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xlcdp's Projects

advertools

advertools - online marketing productivity and analysis tools

apache-mautic-auto-installer-php-7-3

Mautic Auto Installation Script with PHP 7.3

automato

🎉🎉 ( v2 ) Web Application to automate sending Whatsapp, SMS & Email* campaigns

awesome-bigdata

A curated list of awesome big data frameworks, ressources and other awesomeness.

Customer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza. SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services. In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics. By the late 2000s, Facebook, Twitter and all the other socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant. With the digital age things have changed drastically. Customer issuperman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience. This tsunami of data has changed the customer analytics forever. Today customer analytics is not only restricted to marketing forchurn and retention but more focus is going on how to improve thecustomer experience and is done by every department of the organization. A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics. From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation. Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure. Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before. Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical. There are various ways customer analytics is carried out: Acquiring all the customer data Understanding the customer journey Applying big data concepts to customer relationships Finding high propensity prospects Upselling by identifying related products and interests Generating customer loyalty by discovering response patterns Predicting customer lifetime value (CLV) Identifying dissatisfied customers & churn patterns Applying predictive analytics Implementing continuous improvement Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time. Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect. Tomorrow there may not be just plain simple customer sentiment analytics based on feedback or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time. There’s no doubt that customer analytics is absolutely essential for brand survival.

awesome-customer-data-platform

Learning and implementing Customer Data Platform

behaviopy

Behavioral data analysis and plotting in Python.

behaviouralanalysis

Analysis of behaviour

behaviouraleconomics

All the files and data for the experiment performed during the course Behavioural Economics @ University of Amsterdam

cdp-metrics

Introduction to Customer Data Platform Metrics

clickhouse

ClickHouse® is a free analytics DBMS for big data

cloudcherry-r-sdk

CloudCherry is a Customer Experience Management platform that helps you measure experience across the customer journey, and derive insights that drive improvements. This package contains functions that help you get data out CloudCherry and into R for fun and profit!

customer-data-analysis

My reflections on analyzing customer data to learn their behaviors and preferences for making strategic and tactical business decisions.

customer-data-framework

Customer Management Framework for management of customer data within Pimcore

customer-data-platform

Lemnisk is the world's first real-time marketing automation built on an intelligent and secure customer data platform

cxs-cdp

OASIS Context Server (CXS) TC: Developing the Customer Data Platform specification. This repository contains both the normative specification documents as well as an associated GraphQL Javascript API demo. https://github.com/oasis-tcs/cxs-cdp

dash

Anylytics Platform to manage data analysis and serve customers

data-pipeline

Build a data pipeline using Google BigQuery, dbt, Google Sheets, and Supermetrics. It helps you create a monthly reporting toolkit that pulls in data from a variety of marketing channels.

datacamp-generating-keywords-for-google-adwords

Automatically generate keywords for a search engine marketing campaign using Python.

datacleaner

The premier open source Data Quality solution

dataspherestudio

DataSphereStudio is a one stop data application development& management portal, covering scenarios including data exchange, desensitization/cleansing, analysis/mining, quality measurement, visualization, and task scheduling.

dbt

dbt (data build tool) enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

digital-marketing-analysis

A data-driven communication strategy with Email Engagement Model, Cluster Analysis and Churn Prediction

dynamics-365-for-marketing---power-bi-reporting

Download these Power BI templates to start building custom analytics and reports based on your Dynamics 365 for Marketing data. These templates will help you to connect to your Dynamics 365 instance and access its data. The download includes the following templates: Power BI template for Dynamics 365 for Marketing: Includes the code required to connect to your Dynamics 365 for Marketing data, and also includes functions that you can call to load entity and interaction data with just one line of code. This template provides a basic starting point for building your own custom reports. Sample email marketing analytics report: Provides a comprehensive report of your email marketing results, including detailed analytics, charts, and views spread across multiple report pages. You can use this template as-is, or as inspiration for designing your own reports.

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