|Title||ACTIVATING INFECTION ALERT SYSTEM IN RURAL INDIA|
|Brand||HINDUSTAN UNILEVER LIMITED|
|Product / Service||LIFEBUOY|
|Category||A01. Activation by Location|
|Entrant||MINDSHARE Mumbai, INDIA|
|Idea Creation||MINDSHARE Mumbai, INDIA|
|Media Placement||MINDSHARE Mumbai, INDIA|
|Shiv Shetty||Mindshare India||Senior Director|
|Himanshu Jha||Mindshare India||Senior Director|
|Somnath Saha||Mindshare India||Director|
|Rituparna Dasgupta||Mindshare India||Principal Partner|
|Virendra Bapardekar||Mindshare India||Senior Director|
|Vinish Mathews||Mindshare India||Principal Partner|
|Zubin Tatna||Mindshare India||Principal Partner|
We focused on a simple insight that most people have access to soap, but the number of people who regularly wash their hands before eating and after using the toilet – is very low; leading to number of communicable diseases. We had to break this inertia by ‘INFLUENCING AND EDUCATING’ our consumers on the benefits of hand-washing at the most relevant times. For this, we first identified 21 communicable diseases that can be reduced by hand washing. Post this, we needed a more personalized medium to reach out to our target audience. Mobile phones with penetration of 78%, gave us the perfect platform as a vehicle to influence and educate consumers. Keeping Mobile central to our Media solution to drive education, we created a data-led infection alert system - “The Adaptive Data Lighthouse (ADL)”.
The ADL was implemented in 4 steps Step 1 – Disease Database Management – We tapped into Government of India’s two largest databases which are part of the National Health Mission Integrated Disease Surveillance Program: Tracks variety of disease outbreaks within India down to the village and block level on a weekly basis. Health Management Information Systems: Tracks the quality of health care available to rural populations throughout India Public Health care system, at a monthly level. Step 2 – Predictive Analysis – Using combination of the historical trends (past 3 years) and current outbreak cases at a given district, we defined the predictive incidence rate against each of the 21 communicable diseases. Step 3 – Micro-targeting –We created a proprietary dashboard system to understand the Intensity, Magnitude and Trends of these diseases. Step 4 – Communicating – We created contextual preventive messages for each of the identified diseases. The message was communicated only to consumers of sub-districts where Disease outbreak went beyond the threshold using Mobile Outbound Dialer. In 8 Weeks of activity we made over 60 million calls in relevant sub districts across Uttar Pradesh and Bihar to ensure consumers take preventive measures against infections.
The post campaign results spoke the success of the campaign: - 1. The key brand attribute ‘Protect effectively from Germs’ scores increased by 500 basis points 2. Additionally, brand trust attribute ‘Trust more than other brands’ grew by 200 basis points 3. Post campaign dipstick study in rural districts of Uttar Pradesh & Bihar revealed: - a. 98% of the listeners had Spontaneous Brand recall for Lifebuoy b. 65% of the listeners had Spontaneous message recall 4. The campaign received listenership of 43% against the industry average of 27% without any celebrity voice (which is a regular norm for any Voice related campaign) 5. The overall approach helped us delivering a 32% business efficiency. Given the campaign success in 2 states, we implemented the Adaptive Data Light House Model in 6 additional states to keep the momentum going.
Lifebuoy’s core consumer resides in rural India. They are spread across 6 lakh Indian villages having a population of 5000 +/ village which shows the physical strength of the brand in the country. The target audience, being from rural areas with lower income group, posed a challenge because they were users of basic feature phones, which have no Internet connectivity, hence Lifebuoy used audio communication to deliver greater impact. Our ADL was fueled with data from 34,000 hospitals across 822 sub-districts of Uttar Pradesh and Bihar. This data was simplified into a simple data structure on our proprietary dashboard. This dashboard was updated on weekly basis for us to track incidence rate for every district/sub-district which helped us in targeting consumers at the most granular level of markets (i.e. Villages/Sub-Districts). We only targeted audience whenever the disease incidence rate went above government threshold limits in their respective districts, through mobile.