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The future of Customer Data Platform(CDP) an interview with Michael Katz (mParticle)

Who is Michael Katz? He was a founder and CEO of interclick, which took it public in 2009 and sold to Yahoo in 2011 for $270M. Also co-founder and CEO of mParticle, the leading Customer Data Platform helping the worlds best brands unify data across all consumer touch points to optimize marketing outcomes and customer experience (CX).  MParticle started in 2013 as a customer data platform for brands to accelerate growth. It improves marketing, advertising, and analytics by connecting data seamlessly across channels.   How we know him? We as Data Scientists are working with different and various sources of data in #eCommerce and #Retail and develop a solution called Catwing, which helps us to make Data Science, structuring various data inputs, but soon we realize that there is a niche called #CustomerDataPlatform which we can keep eye on it and while we were researching we came up to one of the best videos explaining what is the Customer...
Customer Data Platform (CDP) which vendor to choose?

Customer Data Platform (CDP) which vendor to choose?

1. Definition A CDP (Customer Data Platform) is a marketing platform that combines customer data from any source and create unified customer profiles (Golden Records), model behavior, and share that data with any source that needs it.   2. Benefits 1. Combine all data sources into one repository Each digital business uses various channels like emails, CRMs, mobile apps, web sites, scraped data and it takes ages to consolidate them. We use smart interfaces and deliver the results within a day without developers involvement.  2. Having a Single View of Your Customers Single Customer View is the main ingredient for leveraging the customer experience by monitoring all customer interaction in one place and being able to go deeper by applying Data Science and Machine Learning.  3. Personalize Customer Interactions Each customer has its preferences, interests and patterns, based on Catwing AI we are able to segment them based on their behaviour and create various personas, which can be targeted...
How to build your data science or machine learning model, when you store data into relational database for your e-commerce site.

How to build your data science or machine learning model, when you store data into relational database for your e-commerce site.

Scope: The scope of this article is to share how to dive deeper into your e-comerce client data and start making data science relatively easy and cheap, when you need to deal with data from your database and you want to work with Python relying on Jupyter Notebook, where time of execution is important. Especially focusing on the relational database. We all store data into files or into databases and as Data Scientists we need to have fast access to it and to be able to train our models. Our task Now we have a task to build a model combining different sources from emails, click data which we capture, mobile apps and sales, see the diagram above. We have several different sources all stored into Postgres Database Tables with various foreign keys between each table. In order to process the data first we need to access it from the database, structure it and then start playing with it....
Deploy Data Science model in production on AWS Elastic Beanstalk using flask application for your ecommerce or retail site

Deploy Data Science model in production on AWS Elastic Beanstalk using flask application for your ecommerce or retail site

“Every Data scientist soon or later is challenged by flask 🙂” Sergi Sergiev loves to say. Many of us data scientists are doing various models with exposure to different segments like Retail, Ecommerce, Finance, Gaming industry or many other, but we all want to see our models live in production and normally there is a need to deal with developers. Our work as a consultant is to define(finalize) the task, dive deeper, explore and clean the data, redefine the requirements, speak with the domain experts and then start making or using AI magic with our tools. We can produce different models for prediction, forecasting, item recommendations, object detection or many others, but we don’t really know how to deploy it in production. Therefore we decided at Shopup to show you how to setup your first ready to deploy model by yourself without the support of developers or devops. It is so easy to create your API interface which can...
Analyze and predict physical customers’ patterns in Hypermarkets with high precision using the latest advancement in technologies

Analyze and predict physical customers’ patterns in Hypermarkets with high precision using the latest advancement in technologies

The time has come to announce that after more than a year and a half of research and tests we finally have a proof of concept to launch our new product – Sens.ai – with the main focus on  Retail. Sens.ai provides insights and analytics on the customers’ and employees’ behaviour in physical stores with high precision and reasonable pricing. The product is based on the latest technologies and innovation in IoT, Robotics and AI. It is a great example of how math and technology can be combined in order to obtain top class quality solutions with limited  data in the niche of hypermarkets.   Background information  The project started in the summer of 2018. We were contacted by a leading retail consultant working with one of the largest retail chains in Bulgaria about a very specific task. The scope was to measure and analyse the customers’ behavior after layout change in one of their hypermarkets and to optimize...