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A Social Listening Case Study on Luxury Watches

7 Jun 2017 1:18 PM | Kathy Doering (Administrator)

Posted October 28, 2016, DigitalMR


Challenge

In 2014, a renowned maker of luxury wrist watches commissioned DigitalMR to harvest and analyse social media data related to their product category, in order to understand what people were saying about their brand and discover some actionable business insights.

A brand in an exclusive product category such as luxury watches is not expected to have a huge volume of posts or a large share of negatives, so it can mainly benefit from analysis that is granular enough to extract all the precious insights that a relatively small dataset can offer.

Solution

41,794 posts were harvested using the brand names. This was reduced to 12,520 after round 1 of noise elimination, to 4,423 after removing commercial (i.e. not consumer) posts, and finally to 4,134 after round 2 of noise elimination.

In order to discover conversation drivers we created a hierarchical taxonomy of topics for luxury watches customised to the reporting needs of the client. The taxonomy was enhanced by topics which emerged through what people were posting online. This process in addition to showing sentiment by topic and sub-topic within brand, also enabled us to provide our client with Brand Share of Voice and Net Sentiment ScoreĀ© benchmarking (NSS = a DigitalMR metric which takes into account the no. of positive and negative posts) for all topics and subtopics.

Further to that, through our unique process we were able to identify ~513,000 posts from people contemplating buying a watch. This enabled the client to directly come in contact with social media users who could become customers. We also found influencers with a large following discussing watch brands in thousands of online posts, not only on social media, but also on blogs and specialized forums, so that the client could reach out to them and explore co-operation.

Result

This helped the client gain a better understanding of the social landscape around their product category and discover what consumers are saying with high sentiment accuracy. It also gave them the opportunity to engage with consumers behind posts expressing purchase intent, as well as category influencers which could potentially collaborate with the brand.


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