Find out how adidas and other brands are creating personalized CX to connect with customers for better engagement and ultimately, more sales.
It’s a whole new ball game for the American sporting goods industry. Online sales surged to 17.2 percent of the total sporting goods industry in 2015, up from 11.8 percent in 2010 (per National Sporting Goods Association data), escalating competition among omnichannel retailers, ranging from large sporting goods chains to big box stores to specialty online retailers.
Additionally, consumers are more empowered than ever before, taking advantage of price comparisons, customer reviews and more to make their selections. These factors contributed to recent bankruptcies for Sports Authority and Sports Chalet – further illustrating the need for retailers to modernize using data and analytics.
The following use cases reveal some ways in which modern sporting goods retailers are applying a combination of internal data, competitor pricing and assortment data, as well as search data, product reviews, and social media signals to accomplish three fundamental goals:
- Optimize product assortment
- Improve price competitiveness
- Increase web traffic and conversion through coupon campaigns
Increasing sales and conversion by optimizing assortment
Like many of its peers, one of the largest omnichannel sporting goods retailers in the world used to make assortment decisions by consulting with vendors and listening to customer feedback and behavior. The only data analytics the company used were on its own transactional data. The company knew it could have a more comprehensive and less manual system in place.
They decided to fully embrace big data and started collecting external e-demand signals, as well as online competitive information, and blended all of that with the company’s web analytics and transactional data.
The result led to specific and actionable insights for the company – which were forecasted to improve conversions by three to four percent.
Improving price competitiveness using competitive data and consumer e-demand signals
Pricing decisions presented a challenge, since the retailer didn’t have timely and accurate data about the competition.
They deployed a state-of-the-art big data platform to collect prices across retailers for matching products, including private label products. Then they ran analysis to determine the demand for the products and the prices customers were willing to pay.
Based on the recommendations driven by data and not opinions, the company experienced a seven percent rise in margins for multiple categories.
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Data analytics for sports retailers: Increasing web traffic and conversion through coupon campaigns
Digital coupon promotions can be immensely helpful in attracting new and existing customers to e-commerce websites. Additionally, they can lead to more focused marketing spend, since they make it easier to track and measure profitability compared to a sale.
One leading online mass merchant based in the U.S planned to implement digital coupons to help with declining web traffic and sales. However, concern arose that if coupons were sent to people who weren’t a fit, it could lead to additional losses.
So the company reviewed data from previous campaigns, both good and bad, to understand the response rate and incremental sales across different customer segments.
Through the simulation of new campaigns, they identified a specific group of customers that made the most sense to target, combined with the coupon offer that would lead to the best ROI.
The coupon campaign that resulted achieved 25 percent incremental sales versus forecasted projections, boosted the response rate by 0.9 points and provided a foundational strategy for the client to repeat in future campaigns.
By adopting a data-driven analytics approach to optimize product assortment, improve price competitiveness, and increase web traffic and conversion for campaigns, companies can be armed and ready to face their competition in the ever-changing sporting goods industry.