What future for e-commerce? Faced with the pandemic, globalization, new consumer expectations, especially regarding the environmental impact and the web2store, retail is evolving. Artificial intelligence tools developed to meet these challenges exist, but have professionals taken them up? Adoption, deployment, expectations and obstacles are at the heart of this new study proposed by OpenStudio.
To answer these questions, OpenStudio has released the results of a study conducted with 132 retailers from all sectors of activity in September 2021. For Cédric Sibaud, Associate Director of OpenStudio and e-commerce expert:
"Artificial Intelligence has already become indispensable for the big names in e-commerce, and it will become a must for all merchant sites, regardless of their size. It is therefore in the interest of retailers to take an interest in this technology as soon as possible, as it is becoming more and more democratic and opens the door to great opportunities.Discover below the 10 key points of this survey, which you can also download HERE .
You can also find OpenStudio in the issue 5 ofActuIA, the magazine of artificial intelligencecurrently in digital version and in newsstands!
1 - Almost all retailers (98%) believe that AI could improve their e-commerce platform, 62.5% of them significantly 2 - Retailers' expectations of AI are very high: Sales and time saving, the 1st objectives The strong and/or priority expectations are :
- save time through automation: 84
- facilitate decision making through real-time analytics: 83
- increase sales: 81%.
3 - The top 8 priority and/or useful AI solutions: manage data, automate and secure payments For retailers, AI is a real opportunity that will improve the performance of their e-commerce platform. The top 8 items for which they believe AI is a priority and/or useful:
- update, manage & enrich customer databases: 90
- automate order preparation and shipping: 89.7
- detect fraud & anomalies/secure payments: 89.7
- understand/model/predict Internet user behaviour (purchases, etc.): 89.2
- synchronize catalogs with all third-party applications (ERP, marketing tools, etc.): 88.9
- forecast sales and manage inventory: 86.7
- update catalogues and pricing in real time: 85.4
- make the customer journey more fluid (web2store): 85.2
- ensure competitive intelligence: 36
- process Big Data: 34
- analytics: 32
- ex-aequo: real-time predictive: 32
- enriching customer data (cross-referencing with external data such as social networks): 31%.
- AI to process Big Data: 38
- chatbot/voicebot: 35
- AI to enrich customer data (cross-referencing with external data such as social networks): 34.5
- AI to process Big Data: 72
- AI to enrich customer data (cross-referencing with external data such as social networks): 65.5
- chatbot / voicebot: 63.5
- AI for analytics: 63%.
- chatbot/voicebot: + 124
- AI for marketing campaign personalization: + 114.5
- AI to enrich customer data (cross-referencing with external data such as social networks): +111%.
- ethical issues raised by the use of AI: 33
- tie: the cost of implementing AI solutions: 33
- the difficulty to measure/quantify the benefits of AI: 32
- The complexity of implementing AI solutions: 31%.
- storing data in eco-datacenters: 40
- offer customers the opportunity to offset the carbon emissions emitted by their orders: 39
- optimize the website with an eco-design approach: 36%.
- optimising the website in an eco-design approach: 48% (36% have already implemented this measure)
- the use of open source AI models: 47% (already implemented: 34.5%)
- ex-aequo at 47%: hosting the site in a green center (already implemented: 33%).
- limiting customer returns through predictive analysis: 75
- rationalize packaging: 72
- ex-aequo: reducing the carbon footprint by optimizing delivery routes: 72%.
Translated from Enquête sur l’IA et le retail pour un état des lieux et des prévisions pour le e-commerce