Soaring e-commerce growth has made retail more crowded, complex and competitive. Now retailers face an urgent need to keep an eye on more rivals with potential substitute products to maximize their own e-commerce growth.
Consider these recent figures, which illustrate online shoppers’ abundance of product choices:
These statistics prove that in 2021 retailers need to navigate more online players and products. Now retailers need a new approach to stay on top of market trends to keep their e-commerce strategies competitive, profitable and attractive to discerning online shoppers.
In response to online crowding, more leading retailers are turning to similarity matching. Similarity matching is a type of retail analytics that scour global e-commerce sites to find products that exactly match a specific item as well as products that closely match it. Similarity matching insights have grown in strategic significance because they increase retailers’ visibility into potential substitute products, so they can respond to all rivals’ moves with greater agility and efficiency to stay competitive.
In terms of e-commerce applications, similarity matching helps retailers gather insights on potential substitute products so they can adjust their pricing and assortment strategies accordingly. Retailers can align their pricing with rivals’ pricing moves for similar items to protect their margins and maximize profitability. They can also make informed assortment decisions, including which product mix of bestsellers, unique items and private labels could optimize their online sales performance.
Consumer behavior plays a role, as online search habits differ across product categories, which influences the type of similarity matching retailers need. For example, categories like fashion, toys, home and kitchen work best with similarity matching based on text and images. In these highly-visual categories, consumers can quickly determine whether a product fits the design and aesthetic they are looking for. As a result, e-commerce product titles, descriptions and product images play a big role in consumers’ purchase decisions.
By contrast, consumer electronics and furniture are categories in which consumers tend to seek specific product attributes, such as a certain level of resolution for their high-definition TV or a couch with particular dimensions so it fits their living room. For these types of products, consumer purchases are driven by product specifications, so similarity matching takes into account their specific needs as well as a degree of tolerance for exact or near-similar attributes across online competitors.
As more consumers shop online, they are increasingly informed by online product comparison information. A wide variety of product choices means consumers can substitute similar goods with ease, especially if a particular item is out-of-stock. Perceived product differentiation, price sensitivity and private labels can also influence consumers’ purchase decisions.
Across categories, e-commerce growth is outpacing total retail growth. When competition is this fierce, there is an increased risk that numerous and aggressive players will drive down profit margins. Leading retailers are now seizing opportunities to earn consumer loyalty. Using similarity matching helps retailers by offering in-demand products that consumers will actually buy and deliver exceptional online experiences to prevent shoppers from switching to rivals and their comparable products.
As e-commerce traffic and rivalry increase, similarity matching helps retailers stand out and serve online shoppers more effectively.
Retailers gain visibility into their entire competitive landscape to keep their e-commerce strategy responsive to shifts among consumers and rivals. By knowing the full scope of potential substitute products available online, retailers can keep their pricing and assortment strategies in line with rivals’ to reduce their risk of losing sales to rivals, and boost their top line, profitability and cost savings.
The data insights give retailers the flexibility they need to align with online shoppers’ different needs across categories. As a result, retailers can use similarity matching to boost agility and gain a competitive advantage by adapting to online shoppers’ needs, winning their sales and fueling e-commerce growth.DataWeave’s similarity matching capability lets clients
1 US Direct-to-Consumer Ecommerce Sales Will Rise to Nearly $18 Billion in 2020. eMarketer. April 2, 2020.
2 Ochwat, Dan. Shopper study: Private brands purchased because they’re preferred. Store Brands. February 24, 2021
3 Miranda, Leticia. Small businesses who pivoted to e-commerce saw record sales during Black Friday weekend. December 1, 2020.
4 Charm, Tamara, Harrison Gillis, Anne Grimmelt, Grace Hua, Kelsey Robinson and Ramiro Sanchez Caballero. Survey: US consumer sentiment during the coronavirus crisis. McKinsey & Company. March 24, 2021.
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