Influencer Marketing Demand forecasting and Optimisation

在线商务正在迅速发展，然而，在线零售商面临的最大挑战是利润率和转化率的下降。近年来，随着品牌利用社交媒体上有影响力的人的可信推荐来提高投资回报率和销售额，该行业开始接受网红营销。网红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率约为10%，但在促销期间，对产品的高需求可能导致缺货率高达60%。在英国，83%的电商商店开展网红营销活动，但他们面临三个关键挑战：1)跟踪销售和业绩；2)为他们的活动找到合适的网红；3)预测活动的库存水平，以便计划和补充库存。我们通过使品牌与正确的影响者建立联系，以及通过我们现有的平台提供有关性能和转换的分析来解决前两个问题。通过这个项目，GAL旨在解决需求预测的第三个问题。这个问题可以通过两种方式解决：首先，根据产品在活动中的历史表现，预测产品缺货的可能性；其次，如果它仍然缺货，建议从同一家零售商那里购买类似或替代产品。一个新的缺货模块将建立在我们目前的女性创新奖项目上，在我们现有的影响者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是一家快速发展的机器学习初创公司，专注于在线零售。它为在线零售商开发了一个复杂的营销平台。数百家全球零售商店使用该平台，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。通过这个项目，GAL旨在开发最先进的机器学习流程，以支持电子商务基础设施。通过这项技术的发展，网上购物者将能够更好地管理他们的库存，或者在网上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.