TechGig & MapMyIndia Boardroom Dialogue

TechGig, India’s largest tech community and MapMyIndia, India’s leading geo tagging-based service provider, hosted an exclusive Boardroom Dialogue on Aug 23, 2018 in Gurgaon. The purpose of the event was to discuss the optimum use of location intelligence for FMCG domain. Dr. Hemen Goswami, VP Solutions at Infogain, participated as a panelist at the round table event which highlighted how location intelligence can become a crucial competitive advantage for FMCG/Retail & Healthcare companies in the future. Upakul Barkakaty, the head of Infogain’s mobility practice, participated as well.

About MapMyIndia

MapMyIndia is India’s leader in premium quality digital map data, APIs, GPS navigation, telematics, location-based SaaS and GIS Solutions. MapMyIndia’s offerings include:

  • Map data solutions with cutting-edge location technology products and solutions in the areas of Navigation, Tracking, IoT and Analytics for products, services and solutions
  • Fleet Tracking & telematics which allows the monitoring of location, movement, status and behavior of a vehicle or a fleet of vehicles.
  • GeoAnalytics & Decision Support, using location knowledge to frame marketing efforts by adding location intelligence to business analytics provided valuable insights into where to pitch, open a new branch, penetrate a new market.

The main focus of the event was about applications within India with examples of how retailers use MapMyIndia’s offerings for inventory replenishment and location, personalization, and location-based marketing campaigns.

Other location-based intelligence approaches that help businesses in India were discussed as well, including Unified Commerce, a business design that that leverages the integration of retail processes and systems to provide full transparency of consumers on the back end and seamless customer experiences on the front end, regardless of the journey taken to make a purchase.

“Retailers are making a transition from Omnichannel to Unified Commerce,” said Infogain’s Hemen Goswami, “Omnichannel talks about integrating brick and mortar but still at the backend it is diverse enterprise systems.  Unified commerce applies omnichannel to the system environment. The primary focus is to leverage data, captured from distributed sources and multiple channels, to ensure that the end consumer’s experience achieves precedence over everything.”

“An excellent example of Unified Commerce, outlined by Justin Guinn of Software Advice, a Gartner Company, looks like this:”

On the back end:

  • A Retailer tracks customer’s movement throughout the store. RFID tags, shelf labels and perhaps even computer vision and other sensors communicate with a newly designed consumer app on the customers’ phone.
  • Retailers can record products that a customer looks at, picks up and even carries around but doesn’t purchase. Interaction with these products will be stored with previously in-store and online interactions.
  • Past recorded interactions with similar products will prompt a targeted social campaign that features the product(s) in question in an Instagram post. The post includes a link to purchase that opens the retailers’ app to the checkout screen with the item in customers’ cart.

On the front end:

  • Customer revisits a physical store, browsing products on shelves while looking at special in-store deals featured in the store’s app.
  • Customer picks up a few products, even carries one around for a while, but doesn’t make a purchase.
  • While browsing Instagram later in the same day, the customer sees an ad for the product that was almost purchased. There’s a link in the text of the Instagram post stating the customer can get the product for 10 percent off the store price by clicking the included link.
  • Customer clicks the link in the Instagram text and it prompts the retail store’s app to open. The app opens directly to the checkout screen and features the product in question already in the cart. Related items are listed below that can be added to the cart with one click.
  • The customer adds two of the related products to the cart and purchases them with the original product in question. The customer receives a message on the store’s app confirming the purchase and asking if the customer wants to receive a 5 percent-off coupon for use in-store by sharing a notification about the purchase across social channels or directly to contacts.

While intelligent location-based processes for marketing and operations are promising, executives at the Board Room Dialogue discussed the challenges that exist:

  • Internet connectivity issues, particularly in smaller (“tier 2”” and “tier 3”) cities in India.
  • Location information that comes in a convoluted form.   In broad terms, retailers need to obtain the location information, then link it to their customer records, then get insights, then use the insights, then execute targeted actions accordingly.
  • The struggle to use data to get intelligence.  Proper data modeling is needed, as well as data scientists who can use the data to build the intelligence.
  • Determining which devices to use to capture location information, including electronic GSM devices, Radio Frequency Identification (RFID) tags and receivers, GPS satellites.
  • Standard governance.  Location is closely connected to IoT, and location info comes from IoT devices.  We must insure privacy and make sure that government regulations are respected properly.

The event was a great opportunity for participants to collaborate with C-Suite executives and thought leaders from the leading FMCG/Retail & Healthcare industries about driving growth factors, process innovation and technology adoption for tapping growth in both urban/ rural markets.

Infogain’s Unified View platform enables Data convergence and data regulation in conjunction with IoT and location-based solutions. Contact us to learn more.

Microservices: 5 Challenges and How to Solve Them

By : Mohit Kumar Mittal | Senior Architect and Hemen Goswami | Vice President

Introduction

Microservice is an architecture that has been gaining traction in the past few years. MSA (Microservice Architecture) defines an application as a program composed of loosely coupled services. This comes with lots of challenges that we must solve up front.  Below are some such common challenges, patterns to solve, and some potential technical solutions.

Challenge #1: Service Discovery and Registry

Microservices is an architecture style for distributed computing. An application is composed of multiple fine grain independent services that communicate over a network. To use a service, the consumer needs to know its specific network location. This is an issue that will grow even more complex if the network location of services keeps changing.

Some examples of consumers of services are external clients like Mobile App, API Gateway and other (Micro) services.

Recommended Solution

System designers should implement a service Registry and a database of all the services network locations. They can register services themselves or by way of a third party to a common registry. Consumers can use this service registry to dynamically discover network locations of services.

Technical Implementation

Netflix Eureka service registry, Kubernetes Service

Challenge #2: Unified Access of Services

In Microservices architecture, there can be multiple services and multiple consumers. Different consumers may require different services—for example, Mobile App may need to display fine grained information, while web-based applications may need to render coarse grained information.

On top of that, services may be using different protocols like JMS or REST etc. In order to simplify services access, there needs to be a unified access mechanism.

Recommended Solution

A potential solution to this concern would be to implement an API gateway as a single-entry point for all services. All the consumers will call API gateway and API gateway should route them to their required services. Additionally, API gateway can provide services like “Aggregation of Services”, “Security”, “Metering” etc.

Technical Implementation

Spring Cloud Zuul, Amazon API Gateway, Apigee

Challenge #3: Authentication and Authorization

Security is a key aspect in Microservices.

Recommended Solution

Implement a common and centralized authentication service. This service will allow all the consumers to get authenticated before seeking a services access. Individual services should be responsible for the authorization of its consumers.

Technical Implementation

oAuth, JWT

Challenge #4: Logging and Debugging

In a Microservices architecture, there can be use cases that span across services. In a scenario in which each service generates its own logs, it would be nightmare for a developer to troubleshoot a bug, as he/she would have to aggregate all the services log and analyze manually

Recommended Solution

One could instead implement a common logging service, wherein each service can use this service to write logs.

Technical Implementation

Elastich Serach, LogTash, Kibana (ELK)

Challenge #5: Configuration

Configuring services is an added complexity in Microservices. Since each service is independent, it must be configured separately. This will make it difficult to run applications in different environments without making adjustments. Configurations can be anything like DB Credentials, Environment Variables and Network Locations.

Recommended Solution

Implement a common/centralized configuration service. This service can be backed by a common configuration repository so that all services can request this service to read configurations.

Technical Implementation

Spring Cloud Config Server , kubernetes configmap

Infogain Announces Strategic Alliance with Jesta I.S.

Los Gatos, CA – August 28, 2018 – Infogain, a Silicon Valley provider of vertically-specialized software engineering services, is pleased to announce our partnership with Jesta I.S. Headquartered in Montreal, Canada, Jesta I.S. is an international supplier of integrated software solutions for brand manufacturers, wholesalers and retailers specializing in apparel, footwear and hard goods verticals. The company was recently positioned as a “Major Player” in IDC’s report “Worldwide Point-of-Sale Software in Small and Midsize Retail Apparel and Softlines 2018 Vendor Assessment”. The report assesses vendors’ capabilities in omni-channel, technical, integration, customer (record/engagement), product, transaction management, and fulfillment.

In the new alliance, Infogain will support Jesta I.S. to provide best-in-class retail functionality to customers in the United States and Canada as well as responding to for demand for Jesta IS services internationally. Infogain’s deep retail industry expertise combined with new digital approaches such as robotic process automation will improve business operations and enhance customer service for Jesta I.S. clients.

“Our new alliance with Infogain will enable Jesta IS to leverage and further enhance its marketing and sales efforts by strengthening our position in North America and expanding as a market leader in the APAC and Middle East regions. Infogain’s extensive retail client relationships and delivery centers around the world make this partnership a great step in the right direction for Jesta, and will help us meet the increasing demand for our solutions.” said Moris Chemtov, President of Jesta I.S.

“Infogain’s digital transformation and automation solutions, along with our software platform engineering expertise complement Jesta I.S. offerings and further expand value added at Jesta customers. We are excited to partner with a software solution leader that is best-in-class, innovative and recognized for outstanding client service. We look forward to a long and successful partnership,” said Rohit Nagpal, VP & Global Head of Retail, Travel and Hospitality services at Infogain.

About Jesta I.S., Inc.

Jesta I.S. is an international supplier of integrated software solutions for brand manufacturers, wholesalers and retailers specializing in apparel, footwear and hard goods verticals. Jesta’s Vision Suite is a modular software platform that helps simplify the Omni- channel journey for retailers and wholesalers – from PLM to POS. With 50 years in the business, Jesta I.S. has the experience and resources to help with the technology aspect, the human factor and everything in between. Customers include Perry Ellis International, Puma, Harry Rosen, Genesco, Town Shoes, Peter Harris Clothes, , Cole Haan, Canex, DSW, Carter’s and Stokes. For more information, visit www.jestais.com.

About Infogain

Infogain is a Silicon Valley headquartered company with expertise in software platform engineering and deep domain skills in travel, retail, insurance, and high technology. We accelerate the delivery of digital customer engagement systems using digital technologies such as cloud, microservices, robotic process automation and artificial Intelligence for our clients.

Our unique engagement approach of “Listen-Curate-Deliver” helps to accelerate the innovation journey of 5 of the world’s largest 50 companies and 24 of the Fortune 500, with several relationships of over 10 years. We deliver positive business outcomes using rapid prototyping and a solid foundation of DevOps-based software platform engineering that ensure high-quality and on-time delivery. Our 3,500 global employees across the US, UK, Singapore, Middle East and India focus on client value creation, delivery excellence and innovation. For more information, visit www.infogain.com.

For more information, contact:Infogain

Cathy Chandhok
VP – Marketing
Cathy.Chandhok@infogain.com
(408) 355-6028

Can Microservices revolutionize automated testing?

Source: https://dzone.com/articles/what-are-microservices-actually

In the field of test engineering, microservices have been making waves in the testing community over the past year, and with good reason. With many companies investing in DevOps and favoring a more microservice-oriented framework of software development, testing practices will also need to change. Microservices will have various significant impacts on the future of testing.

Before we tackle these changes, let us define microservice. Microservices architecture is a branch of services-oriented architecture (SOA) that consists of several extremely narrowly focused services that, when brought together, function as an application. In contrast, monolithic applications are when a single application is comprised of the client access code, business logic, and the data layer combined.

The problem with monolithic applications is that when a company wants to make minor changes to a single line of code, it requires a complete overhaul of entire application. Microservices present a novel solution to this problem, because rather than redeploying all the code, an arduous and complex process, the target layer they wish to change can be altered individually.

A real story of this positive transition is the Amazon application. In 2001, the Amazon retail website was one large architectural monolith, a huge single code base and a clumsy, frankly outdated way to operate the application. In keeping with their forward-looking culture, they took the truly revolutionary approach of decoupling service architectures to simplify their pipeline and enable them to roll out updates every 11.6 seconds. This is a testament to the value of microservices and demonstrates how it is feasible for scalability, relatability, and availability. Other companies like Netflix and HelloFresh are following Amazon’s example and breaking up their apps as well.

As these development processes transform, optimal testing technology choices also change.  Karate is a simple, elegant open-source tool that simplifies microservices testing and claims that the business of testing web APIs might be fun. It does this by reducing the entry barrier to writing a test, thus increasing the readability of tests and making the test easier to maintain.

Furthermore, microservices will change the methodology of testing. When you want to make a specific change a microservice, you can use stubs to isolate individual integration points of the application from each other for unit testing. This will dramatically simplify the testing process. Also, you can automate testing earlier in the process, because you won’t have to test the code that’s driving the user interface and experience, absolving you of onerous, manual, often subjective evaluation of these components. On the other hand, testing microservice architecture poses a unique challenge that did not really exist with monoliths: when running software tests on a microservice, you need to ensure that not only that specific microservice performs as expected, but also that all the microservices that compose an application behave harmoniously as they were intended.

In summary, as teams adopt microservices, companies will observe significant simplifications in the way in which testing takes place.  Infogain’s testing team provides microservices-based testing for Fortune 100 companies and is a world leader in automated, cognitive-automation-driven automation testing.

Vikas Mittal | Head – Testing Expert Centers and Solution Delivery

https://www.infogain.com/digital-experience/

How to Optimize Price without Marginal Loss for SKU’s with a shorter shelf life

The shelf life of a product is a length of time an SKU can be stored on shelf without becoming unfit for use. Retailers commonly use the strategy of “Stock Rotation and Rationalization” — moving the SKU’s with the earliest sell dates so that shoppers pick them before the expiration date without any loss to the retailer.

Sometimes it becomes necessary to sell the slow-moving SKU’s with a shorter shelf life at a lower price point to increase the velocity of SKU.

When a SKU/Product Line is sold at a lower price point or a discounted price, it results in a Marginal Loss to the retailer. For retailers, coming up with maximum possible discount without incurring a Marginal Loss is a challenge.

Infogain’s Analytical Approach

Co-efficient Estimate for price variables

(Co-efficient = Delta Y / Delta X) Step 1: Co-efficient Estimation
Based on the distribution of the data, the appropriate Statistical Equation is applied to the data to estimate the co-efficient of price Variables.:

Y = Velocity of a Product Line of perishable Product Line/List of SKU

  1. X1 = Discounted Price (Percent)
  2. X2 = Relative Regular Price Index (Our price / Competition regular Price) +
  3. X3 = Digital Media+
  4. X4 = Seasonality
  5. X5 = Non-Digital Media
  6. X6 = Competition Media
  7. X7 = Any Other Macro / Micro Factors

Key Factors of the Co-Efficient Price Equation

  • The Non–Price variable data will help in estimating the co-efficient of Price Variables accurately.
    If Non–Price data is not available; the equation will be run with price variables alone.
  • The contribution to revenue other than price will be attributed to Base.
  • In order to accurately simulate a scenario with an optimal discount without any marginal loss, it is critical to measure the influence of all factors (including competition and media). Based on the category and market, seasonality could be removed from the data if it is not provided.

Infogain’s Price Optimization to estimate Optimal Discount

Infogain’s Algorithm uses the co-efficient obtained from the co-efficient estimate for price variables equation, running Optimization to arrive at an optimal discount price point based on Marginal ROI Analysis:

  • Marginal ROI at a time (t2) = Delta Total Value Sales (t2-t1) / Delta Total Cost (t2-t1)
  • Total Revenue at a time (t) = Total Units sold at a time (t) * Selling Price at time (t)
  • Total Cost = Total Units Sold * Marginal Cost.
    Marginal Cost = Cost of producing one additional unit of product
  • Delta value sales = Values sales at 30% discount rate minus value sales at 25% discount rate

Although the Volume Sales will go up with the increased discount, Value Sales won’t necessarily go up as well.

Infogain’s Optimization Approach Illustrated

Optimization: Mathematical Linear Programming and Optimization

Objective Function: Minimize the Delta Value Sales

Variable: Discount Percent

Constraint: Marginal ROI should be equal to 1

Optimal Discount Percent based on Marginal ROI Analysis = Discount percent 15%
Discount Percent Point without incurring Marginal loss = 30%
Any Discount beyond 30% will lead to Marginal loss

Infogain’s Value Addition—Scenario Planning for data driven decision making

  1. BASE SCENARIO:

What is the base /current scenario? Marginal cost and profit evaluation of current Situation

(Are you currently below optimal Discount or above Optimal Discount or incurring loss? Or is there any scope of improvement)

  1. SCENARIO WITH OPTIMAL DISCOUNT RATE:

What is the Optimal Discount Rate? (15 % as seen above)

  1. MAXIMUM DISCOUNT RATE WITHOUT INCURRING MARGINAL LOSS:

What is the highest possible discount rate that can be offered without incurring marginal loss? (Ex. Scope to increase discount rate up to 30% as seen above)

Assumptions and Complexities

  1. According to the Managerial Microeconomic price-demand theory, price and demand always has an inverse relationship. With a decrease in regular price (increase in discount rate) an increase in demand is observed.

This relationship is mostly linear in nature.  Infogain has suggested Linear Programing for optimization based on the Domain experience, managerial economic price demand function theory and APAC Market Knowledge. If we don’t see a linear relationship between price and demand, however, an appropriate non –linear programming will be applied for Optimization and statistical equation based on trend observed in the data.

  1. Seasonality factor can be extrapolated by Infogain if not provided.
  1. Assumption is made that media and competition data will be available to estimate co- efficient of price accurately

Conclusion

The Marginal ROI Model is used for solving many computational financial problems ranging from Option Pricing to investment decisions such as cost minimization and profit maximization. Several Marketing and Consumer Packaged Goods (CPG) companies have adopted the Marginal ROI Analysis for best pricing in order to achieve Maximize Profitability.

Linear programming Marginal ROI Model can help retailers select the best possible discount strategy for perishable goods without incurring any marginal loss . The Scenario planner can help them evaluate the marginal cost vs profit of various discount alternatives. The Model can also help retailers forecast profitability for future.

To learn more about the Infogain’s  Linear programming Marginal ROI Model, contact analytics@infogain.com.

About Infogain

Infogain is a global business-oriented IT consulting provider of front-end, customer-facing technologies, processes and applications, leading to a more efficient and streamlined customer experience. We want our clients’ interactions with their customers to be fast, efficient, and cost effective.

With close to 4,000 employees in the United States, India, the Middle East, U.K., Singapore and Malaysia, we service 5 of the world’s largest 50 companies, and 24 of the Fortune 500. we have million-dollar engagements with over 25 customers, many of which have been with us for 5 years or more.

Specialty Retailer Optimized their Hybris eCommerce Platform with Infogain

Solutions & Scope of Work

  • Inventory Management Optimization and real time updates
  • Efficient and cost effective dual shore team
  • Implemented processes to increase sales, customer experience and higher customer conversion
  • Test Automation of B2B and B2C web and mobile platforms
  • Implemented processes to reduce abandoned carts
  • Implemented Point-to-Point Tokenization

Infogain Enables Real-Time Predictive Sales Business Intelligence (BI) to Enhance Productivity for Trilogy

Infogain Helps Trilogy to Deploy an Interactive Business Intelligence Tool for its Enterprise Lead Management System

 

The client is a U.S-based provider of enterprise-class software and service solutions to Global 1000 companies in the automotive industry. The client’s Enterprise Lead Management System (ELMS) delivers the highest quality new and used vehicle leads to dealers and automotive manufacturers. Infogain partnered with the client to develop and deploy a BI tool for its ELMS. The Infogain team developed a rich functional BI interface that provided end-to-end analysis and basic analytics of Lead performance along with operational day-to-day reports.

Infogain’s Guide to Customer Loyalty Models

How Infogain developed a Loyalty Model of our own

Are you an iPhone or Android person? Do you swear by Coke over Pepsi? Mac or PC?

What was it that made you choose one over the other?

Customer Loyalty is all about turning satisfied customers into brand advocates, to achieve this, it is critical to strengthen the bond with customers and it is necessary to compel customers to participate in loyalty programs—most commonly through the use of credit cards that provide rewards and incentives for the amount of money you spend.

Loyalty Analytics

Loyalty Analytics has become a significant area of study. Rewards programs that are solely based on discount economies can chip away at profitability, especially when they are not aligned with customer preference and needs.

According to a 2009 study published in the Journal of Brand Management, there is a three-dimensional approach for auditing brand loyalty:

Behavior loyalty: A consumer prefers to buy certain brand and continue to purchase that brand and earns points toward discounts. A model based on behavior alone inflates price sensitivities.

Cognitive loyalty:  A consumer is conditioned to buy a brand with reduced sensitivity to the price and will even pay a premium for their preferred brand.  Cognitive Loyalty is linked to perceived quality of certain features which the customer feels is more advantageous than competition. (Think Mac vs. PC)

Attitudinal loyalty involves brand intimacy. A consumer acts as a brand ambassador by referring the brand on social media. Attitudinal loyalty is non-transactional in nature, as brand ambassadors expect to be rewarded for their non–purchase actions such as referrals on Facebook, Twitter and other social media outlets.

When Loyalty Models fall short

Infogain was contacted by a large conglomerate which operates Retail, Insurance, Loyalty and other business lines. A Loyalty program for an Oil & Gas – Distribution & Marketing company

was not paying off, as customers were not using points from the program to re-purchase fuel. The program became a cost center rather than being a profit center.

Infogain studied the Amex Membership Milestones program which had experienced a similar problem. They had launched the Milestones program due to competition pressure—not desired customer behavior. The Milestone membership program also turned into a cost center rather than being a profit center. When Amex understood the link between rewards and desired customer behavior, it began encouraging profitable customer behavior, including referrals and social sharing.

The Infogain Loyalty Model

The Infogain Loyalty model addresses behavioral, cognitive and attitudinal motivators and will reduce churn and increase repeat purchase. In order to convert a rewards program center into a profit center from being a cost center, it is critical to link rewards with desired behavior:

  • A person who repurchases a product regularly by redeeming points is behaviorally loyal
  • A customer who is ready to pay a premium price for product exhibits cognitive loyalty
  • A person who is happy about the product and refers the product to others exhibits attitudinal loyalty

Infogain suggests a Fixed Effects Model to measure the impact of past loyalty programs and to capture the differences that exists in locations, thereby reducing the location bias. Infogain set the dependent variable as “Profit  Ratio” and borrowing the concept of linking the rewards to desired behavior from the Amex Case:

Profit Ratio = Revenue earned from re-purchase / Cost of loyalty and rewards. Profit ratio should be greater than 1 for profits, at 1 there is a Break Even Point

How Infogain’s Customer Loyalty Model increased the repeat purchase ratio of the Oil & Gas – Distribution & Marketing company

It is difficult to measure cognitive loyalty. If the customer is ready to pay a higher price for the fuel at the oil and gas company, they will not worry about accumulating points there.
The perceived value – cognitive loyalty could be anything from:
1. Employee quality and delight
2. Car wash or other facilities available in the station
3. Ambience
4. Other products like Techron

Conclusion


Loyalty Programs should be a win-win proposition.

The value created should always exceed the costs created on a balance sheet, and companies should understand the links between value created for customer and value created for company, else profits will only be illusory and cost will be real.

Loyalty is not only concerned with rewarding customers with personalized offers through loyalty programs, but also with turning satisfied customers into successful brand advocates.

To learn more about the Infogain Loyalty Models, contact analytics@infogain.com.

About Infogain

Infogain is a global business oriented IT consulting provider of front-end, customer-facing technologies, processes and applications, leading to a more efficient and streamlined customer experience. We want our clients’ interactions with their customers to be fast, efficient, and cost effective.

With close to 4,000 employees in the United States, India, the Middle East, U.K., Singapore and Malaysia, we service 5 of the world’s largest 50 companies, and 24 of the Fortune 500. we have million-dollar engagements with over 25 customers, many of which have been with us for 5 years or more.

National Specialty Retailer Reduces Costs and Achieves Greater Efficiency with AMS-Shared Support Services from Infogain

Solutions and Scope of Work

Infogain was already a trusted partner, having supported the client’s Oracle Retail Stores Solutions Suite (ORSS) applications and delivered various improvements including as Returns Management implementation and enhanced key customer and security-related functionality. Based on Infogain’s strong delivery, defined processes and pro-active initiatives, the client elected to expand Infogain’s responsibilities to include Oracle Retail Merchandising (MOM). The scope of work included:

  • Process improvements to reduce costly manual efforts that will lead to greater efficiency
  • Implementing onsite-offshore shared support service model for Retail Applications Maintenance Services
  • Leveraging Infogain’s CoE in Retail domain for to Database Administration

Media Fact Sheet

Our Solutions

  • End-to-end implementation and support with embedded accelerators to enhance the efficiency of the Stores experience
  • Business Assurance using Unified Automation Platform (UAP)
  • Integrated Retail Dash boards
  • Merchandising and Omni-channel E-commerce capabilities including Loss prevention, Mobility, EBS value chain
  • optimizations
  • Robotic Process Automation (RPA)/Bots
  • Shared Services Support Model with L2 Automation
  • UI/UX for the art of frictionless consumer experience