With E-Commerce Growth Slowing, Retailers Need AI to Make it Profitable and Permanent
During the pandemic-induced online shopping boom, retailers invested in e-commerce capabilities out of necessity. As consumers shifted to retailers with online ordering, merchants were scrambling to provide new digital order and delivery options. These investments helped capture more market share, but now that stores are reopening and there's little market share remaining to capture, retailers are left with a realization: e-commerce is expensive.
Increased costs of delivery, warehousing and higher return rates, along with investment in web development and digital ad spend add up quickly. To offset these expenses, retailers need to improve e-commerce operating costs. This requires focusing on improving efficiencies, retaining customers, reducing returns, and improving personalization. Artificial intelligence (AI) will be integral in each phase of this reimagined commerce.
Improving Operational Efficiency
As retailers look to cut e-commerce operating costs, using AI to make order picking and delivery more efficient is a prime example. Applying advanced algorithms to order data, retailers can cluster items and delivery times to help consolidate trips and reduce picking and delivery costs.
Optimizing inventory is another critical component facing heightened strain from recent shipping and warehouse capacity challenges. While many companies had models in place before the pandemic to help optimize inventory, sharp changes in consumer behavior have rendered these models outdated and inaccurate. A company may have previously run forecasts in aggregate, but in an era when consumers can switch e-commerce sites that lack items they're looking for with a simple click, companies need the ability to predict demand daily at the store level. This level of accuracy is only possible with AI.
Improving Personalization to Reduce Customer Churn
Prior to the pandemic, brand affinity was a top consideration in purchase decisions. As shopping shifted online and other people couldn’t see what was in their cart, consumers were more willing to switch brands. Engaging these customers to strengthen loyalty is a priority moving forward, and these efforts will depend on data.
AI offers new ways of analyzing customers to understand what factors lead to retention as well as identify when and why customers churn. Bain & Company research found that increasing customer retention rates by 5 percent increases profitability by 25 percent to 95 percent. Additionally, marketers can identify those customers that are most likely to purchase and concentrate promotional investments there to maximize marketing lift.
Capturing and retaining customers — and increasing incremental sales — often relies on creating a personalized experience that drives engagement. Personalization isn't a new concept, but with richer data available from online transactions, retailers can significantly enhance effectiveness. Using AI, retailers can comb through vast amounts of behavioral data to deliver highly relevant recommendations that lead to higher customer retention and revenue.
Product returns are among the most expensive aspects of e-commerce. Solving for this best begins, surprisingly, during the consideration stage. It’s important to instill confidence in consumers that they're making the right decision when considering their purchase. AI models can help consumers make smarter, more confident purchase decisions.
For example, if an apparel retailer can build AI-powered tools to show how a piece will fit — accounting for their actual measurements and body type — customers are less likely to need to return items for fit. Even after a customer decides to return a purchase, leveraging data to automate the process will cut return and reshelving costs significantly.
Retailers that excelled during the significant recent shifts in e-commerce all have one thing in common: embracing rapid experimentation with data and AI. It's clear that in a post-COVID world, the same holds true. Leveraging AI in tailor-made ways will be essential to remaining competitive in retail.
Rob Saker is global industry leader, retail and CPG at analytics company Databricks.
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Rob Saker is Global Industry Leader, Retail and CPG at analytics company Databricks.