Influencer Marketing Demand forecasting and Optimisation

在线商务正在快速增长，然而，在线零售商面临的最大挑战是利润率和转化率不断下降。近年来，随着品牌利用社交媒体影响力人士的值得信赖的推荐来推动更高的ROI和销售额，该行业已经接受了有影响力的营销。有影响力的营销活动的本质导致对产品的需求恶化，导致缺货情况。大多数在线零售商的缺货水平约为10%，但在促销期间，对产品的高需求可能导致缺货水平高达60%。在英国，83%的电子商务商店开展有影响力的营销活动，但他们面临三个关键挑战：1)跟踪销售和业绩；2)为活动找到合适的影响者；3)预测库存水平，以计划和补充库存。我们解决前两个问题的方法是让品牌与合适的有影响力的人建立联系，并通过我们现有的平台提供有关业绩和转化率的分析。通过这个项目，GAL的目标是解决需求预测的第三个问题。这个问题将通过两种方式解决：第一，根据产品与活动中有影响力的人的历史表现，预测产品脱销的可能性；第二，如果产品仍然脱销，建议来自同一零售商的类似或替代产品。我们将在我们目前的女性创新奖项目基础上建立一个新的脱销模块，在我们现有的有影响力的营销平台上增加一个创新层，以增强用户体验和在线商店的销售。在产品列表页面上实时动态推荐替代产品将是一个全新的概念。GAL是一家专注于在线零售的快速增长的机器学习初创公司。它为在线零售商开发了一个复杂的营销平台。数百家全球零售店使用该平台，为全球数百万用户提供服务。研究和开发领域包括使用机器学习、先进的数据处理和事件处理，这是电子商务系统研发的前沿。GAL的项目旨在开发最先进的机器学习过程，以支持电子商务基础设施。通过这项技术开发，在线购物者将能够更好地管理他们的库存，或者在在线零售网站上发现类似或替代的产品。

Online commerce is growing rapidly, however the biggest challenge for online retailers is dwindling margins and conversions. In recent years, the industry has embraced influencer marketing as brands leverage trustworthy recommendations from social media influencers to drive better ROI and sales. The very nature of influencer marketing campaigns causes the demand for products to sour, resulting in out-of-stock situations. Most online retailers experience out of stock levels of around 10%, but during promotions the high demand for products can cause the out of stock levels to reach as high as 60%.In the UK 83% of the eCommerce stores run influencer marketing campaigns but they face three key challenges i) tracking sales and performance ii) finding the right influencer for their campaign and iii) predicting the stock levels for the campaign to plan and replenish stock. We address the first two issues by enabling brands to connect with the right influencers, as well as providing analytics regarding performance and conversions through our existing platform. With this project, GAL aims to address the third problem of demand prediction. The problem will be resolved in two ways: first predict the likelihood of a product going out of stock based on its historic performance with the influencers in the campaign; second, if it still goes out of stock, suggest a similar or substitute product from the same retailer.A new out-of-stock module will be built on our current Women in Innovation Award project by adding an innovative layer on top of our existing influencer marketing platform to enhance user experience and sales for online stores. It will be a completely new concept to recommend substitute products dynamically in real time on the product listing page.GAL is a fast-growing machine learning startup focused on online retail. It has developed a sophisticated marketing platform for online retailers. Hundreds of global retail stores use the platform, which serves millions of users around the world. Areas of research and development include use of machine learning, advanced data processing and event processing which is at the leading edge of R&D in eCommerce systems. With its project, GAL aims to develop state-of-the- art machine learning processes to support eCommerce infrastructure. Through this technology development, online shoppers will be able to better manage their stock or discover similar or substitute products at the online retail sites.