Answer 80% of the answers are provided
Posted: Thu Feb 13, 2025 5:46 am
Adaptability: Speed in enriching information
AI for Retail: Best Practices for Customer Relations and Marketing by Digitaleo
The two speakers talk about specific cases of the use of the tool. Management of customer reviews left on web platforms:
by AI with a set of criteria defined in the tool. The 20% are manual processing of difficult cases that require special attention.
Analyze : On average, their customers receive more than 1000 reviews per month. Manual processing is very complicated. So it is an AI that gives information on trends.
Promote : There is a mechanism for proposing the publication of relevant opinions on social networks.
Another feature is the suggestion of communication ideas on social networks. A brand must be russia telegram data present at least once a month on the networks and it is sometimes difficult to be original. This is where AI comes into play. From a set of criteria (type of product, consideration of seasonality, type of emotion of the message, etc.), AI suggests a striking post with content and image.
Retail Media: Creating a Seamless Experience in a Fragmented Universe by Criteo
The conference was a little harder to follow for someone who is not used to the jargon of retail media. In fact, the problem was posed but the solution was not mentioned. The promise was as follows: Faced with complex and fragmented consumer journeys, with extremely heterogeneous shopper typologies, multiple points of contact and completely non-linear conversion funnels, what are the best practices for navigating this ecosystem and taking full advantage of Commerce Media?
We stuck to ideas like: targeted advertising on premium spaces, multiplying points of contact with the customer, finding the right omnichannel digital experience, solving the problem of consistency between brand and retailer advertising, etc.
Salomon: Accelerating Machine Learning Projects with Snowflake and Involving Business Teams
The purpose of the conference is to show the implementation of machine learning (ML) projects thanks to the power of Snowflake. The conference begins with a statistic: " 85% of machine learning projects do not succeed " and tries to explain how to make them succeed. So, we see a lot of architectural diagrams, slides in font 6 and we do not really understand what all this power is going to be used for.
AI for Retail: Best Practices for Customer Relations and Marketing by Digitaleo
The two speakers talk about specific cases of the use of the tool. Management of customer reviews left on web platforms:
by AI with a set of criteria defined in the tool. The 20% are manual processing of difficult cases that require special attention.
Analyze : On average, their customers receive more than 1000 reviews per month. Manual processing is very complicated. So it is an AI that gives information on trends.
Promote : There is a mechanism for proposing the publication of relevant opinions on social networks.
Another feature is the suggestion of communication ideas on social networks. A brand must be russia telegram data present at least once a month on the networks and it is sometimes difficult to be original. This is where AI comes into play. From a set of criteria (type of product, consideration of seasonality, type of emotion of the message, etc.), AI suggests a striking post with content and image.
Retail Media: Creating a Seamless Experience in a Fragmented Universe by Criteo
The conference was a little harder to follow for someone who is not used to the jargon of retail media. In fact, the problem was posed but the solution was not mentioned. The promise was as follows: Faced with complex and fragmented consumer journeys, with extremely heterogeneous shopper typologies, multiple points of contact and completely non-linear conversion funnels, what are the best practices for navigating this ecosystem and taking full advantage of Commerce Media?
We stuck to ideas like: targeted advertising on premium spaces, multiplying points of contact with the customer, finding the right omnichannel digital experience, solving the problem of consistency between brand and retailer advertising, etc.
Salomon: Accelerating Machine Learning Projects with Snowflake and Involving Business Teams
The purpose of the conference is to show the implementation of machine learning (ML) projects thanks to the power of Snowflake. The conference begins with a statistic: " 85% of machine learning projects do not succeed " and tries to explain how to make them succeed. So, we see a lot of architectural diagrams, slides in font 6 and we do not really understand what all this power is going to be used for.