How to Boost Revenue and Gross Margin of Lifecycle Products
Nowadays, consumer culture is both fueling the retail industry and gradually taking its toll on it. While it gives retailers an excellent opportunity to generate revenue streams from consumers' whims and needs, it still plays against them. The latter is well-reflected by the growing number of product categories whose life cycle keeps declining. Once, it was fashion apparel, agricultural products, and foods. Later, technology advancement extended the list by adding electronic goods and devices. And finally, let's not forget about seasonal products that have always had an extremely limited life span, such as annual holiday items, back-to-school supplies, and home-care products.
Such a trend puts retailers on a quest to release inventory within its life cycle and boost bottom-line business metrics all at the same time. In pursuit of intended rate of sales, retailers often turn to blanket discounts. But besides quick oversell, such an approach often leads to significant gross margin losses. Here, a markdown campaign with a primary focus on total gross margin for a period and managed sellout can deliver dramatic results on lifecycle products.
The Many Facets of a Markdown Campaign
In an ideal world, to run a markdown campaign, teams should consider items' price elasticity and their cross impacts, changes in consumer demand, market trends, inventory availability, website analytics (for online retailers), internal pricing structure, past transactional data, and depth of discounts in other channels. Furthermore, to ensure it runs smoothly and in line with the projected sales curve, retailers should plan markdown cadences (i.e., optimal frequency, timing and discount depth).
Complex as it may sound, these are only some of the data points worth considering when calculating an optimal discount price. For example, when talking about internal pricing structure and formations, the approach implies assigning the "anchor" and dependent products to build the so-called "price ladders" or "price architecture" and evaluating the impact of price changes on the sales of a product family.
How can one embrace all of these components to make optimal markdown pricing decisions? Here's where a cold mind of a price optimization machine comes into play. Nowadays, smart algorithms can do all the work by processing billions of pricing and nonpricing factors to eventually calculate an optimal price reduction, helping retailers hit their business goals.
Making the Most of Product Life Cycles
Balsam Brands, a $200 million home decor retailer, partnered with retail pricing platform Competera to manage sellout and profitability of artificial Christmas trees and related holiday decorations. Some of the other goals were absolute gross margin and revenue.
Eighty percent of Balsam Brands' annual sales happen during the last three months of the year, meaning that its products' life cycle is only somewhere between two months and three months. Therefore, conducting a traditional A/B test in 2020 would mean scaling the new approach to price management only in 2021. The Competera team reimagined the tradition with the help of backcasting, also known as "a planning method that involves predicting the unknown values of the independent variables that might have existed, to explain the known values of the dependent variable."
In a nutshell, Competera's artificial intelligence (AI) models had to forecast sales as if the project was launched not in 2020, but in 2019. The experiment resulted in 96 percent forecast accuracy, and turned out to be a good way for a cost-effective and high-quality demonstration of the solution's feasibility for a seasonal business.
Balsam Brands switched from a 12-hour weekly repricing process to automated data-driven discount calculations performed by a trainable AI model. To generate new prices, it processed the latest sales data, item availability in stock, and a number of smart business constraints set by the retailer, such as share of assortment on promotion, smart price rounding rules, and the existing price ladders.
As a result, Balsam Brands smoothly managed inventory sellout and generated a 3.5 percent revenue growth and a 3 percent gross margin uptick during the 2020 holiday season. Matt Lewis, senior e-commerce business strategist at Balsam Brands, shared about his repricing experience with Competera.
"Its usability is a standout point to highlight," Lewis said. "Competera would let you embrace all the key SKUs at first glance, track inner pricing architecture, and set business constraints — all this while redirecting resources to strategic planning and long-term business growth."
Speaking about the impact of the price optimization solution on Balsam Brands' business processes, Joyce Lin, senior e-commerce business manager at Balsam Brands, added, "Competera's smart algorithms made our price management data-powered and proactive, and saved the team 50 percent time from routine tasks. Competera has shown how AI is revolutionizing traditional pricing processes and strategies."
After the project's success, Balsam Brands agreed to adopt the technology for other regions.
Vladimir Kuchkanov is a pricing solutions architect at Competera, pricing software for online and omnichannel retailers. He is a Data Scientist, a top-rated domain expert in business analytics, pricing and media management with a successful track record in world-class FMCG companies.