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Omnichannel CX and Intelligent Innovation – The Future of Retail
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Highlights
- Amid inflation, raising prices without taking a macro view of the complete purchase basket can be detrimental.
- To avoid alienating customers and drive long-term value, retailers should consider the impact of price changes on three key dimensions—customers, financials, and market.
- A pricing discipline backed by an understanding of the overarching pricing strategy and training for merchants, store operations, and planning teams can help maximize pricing outcomes.
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Price optimization in times of inflation
In times of inflation, raising prices to safeguard gross margins can hurt customer sentiment.
Fashion retailers are reeling under the headwinds of inflation. Mounting production costs due to rising raw material, energy, shipping, and labor rates and hypersensitive consumers trading down to less expensive brands are forcing retailers to increase prices. However, raising prices to safeguard gross margins without taking a macro view of the complete purchase basket can lead to dissatisfied customers and loss of revenue.
Let us assume that a retailer raises the price of cotton T-shirts to counter the rising price of cotton. While regular shoppers may accept a marginal price rise because a cotton T-shirt is an essential purchase, price-conscious shoppers may look elsewhere. While crafting pricing strategies during inflation, it is important to consider the impact of price changes on the basket size and how the mix of products will together drive up profit through the customer’s full purchase.
Take a holistic approach to pricing with AI
Retailers can surgically balance price with consumers’ price perceptions, category goals, and competitor pricing to meet their own strategic objectives.
To avoid alienating customers and drive long-term value, retailers should consider the impact of price changes on three key dimensions—customers, financials, and market (see Figure 1). AI-powered pricing systems can triangulate these dimensions at scale and in near real time to determine when, where, and how to pass price increases to customers.
Figure 1
Key dimensions for price optimization
Figure 1
Customer behavior
Understand customers’ price sensitivity for each product category.
Analyzing past customer behaviors to price changes by segment and product categories can help gauge the customers’ likely reaction to price increases (see Figure 2). Good AI-ML analysis can help delve deeper into product categories and customer segments and identify additional factors that impact demand, allowing merchants to determine the impact of pricing. Some of these additional impacts are:
Seasonality: The time of year and demand for certain product categories will determine the pricing strategy. For example, during the fall season, the price elasticity of jeans is high, but in summer, no amount of discount will increase demand.
Location and income: Customers’ location also determines price reactivity. For example, customers in cities with higher incomes are willing to accept price hikes than customers from rural or suburban markets lacking buying power.
Fashion trends: Demand for apparel that is out of fashion is likely to be less elastic than product lines aligned with the latest trend (for example, skinny jeans vs high-waisted jeans).
Brand relevancy: Price-conscious shoppers may perceive Polo, the private brand owned by US Polo Association, as an essential product for its affordability, while the original Polo owned by Ralph Lauren may drive traffic due to its brand value.
Convenience: Customers may pay more for a T-shirt in a store where additional purchases like groceries can be conveniently made than in a store where only a T-shirt can be purchased.
Customer segment: Different customer segments—value-driven customer, fashion-conscious, or convenience shopper— react differently to price changes.
Figure 2
Elasticity estimates for price optimization for private brand apparel categories
Figure 2
Financial metrics
Create baseline financial expectations to benchmark sales performance.
When setting retail prices, merchants typically consider multiple factors: the initial markup for margin targets, recent performance of similar styles, price required to turn inventory at the target rate, and competitor prices for similar styles. However, even the best merchants can fall into one of two pricing traps.
The first one is that they often pay disproportionate attention to one factor and miss out on others. For example, they may set the initial price to achieve a margin but fail to check competitor pricing. The second is that they often have misconceptions about factors that really generate sales. They could, for example, attribute the increase in outerwear sales to a 10% price cut when a cold spell and aggressive promotions may have been the key contributors. Traditional sales metrics alone will not reveal true incrementality. To accurately measure incrementality, it is important to establish a clear baseline—the expected sales performance in the absence of other factors that may influence performance. For an accurate AI-ML foundation, an initial analysis of transactional performance for all items across several months, preferably more than a year, is recommended. When this data is established, price optimization engines can automatically attribute accurate reasons for sales performance, enhancing the accuracy for pricing-related items as well as predicting price reactivity and true incrementality.
Market price
Mind the market gap to drive higher market share and profitability.
Price gap to market refers to the difference between the price at which a category or product is sold and the prevailing market price. This gap can be positive or negative and has a significant impact on profitability and market share. Companies that price their products higher than the market price may have difficulty competing against lower-priced competitors, while those that price their products lower may struggle to recover costs. For retailers adopting high-low pricing, the retail price gap may be high, but the overall out-the-door price (the price the customer pays) is the value that must be compared, adding another layer of complexity that standard analysis might not capture.
Two factors make competitive benchmarking even more complex for fashion retailers: comparing private brand pricing to national brand pricing and seasonal changes in fashion assortments. How can fashion retailers establish competitive baselines for their private-label products? Merchants must determine their competitors, brands, and the level at which items will be compared. For example, attributes important for comparison of a private brand shirt include the fabric, silhouette, and customer segment. Color, on the other hand, may not be a crucial factor. Advances in natural language processing (NLP) are significantly speeding up item matching in apparel through automation.
Reviewing market pricing at a regular cadence is key to ensuring data is not stale. One key issue retailers face is determining who can complete this work. Establishing a pricing analytics team can facilitate this effort, reducing the burden on merchants.
Create a team of pricing analysts
Creating accurate pricing strategies must not be the onus of pricing managers alone.
The speed of assortment changes in fashion is higher than in general merchandise and can hinder the accuracy of historical data, proper planning, and categorization of products. To alleviate the complexities of the price optimization process, retailers should create a team of pricing analysts who focus on data, analytics, and competition.
AI pricing system improves agility, CLV
A state-of-the-art pricing system infuses pricing agility, enabling retailers to balance cost inflation and price investment to maximize margin, sales, and customer long-term value (CLV).
AI-ML balance multiple components that affect pricing, including segment elasticity, demand transference, cannibalization, and customer segmentation to help retailers meet their strategic margin goals. Pricing analysts can apply AI in pricing to determine the starting price point of an item or the price of items sold as a set. For instance, by analyzing market trends and product strategies, AI can help determine the optimal starting price. For items sold as a set such as a dress and jacket, it can help drive traffic into the store with a well-priced dress, while ensuring margins are higher with a matching jacket.
AI-ML optimization techniques also help with timing and depth of pricing on promotions. An underperforming product can be added to a promotional marketing event to reduce the impact of a final markdown. Finally, clearance markdowns can be optimized through localization and personalization efforts for effective inventory turnover.
Beyond improving gross margin, price optimization is great for driving customer lifetime value (CLV) if the platform can localize prices. Highly loyal customers can receive discounts more frequently or receive deep discounts; any short-term margin erosion is balanced out because their average order value is higher than regular customers. By harnessing customer transaction data across channels, retailers can also define channel-specific pricing strategies based on loyalty, customer preferences, and traffic patterns.
Maximizing pricing outcomes
A pricing discipline backed by an understanding of the overarching pricing strategy and training for merchants, store operations, and planning teams can help maximize pricing outcomes.
Pricing is the fastest lever retailers can use for an immediate impact on margins and sales. But fashion retailers’ price is based on product costs and margins. The best pricing strategies consider revenue and profit goals, customer profiles, brand perception, product life cycle, consumer demand curve, and macroeconomic trends.
Applying all the factors discussed into AI-ML models enables sophisticated, objective decisions quickly beyond just incremental improvements. AI-ML techniques add a critical component of establishing potential demand transference of products to another or cannibalization from one product to another. This analysis is critical for establishing a multi-dimensional, science-based approach to price optimization.
Maximizing pricing outcomes is dependent on a pricing discipline focusing on the three key dimensions—customer, finance, market, backed by an understanding of the overarching pricing strategy and training for merchants, store operations, and planning teams. This commitment can yield 3-10% margin increase each year, while protecting sales revenue, driving 30-60% of margin directly to the bottom line.
