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A global media and entertainment company wanted to measure levels of fan engagement for established artists, assess their competitive standing across select geographies and identify promising amateur talent early on
The larger objective was to remove human bias in artist selection and contract renewals, realize operational savings and reduce artist acquisition costs
WNS SocioSEERTM was deployed to get a unified view of artists’ popularity across geographies. The Music and Entertainment module of the analytics platform enabled creation of granular fan profiles and interpretation of fans’ sentiments across social media platforms
The Client's Challenge
Our client, a global media and entertainment company, had multiple growth objectives. First to identify and track the ‘true1’ engagement of their established artists with their vast fan base. Second, to compare and contrast the engagement of these artists with the competitive landscape across defined geographies. Third, to identify amateur and promising talent.
An overarching objective was to eliminate any scope for human bias in artist selection or contract renewals.
WNS deployed its unique big data and social media analytics platform, SocioSEER™, to solve the client's challenges.
SocioSEER™ used its proprietary machine-learning algorithms, in-house taxonomies and deep domain infusions to benchmark artists on their 'virality' using social media engagement scores (for example, the number of fans, favorites, likes, dislikes and comments). The cloud-based interface of SocioSEER™ effortlessly managed complex, unstructured information in real-time for both timely action and decision-making across millions of
impressions and hundreds of artist pages across platforms.
SocioSEER™ tracked 1600 artists across seven social media platforms and 13 geographies. More than 50 Million structured and unstructured impressions / records were analyzed through SocioSEER™ to categorize viewer comments and determine sentiments associated with each artist across platforms. Competitor benchmarking was done across 20 other areas.
The Music and Entertainment module of SocioSEER™ enabled:
Text analytics for sentiment and theme classification: Categorized all comments, including video text, for theme generation and predicting artist sentiments
Face detection and AI for audience analytics: Created insights related to the audience — their music preferences, genre, demographics and so on. Leveraged these insights to create a single view into each investment by the record label to ascertain its effectiveness
Machine-learning to enable language detection and translations: Deployed the platform across multiple geographies to create a unified view for deeper insights and effective decision-making
Creation of the 'SocioSEER™ Index' (social media standing of an artist vis-à-vis competition)
100 percent reduction in bias while signing and renewing established artists
~20 percent optimization in channel spends for promoting artists
Operational savings made by identifying and dropping non-performing artists
Reduction in acquisition cost by identifying and signing amateur and promising talent early-on
Creation of a 'decision workbench' to allow for optimized and accurate marketing spends among artists
With social media usage growing at an astounding rate, brands are inundated with data and information, but have very little insights into relevant customer conversations.
WNS SocioSEER™ provides a single and seamless platform for global brands across industries to:
Cut through the 'white noise' with the help of our proprietary RECOINSM (Refine, Contextualize, Index) methodology
Filter conversations that truly matter — from both structured and unstructured sources including social media, call center data and blogs
Avoid brand blind spots and build industry-specific, relevant and actionable context
Leverage advanced analytics to track online reputation and focus on brand equity drivers
Measure brand impact and track progress with a single unified metric
In simple terms, WNS SocioSEER™ has the power to impact your top-line and brand equity for real. It goes beyond attitudinal to cover behavioral insights; it goes beyond benchmarking against co-brands to benchmarking an entire market; and goes beyond white noise to uncover true customer conversations.