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3 Ways To Use Data For A More Humanized Brand Experience

Last updated: 08-13-2019

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3 Ways To Use Data For A More Humanized Brand Experience

When thinking of data and the broader customer experience, we often think of a very impersonal set of numbers and figures. However, branded data that is analyzed through artificial intelligence can tell a compelling story that brings a human element to a brand’s relationship with their customer base. Here are 3 great ways that data can be used to create a more pleasantly human experience for larger brands like Foot Locker, Home Depot and Silicon Labs.

For anyone who has ever run a business, when there is a flood of questions from customers, it’s nearly impossible to keep up manually. Using a solution like Query Service in the AEM (Adobe Experience Platform) allows the brand to answer complex questions that require diving into the data and allows for scaling both the number of responses and allows for deep personalization. Two out of five people find non-personalized content that is served to them is "annoying." Having a strong customer experience management (CXM) tool smooths over these more in-depth questions that are tied with B2B and large B2C companies. Customers get a faster response and analysts spend less time trying to grab that response. Win-win.

As more brands leverage AI (artificial intelligence) to help understand who their customers are and to make the customer experience better, using AI is no longer “scary” but a real part of a growing business. Businesses can now use Adobe Sensei pre-built models in the Data Science Workshop to understand and predict customer data, allowing businesses like Home Depot and Silicon Labs to make decisions in mere days or weeks instead of months. I recently spoke with Karthik Muralidharan, Sr. Product Marketing Manager at AEM sites about how a global B2C brand like Foot Locker is able to use AEM to quickly discover the best content to serve to their partners like Lady Foot Locker, Champs Sports, and direct-to-customer channels, including Eastbay.com, leaving them more time to sell the latest sneaker trend. 

Instead of having customer service work overtime on sending customer questions to the right place, ML (machine learning) can now automatically route a customer’s question to the right department - like sales, human resources or IT. One company doing that is DXC Technology. By learning with each interaction, ML can predict even better what route a customer inquiry or question can take.

Using fast, intelligent data analysis, AI and ML, brands can now go through customer questions and deliver the right personalized content.

When thinking of data and the broader customer experience, we often think of a very impersonal set of numbers and figures. However, branded data that is analyzed through artificial intelligence can tell a compelling story that brings a human element to a brand’s relationship with their customer base. Here are 3 great ways that data can be used to create a more pleasantly human experience for larger brands like Foot Locker, Home Depot and Silicon Labs.

For anyone who has ever run a business, when there is a flood of questions from customers, it’s nearly impossible to keep up manually. Using a solution like Query Service in the AEM (Adobe Experience Platform) allows the brand to answer complex questions that require diving into the data and allows for scaling both the number of responses and allows for deep personalization. Two out of five people find non-personalized content that is served to them is "annoying." Having a strong customer experience management (CXM) tool smooths over these more in-depth questions that are tied with B2B and large B2C companies. Customers get a faster response and analysts spend less time trying to grab that response. Win-win.

As more brands leverage AI (artificial intelligence) to help understand who their customers are and to make the customer experience better, using AI is no longer “scary” but a real part of a growing business. Businesses can now use Adobe Sensei pre-built models in the Data Science Workshop to understand and predict customer data, allowing businesses like Home Depot and Silicon Labs to make decisions in mere days or weeks instead of months. I recently spoke with Karthik Muralidharan, Sr. Product Marketing Manager at AEM sites about how a global B2C brand like Foot Locker is able to use AEM to quickly discover the best content to serve to their partners like Lady Foot Locker, Champs Sports, and direct-to-customer channels, including Eastbay.com, leaving them more time to sell the latest sneaker trend. 

Instead of having customer service work overtime on sending customer questions to the right place, ML (machine learning) can now automatically route a customer’s question to the right department - like sales, human resources or IT. One company doing that is DXC Technology. By learning with each interaction, ML can predict even better what route a customer inquiry or question can take.

Using fast, intelligent data analysis, AI and ML, brands can now go through customer questions and deliver the right personalized content.


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