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：为什么这项工作与媒体相关？ 这项运动智能地利用了从农村保健中心收集的关于疾病爆发的实时非结构化数据，设计了一个 “感染警报系统”。 该系统帮助救生圈主动识别感染高风险的村庄。 救生圈随后通过他们的 “功能电话” 接触到这些偏远村庄的弱势人群 -- 媒介比农村的任何其他媒介都要高 6 倍，从而克服了传统媒介接触不足的障碍。 感染警报通过手机上的上下文音频通信实时提供，让消费者了解用肥皂洗手作为最具成本效益的致命疾病预防措施的重要性。 背景 印度十分之七的人使用救生圈，这是印度第一肥皂品牌。救生圈的业务反映了印度城乡人口的分裂，即70% 的业务来自印度农村。 该品牌的宗旨是增强人们的能力，保护自己免受危及生命的疾病的侵害，特别是在印度农村，大多数家庭每天收入不到 1 美元，家庭中的疾病/感染可能会严重削弱-情绪和经济。 救生圈的目标是帮助这些人改变手卫生行为，减少疾病和儿童死亡的发生率。 简报是为了克服印度农村特有的媒体障碍，即低电视普及率和低识字率使得打印和外出无效且可以忽略不计的互联网/智能手机普及率能够在消费者最脆弱的时候接触到他们。 描述创意/见解 (30% 的选票) 这个想法是为印度农村创建一个实时数据驱动的 “感染警报系统”，帮助救生员在消费者最容易感染致命疾病时主动教育他们，并通过音频激活它移动通信。 研究和数据收集涉及两个关键步骤: 1.疾病数据库管理 印度政府从人口最多的北方邦和比哈尔邦的 34,000 个分区/村庄的 822 个农村社区卫生中心收集了疾病爆发的数据。这些数据是非结构化的，以纸质形式、当地语言保存，没有元数据标准。 旧的纸质记录被数字化，然后算法被用来读取数据并将其加载到 21 种传染病的结构化数据库中。 通过数据管道以每周的频率将新数据添加到数据库中。 2.预测分析 使用分层时间序列模型，对疾病发病率进行建模，得出村级的预测发病率。 描述策略 (20% 的选票) 目标受众来自印度农村，那里的婴儿死亡率 (IMR) 比全球平均水平高 13%。北方邦和比哈尔邦的情况更糟，IMR分别比全球平均水平高出 43% 和 27%。 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。 媒体规划方法涉及两个关键步骤: 1.超本地定位 对创建的疾病数据库进行预测分析，以确定每个村庄激活优先级的风险程度。如果某一村庄的疾病发病率的预测严重程度高于某一阈值，则警告呼叫将通过自动呼叫系统激活。 2.在媒体中沟通 收入较低的目标受众面临挑战，因为他们是没有互联网连接的基本功能电话的用户，因此救生圈使用呼出电话来产生更大的影响。 描述执行情况 (20% 的选票) 我们与领先的电信公司合作，利用一个 100 移动数据库，该数据库映射到感染警报系统的每周爆发预测，以确保疾病上下文音频通信仅通过自动呼叫系统。 平均而言，每周拨打 800万个电话，覆盖北方邦和比哈尔邦 822 个优先地区和相关分区中的 60 个。在活动的前 8 周，超过 6400万个电话 (感染警报) 到达 1900万个家庭，以确保消费者采取预防措施防止感染。 这些呼吁与特定村庄的流行疾病相关。 在为期 8 周的第2 阶段，救生圈扩大了印度另外六个州的感染警报系统，拨打了 9000万个感染警报，覆盖了 3600万个家庭。现在这是一场持续的运动，是未来可持续的成功模式。 列出结果 (30% 的选票) 感染警报系统推动了救生圈的目的和发展。 98% 记得电话的人会自动召回救生圈 65% 记得这个电话的人都表示火暴消息召回 北方邦和比哈尔邦在竞选期间减少了 178,000 例最致命的疾病。 救生圈的游戏规则改变者，该品牌也获得了有希望的商业成果: 1.“有效保护免受细菌侵害” 增长了 500 个基点，从 69 个增加到 74 个基点 (目标是 300 个基点) 2.UP和比哈尔邦的销售收益分别为 19% 和 14% (目标 10%) 3.UP中的渗透增益为 220 个基点 (目标 100 个基点) 感染警报系统在UP和比哈尔邦这两个最大的州的成功，几乎 75% 的人口居住在农村，导致它在另外六个州的范围内扩大，以产生更大的影响。 描述数据的使用，或数据如何增强市场活动输出 Insights旅程的原始数据利用了数据质量、地理智能工具和实体识别引擎，其中涉及多个流程，包括: 1.每周一次的健康中心数据整理， 2.从纸质表格中提取数据， 3.洁面， 4.结构化， 5.标准化， 6.编目， 7.建模疾病的历史数据和使用时间序列模型导出预测发病率 8.主动发出感染警报的可视化 专有算法将大数据数字化和简化，以帮助了解 822 个地区中每个地区每周 21 种传染病的强度，幅度和趋势。 当预测爆发时，我们启动了一个自动呼叫系统，平均每周打 800万个电话，提醒农村消费者注意他们所在村庄的流行疾病，并教育他们用肥皂洗手作为预防措施的重要性。
感染警报系统
案例简介：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.
The Infection Alert Syste
案例简介：为什么这项工作与媒体相关？ 这项运动智能地利用了从农村保健中心收集的关于疾病爆发的实时非结构化数据，设计了一个 “感染警报系统”。 该系统帮助救生圈主动识别感染高风险的村庄。 救生圈随后通过他们的 “功能电话” 接触到这些偏远村庄的弱势人群 -- 媒介比农村的任何其他媒介都要高 6 倍，从而克服了传统媒介接触不足的障碍。 感染警报通过手机上的上下文音频通信实时提供，让消费者了解用肥皂洗手作为最具成本效益的致命疾病预防措施的重要性。 背景 印度十分之七的人使用救生圈，这是印度第一肥皂品牌。救生圈的业务反映了印度城乡人口的分裂，即70% 的业务来自印度农村。 该品牌的宗旨是增强人们的能力，保护自己免受危及生命的疾病的侵害，特别是在印度农村，大多数家庭每天收入不到 1 美元，家庭中的疾病/感染可能会严重削弱-情绪和经济。 救生圈的目标是帮助这些人改变手卫生行为，减少疾病和儿童死亡的发生率。 简报是为了克服印度农村特有的媒体障碍，即低电视普及率和低识字率使得打印和外出无效且可以忽略不计的互联网/智能手机普及率能够在消费者最脆弱的时候接触到他们。 描述创意/见解 (30% 的选票) 这个想法是为印度农村创建一个实时数据驱动的 “感染警报系统”，帮助救生员在消费者最容易感染致命疾病时主动教育他们，并通过音频激活它移动通信。 研究和数据收集涉及两个关键步骤: 1.疾病数据库管理 印度政府从人口最多的北方邦和比哈尔邦的 34,000 个分区/村庄的 822 个农村社区卫生中心收集了疾病爆发的数据。这些数据是非结构化的，以纸质形式、当地语言保存，没有元数据标准。 旧的纸质记录被数字化，然后算法被用来读取数据并将其加载到 21 种传染病的结构化数据库中。 通过数据管道以每周的频率将新数据添加到数据库中。 2.预测分析 使用分层时间序列模型，对疾病发病率进行建模，得出村级的预测发病率。 描述策略 (20% 的选票) 目标受众来自印度农村，那里的婴儿死亡率 (IMR) 比全球平均水平高 13%。北方邦和比哈尔邦的情况更糟，IMR分别比全球平均水平高出 43% 和 27%。 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。 媒体规划方法涉及两个关键步骤: 1.超本地定位 对创建的疾病数据库进行预测分析，以确定每个村庄激活优先级的风险程度。如果某一村庄的疾病发病率的预测严重程度高于某一阈值，则警告呼叫将通过自动呼叫系统激活。 2.在媒体中沟通 收入较低的目标受众面临挑战，因为他们是没有互联网连接的基本功能电话的用户，因此救生圈使用呼出电话来产生更大的影响。 描述执行情况 (20% 的选票) 我们与领先的电信公司合作，利用一个 100 移动数据库，该数据库映射到感染警报系统的每周爆发预测，以确保疾病上下文音频通信仅通过自动呼叫系统。 平均而言，每周拨打 800万个电话，覆盖北方邦和比哈尔邦 822 个优先地区和相关分区中的 60 个。在活动的前 8 周，超过 6400万个电话 (感染警报) 到达 1900万个家庭，以确保消费者采取预防措施防止感染。 这些呼吁与特定村庄的流行疾病相关。 在为期 8 周的第2 阶段，救生圈扩大了印度另外六个州的感染警报系统，拨打了 9000万个感染警报，覆盖了 3600万个家庭。现在这是一场持续的运动，是未来可持续的成功模式。 列出结果 (30% 的选票) 感染警报系统推动了救生圈的目的和发展。 98% 记得电话的人会自动召回救生圈 65% 记得这个电话的人都表示火暴消息召回 北方邦和比哈尔邦在竞选期间减少了 178,000 例最致命的疾病。 救生圈的游戏规则改变者，该品牌也获得了有希望的商业成果: 1.“有效保护免受细菌侵害” 增长了 500 个基点，从 69 个增加到 74 个基点 (目标是 300 个基点) 2.UP和比哈尔邦的销售收益分别为 19% 和 14% (目标 10%) 3.UP中的渗透增益为 220 个基点 (目标 100 个基点) 感染警报系统在UP和比哈尔邦这两个最大的州的成功，几乎 75% 的人口居住在农村，导致它在另外六个州的范围内扩大，以产生更大的影响。 描述数据的使用，或数据如何增强市场活动输出 Insights旅程的原始数据利用了数据质量、地理智能工具和实体识别引擎，其中涉及多个流程，包括: 1.每周一次的健康中心数据整理， 2.从纸质表格中提取数据， 3.洁面， 4.结构化， 5.标准化， 6.编目， 7.建模疾病的历史数据和使用时间序列模型导出预测发病率 8.主动发出感染警报的可视化 专有算法将大数据数字化和简化，以帮助了解 822 个地区中每个地区每周 21 种传染病的强度，幅度和趋势。 当预测爆发时，我们启动了一个自动呼叫系统，平均每周打 800万个电话，提醒农村消费者注意他们所在村庄的流行疾病，并教育他们用肥皂洗手作为预防措施的重要性。
The Infection Alert Syste
案例简介：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.
感染警报系统
暂无简介
The Infection Alert Syste
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
