OML 1.0 via Fingerprinting: Open, Monetizable, and Loyal AI

Airline Booking is a sample web application that provides Flight Search, Flight Payment, Flight Booking and Loyalty points including end-to-end testing, GraphQL and CI/CD. This web application was the theme of Build on Serverless Season 2 on AWS Twitch running from April 24th until end of August in 2019.

Catima, a Loyalty Card & Ticket Manager for Android

Django web application to store and manage vouchers, coupons, loyalty and gift cards digitally. Supports PWA, offline caching, expiry notifications, transaction histories, file uploads and OIDC SSO.

Sparkle Proof of Loyalty Contract

Stores your barcode-based store/loyalty cards on your phone

🌟 Open Source Referral and Affiliate Marketing Platform - Launch your referral program in minutes!

Sparkle Loyalty Images and Resources

Unicorn Loyalty: E-Commerce Serverless GraphQL Loyalty Sample App

Customer loyalty program

A home for all your coupons and loyalty cards. https://coupon.metikular.ch

A user points package for Laravel that can be used to give rewards, loyalty or experience points with real time support

Shoe Store Loyalty Engine - Flink SQL Workshop

# WARNING: This repository is no longer maintained :warning: This pattern focuses on older technology (e.g. Hyperledger Fabric APIs prior to Fabric 1.4). Therefore, there is no support for this pattern and it will be archived on May 1, 2019. You are welcome to use up to that date, but we recommend that you begin working with the updated release found at https://developer.ibm.com/patterns/customer-loyalty-program-with-iks-saas-v2-fabric/.

Generation and verification of card numbers using Luhn's algorithm.

In the customer management lifecycle, customer churn refers to a decision made by the customer about ending the business relationship. It is also referred as loss of clients or customers. Customer loyalty and customer churn always add up to 100%. If a firm has a 60% of loyalty rate, then their loss or churn rate of customers is 40%. As per 80/20 customer profitability rule, 20% of customers are generating 80% of revenue. So, it is very important to predict the users likely to churn from business relationship and the factors affecting the customer decisions. We are going to show how logistic regression model using R can be used to identify the customer churn in the telecom dataset.

Projeto completo de Data Science 2025 no canal Téo Me Why

Deploy a customer loyalty back-end application with Java and Node microservices on IBM’s managed OpenShift.

User Engagement and Gamification Framework

Cardholder PWA is a self-hosted app for your loyalty and discount cards

Sample use of Ethereum smart contract in Hyperledger Fabric

App for saving your loyalty card📱💳

Running for Brews Loyalty Application

Loyalty - Hide chats in the Telegram messenger

Revolutionizing Customer Loyalty through Polkadot Blockchain and Artificial Intelligence | Proof of Concept (POC) developed during the intense 3-day NERDATHON Polkadot hackathon

API Component for Loyalty Blockchain based on IBM HyperLedger Fabric to provide a distributed ledger for loyalty assets like points, vouchers, gift cards, prepaid cards, utility tokens and more

hold your cards, identity cards, student cards, bank cards, loyalty cards, credit ,gift,cheque,debit card, passport and more with original photo

Generates McDonalds 'Belgium Loyalty Card' codes

A blockchain-based NFT loyalty program where businesses can reward customers with NFTs that unlock exclusive discounts, perks, and experiences.

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.