The long-tail opportunity for online retail pricing strategies

Introduction to the long-tail theory

The term "long tail" in online commerce refers to the phenomenon of a large number of niche products collectively making up a significant portion of the overall market demand, alongside the relatively few popular items that dominate sales. This concept was popularised by Chris Anderson, the former editor-in-chief of Wired magazine, in his book "The Long Tail: Why the Future of Business Is Selling Less of More."

In the traditional brick-and-mortar retail environment, physical shelf space is limited, which means that retailers tend to stock only the most popular items that will generate the most revenue. However, in the online world, where shelf space is virtually unlimited, retailers can offer a much wider range of products, including those that cater to niche markets. This can result in a "long tail" of products that collectively make up a significant portion of sales, even though each individual product may sell relatively few units.

For pricing strategies, the long tail implies that companies can profit from offering a wide range of products at varying prices. In particular, companies can benefit from offering higher prices on niche products, which may have lower demand but even fewer competitors and can there still generate significant revenue when aggregated with other niche products. This is in contrast to a traditional pricing strategy where a company might focus on a small number of high-demand products and discount against competitors to acquire new customers and build customer lifetime value (CLTV).

Approaches to dynamic pricing across the long-tail

Dynamic pricing algorithms for ecommerce typically use a variety of data and machine learning techniques to optimize pricing in real-time based on market demand, customer behaviour, and other factors. Here are some of the main ways of building a dynamic pricing algorithm:

1. Data collection: Collecting and analysing data on customer behavior, market trends, and competitor pricing is critical for developing an effective dynamic pricing algorithm. This may involve scraping data from competitor websites, collecting channel data (e.g. Google Analytics, Facebook advertising), and tracking user behaviour on your own website (e.g. searches for products even if they do not result in sales)

2. Demand forecasting: Predicting demand based on historical sales data, seasonal trends, and market events can help determine optimal pricing for each item.

3. Customer segmentation: Dividing customers into groups based on their behavior and preferences can help tailor pricing to specific customer segments. For example, offering discounts to customers who have previously purchased similar items, or who have abandoned a book in their cart, can encourage them to complete their purchase.

4. Competitive pricing: Monitoring competitor pricing and adjusting your own prices accordingly can help ensure that your store remains competitive in the market.

5. Real-time adjustment: Prices should be adjusted in real-time based on changes in market demand, competitor pricing, and other factors. This requires a high degree of automation and machine learning algorithms to make these adjustments quickly and effectively and typically relies on building yield curves to scale prices up and down depending on demand.

6. Experimentation: Testing different pricing strategies and measuring their impact on sales can help refine the pricing algorithm over time, and identify the optimal pricing strategy for each book and customer segment.
   
   

The importance of understanding demand

By understanding customer demand, you can identify the optimal pricing strategy for each product, avoid overpricing or underpricing, manage inventory levels more effectively, and improve customer satisfaction. Ultimately, a pricing algorithm that takes into account customer demand can help maximize revenue and profits, while providing customers with fair and reasonable prices.

When developing pricing algorithms at QuantSpark we primarily focus on understanding latent demand and we follow these general steps:

1. Define your target variable: In the case of an online retailer we likely use historical sales as a baseline for understanding demand however we then consider additional data sources such as searches for products on your own site and referral searches from Google. Historical sales volumes are partially dependent on the pricing strategy (and elasticity) at the time so should be used cautiously.

2. Feature engineering: We features that represent the user interactions with your ecommerce site. This could include features such as the number of searches for each product and product category, the frequency of searches, and the time of day or day of the week when searches occur. You may also want to include other features, such as the price of the product or the number of reviews.

3. Model selection and training: We select an appropriate machine learning model to predict latent demand for each product. Depending on the size of the data set and the complexity of the features, we could consider models such as linear regression, decision trees, or neural networks.

4. Model evaluation and tuning: We evaluate the performance of the model on a holdout validation set, and compare it to a baseline model to see if the model is adding any value. We may also want to tune the hyperparameters of the model to improve its performance.

5. Deployment and monitoring: Once we have built a model that performs well, we will monitor its performance over time and we may need to retrain the model periodically as new data becomes available or as user behaviour changes.

Using a yield curve approach to automate real-time prices

Building yield curves for an online retailer is a complex process that involves multiple steps. The first step is to collect comprehensive data on sales history, inventory levels, and pricing information over a period long enough to identify trends and patterns in customer behavior.

Next, the data needs to be analysed to identify correlations between pricing and sales, as well as other factors that may affect demand, such as seasonality and marketing promotions. Once the data has been analyzed, a yield curve can be built that shows the relationship between price and sales volume. This curve can be used to identify the optimal price point for each product. Before implementing the yield curve, it is important to test it to ensure its accuracy and effectiveness.

After implementing the yield curve, it is important to monitor its performance and make adjustments as necessary. This may involve re-evaluating the data and making changes to the curve, or it may involve adjusting prices in response to changes in demand or competitive pricing.

The yield curves can be used to inform dynamic pricing by setting prices optimized for each product and customer segment. This may involve using automated pricing tools that adjust prices in real-time based on changes in demand, inventory levels, and competitive pricing.