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The Infection Alert Syste

basics
Industry: Retail & Distribution
Media:Radio
Style: Minimalism
descriptioncnen
Why is this work relevant for Media?

This campaign made intelligent use of real-time unstructured data on disease outbreaks collected from rural health centres to devise an “Infection Alert System”.

That system helped Lifebuoy proactively identify villages at high-risk of infections.
Lifebuoy then reached vulnerable people in these remote villages on their “feature phones” - medium with 6X higher reach than any other medium in rural and thereby overcoming the barriers of low traditional media reach.

Infection Alerts educating consumers on the importance of hand-washing with soap as the most cost-effective preventive measure against life-threatening diseases were delivered real-time through contextual audio communication on their mobile phones.

Background

Seven out of ten people in India use Lifebuoy which is India’s #1 soap brand. Lifebuoy’s business is reflective of India’s rural-urban population split i.e. 70% of business comes from rural India.

The brand is guided by its purpose of empowering people to safeguard themselves against life threatening diseases, especially in rural India where most families earn less than 1 dollar a day and a disease/infection in the family can be severely debilitating – emotionally and financially.

Lifebuoy’s objective was to reach these people to drive hand-hygiene behaviour change and reduce the incidence of illness and child deaths.

The brief was to overcome media barriers characteristic of rural India i.e. low television penetration and low literacy rates rendering print and out of home ineffective and negligible internet / smartphone penetration to reach consumers when they were most vulnerable.

Describe the creative idea/insights (30% of vote)

The idea was to create a real-time data-driven “Infection Alert System” for rural India to help Lifebuoy proactively educate consumers when they are most vulnerable to fatal diseases – and activate it through audio communication on mobile.

The research and data gathering involved 2 key steps:

1. Disease database management
Government of India data on disease outbreaks was collected from 34,000 rural community health centres across 822 sub-districts/villages of the most populous states of Uttar Pradesh & Bihar. This data was unstructured, maintained in paper-forms, in local languages with no metadata standards.
Old paper records were digitised and then algorithms used to read and load data into a structured database of 21 communicable diseases.
Fresh data was added to the database via a data-pipeline at a weekly frequency.

2. Predictive analytics
Disease incidence was modelled to arrive at predictive incidence rates at a village level, using hierarchical time series models.

Describe the strategy (20% of vote)

The target audience was from rural India where Infant Mortality Rate (IMR) is 13% higher than the global average. The situation is worse in Uttar Pradesh and Bihar with an IMR of 43% and 27% higher than global average respectively.
Half of these deaths are caused by preventable diseases like diarrhoea and pneumonia.

The media planning approach involved 2 key steps:

1. Hyper-local targeting
Predictive analytics on the disease database created was used to determine the degree of risk for each village for prioritisation of activation. If predicted severity of disease incidence for a given village was above a certain threshold then warning calls would be activated through an automatic calling system

2. Communicating in media
The target audience, being in the lower income bracket, posed a challenge because they were users of basic feature phones with no internet connectivity, hence Lifebuoy used outbound calls to deliver greater impact.

Describe the execution (20% of vote)

We partnered with leading telecom players to leverage a 100M mobile database which was mapped to the weekly outbreak predictions from the Infection Alert System to ensure disease contextual audio communication was dialled ONLY to infection affected villages through an automatic calling system.

On average, 8 million calls were dialled every week covering ~60 out of 822 prioritised and relevant sub-districts across Uttar Pradesh and Bihar. In the first 8 weeks of activity over 64 million calls (infection alerts) were made which reached 19 million families to ensure consumers took preventive measures against infections.

The calls were contextual to the prevalent disease in the given village.

In the 2nd phase of 8 weeks, Lifebuoy scaled up the Infection Alert System across six additional states in India dialling 90 million infection alerts & reaching 36 million families. It is now an ongoing campaign which is a sustainable success model for future.

List the results (30% of vote)

The Infection Alert System drove both purpose and growth for Lifebuoy.

98% of people who remembered the call displayed spontaneous recall for Lifebuoy
65% of people who remembered the call displayed spontaneous message recall

Uttar Pradesh and Bihar saw a drop of 178,000 cases of the deadliest diseases during the campaign period.

A game-changer for Lifebuoy, the brand reaped promising business results as well:
1. 'Protects effectively from germs' grew by 500 bps, from 69 to 74 (target 300 bps)
2. Sales gain for UP and Bihar was 19% and 14% respectively ( target 10%)
3. Penetration gain in UP was 220 bps ( target 100 bps)

The success of the Infection Alert System in the two largest states of UP and Bihar with almost 75% of population residing in rural led to the scaling it up across six additional states for bigger impact.

Describe the use of data, or how the data enhanced the campaign output

The raw data to insights journey leveraged data quality, geo intelligence tools and an entity recognition engine which involved multiple processes including:
1. Data collation across health centres at a weekly frequency,
2. Extraction of data from paper forms,
3. Cleansing,
4. Structuring,
5. Standardisation,
6. Cataloguing,
7. Modelling historical data on diseases and deriving a Predictive Incidence Rate using a time series model
8. Visualisation for signalling infection alerts proactively

The proprietary algorithm digitised and simplified big data to help understand the Intensity, Magnitude and Trends of each of 21 communicable diseases, at a weekly level, for each of the 822 districts.
When an outbreak was predicted, we activated an automatic calling system that made on average 8 million calls every week, alerting rural consumers contextually on the prevalent disease in their village and educating them on the importance of hand washing with soap as a preventive measure.
credit
Brand:
Agency:
awards
Cannes Lions International Festival of Creativity 2019
Silver Media
Use of Real-time Data
Cannes Lions International Festival of Creativity 2019
Shortlist Media
Social Behaviour & Cultural Insight
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