A large restaurant chain based in the US with stores across geographies wanted to improve the customer experience as well as become the first choice to dine out. They began to realize that customers are beginning to trust online reviews to identify the best place to dine and positive reviews had a significant impact on their revenue, reputation, and overall online visibility. With multiple chains across locations, it was getting difficult for this restaurant to analyze sentiments, respond to negative reviews, and manage reputation effectively.
That is when they came across BuzzSense.io and decided to implement it for their business.
Identify and aggregate customer reviews across channels and analyze current customer sentiments
Improve customer engagement through online review monitoring and devise automated responses
Improve overall brand image, achieve better visibility, and growth in revenue
Prior to implementing Buzzsense.io to manage their online reputation, the restaurant chain had difficulties in managing the process in-house due to the lack of the following:
It was a multi-location business with 4300+ franchisees. Customers leave digital footprints on multiple platforms in the form of reviews and comments, newsfeed and listings, and more. They did not have a scalable solution to monitor this
There were negative reviews and comments that needed to be sorted out quickly. They did not have an automated response mechanism to manage negative reviews (responded only to 33% reviews)
Lack of technical expertise and consultation to help them manage the impact of negative reviews on search engine rankings
The reputation experts at Buzzsense.io analyzed the challenges and implemented a step-by-step solution.
1 There were 1M+ reviews across social channels, review sites, newsfeed, and various other sources
We implemented our cloud-based data aggregation platform to aggregate reviews across channels/geographies. The solution contained 32 automated bots.
2 The reviews were mixed - positive, negative, and neutral.
Our NLP-based sentiment analysis tool classified reviews into - positive, negative, and neutral. This helped in automating a tailored-response. Eg., for a negative review, the response was ” Sorry for the inconvenience caused. Please reach out at firstname.lastname@example.org for further discussion and grievance resolution ” and if the review was positive the response was “ Thank you for the feedback. This will help us serve you better”.
3 A thorough analysis of the sentiments and geography-based store performance analysis data from the customer revealed some critical insights to our reputation experts, based on which they made recommendations.
Using heatmaps, the experts were able to identify stores with low customer satisfaction and map it to performance data and geography-based sentiments.
4 Missing out on brand mentions, negative feedback, and other significant happenings.
A dynamic dashboard with key metrics made monitoring quick and easy. An automatic response system was put in action for timely response to customer feedback and an alert system to notify if a particular franchise is going below a threshold in terms of overall sentiment.
Buzzsense.io helped the restaurant chain respond to reviews across channels thereby significantly improving their brand image. The results are quantified and are as follows.
The response rate to customer reviews increased from 33% to 100%, which had a positive impact on the overall brand image
The brand was able to take timely action on the franchise with a low Customer satisfaction index/rating
23 Franchises were removed due to poor rating continuously and the client was able to warn 124 franchises regarding certain aspects of their operation to improve the customer conversion rate
Real-time data on the dashboard helped them take timely actions regarding a particular franchise
Resolving negative customer reviews and feedback improved the search results rankings with other favorable information
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