions can provide valuable insights into price elasticity within various categories. Price optimization tools can even give weightage to each attribute within an apparel line like color, fabric, closure styles etc., based on identified customer preferences. These can additionally help the analytical tools come up with more accurate pricing decisions.
Data science is changing the retail industry as a whole. In the complex and unpredictable environment of fashion retail, it becomes even more relevant to come up with better merchandising and price optimization. Pricing fashion becomes less of a challenge with data science and advanced analytics. The worries about cost of implementation is removed by the availability of cloud-based SaaS solutions, so even the smaller retailers can leverage these new technologies to attract more customers and increase revenue by pricing smart.
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