Tackle Supply Chain Challenges and Boost Ecommerce Conversions with AI

Snags in supply chains have become a widespread hurdle for ecommerce brands to clear since the beginning of the pandemic. As we entered the 2021 holiday shopping season, many brands found themselves scrambling to ensure customers hit the check-out of their online store with something they wanted in spite of many supply shortages.

The Retail Industry is Weathering the Toughest Supply Chain Challenges

The unfortunate part of supply chain challenges is the detrimental “chain” of effect – trouble in one area causes for another down the line. Between major holiday shopping seasons and the ongoing pandemic, the issues below have hit brands the hardest:

• Logistic, material, and labor bottlenecks
• Lack of visibility and transparency of goods
• Forecasting challenges
• Resiliency and agility
• Increasing costs

The Supply Chain Impact On Ecommerce Is Significant

The ecommerce experience has just as much of a knock-on effect as a direct result from the above challenges. The cascading results are the nightmare scenarios that can cost brands dearly:

• Out of stock dead-ends and zero results when searching for a product
• Fulfillment or shipping delays
• Loss of customers to competitors
• Lower conversions and revenues
• Frustrated employees
• Lower overall brand sentiment,, Net Promoter Scores (NPS), and more

AI To The Rescue

AI has become a reliable technology for product discovery even at a time of critical supply chain disruption. AIcan help brands tackle supply chain challenges and improve ecommerce KPIs in four important ways:

1. Driving preparedness by identifying trends in advance

AI and machine learning provide an exciting opportunity to get ahead of the game in terms of what will be in demand. Part of this involves simply analyzing the content that already exists out on the web. This has been referred to as semantic and concept extraction or sentiment analysis, but the idea is simple: the technology can crawl various social and community channels, search engines, marketplaces, and more to conduct an omnichannel analysis of what is trending. This provides multi-dimensional analytics for brands to apply to supply and manufacturing processes. If brands know what’s going to be hot ahead of time, they can get ahead of the boom and bust.

2. Automating predictive merchandising strategies

Machine learning rescues merchandisers from n manually curating experiences for shoppers. It shows shoppers what they want based on their intent in the moment as well as behavioral signals . This can be incorporated with supply chain data such as demand forecast and inventory planning and optimization. The AI component comes in by automatically adjusting the presentation and position of products, such as gradually burying items based on demand or supply ratios, exposure, and bias. What’s more is AI can work to achieve targets such as boosting high demand inventory to loyal customers and burying for transient, low propensity shoppers.

3. Predicting shopper intent and personalization to improve conversion

AI can predict shopper intent to enhance personalization, which can be used to further optimize supply chain processes.

• Semantic vector search uncovers alternatives and replacements for items that are out of stock or not carried, avoiding hitting zero results.
• Neural networks based recommenders individualize suggestions or alternatives based on behaviors.
• Affinity modeling presents optimal offers and promotions to encourage shoppers to convert. This can be used to surface relevant content to educate a shopper toward a purchase.

All of the above contribute to more conversations, including in the long tail, while also increasing revenue per visit. How does each work?

• Product and content is analyzed through machine learning.
• Entity extraction of queries predicts characteristics of products that a shopper is interested in.
• Concept extraction of queries predicts what a shopper’s ultimate use case is beyond just the product
• Clickstream behavior reveals attributes and parameters specific to the shopper such as category, brand, type, etc.
• Context provides surrounding conditions around interaction such as location, season, and climate.

All of these are encodings that can be used to show products based on goal and intent as opposed to lexical query matching.

4. Uncovering insights and allowing brands to act accordingly

All of the above tools and capabilities can be used in service to connect, enrich, and feed data into forecasting, planning, and actionable outcomes. For example, data gathered from purchase and shipping details can inform inventory, warehouse, and supply chain planning in advance of restocking. Product interactions can indicate product insights, which can inform planning of raw materials.

Even actions that are seemingly passive or less direct can provide valuable insights. Browsing and navigation data can inform cataloging and assortment planning for taxonomy and enrichment. Traffic insights can feed back into insights for marketing campaigns, while pricing and discount interactions can point to financial insights and price elasticity. Finally, the insights received from support and customer service are incredibly important for gathering supplier diagnostics, which enriches the knowledge base and reverse logistics (such as understanding returns and why).

Supply chain challenges are not going away any time soon. But brands can tackle them heads-on with AI-driven ecommerce strategies that result in happier shoppers and employees.

Curious to learn more about how Lucidworks can help your brand achieve all of the above and more? Head of Industry, Commerce, Sanjay Mehta breaks it all down in his commerce webinar, which you can stream on-demand here. If you’re curious how our AI-powered ecommerce solutions can support your brand through supply chain challenges, please get in touch with us.