Consumer Fusion Launches N.E.W. AI Reputation Management Tool to Tackle Negative Reviews
Consumer Fusion, announced the launch of its N.E.W. (Negative Evaluation Widget) AI Reputation Management Tool, which helps businesses identify illegitimate reviews and predict the likelihood of their removal. “We’re thrilled to present this exciting new feature representing the future of reputation management,” said Brigitte Rubidoux, Director of Marketing and Product for Consumer Fusion. “The tool […]
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Consumer Fusion, announced the launch of its N.E.W. (Negative Evaluation Widget) AI Reputation Management Tool, which helps businesses identify illegitimate reviews and predict the likelihood of their removal.
“We’re thrilled to present this exciting new feature representing the future of reputation management,” said Brigitte Rubidoux, Director of Marketing and Product for Consumer Fusion. “The tool will play a critical role internally and for our clients, as we continue to help them manage their online presence by maintaining honest reviews. We’ve been able to produce a tool with a high accuracy rate and we continue to train our system with more data to raise its capabilities.”
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Consumer Fusion utilised years of data from their CRM system to train the deep learning model to predict the probability of a review getting removed through a review dispute process. The company used eighty thousand records for the first round of training and is continuously loading more review data, so the model may continue to learn to improve performance and accuracy.
“In the near future, we will be adding the new tool to our backend for our team to utilise during their dispute process,” added Rubidoux. “The next step will be adding the tool to our real-time review tab so that clients may see the percentage probability of a negative review being removed.”
Consumer Fusion is projected to create a version of the AI tool that will be available to the public by late 2021 or early 2022. This will allow companies to check their own reviews at their convenience and take the appropriate actions.
“Our clients often ask our team the likelihood of a review being removed – and now we have a data-backed response,” concluded Rubidoux.