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Social Listening for Hospitals

How AI/NLP based “Voice of the Customer” Technology helped a leading hospital brand manage crisis

We used our proprietary social listening and market research tool for a leading hospital brand to monitor mentions, provide customer service and react to crises in real time

The Brand

The brand is Asia’s largest healthcare services provider. Founded in 1983, it is a trusted name in the industry with over 45 million beneficiaries from 121 countries. Considering the mammoth size of its operations and daily engagement with its customers, managing customer response efficiently across touch points was crucial to its business and bottom line.

The Problem statement:

Taking social listening to the next level

A large percentage of the brand’s customers are active netizens, commenting, liking, discussing, reviewing and rating various products and services. To reach out to these customers and leverage the brand’s online presence, it was pertinent to create a central social listening system, which could draw actionable insights from the buzz around the brand, like quickly categorize issues, inform relevant stakeholders, escalate wherever needed and reach out to customers with solutions within defined timelines. Most importantly, it would add a human voice to the brand which would result in sustainable customer engagement.

At a basic level, the following needed to be taken care of.

  • A high volume of customer enquiries, complaints, news mentions need to be listened to and addressed.
  • The responsiveness to consumer enquiries need to be increased significantly
  • In addition, the brand wished to make more sense of the buzz for upstream market research and crisis prediction.
  • Crisis needs to be predicted and managed
  • Insights which range from operational level insights (e.g. which location and department has the most consumer issues) need to be gathered
  • Consumer perception about the brand needs to be mapped and marketing campaigns  devised with that input

Strategy

The choice was between using an off-the-shelf social media monitoring tool or creating something which is industry specific, thereby better poised to provide insights. We created an AI/NLP based social listening technology for hospitals and deployed this for brand.

Execution

GenY Labs deployed. It is an AI/NLP powered SaaS product that helps social listening through a defined framework created from GenY Medium’s experiences with different customers.

Coverage: Ability to listen-in to data from multiple sources including social, review boards, news, blogs and emails. GenY Labs uses the Twitter / FB ‘fire-hose’ rather than a polling method, thereby providing 100% real-time access to the buzz.

Industry-specific AI/NLP models: A state-of-the-art sentiment analysis engine with between 80%-90% accuracy (best in class). Different models for multiple industries.

Attribution beyond sentiment: Ability to add more dimensions to the data – where is the complaint from? What is the root cause?

Intuitive visualization: Use intuitive charts and graphs to be able to visualize data, trends and draw insights for own brand plus competition

Action workflows: Insights are actionable right from the platform – automated alerts to identified SPOCS, ability to integrate to existing CRM solutions being used (Sales-force, Microsoft Dynamics, Zendesk etc.)

Technology

  •  Cloud-based so that people can access it in real time
  •  Fire-hose to listen versus polling, to ensure we listen real time instead of with a lag
  •  Connect with a CRM (Freshdesk) so that the team at the hospital can action and report back

CRM Integration

Powerful search available to identify issues related to a particular location, time, prob-lem-type, root-cause and so on. Queries complaints straightaway pushed to CRM where a ticket is created, tracked, deadline assigned and finally ensured its closure

Push to CRM

Training and deployment amongst the team

  •  60 people connected organization-wide to receive and respond to the queries
  •  Establishment of an escalation matrix going all the way to the CEO
  •  Training on select aspects of the solution as appropriate

Outcome

Overall sentiment analysis

Overall buzz volume, sentiment (real time) for the senior most management

Reduction in Turn Around Time (TAT)

The implementation of the tool, integration with the CRM and the escalation mechanism (with disincentives), ensured that the TAT for the brand was the lowest (i.e., most responsive amongst peers). Monthly 50-60 queries recorded. A drastic drop in response time from 2 days to below 1-2 hours.

During crisis, surge in traction

Whenever there’s surge in traction, the brand is alerted so that necessary measures can be planned on time.

Deep insights

User timeline at the individual level which provides context while taking an action

Crisis Management Examples

When ex-TN CM Jayalalitha was hospitalized at the brand’s Chennai branch, continuous updates pushed through social channels.

During cyclone Vardah, guided people with details of necessary measures they can take to stay safe.

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