Despite growing adoption of Cloud across computing and data needs most internal and external users face a slow and frustrating dependency on internal IT due to lack of Self-Service models without compromising on security, governance and cost management controls. Time to Unlock the Power with RLCatalyst Cloud Portal.
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While there is rapid momentum for every enterprise in the world in consuming more Cloud Assets and Services, there is still lack of maturity in adopting an “Automation-First” approach to establish Self-Service models for Cloud consumptions due to fear of uncontrolled costs, security & governance risks and lack of standardized Service Catalogs of pre-approved Assets & Service Requests from Central IT groups. Lack of delegation and self-service has a direct impact on speed of innovation and productivity with higher operations costs.
Working closely with AWS Partnership we have now created a flexible platform for driving faster adoption of Self-Service Cloud Portals. The primary needs for such a Self-Service Cloud Portal are the following.
Adherence to Enterprise IT Standards
Governance and Cost Management
Deployment and license management
Identity and access management
Common Integration Architecture with existing platforms on ITSM and Cloud
Support for ServiceNow, Jira, Freshservice and Standard Cloud platforms like AWS
Ability to add specific custom functionality in the context of Enterprise Business needs
The flexibility to add business specific functionality is key to unlocking the power of self-service models outside the standard interfaces already provided by ITSM and Cloud platforms
A common way of identifying the need for a Self-Service Cloud portal is based on following needs.
Does your enterprise already have any Self-Service Portals?
Do you have a large user base internally or with external users requiring access to Cloud resources?
Does your internal IT have the bandwidth and expertise to manage current workloads without impacting end user response time expectations?
Does your enterprise have a proper security governance model for Cloud management?
Are there significant productivity gains by empowering end users with Self-Service models?
Working with AWS partnership and with our existing customer we see a growing need for Self-Service Cloud Portals in 2020 predominantly centred around two models.
Enterprises with existing ITSM investments and need to leverage that for extending to Cloud Management
Enterprises extending needs outside enterprise users with custom Cloud Portals
The roadmap to Self-Service Cloud portals is specific to every enterprise needs and needs to leverage the existing adoption and maturity of Cloud and ITSM platforms as explained below. With Relevance Lab RLCatalyst products we help enterprises achieve the maturity in a cost effective and expedited manner.
Professional and responsive UI Design with multiple themes available, customizations allowed
Standards based Architecture & Governance
Tightly Built On AWS products and AWS Well Architected with pre-built Reference Architecture based Products
Pre-built Minimum Viable Product Needs
80-20 Model – Pre-built vs Customizations based on key components of core functionality
Proprietary vs Open Source?
Open-source foundation with source code made available built on MEAN Stack
Access Control, Security and Governance
Standard Options Pre-built, easy extensions (SAML Based). Deployed with enterprise grade security and compliances
Rich Standard Pre-Build Catalog of Assets and Services
Comes pre-built with 100+ catalog items covering all standard Asset and Services needs catering to 50% of any enterprise infrastructure, applications and service delivery needs
Explained below is a sample AWS Self-Service Cloud for driving Scientific Research.
To make is easier for enterprises for experiencing the power of Self-Service Cloud Portals we are offering two options based on enterprise needs.
Hosted SAAS offering of using our Multi-tenant Cloud Portal with ability to connect to your existing Cloud Accounts and Service Catalogs
Self-Hosted RLCatalyst Cloud Portal product with option to engage us for professional services on customizations, training, initial setup & onboarding needs
Pricing for the SAAS offering is based on user based monthly subscription while for self-hosting model an enterprise support model pricing is available for the open source solution that allows enterprises the flexibility to use this solution without proprietary lock-ins.
The typical steps to get started are very simple covering the following.
Setup an organization and business units or projects aligned with your Cloud Accounts for easy billing and access control tracking
Setup users and roles
Setup Budgets and controls
Setup standard catalog of items for users to order
With the above enterprises are up to speed to use Self-Service Cloud Portals in less than 1-Day with inbuilt controls for tracking and compliance
Cloud Portals for Self-Service is a growing need in 2020 and we see the momentum continuing for next year as well. Different market segments have different needs for Self-Service Cloud portals as explained in this Blog.
Scientific Research community is interested in a Research Gateway Solution
University IT looks for a University in a Box Self Service Cloud
Enterprises using ServiceNow want to extend the internal Self Service Portals
Enterprises are also developing Hybrid Cloud Orchestration Portals
Enterprises looking at building AIOps Portal needs monitoring, automation and service management
Enabling Virtual Training Labs with User and Workspace onboarding
Building an integrated Command Centre requires an Intelligent Monitoring portal
Enterprise Intelligent Automation Portal with ServiceNow Connector
We provide pre-build solutions for Self-Service Cloud Portals and a base platform that can be easily extended to add new functionality for customization and integration. A number of large enterprises and universities are leveraging our Self Service Cloud portal solutions using both existing ITSM tools (Servicenow, Jira, Freshservice) and RLCatalyst products.
Relevance Lab’s SPECTRA analytics platform uses machine learning models to predict customer churn for subscription businesses, thereby enabling them to take advance corrective actions.
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If you are a business with a digital product or a subscription model, then you are already familiar with this key metric – “Customer Churn”.
Customer Churn is the percentage of customers who stopped using your product during a given period. This is a critical metric, as it not only reflects customer satisfaction but it also has a big impact on your bottom line. A common rule of the thumb is that it costs 6-7 times to get a new customer versus keeping the customers you already have. In addition, existing customers are expected to spend more over time, and satisfied customers lead to additional sales through referrals. Market studies show that increasing customer retention by small percentage can boost revenues significantly. Further research reveals that most professionals consider that Churn is just as or more important a metric than new customer acquisitions.
Subscription businesses strongly believe customers cancel for reasons that could be managed or fixed. “Customer Retention” is the set of strategies and actions that a company follows to keep existing customers from churning. Employing a data-driven customer retention strategy, and leveraging the power of big data and machine learning, offer significant opportunities for businesses to create a competitive advantage versus their peers that don’t.
Relevance Lab (RL) recently helped a large US based Digital learning company benefit from a detailed churn analysis of its subscription customers, by leveraging the RL SPECTRA platform with machine learning. The portfolio included several digital subscription products used in school educational curriculums which are renewed annually during the start of the school calendar year. Each year, there were several customers that did not renew their licenses and importantly, this happened at the end of the subscription cycle; typically too late for the sales team to respond effectively.
Here are the steps that the organisation took along the churn management journey.
Gather multiple data points to generate better insights
As with any analysis, to figure out where your churn is coming from, you need to keep track of the right data. Especially with machine learning initiatives, the algorithms depend on large quantities of raw data to learn complex patterns. A sample list of data attributes could include online interactions with the product, clicks, page views, test scores, incident reports, payment information, etc, it could also include unstructured data elements such as reports, reviews and blog posts.
In this particular example, the data was pulled from four different databases which contained the product platform data for our relevant geography. Data collected included product features, sales and renewal numbers, as well as student product usage, test performance statistics etc, going back to the past 4 years.
Next, the data was cleansed to remove trial licenses, dummy tests etc, and to normalize missing data. Finally, the data was harmonized to bring all the information into a consolidated format.
All the above pipelines were established using the SPECTRA ETL process. Now there was a fully functional data setup with cleaned data ordered in tables, to be used in the machine learning algorithms for churn prediction.
Predictive analytics use Machine Learning to know who is at risk
Once you have the data, you are now ready to work on the core of your analysis, to understand where the risk of churn is coming from, and hence identify the opportunities for strengthening your customer relationships. Machine learning techniques are especially suited to this task, as they can churn massive amounts of historical data to learn about customer behavior, and then use this training to make predictions about important outcomes such as retention.
On our assignment, the RL team tried out a number of machine learning models built-in within SPECTRA to predict the churn and zeroed in on a random forest model. This method is very effective when using inconsistent data sets, where the system can handle differences in behavior very effectively by creating a large number of random trees. In the end, the system provided a predicted rating for each customer to drop out of the system and highlighted the ones most at risk.
Define the most valuable customers
Parallel to identifying customers at risk of churn, data can also be used to segment customers into different groups to identify how each group interacts with your product. In addition, data regarding frequency of purchase, purchase value, product coverage helps you to quickly identify which type of customers are driving the most revenue, versus customers which are a poor fit for your product. This will then allow you to adopt different communication and servicing strategies for each group, and to retain your most valuable customers.
By combining our machine learning model output with the segmentation exercise, the result was a dynamic dashboard, which could be sorted/filtered by different criteria such as customer size and geographical location. This provided the opportunity to highlight the customers which were at the highest risk, from the joint viewpoint of attrition and revenue loss. This in turn enabled the client to effectively utilize sales team resources in the best possible manner.
Engage with the customers
Now that you have identified your top customers who you are at risk of losing, the next step is to actively engage with them, to incentivise the customers to stay with you, by being able to help the customer achieve real value out of your product.
The nature of engagement could depend on the stage the customer is in the relationship. Is the customer in the early stage of product adoption? This could then point to the fact that the customer is unable to get set up with your product. Here, you have to make sure that the customer has access to enough training material, maybe the customer requires additional onboarding support.
If the customer is in the middle stage, it could be that the customer is not realizing enough business value out of your product. Here, you need to check in with your customer, to see whether they are making enough progress towards their goals. If the customer is in late stage, it is possible that they are looking at competitor offerings, or they were frustrated with bugs, and hence the discussion would need to be shaped accordingly.
To tailor the nature of your conversation, you need to take a close look at the customer product interaction metrics. In our example, all the customer usage patterns, test performance, books read, word literacy, etc, were collected and presented as a dashboard, as a single point of reference for the sales and marketing team to easily review customer engagement levels, to be able to connect constructively with the customer management.
Conclusion If you are looking at reducing your customer churn and improving customer retention, it all comes down to predicting customers at risk of churn, analyzing the reasons behind churn, and then taking appropriate action. Machine learning based models are of particular help here, as they can take into account hundreds and even thousands of different factors, which may not be obvious or even possible to track for a human analyst. In this example, the SPECTRA platform helped the client sales team to predict the customers’ inclination for renewal of the specific learning product with 92% accuracy.
Onboard a new university with a 1-Click model and core applications bundled into a Self-Service “box” powered by RLCatalyst Cloud Portal. Innovative new concept to solve any university standard IT needs with secure infrastructure and productivity apps pre-packaged.
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With need for business agility in the current challenging times, we leveraged Shopify cloud platform to help roll out a new Digital Commerce solution for our key customer.
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As universities deal with the challenging situation in 2020 with remote assets, workforce and students, there is a need to make education frictionless by leveraging cloud based solutions in a pre-packaged model. Working closely with the AWS partnership in trying to make Digital Learning frictionless, Relevance Lab is bringing a unique new concept to the market of University in a Box, that extends a self-contained Cloud Portal with basic applications to power the needs of a university. This new, radical and innovative concept is based on the idea of a school, college and university going from zero (no AWS account) to cloud native in hours. This enables the Cloud “Mission with Speed” with a mature, secure and comprehensive adoption very fast.
A typical university starting on their cloud journey needs a Self-service interactive interface with user logins, tracking and offering the deployed products, provide actions for connectivity after assets are deployed, ability to have lifecycle interactions in UI of Cloud Portal with no need to go to the AWS Console and with a comprehensive view of cost and budgets tracking.
The key building blocks for University In A Box comprise the following
University Catalog – Cloud Formation Templates useful to Higher Education packaged as Service Catalog Products
Self-Service Cloud Portal for University IT users to order items with security, governance and budget tracking
Easy onboarding model to get started with a hosted option or self-managed instances of Cloud Portal
Leverage existing investments in AWS and standard products the foundational pieces includes a Portfolio of useful software and architectures often used by universities.
Deploy Control Tower
Deploy Security Hub
Deploy VPC + VPN
Deploy AD Extension
Deploy Web Applications SSO, Shibboleth, Drupal
Deploy FSx File Server
Deploy S3 Buckets for Backup Software
Deploy HIPAA workload
Deploy Other solutions as needed, Workspaces, Duo, Appstream, etc
WordPress Reference Architecture
Drupal Reference Architecture
Moodle Reference Architecture
Shibboleth Reference Architecture
How to set up and use University in a Box?
The RLCatalyst Cloud Portal solution enables a University with no existing Cloud to deploy a self-service model for internal IT and consume standard applications seamlessly.
Steps for University Specific Setup
Time Taken (Approx)
A new University wants to enable core systems on AWS Cloud and the Root account is created
Launch Control Tower and Create Core OU & University OU
User and Access Management, Account Creation, Budget Enablement
Network Design of the University Landing Zone (Creation + Configuration)
Provision of basic assets (Infra & Applications ) from the standard catalog
Enable Security and Governance (Includes VA, PM, Security Hub)
User Training and Handover
The following diagram explains the deployment architecture of the solution.
University Users, Roles and Organization planning
Planning for university users, roles and organizations requires mapping to existing departments, IT and non-IT roles and empowering users for self-service without compromising on security or governance. This can vary between organizations but common patterns are encountered as explained below.
Common Delegation use cases for University IT
Delegate a product from a Lead Architect to Helpdesk, or a less skilled co-worker
Delegate a product from Lead Architect or Central IT, to another IT group, DBA team, Networking Team, Analytics Team
Delegate a product to another University Department – Academic, Video, etc
Delegate a product to a researcher or faculty member
Setup planning considerations on deployment and onboarding
Option:1 – Dedicated Instance per Customer
Option:2 – Hosted Model, Customer brings their AWS account
Option:3 – Hosted Model, RL (Relevance Lab) provides a new AWS account
Initial Catalog Setup
Option:1 – Customer has existing Service Catalog
Option:2 – A default Service Catalog items are loaded from a standard library
Option:3 – Combination of above
Optimizing Setup parameters and Catalog binding for ease of use
Option:1 – Customer fills up details based on best practices and templates provided
Option:2 – RL sets up the initial configuration based on existing parameters
Option:3 – RL as part of new setup, creates an OU, new account and associated parameters
Additional Setup considerations
DNS mapping for Cloud Portal
Authentication – Default Cognito with SAML integration available
Mapping users to roles, organizations/projects/budgets
Standard Catalog for University in a Box leverages AWS provided standard architecture best practices
The basic setup leverages AWS Well Architected framework extensively and builds on AWS Reference Architecture as detailed below.
Sharing a sample Products Preview List based on AWS Provided University Catalog under Open Source Program.
University Catalog Portfolio
Portfolio of useful software and architectures often used by colleges and universities.
WordPress Product with Reference Architecture
This Quick Start deploys WordPress. WordPress is a web publishing platform for building blogs and websites. It can be customized via a wide selection of themes, extensions, and plugins. The Quick Start includes AWS Cloud Formation templates and a guide that provides step-by-step instructions to help you get the most out of your deployment. This reference architecture provides a set of YAML templates for deploying WordPress on AWS using Amazon Virtual Private Cloud (Amazon VPC), Amazon Elastic Compute Cloud (Amazon EC2), Auto Scaling, Elastic Load Balancing (Application Load Balancer), Amazon Relational Database Service (Amazon RDS), Amazon ElastiCache, Amazon Elastic File System (Amazon EFS), Amazon CloudFront, Amazon Route 53, Amazon Certificate Manager (Amazon ACM) with AWS Cloud Formation.
Scale Out Computing Product
Amazon Web Services (AWS) enables data scientists and engineers to manage their scale-out workloads such as high-performance computing (HPC) and deep learning training, without having extensive cloud experience. The Scale-Out Computing on AWS solution helps customers more easily deploy and operate a multiuser environment for computationally intensive workflows such as Computer-Aided Engineering (CAE). The solution features a large selection of compute resources, a fast network backbone, unlimited storage, and budget and cost management directly integrated within AWS. This solution also deploys a user interface (UI) with cloud workstations, file management, and automation tools that enable you to create your own queues, scheduler resources, Amazon Machine Images (AMIs), and management functions for user and group permissions. This solution is designed to be a production ready reference implementation you can use as a starting point for deploying an AWS environment to run scale-out workloads, enabling users to focus on running simulations designed to solve complex computational problems. For example, with the unlimited storage capacity provided by Amazon Elastic File System (Amazon EFS), users won’t run out of space for project input and output files. Additionally, you can integrate your existing LDAP directory with Amazon Cognito to enable users to seamlessly authenticate and run jobs on AWS.
Drupal Reference Architecture
Drupal is an open-source, content management platform written in the PHP server-side scripting language. Drupal provides a backend framework for many enterprise websites. Deploying Drupal on AWS makes it easy to use AWS services to further enhance the performance and extend functionality of your content management framework. This reference architecture provides a set of YAML templates for deploying Drupal on AWS using Amazon Virtual Private Cloud (Amazon VPC), Amazon Elastic Compute Cloud (Amazon EC2), Auto Scaling, Elastic Load Balancing (Application Load Balancer), Amazon Relational Database Service (Amazon RDS), Amazon ElastiCache, Amazon Elastic File System (Amazon EFS), Amazon CloudFront, Amazon Route 53, Amazon Certificate Manager (Amazon ACM) with AWS Cloud Formation.
Moodle Reference Architecture
Moodle is a learning platform designed to provide educators, administrators and learners with a single robust, secure and integrated system to create personalised learning environments. This repository consists of a set of nested templates which deploy a highly available, elastic, and scalable Moodle environment on AWS. Moodle is a learning platform designed to provide educators, administrators and learners with a single robust, secure and integrated system to create personalized learning environments. This reference architecture provides a set of YAML templates for deploying Moodle on AWS using Amazon Virtual Private Cloud (Amazon VPC), Amazon Elastic Compute Cloud (Amazon EC2), Auto Scaling, Elastic Load Balancing (Application Load Balancer), Amazon Relational Database Service (Amazon RDS), Amazon ElastiCache, Amazon Elastic File System (Amazon EFS), Amazon CloudFront, Amazon Route 53, Amazon Certificate Manager (Amazon ACM) with AWS Cloud Formation. This architecture may be overkill for many Moodle deployments, however the templates can be run individually and/or modified to deploy a subset of the architecture that fits your needs.
Shibboleth Reference Architecture with EC2
This Shibboleth IdP reference architecture will deploy a fully functional, scalable, and containerized Shibboleth IdP. This reference architecture includes rotation of IdP sealer keys, utilizing AWS Secrets Manager and AWS Lambda. In addition, the certificates that are part of the IdP as well as some of the LDAP settings (including the username/password) are stored in AWS Secrets Manager. This project is intended to be a starting point for getting the Shibboleth IdP up and running quickly and easily on AWS and provide the foundation to build a production ready deployment around. Be aware that if you do delete the stack, it will delete your CodeCommit repository so your customizations will be lost. Therefore, if you intend to use this for production, it would be a good idea to make a copy of the repo and host it in your own account and take precautions to safeguard your changes.
REDCap on AWS Cloud Formation
This repository contains AWS Cloud Formation templates to automatically deploy a REDCap environment that adheres to AWS architectural best practices. In order to use this automation, you must supply your own copy of the REDCap source files. These are available for qualified entities at projectredcap.org. Once you have downloaded your source files then you can follow the below instructions for deployment. In their own words – REDCap is a secure web application for building and managing online surveys and databases. While REDCap can be used to collect virtually any type of data,including 21 CFR Part 11, FISMA, and HIPAA-compliant environments, it is specifically geared to support online or offline data capture for research studies and operations.
University in a Box is a powerful example of a specific business problem solved with leverage of Cloud integrated with existing customer specific use cases and easy deployment options to save time, money and achieve quick maturity.
For Universities, colleges and schools trying to use AWS Cloud infrastructure, applications and self-service models the solution can bring significant cost, effort and compliance benefits to help them focus on “Driving Effective Learning” than worrying about enabling cloud infrastructure, basic day to day applications and delegation of tasks to achieve scale. With a combination of pre-built solution and a managed services model to handhold customers with a full lifecycle of development, enhancement and support services, Relevance Lab can be your trusted partner for digital learning enablement.
With the growing need for cloud adoption from various enterprises, there is a need to move end-user computing workload and traditional data center capacity to the cloud. Relevance Lab is working with AWS partner groups to simplify the cloud adoption process and bring in best practices for the entire lifecycle of Plan-Build-Run on the cloud. Following is the suggested blueprint for cloud adoption and moving new workload on to the cloud.
CloudEndure to enable automated Cloud Migration
AWS Control Tower is used to set up and govern a new, secure multi-account AWS environment
AWS Security, Identity and Compliance
AWS Service Management Connector for ServiceNow with Service Catalog management
AWS Systems Manager for Operational Insights
RLCatalyst Intelligent Automation
As part of our own organization’s experience to adopt AWS for both our workspace and server needs, we have followed the following process to cater to needs, of multiple organization roles.
Since we already had an AWS Master account but did not use AWS Control tower initially, the steps followed were as follows.
Setup & launch AWS Control Tower in our Master Account and build multiple Custom OUs (Organizational Units) & corresponding accounts using account factory
Use CloudEndure to migrate existing workloads to the new organizations under Control Tower
For two different organizational units, there is a need to publish separate service catalogs and access to the catalogs controlled by User Roles defined in AD integrated with ServiceNow. Based on the setup only approved users can order items relevant to their needs
Used AWS Service Management Connector to publish the catalogs and integrate with AWS resources
Implementation of RLCatalyst BOTs Automation for 1-Click provisioning
Different guardrails for workload being provisioned for AWS Workspaces and AWS Server Assets based on organization needs
Management of AWS server assets by AWS Systems Manager
Mature ITSM processes based on ServiceNow
Proactive monitoring of workspaces and servers for any incidents using RLCatalyst Command Centre
Based on our internal experience in adopting full-lifecycle of Plan-Build-Run use cases, it is evident that multiple solutions from AWS integrated with ServiceNow and automated with RLCatalyst product provides a reusable blueprint for intelligent and automated cloud adoption. Answering the following quick questions can get your Cloud adoption jumpstarted.
List down your desktop assets and server assets to be migrated to the cloud with an underlying OS, third party software and applications
Designing your AWS Landing zone with security considerations between public- facing and private facing assets
Designing your networking elements between your organization’s business unit segmentation of assets and different environments needed for development, testing and production
List down your cloud cost segmentation and governance needs based on which a multi-organization setup can be designed upfront, and granular asset tags may be implemented
Capacity planning and use of Reserved Instances for Cost optimization
User Management and Identity management needs with possible integration to existing Microsoft AD infrastructure (On-Cloud or On-Prem) and Single Sign-On
Capture the needs from the IT department to provide the organization with Self-Service Portals to be able to Order Assets and Services in a frictionless manner with automated fulfilment using BOTs
The use of Systems Manager, Runbook design & automation and Command Center are used to proactively monitor any critical assets and applications to manage incidents efficiently
Ability to provision and deprovision assets on-demand with automated templates
Automation of User Onboarding and Off-boarding
ITSM Service management with Change, Configuration management database, Asset Tracking and SecOps
Disaster Recovery strategy and internal assessments for readiness
Cloud Security, Vulnerability testing, Ongoing patch management lifecycle and GRC
DevOps adoption for higher velocity of achieving Continuous Integration and Continuous Deliveries
Most organizations moving to cloud is a competency discovery process which lacks best practices and a maturity model. A better approach is to use a solid framework of technology, people and processes to make your cloud adoption frictionless. Relevance Lab with its pre-built solution in partnership with AWS and ServiceNow can help enterprises adopt cloud faster.
Working with a large enterprise customer supporting B2B and B2C business we leveraged Shopify to launch fully functional e-commerce stores enabling new digital channels in a very short window. Post Covid-19 pandemic disrupting existing business and customer reach, large companies had to quickly realign their digital channels and supply chains to deal with disruption and changes in the consumer behaviour. Businesses needed to have a frictionless approach to enable new digital channels, markets and products to reach out in a touchless manner while rewiring their backend fulfilment systems to deal with the supply chain disruptions. Relevance Lab worked closely with our customers during these challenging times to bring in necessary changes of empowering e-commerce, enterprise integrations, and supply chain insights helping create and maintain business continuity with a new environment.
The existing customer had invested in a full fledged but heavy e-commerce platform that was slow and costly to change. With Shopify we quickly enabled them to achieve setting up a fully functional e-commerce store in Canada with standard integrations with region specific context and positive revenue impacts.
It all boiled down to identifying an e-commerce platform which
Is easy and fast to set-up
Is secure and scalable
Incur least total cost of ownership
Provides the convenience to shop on multiple devices
Customizable as per requirement
We have configured the Shopify built-in theme to meet branding requirements and purchase workflows. Payment was enabled through multiple channels, including credit card, PayPal, and GPay. The store was also multilingual supporting two languages x`– English and French. We were able to go live in just four weeks and provide complete functionalities covering over 500 products delivered with a very cost optimized Shopify monthly subscription plan.
In parallel to building the storefront, the operations team simultaneously enabled
Adding new products to the online store
Configuring customer support
Validating standard reports such as sales reports etc
The merchant had a complicated tax calculation GST, PST, QST across 13 regions which were simplified by the out of the box country-specific tax configuration in Shopify.
Feature Configuration and Customization Details
Customization of Shopify theme to make the store stand out and look great on web and mobile
Extended store functionalities such as translation, user review, product quick view and product pre-order using apps from Shopify Marketplace
Shopify’s own payment provider to accept credit card payments
Blog publishing through Shopify native blog features to help customers make informed decisions
Enabled multiple languages from Shopify admin and created separate URLs for translated content
Shopify Fulfilment Network offered a dedicated network of fulfilment centers that ensure timely deliveries, lower shipping costs, and a positive customer experience
Shipping suite provides tools to calculate real-time shipping rates, purchase and print shipping labels, and track shipments
Using Shopify built in tax engine to automatically handle most common sales tax calculations
Shopify native Notifications Module to automatically sent email or SMS to customers for confirmation of their order and shipping updates
With minimal effort we have configured Shopify Email to create email marketing campaigns and send them from Shopify
Over 500 products were imported in a matter of minutes using the product Import feature. More advanced features including associating multiple product images to product and meta data were out of the box
Advanced store navigation was configured using collections and tags which helped customers to easily discover products of their choice
Shopify’s analytics and reports provide means to review store’s recent activity, get insight into visitors, analyze online store speed, and analyze store’s transactions
Solution Architecture Key components of Shopify platform are
Partner Dashboard: This provides capabilities including API credentials, track metrics for your published apps, create development stores, and access resources that help you to build your business
Shopify App CLI: Bootstrap a working Shopify app with Shopify command-line tool
Shopify App Generator for Rails: A Rails engine for building Shopify apps
Shopify Admin API Library for Ruby: A handy software to simplify making Admin API calls in Ruby apps
Shopify Admin API Library for Python: A Python library to simplify making Admin API calls in Python apps
Shopify Admin API GraphiQL explorer: Interactive tool to build GraphQL queries using real Shopify API resources
Shopify Storefront API GraphiQL explorer: Interactive tool to build GraphQL queries for Shopify’s Storefront API
Android Buy SDK: Add Shopify features to Android apps
iOS Buy SDK: Add Shopify features to iOS apps
Polaris: Create great user experiences for your apps with Shopify’s design system and component library
Leveraging the above standard Shopify components, the solution was delivered with following storefront architecture.
Relevance Lab Differentiator
Relevance Lab empowers Digital Solutions covering e-commerce, Content, CRM and E-Business. Within e-commerce platforms there are deep specializations on Salesforce Commerce Cloud, Adobe Experience Manager, Shopify.
With a complementary expertise in Cloud Infrastructure, Business Analytics and ERP Integration we help our customers achieve the necessary flexibility, scalability and cost optimization to adopt Cloud platforms covering SAAS, PAAS and IAAS. Based on the context of the business challenge, we provide an end to end perspective in identifying areas of friction and leveraging technology to address the same. In this case there was a quick recovery from Covid-19 induced disruptions and a solution was delivered at a fraction of regular costs with quick ROI achieved. The collaborative approach to deeply understanding customer business problems, ability to consult on multiple solutions and bring in deep expertise to enable the outcome is part of Relevance Lab unique capabilities.
For more details on how we have help achieve frictionless digital business and leverage Cloud based platforms like Shopify for e-commerce feel free to contact email@example.com
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