By Henry Lee, Solutions Architect, and Charles Tse, Staff Solutions Architect
Campaign data analysis is important not only to understand event effectiveness, but also to support offline store intelligence such as shop/booth layout design, dropout ratio in relation to customer flow. It provides another channel to observe customer demographic and buying behavior. With all these information, we can provide product recommendations with a higher level of confidence.
To drive the event with data and AI, we can follow 4 simple steps:
Attract, Observe, Recommend, Analyze
For customer attraction, we see a number of retailtainment technology being used such as AR that is applied to "magic mirrors", or interactive games or company apps. Smart shelf/vending machines for promotional products in popup shops, which may also be integrated with product recommendation to provide personal advice.
All these attraction channels include online/offline interaction with customers. It provides a good chance for businesses to observe customer behavior or to better understand their interests. The observation can be related to online comments for the event activities, coupon redeem ratio, customer flow track in popup shops or demographic recognition both online and offline.
Through attraction interaction points and observe process, correlating history customer behavior information. We are more confident to provide product recommendation in the campaign.
We can generate product recommendations based on 3 types of data inputs with Alibaba Cloud's Machine Learning Platform for AI (PAI) service. The data types include customer demographic, product information, and customer behavior such as purchase history. With customer demographic, it gives us another way to provide recommendations, independent of history, which can be beneficial when dealing with new customers who have not completed a transaction before. We can provide single item recommendations, top items recommendations, or item-to-item relationship suggestions for different scenarios. Recommendation results can be used in company websites, mobile applications, or booth devices in the event.
Recommendation models can also be fine-tuned with customer transactions in the event, improving the hit rate of the data model and supporting us to provide a better model for future events.
Popup store would be another good observation point for data analysis. One of the most common measure would be measuring user behavior through camera devices. This supports not only the visitor count, but also help to capture basic demographic information such as age range and gender. Another part of data analysis would be on route and counter stay time within popup shops. We can also perform more detailed analysis such as estimating the conversion rate of purchases by measuring the number of visitors to the cashier area. All these can help us to understand the customer flow and measure the effectiveness of attraction points.
To prepare for future campaigns, we can also reference on the event data to locate high value customers. We can target those who are responsive or active in an event and those with high conversion rates. With advanced data analytics and big data, we can understand the users' common demographic and look for similar (lookalike) customers.
This article only lists out part of the possible areas for data analysis in retail campaigns. With better technology and innovative ideas, we could do even more to drive events with data. All these ideas are only possible with a powerful, cloud-based platform to support the data analysis. Learn how Alibaba Cloud helps you achieve this with our Machine Learning Platform for AI (PAI), MaxCompute, and QuickBI platform to have a quick start on your campaign data analysis.
Redefining Mobile Verification with Phone Number Verification Service
SAML Integration between Windows Active Directory and Alibaba Cloud
2,605 posts | 747 followers
FollowNick Patrocky - February 17, 2024
Lana - March 24, 2023
Nick Patrocky - March 7, 2024
Nick Patrocky - February 5, 2024
Alibaba Clouder - January 5, 2017
Nick Patrocky - January 24, 2024
2,605 posts | 747 followers
Follow
MaxCompute
Conduct large-scale data warehousing with MaxCompute
Learn More
Platform For AI
A platform that provides enterprise-level data modeling services based on machine learning algorithms to quickly meet your needs for data-driven operations.
Learn More
DataWorks
A secure environment for offline data development, with powerful Open APIs, to create an ecosystem for redevelopment.
Learn MoreMore Posts by Alibaba Clouder
