E-Commerce

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Consumers meet Artificial Intelligence

In the retail sector you are constantly challenged to optimize processes, reduce costs, mitigate churn, improve customer experience and much more.

Gaining deep understanding from customer engagement and extracting fast insights is a never-ending race. Commerce platforms remain challenged by needs to be as close to consumers as possible.

Does your Commerce Platform look like this?

Two ways to empower your Platform

Traditional Approach

1

Establish AI project team

2

Discover use case

3

Perform feasibility study

4

Fine-tune project planning

5

Develop data model

6

Implement prototype

7

Make production ready

8

Advise business users

9

Generate actionable insights

Blueprint Approach

1

Search market place and find matching problem description

2

Select recommended blueprints and load into orchestrator

3

Orchestrate blueprints to match project needs

4

Upload blueprints to data fabric and fine-tune before execution

5

Execute blueprints and generate actionable insights

Use Case: Visual Listening

Understanding and improving customer experience is the key competitive differentiator.

Suppose, you need to understand your consumers' brand experience, but most of the social media images that include your logo are neither tagged nor mention your brand in the text.

The Data Management Platform you operate, supports automated metadata extraction, but is not able to detect all these positive and negative mentions of your brand.

You are at risk to miss what consumers really think about your products.

Closing this gap is easy: Visit the blueprint market place, select an image recognizer that supports your data management platform, pick your favorite search engine and arrange these components into a data workflow.

Feed Predictive Works' big data fabric with this blueprint and execute it. Then, you are ready to go beyond metadata and tag extraction and learn what your customers really think.

View other Use Cases

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Internet of Things

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Publishing

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