E-commerce today as a sub-sector of the national economy is represented by almost every major retailer, and therefore requires from entrepreneurs additional efforts to overcome competition and increase consumer loyalty. The use of modeling and customer analysis – with predictive analytics at its core – has long been a major driver of innovation in this field. One of the reasons behind this success is the availability of large amounts of historic data collected by major online retailers. On the other hand, companies launching new products and services or entering a new market are often confronted by the chicken-or-egg problem: How to apply predictive analytics if historical data is lacking? This use case will therefore showcase special approaches developed by ELEKS in multiple projects on so-called “cold start models”. This class of methods uses for example historical data from related, external sources to allow the prediction and analysis customer behavior for new products right from the start. This presentation will cover modules such as customer analysis, prediction of future consumer behavior and recommender systems and will highlight possible bottlenecks and benefits. It will also focus on models for consumer segmentation, churn risk, price sensitivity, and recommender systems to predict and steer consumer behavior are today state of the art.
Tradera is one of Sweden’s largest online marketplaces with over two million users. Dativa worked with the team at Tradera to implement image based product search and automatic classification of auctions. The major question was whether it’s more cost effective and more accurate to use the standard Google Cloud APIs or to implement from scratch. We tested both approaches – using state of the art TensorFlow neural networks and the Facebook FAISS database, and the standard Cloud Vision APIS. In this presentation we share the journey and the results of the evaluation and demonstrate the service.
The deep dive will cover 4 main topics:
* introduction of Tradera’s ambition with the project and live test by the participants
* development of image classification using 25M images and techniques for achieving higher accuracy
* comparison of a FAISS based solution with Google Cloud Vision APIs and help answer the question what are the main considerations when deciding to build from scratch or using cloud services
* if choosing own FAISS based solution setting up a robust scaleable API in GCP
Attribution is about crediting touchpoints in customer interactions with their impact in the sale process, hence the core element of performance marketing. But today, the choice of the model is often driven by believe and guessing, rather than data and analytics. This explains why to date we find basic models, like last-click or last-non-direct. In this deep dive, Alwin will analyze the different models seen in practice, how they perform in different contexts, what are their core ideas (from statistics, game theory, marketing science and machine learning) and what are their pros & cons. Finally, you will learn how to turn descriptive attribution into successful predictive usage.
True customer centricity is a mainstream goal for any company dealing in b2c or b2b-many markets today. But what does it take to really get there, especially machine-learning-wise? Join this deep dive to learn about the phases to getting to true machine-learning-powered customer centricity. You will learn what it takes to proceed through the phases and find out where your company is today. You will get practical advice on best practices and insights on common pitfalls that block progress. From the exciting start of limitless possibilities – through the deep valley of expecting too much, too soon – to the point where you really get a glimpse of the giant upside potential for the first time – to finally running your first automated decision-making campaign that drives real uplift. Dennis will map out this journey together with you and share the crucial learnings from 10 years of building hundreds of machine learning systems for CRM practice.