本案例默认翻译为中文，点击可切换回原语言
已切换成原语言，点击可翻译成中文
感染警报系统
案例简介：为什么这项工作与创意数据相关？ 这项运动巧妙地利用了来自34,000农村卫生中心的疾病暴发的非结构化数据来设计感染警报系统。 该系统帮助救生圈在最佳时机到达印度农村最偏远村庄的人们，以教育他们用肥皂洗手作为预防腹泻和肺炎等威胁生命的最具成本效益的预防措施的重要性。 此预测性感染警报系统可帮助根据发病率的严重程度优先考虑村庄的超局部目标。 感染警报是通过消费者手机上的上下文音频通信发出的，克服了传统媒体覆盖面低和识字率差的障碍。 背景 印度十分之七的人使用Lifebuoy，这是印度排名第一的肥皂品牌。Lifebuoy的业务反映了印度城乡人口的分裂，即70% 业务来自印度农村。 该品牌的宗旨是使人们有能力保护自己免受威胁生命的疾病的侵害，尤其是在印度农村地区，大多数家庭每天的收入不到1美元，并且家庭中的疾病/感染可能会在情感和经济上严重削弱。 救生圈的目标是帮助这些人推动手部卫生行为的改变，并减少疾病和儿童死亡的发生率。 简介是为了克服印度农村地区特有的媒体障碍，即电视普及率低和识字率低，使印刷和外出工作效率低下，互联网/智能手机普及率微不足道，无法在消费者最脆弱的时候到达消费者手中。 描述想法/数据解决方案 (投票20%) 印度的婴儿死亡率 (IMR) 比全球平均水平高出13%。北方邦和比哈尔邦这两个人口最多的州的情况更糟，其IMR分别为43% 和27%，高于全球平均水平。 这些死亡中有一半是由腹泻和肺炎等可预防的疾病引起的，发生在农村地区。 印度人主要是用手吃饭和喂养孩子; 不是餐具。饭前用肥皂洗手不是他们习惯或习惯的一部分。未洗的手使他们和他们的孩子容易患病。 用肥皂洗手是防止儿童死亡的最具成本效益的干预措施。它甚至被证明可以减少45% 引起的腹泻和23% 引起的肺炎。 这个想法是为印度农村地区创建一个数据驱动的 “感染警报系统”，该系统看似 “数据黑暗” 和 “媒体黑暗”。该警告系统将近乎实时地向脆弱村庄的消费者激活感染警报。 该想法的创新和独创性是使用从34,000农村卫生中心收集的疾病暴发的非结构化数据，并利用功能电话作为功能强大的通信介质，其覆盖范围比农村地区的任何其他介质高6倍。 描述数据驱动策略 (投票30%) 有4个关键流程: 1.疾病数据库管理 从822个分区/村庄的34,000个农村社区卫生中心收集了疾病暴发数据。将旧的纸质记录数字化，然后使用算法将数据读取并加载到包含21种传染病的结构化数据库中。通过数据管道添加了新数据，该管道将原始数据类似地处理到结构化数据库中。 2.预测分析 使用分层时间序列模型 (未观察到的成分模型) 对历史疾病发病率进行建模，以得出村庄一级的预测发病率。 3.超局部定位 使用预测分析来确定每个村庄的风险水平。如果给定村庄的疾病发病率的预测严重程度高于某个阈值，则呼叫将被激活。 4.媒体交流 处于较低收入阶层的目标受众构成了挑战，因为他们是没有互联网连接的基本功能电话的用户，因此Lifebuoy使用呼出电话来产生更大的影响。 我们与领先的电信公司合作，利用一个100万移动数据库，该数据库被映射到感染警报系统的每周报告，以确保疾病上下文音频通信仅拨给受感染的村庄。 描述数据的创造性使用，或者数据如何增强创造性输出 (投票30%) 我们利用了印度政府的两个最大的健康数据库 1.综合疾病监测计划 (IDSP) 每周跟踪印度境内各种疾病暴发，从村庄到街区。 2.卫生管理信息系统 (HMIS) 跟踪整个印度公共卫生保健系统中农村人口可获得的卫生保健质量。 原始数据到洞察之旅利用数据质量、地理智能工具和实体识别ition引擎涉及多个过程，包括: 1.各保健中心每周一次的数据整理， 2.从纸质表格中提取， 3.清洗， 4.结构化， 5.标准化， 6.编目， 7.预测分析和 8.信号感染警报的可视化 平均而言，每周拨打800万个电话，覆盖北方邦和比哈尔邦822个优先和相关分区中的约60个。在活动的前8周，进行了超过6400万个电话 (感染警报)，以确保消费者采取预防措施预防感染。 该电话与给定村庄的流行疾病有关。 感染警报系统是大规模数据驱动营销的完美示例，并在脆弱性最高的最相关时间帮助警告消费者。 列出数据驱动结果 (投票20%) 典型感染警报呼叫: “救生圈叮当声……。你好，我是救生圈博士。腹泻在你所在的地区迅速蔓延。只有开水和熟食。最重要的是。吃饭前用救生圈洗手” 当消费者最脆弱，因此最容易接受交流时，他们就成为了目标，这导致了记忆力的提高和行为的改变。 这场运动推动了救生圈的目标和增长。 记得电话的人的98% 显示了对救生圈的自发回忆 记得通话显示自发消息的人的65% 回忆 在竞选期间，北方邦和比哈尔邦减少了178,000例最致命的疾病。 作为救生圈的游戏规则改变者，该品牌获得了令人鼓舞的业务业绩: 1.“有效保护种质” 增长了500个基点，从69个基点增加到74个基点 (目标300个基点) 2.UP和Bihar的销售收益分别为19% 和14% (目标10%) 3.上涨的渗透率收益为220个基点 (目标100个基点) 鉴于这项运动在UP和Bihar取得了成功，Lifebuoy扩大了印度另外六个州的感染警报系统，现在这是一项正在进行的运动，是未来可持续的成功典范。
感染警报系统
案例简介：Why is this work relevant for Creative Data? This campaign made intelligent use of unstructured data on disease outbreaks from 34,000 rural health-centres to devise an Infection Alert System. That system helped Lifebuoy reach people in the remotest villages of rural India at the best time to educate them on the importance of hand-washing with soap as the most cost-effective preventive measure against life threatening diseases like Diarrhoea and Pneumonia. This predictive Infection Alert System helped prioritise villages for hyper-local-targeting based on incidence severity. Infection Alerts were delivered through contextual audio communication on consumers’ mobile phones, overcoming the barriers of low traditional media reach and poor literacy rates. 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 behavioural 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 idea/data solution (20% of vote) India’s Infant Mortality Rate (IMR) is 13% higher than the global average. The situation is worse in its two most populous states, 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 and occur in rural areas. Indians primarily eat and feed their children with their hands; not cutlery. Hand-washing with soap before meals is not a part of their regime or habit. Unwashed hands makes them and their children susceptible to diseases. Hand-washing with soap is the single most cost-effective intervention to prevent child deaths. It is even proven to reduce diarrhoea by 45% and pneumonia by 23%. The idea was to create a data-driven “Infection Alert System” for rural India, which is seemingly “data-dark” and “media-dark”. This warning system would activate Infection Alerts to consumers in vulnerable villages in near-real-time. The innovation and originality of the idea was the use of unstructured data on disease outbreaks collected from 34,000 rural health centres and leveraging the feature phone as a powerful communication medium which had 6X higher reach than any other medium in rural areas. Describe the data driven strategy (30% of vote) There were 4 key processes : 1. Disease database management Data on disease outbreaks was collected from 34,000 rural community health centres across 822 sub-districts/villages. 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 via a data-pipeline, which similarly processed raw data into the structured database. 2. Predictive analytics Historical disease incidence was modelled to arrive at predictive incidence rates at a village level, using hierarchical time series models (Unobserved Component Model). 3. Hyper-local targeting Predictive analysis was used to determine the level of risk for each village. If predicted severity of disease incidence for a given village was above a certain threshold then the calls would be activated. 4. 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. We partnered with leading telecom players to leverage a 100M mobile database which was mapped to the weekly report from the Infection Alert System to ensure disease contextual audio communication was dialled only to infection affected villages. Describe the creative use of data, or how the data enhanced the creative output (30% of vote) We tapped into the Government of India’s two largest health databases 1. Integrated Disease Surveillance Program (IDSP) tracks a variety of disease outbreaks within India down to village and block level on a weekly basis. 2. Health Management Information Systems (HMIS) tracks the quality of health care available to rural populations throughout India's public health care system. 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 from paper forms, 3. Cleansing, 4. Structuring, 5. Standardisation, 6. Cataloguing, 7. Predictive analytics and 8. Visualization for signalling infection alerts 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 to ensure consumers took preventive measures against infections. The call was contextual to the prevalent disease in the given village. The Infection Alert System was a perfect example of data-driven marketing at scale and helped warn consumers at the most relevant time when vulnerability was highest. List the data driven results (20% of vote) Typical infection alert call: “Lifebuoy jingle….Greetings I am Dr. Lifebuoy. Diarrhoea is spreading rapidly in your area. Have only boiled water and cooked food. The most important thing. Before eating wash your hands with Lifebuoy” Consumers were targeted when they were most vulnerable and hence most receptive to the communication which resulted in increased memorability and behaviour change. The campaign 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: 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) Given the success of the campaign in UP and Bihar, Lifebuoy scaled up the Infection Alert System across six additional states in India and it is now an ongoing campaign which is a sustainable success model for the future.
The Infection Alert System
案例简介：为什么这项工作与创意数据相关？ 这项运动巧妙地利用了来自34,000农村卫生中心的疾病暴发的非结构化数据来设计感染警报系统。 该系统帮助救生圈在最佳时机到达印度农村最偏远村庄的人们，以教育他们用肥皂洗手作为预防腹泻和肺炎等威胁生命的最具成本效益的预防措施的重要性。 此预测性感染警报系统可帮助根据发病率的严重程度优先考虑村庄的超局部目标。 感染警报是通过消费者手机上的上下文音频通信发出的，克服了传统媒体覆盖面低和识字率差的障碍。 背景 印度十分之七的人使用Lifebuoy，这是印度排名第一的肥皂品牌。Lifebuoy的业务反映了印度城乡人口的分裂，即70% 业务来自印度农村。 该品牌的宗旨是使人们有能力保护自己免受威胁生命的疾病的侵害，尤其是在印度农村地区，大多数家庭每天的收入不到1美元，并且家庭中的疾病/感染可能会在情感和经济上严重削弱。 救生圈的目标是帮助这些人推动手部卫生行为的改变，并减少疾病和儿童死亡的发生率。 简介是为了克服印度农村地区特有的媒体障碍，即电视普及率低和识字率低，使印刷和外出工作效率低下，互联网/智能手机普及率微不足道，无法在消费者最脆弱的时候到达消费者手中。 描述想法/数据解决方案 (投票20%) 印度的婴儿死亡率 (IMR) 比全球平均水平高出13%。北方邦和比哈尔邦这两个人口最多的州的情况更糟，其IMR分别为43% 和27%，高于全球平均水平。 这些死亡中有一半是由腹泻和肺炎等可预防的疾病引起的，发生在农村地区。 印度人主要是用手吃饭和喂养孩子; 不是餐具。饭前用肥皂洗手不是他们习惯或习惯的一部分。未洗的手使他们和他们的孩子容易患病。 用肥皂洗手是防止儿童死亡的最具成本效益的干预措施。它甚至被证明可以减少45% 引起的腹泻和23% 引起的肺炎。 这个想法是为印度农村地区创建一个数据驱动的 “感染警报系统”，该系统看似 “数据黑暗” 和 “媒体黑暗”。该警告系统将近乎实时地向脆弱村庄的消费者激活感染警报。 该想法的创新和独创性是使用从34,000农村卫生中心收集的疾病暴发的非结构化数据，并利用功能电话作为功能强大的通信介质，其覆盖范围比农村地区的任何其他介质高6倍。 描述数据驱动策略 (投票30%) 有4个关键流程: 1.疾病数据库管理 从822个分区/村庄的34,000个农村社区卫生中心收集了疾病暴发数据。将旧的纸质记录数字化，然后使用算法将数据读取并加载到包含21种传染病的结构化数据库中。通过数据管道添加了新数据，该管道将原始数据类似地处理到结构化数据库中。 2.预测分析 使用分层时间序列模型 (未观察到的成分模型) 对历史疾病发病率进行建模，以得出村庄一级的预测发病率。 3.超局部定位 使用预测分析来确定每个村庄的风险水平。如果给定村庄的疾病发病率的预测严重程度高于某个阈值，则呼叫将被激活。 4.媒体交流 处于较低收入阶层的目标受众构成了挑战，因为他们是没有互联网连接的基本功能电话的用户，因此Lifebuoy使用呼出电话来产生更大的影响。 我们与领先的电信公司合作，利用一个100万移动数据库，该数据库被映射到感染警报系统的每周报告，以确保疾病上下文音频通信仅拨给受感染的村庄。 描述数据的创造性使用，或者数据如何增强创造性输出 (投票30%) 我们利用了印度政府的两个最大的健康数据库 1.综合疾病监测计划 (IDSP) 每周跟踪印度境内各种疾病暴发，从村庄到街区。 2.卫生管理信息系统 (HMIS) 跟踪整个印度公共卫生保健系统中农村人口可获得的卫生保健质量。 原始数据到洞察之旅利用数据质量、地理智能工具和实体识别ition引擎涉及多个过程，包括: 1.各保健中心每周一次的数据整理， 2.从纸质表格中提取， 3.清洗， 4.结构化， 5.标准化， 6.编目， 7.预测分析和 8.信号感染警报的可视化 平均而言，每周拨打800万个电话，覆盖北方邦和比哈尔邦822个优先和相关分区中的约60个。在活动的前8周，进行了超过6400万个电话 (感染警报)，以确保消费者采取预防措施预防感染。 该电话与给定村庄的流行疾病有关。 感染警报系统是大规模数据驱动营销的完美示例，并在脆弱性最高的最相关时间帮助警告消费者。 列出数据驱动结果 (投票20%) 典型感染警报呼叫: “救生圈叮当声……。你好，我是救生圈博士。腹泻在你所在的地区迅速蔓延。只有开水和熟食。最重要的是。吃饭前用救生圈洗手” 当消费者最脆弱，因此最容易接受交流时，他们就成为了目标，这导致了记忆力的提高和行为的改变。 这场运动推动了救生圈的目标和增长。 记得电话的人的98% 显示了对救生圈的自发回忆 记得通话显示自发消息的人的65% 回忆 在竞选期间，北方邦和比哈尔邦减少了178,000例最致命的疾病。 作为救生圈的游戏规则改变者，该品牌获得了令人鼓舞的业务业绩: 1.“有效保护种质” 增长了500个基点，从69个基点增加到74个基点 (目标300个基点) 2.UP和Bihar的销售收益分别为19% 和14% (目标10%) 3.上涨的渗透率收益为220个基点 (目标100个基点) 鉴于这项运动在UP和Bihar取得了成功，Lifebuoy扩大了印度另外六个州的感染警报系统，现在这是一项正在进行的运动，是未来可持续的成功典范。
The Infection Alert System
案例简介：Why is this work relevant for Creative Data? This campaign made intelligent use of unstructured data on disease outbreaks from 34,000 rural health-centres to devise an Infection Alert System. That system helped Lifebuoy reach people in the remotest villages of rural India at the best time to educate them on the importance of hand-washing with soap as the most cost-effective preventive measure against life threatening diseases like Diarrhoea and Pneumonia. This predictive Infection Alert System helped prioritise villages for hyper-local-targeting based on incidence severity. Infection Alerts were delivered through contextual audio communication on consumers’ mobile phones, overcoming the barriers of low traditional media reach and poor literacy rates. 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 behavioural 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 idea/data solution (20% of vote) India’s Infant Mortality Rate (IMR) is 13% higher than the global average. The situation is worse in its two most populous states, 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 and occur in rural areas. Indians primarily eat and feed their children with their hands; not cutlery. Hand-washing with soap before meals is not a part of their regime or habit. Unwashed hands makes them and their children susceptible to diseases. Hand-washing with soap is the single most cost-effective intervention to prevent child deaths. It is even proven to reduce diarrhoea by 45% and pneumonia by 23%. The idea was to create a data-driven “Infection Alert System” for rural India, which is seemingly “data-dark” and “media-dark”. This warning system would activate Infection Alerts to consumers in vulnerable villages in near-real-time. The innovation and originality of the idea was the use of unstructured data on disease outbreaks collected from 34,000 rural health centres and leveraging the feature phone as a powerful communication medium which had 6X higher reach than any other medium in rural areas. Describe the data driven strategy (30% of vote) There were 4 key processes : 1. Disease database management Data on disease outbreaks was collected from 34,000 rural community health centres across 822 sub-districts/villages. 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 via a data-pipeline, which similarly processed raw data into the structured database. 2. Predictive analytics Historical disease incidence was modelled to arrive at predictive incidence rates at a village level, using hierarchical time series models (Unobserved Component Model). 3. Hyper-local targeting Predictive analysis was used to determine the level of risk for each village. If predicted severity of disease incidence for a given village was above a certain threshold then the calls would be activated. 4. 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. We partnered with leading telecom players to leverage a 100M mobile database which was mapped to the weekly report from the Infection Alert System to ensure disease contextual audio communication was dialled only to infection affected villages. Describe the creative use of data, or how the data enhanced the creative output (30% of vote) We tapped into the Government of India’s two largest health databases 1. Integrated Disease Surveillance Program (IDSP) tracks a variety of disease outbreaks within India down to village and block level on a weekly basis. 2. Health Management Information Systems (HMIS) tracks the quality of health care available to rural populations throughout India's public health care system. 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 from paper forms, 3. Cleansing, 4. Structuring, 5. Standardisation, 6. Cataloguing, 7. Predictive analytics and 8. Visualization for signalling infection alerts 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 to ensure consumers took preventive measures against infections. The call was contextual to the prevalent disease in the given village. The Infection Alert System was a perfect example of data-driven marketing at scale and helped warn consumers at the most relevant time when vulnerability was highest. List the data driven results (20% of vote) Typical infection alert call: “Lifebuoy jingle….Greetings I am Dr. Lifebuoy. Diarrhoea is spreading rapidly in your area. Have only boiled water and cooked food. The most important thing. Before eating wash your hands with Lifebuoy” Consumers were targeted when they were most vulnerable and hence most receptive to the communication which resulted in increased memorability and behaviour change. The campaign 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: 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) Given the success of the campaign in UP and Bihar, Lifebuoy scaled up the Infection Alert System across six additional states in India and it is now an ongoing campaign which is a sustainable success model for the future.
感染警报系统
暂无简介
The Infection Alert System
暂无简介
基本信息
暂无评分
已有{{caseInfo.tatolPeople}}人评分
创作者
案例详情
涵盖全球100万精选案例，涉及2800个行业，包含63000个品牌
热门节日97个，23个维度智能搜索
-
项目比稿
品类案例按时间展现，借鉴同品牌策略，比稿提案轻松中标
-
创意策划
任意搜索品牌关键词，脑洞创意策划1秒呈现
-
竞品调研
一键搜索竞品往年广告，一眼掌握对手市场定位
-
行业研究
热词查看洞悉爆点，抢占行业趋势红利
登录后查看全部案例信息
如果您是本案的创作者或参与者 可对信息进行完善
