Price is still the most effective earnings and sales lever for you as a retailer. Category managers face the challenge of finding the right pricing strategy for their range of products. The selection of the appropriate pricing strategy depends on the price sensitivity and competitiveness of the items as well as your objective. One product group for which an optimized pricing strategy is particularly relevant is the so-called “long tail” group. Read this article to find out everything you need to know about price optimization for niche products.
Why pricing for long tail products?
Hardly anyone can identify the numerous influencing factors and take into account the pricing of thousands of items. Automated data analysis and price response processes implemented in a timely manner are indispensible when it comes to keeping administrative costs in check and determining fair market prices.
When pricing the range of products, category managers must consider the price sensitivity and competitiveness of the items. This can be described, for example, using price elasticity. Price elasticity shows how demand changes in the face of a price increase or reduction. Long tail products are generally inelastic. In other words, sales are only slightly affected by a change in price.
For example, think about table and bed linens, garden pond accessories and traditional attire. Your customers rarely inquire about these products. That is why long tail products are also referred to as “slow-moving” items. As the items in question are often niche products, the competitive dynamic is moderate, which has a positive effect on profitability. Your customers also focus less on the price in this situation. Low sales figures per product are relativized by the large number of long tail products. Thus, the turnover volume of long tail products compared to the overall turnover is not insignificant. In light of these factors, it pays for you as a retailer not to leave long tail products out of the equation and instead to include them in the pricing strategy.
The theory of the long tail is attributed to journalist Chris Anderson and states that e-commerce companies in particular generate a significant portion of their turnover through niche products. The following graph illustrates the role of long tail products:
Example of the distribution of long tail turnover, graph: cf. business blog http://handels.blog/diskussion/long-tail/
Translation: Anzahl = count, Umsatz = sales
What pricing strategy is appropriate for long tail products?
The pricing of long tail products is usually less of a focus for category managers. However, automated individual price optimization thanks to intelligent optimization methods is well suited to this task.
Gross profit optimization is the goal function behind automated individual price optimization in this scenario. The algorithm increases prices where possible (return effect above the price) and lowers the price where necessary (return effect above the quantity), always measured by price acceptance and thus customer appreciation. In these product ranges the algorithm demonstrably raises gross profit potential by up to 8 percent. The advantages are clear: On the one hand, automation unburdens category management and on the other hand it achieves a relevant gross profit effect.
Pricing is fully automated, non-personalized and occurs across all common sales channels: offline, online, via e-mail and using mobile end devices. The retailer sets the frequency of the automated price updates as desired, from several times a week to weekly. The prudsys Realtime Decisioning Engine (prudsys RDE for short) optimizes prices in view of the specified target function while adhering to rounding rules and in the specified price corridor.
As previously mentioned, what is special about long tail products is the low demand. That means there is only very little transaction data available to the prudsys RDE for the analysis of price-sales-correlations. To rise to this challenge, the prudsys RDE uses product similarities.
The mathematics behind it
The prudsys RDE, an AI-based solution, achieves individual price optimization on the basis of intelligent cluster and regression algorithms. They predict consumer price acceptance using product-specific price-sales functions in real time. Calculations are based on historical transaction data, real-time click data, product information and many other relevant pricing factors.
The prudsys RDE rises to the challenge of the thin database at the product level in the long tail by analyzing what brings individual products together. To do this, a variety of cluster algorithms is used. In this step, intelligent methods identify significant product attributes or environmental conditions that allow for a target-orientated grouping of all long tail products.
If you would like to learn more about the possibilities of dynamic price optimization in retail and the mathematics behind it, we recommend our Pricing Whitepaper.