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


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

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.

Will Mid-Tier Indian IT Companies Sink or Swim in the Era of Digital and Automation?

IT firms that are still not the billion-dollar babies they wanted to be have rebooted to seize the digital opportunity in a choppy sea.

Last Friday, Bengaluru-headquartered Mindtree BSE -4.36 % informed stock markets about independent director VG Siddhartha stepping down from the board to focus on his business, Coffee Day Group.

Siddhartha continues to be invested in the $780-million IT services firm, but it was the end of a tenure spanning 18 years. During this period, he witnessed the abrupt departure of executive chairman and Mindtree’s first chief executive Ashok Soota in 2011 (when the company was less than half its current size).

In 2016, the Mindtree board appointed its third chief executive, Rostow Ravanan, who succeeded Krishnakumar Natarajan. However, for at least a decade, each of these top managers often faced one question — when will Mindtree cross $1 billion in revenue? It hasn’t happened in 19 years.

In its first decade, a couple of investors rued the fact that Mindtree’s annual business was what the larger IT companies did in a couple of months. The IT services providers were competing to expand repeatable outsourcing services, known within industry as ‘application development and maintenance.’ Such deals were typically multi-year contracts, which meant predictability of revenue.

A similar wave — infrastructure management — followed, which HCL Technologies BSE -4.20 % focused on to sneak into the Big IT Club, behind TCS BSE 0.12 %, Cognizant, Infosys BSE -2.16 % and Wipro BSE -2.60 %. The top four had ridden the Y2K wave at the turn of the century.

For companies such as Mindtree, which haven’t touched the $1-billion mark yet, the question now is, how prepared are they to win business in the era of digital and automation? For enterprise clients globally, the new wave involves amortising investments that have been made in the past decade or so, while building for consumers who spend more time online and on smart devices.

For the IT companies that serve them, it is about winning digital IT dollars – which begin as projects – while applying automation to rein in productivity and improve margins. And by the way, it is still intensely competitive. It is also more fragmented. Amit Singh, who heads the IT practice at advisory Avendus, says the IT services outsourcing business is worth around $1 trillion.

“The 25th company in the pecking order is about $1 billion in size and the top 25 corner $350 billion of the market,” he explains. That leaves a considerable $650 billion to fight for between companies that make less than $1 billion in annual revenues. “This is a hugely fragmented market, with a whole lot of small- and midtier companies,” Singh adds. Among these fragmented players are lots of “relics of the past,” says Sanjay Kukreja, partner, ChrysCapital. “In IT services, it is very easy not to change as you make good margins. But these (companies which don’t change) are the ones which will be irrelevant very soon.”

Even as most might perish, large companies will emerge from this fragmented heap. “The death knell for mid-tier has not been rung. But they should not try to be the next labour arbitrage guy. You need the right technology, people and platforms to succeed,” says Kukreja.

Hexaware is one of the pack that has grown and expanded its senior leadership. The process began after founder Atul Nishar brought in R Srikrishna as chief executive from HCL Technologies in 2014. Hexaware crossed the $600-million mark in financial year ending December 2017. “We are not just going up against somebody our size. But we fancy our chances to beat the Goliaths each time,” Srikrishna says, referring to the importance of culture to run small multi-disciplinary teams. “This is David versus Goliath.”

At a time when digital project sizes are small, that is not a bad analogy. Mid-sized companies aim for projects in the $0.5-5 million range—not the $10-50 million a year deals like before. Liken these to the stones for Davids’ slings. “Earlier, the biggest concern for the market was that mid-sized companies’ revenue were concentrated with one or two clients,” Srikrishna says. “So growing in the middle is a positive long-term win—each of these clients has an opportunity to go upward in deals pipeline.”

In financial year 2017, Hexaware took its $1-5 million deal tally to 71, from 64 in the previous year. Srikrishna says this tastes just as sweet as the six deals won in the $10-20 million bracket (twice the number of deals in 2016). “‘Land and expand’ is a big thing,” says Sunil Bhatia, chief executive of Infogain, a Silicon Valley headquartered firm, backed by ChrysCapital. Estimated to be less than $200 million in revenue, it employs 4,000 persons, mostly operating in Noida and Bengaluru. “We have 25 of the Fortune 1000 companies as customers.

The first strategy is to land the business and second is to get repeat business and expand. We have to bring high-touch services to the customer.” Arvind Thakur, managing director of NIIT Technologies, agrees, emphasising the need for a culture shift inside the company to provide high-touch services outside. Digital revenue accounts for a quarter of NIIT’s business, up from 11% in the previous year.

The digital business has five paths: first, creating omni-channel experiences for enterprise clients, or designing solutions that use technology to deliver a great physical experience for clients’ end customers. Second, analytics and business intelligence. Third, helping clients migrate applications to cloud. Fourth, digital orchestration and integration.

“Data resides in legacy systems — we are helping by integrating digital with legacy and orchestrating new processes to offer hyper specialisation to customers,” Thakur says. Finally, acquisitions such as Incessant Technologies in 2015 and RuleTek last year. Incessant enables clients to automate and integrate back-end systems with a digital front end, through alliance partnerships with platform providers like Pegasystems and Appian, while RuleTek in the US helped NIIT improve digital integration capabilities and to expand its North America footprint.


Last year, NIIT invited management thinker Ron Kaufman, who runs a company that specialises in customer experience. “Based on interactions with Kaufman, we put in place the building blocks to drive new ideas, behaviour traits and create new role models. We educated everyone on service excellence experience,” Thakur says.

Bhatia says building this culture is a huge opportunity for mid-sized companies while competing against the service providers that generate more than $10 billion revenue per year. “Changing the culture of a 10,000-people company is far easier than trying to change the mindset of 2,00,000 employees,” points out Thakur. Bhatia cites ChrysCapital-backed LiquidHub, which was acquired by Capgemini recently. LiquidHub focused on a few areas and created a position in digital transformation that Capgemini found attractive. “In the new era, you don’t need truckloads of people—you need more brain than brawn,” says Bhatia.

Not everyone is convinced. Sudheer Guntupalli, equity analyst, Ambit Capital, says scale begets scale in IT services. “Clients seek to do business with vendors who have a certain number of client references from the same industry segment and geography,” he explains. They check for adequate number of trained resources and cutting-edge capabilities in relevant service lines. “That’s why they prefer working with vendors who have the highest scale in the relevant geography, vertical (industry) or service lines.”

Prateek Majumdar, partner, Bain & Company, begs to differ. Scale is no longer as important as it used to be. “What matters more are the capabilities and IP the company has. That’s why some digitalfocused mid-tier companies have been doing really well.” According to Sid Pai, managing partner, Tekinroads Consulting, mid-sized companies need to be aware of the ratio of traditional to new business (digital). In his career as an enterprise client advisor, he saw the likes of Infosys and TCS seize the Y2K wave, followed by the infrastructure services wave that HCL pounced upon.

“While absolute size still matters, the ability to harness the current opportunity depends on relative exposure to traditional business,” Pai says. “There is a large-scale transformation underway in the market, which will take 3-5 years to play out. Some organisations will move faster than other organisations. So, it is about being faster and nimbler players.” Mindtree, which started building its digital story in 2011-12, referred to the lessons during its December 2017 analyst earnings call. Digital revenue is realised differently from the traditional multi-year (predictive) revenue model.

“Price realisation increased mainly due to additional revenue generated with some projects moving from transition to steady state,” said chief financial officer Jagannathan CN. During the transition phase, the client is paying multiple vendors, so transition pricing tends to be lower than “steady-state” pricing. “As (more) work gets picked up and it moves into steady state, revenues are higher.” Equally, there is a danger for the middle rung. With vendor consolidation, midsized companies get pushed out. “Clients can re-evaluate outsourcing partners at any time and if they want to reduce the number of vendors, it’s usually the small, mid-tier ones,” says Milan Sheth, technology leader at advisory EY India.

Remember, mid-sized firms lack pricing power as they don’t have the economies of scale of larger IT firms. “It puts pressure on margins as they offer services at costs lower than their larger peers,” Sheth explains. “For a long time, their business grew because they were cheaper than larger players, not because they differentiated.” So, how will the Davids fight the Goliaths? The big IT companies are organised— to be harsh, even siloed. Pai says, “What is going to make a difference in the market for mid-sized companies is to go after deals with smaller teams of highly capable people who can go all the way, from assessing customer need to design needs to actually building technically, and then managing and delivering it right across the stack.” Picture a rugby scrum.

Srikrishna is confident his teams at Hexaware are already making an impact. “Team and solutions go hand in hand. Its strategy is focused on “bringing together man and machine” to deliver for clients. “It’s very hard for a legacy player to execute that way. Digital is not the only thing we will do. We will focus on traditional outsourcing but deliver it in differently.” The $1 billion milestone isn’t the vanity metric anymore, as execution on a projectby-project basis has taken precedence, especially for an industry which trade body Nasscom estimates will grow 7-9% over the next fiscal. “Growth has slowed down in the industry overall, for large and midtier players. But, we should see at least a couple of players hit the $1-billion mark relatively soon,” says Majumdar of Bain.

Mphasis has breached the billion-dollar mark and Larsen & Toubro Infotech (LTI) is not far behind. The latter is spearheaded by Sanjay Jalona, who ChrysCapital’s Kukreja describes as “among the brightest talent in IT services business.” Jalona came from Infosys and has, in turn, hired 30 others in the last 12 months from across companies to beef up digital services. “The changing technology landscape is resulting in shrinking of deal sizes, bringing mid-sized and large sized players onto a level playing field,” Guntupalli says. “In that context, high quality mid-sized companies can overcome the scale disadvantage that they earlier had.”

News Originally Posted on: TheEconomicTimes

Mobile Business Intelligence Empowers Employees With Anytime, Anywhere Access To Critical Data

Business & Technical Challenges

The client’s user base typically utilizes several business intelligence applications on a regular basis. Inefficiencies existed due to the lack of a consistent framework and no integration across the apps, causing users to perform low-value tasks on a repetitive basis to get the information they needed most.

On the technical front, the BI dashboard applications needed to be made available on the mobile platform, and each had its own set of interfaces and access/launch processes, as they originated from several third-party solution providers. Users also had different access permissions for the various applications which needed to be managed securely, according to the client’s established application security guidelines.

A number of common functionalities were being rewritten multiple times by different teams working n different mobile dashboard thus increasing development costs and inconsistency.

The umbrella application would also need to add value to categorize, launch and maintain the applications from a single interface.

Infogain implemented SAS and BI solutions with leading mobile network operator

Business Challenges

The client was implementing marketing campaigns with an Excel spreadsheet, IBM Database 2 (DB2) and a solution called “IRIS.” They were experiencing cumbersome and slow manual analytics. As a result, their subscribers would receive information that was 15 days or older. Launching activity was slow, with a reach of 8 out of 22 telecom circles, and 50 campaigns.

The client wanted an automated solution that was efficient and effective for building and maintaining a loyal customer base. They chose the SAS® Campaign management solution with innovative features consisting of a user-friendly interface, advanced analytics, superior information management and custom campaign processes. The client chose the SAS® product and the services of Infogain to meet multiple business objectives including:

  • Customized and tailored campaigns
  • Better customer segmentation
  • Targeted and personalized messages with best offers
  • Increased effectiveness and efficiency
  • Increased return-on-investment
  • Cross-sell and upsell services
  • Churn analysis and forecasting

Business Analytics Practice

  • Application of big data technology on unstructured data to perform sentiment analytics
  • Optimized cost of analytics infrastructure by deploying low cost distributed machines
  • Generating New Revenue Streams for the clients by creating analytical products
  • Keeping the tool-set in line with technology to achieve increased benefits at lower cost

Business Transformation Into Virtual

Virtual reality, Artificial Intelligence, Augmented Reality and virtual reality are becoming growing examples of cutting edge applied technologies. In an exclusive conversation with Ramesh Subramanian, CTO, Infogain, Faiz Askari of SMEStreet explored latest trends in the uprising segment.

We are witnessing a major transformation in almost every field. In the area of technology, this transformation is happening in the form of intelligence and experiential learning. Virtual reality, Artificial Intelligence, Augmented Reality and virtual reality are becoming growing examples of cutting edge applied technologies. In an exclusive conversation with Ramesh Subramanian, CTO, Infogain, Faiz Askari of SMEStreet explored latest trends in the uprising segment of Machine Learning, Artificial Intelligence, IoT and Virtual Reality.

The edited excerpts:

Faiz Askari: How are enterprises today adopting Machine Learning and Artificial Intelligence?

Ramesh Subramanian: Machine Learning and AI has advanced significantly in the past few years and enterprises are constantly working on models that would help in analysing large volumes of structured and unstructured data type with the ultimate goal to improve business performance. According to a latest Gartner prediction Artificial intelligence will be the top investment priority for 30% CIOs by 2020 as AI technologies will be virtually pervasive in almost every new software product and service.Effective adoption is still in the nascent stage though, with evolving problem definitions, drivers, data sets and ROI expectations. ML is at a similar point on its adoption curve as the internet was in the late 1990s or cloud computing was in the mid-2000s. These disruptive technologies hold tremendous potential but enterprises need to step with caution and should have a well-though out, strategic approach before adopting it.

Faiz Askari: Enterprises has seen tremendous changes in the last few years from adoption SaaS to Mobile and Analytics. How do you foresee the market changing with automation and intelligence?

Ramesh Subramanian: The adoption of SaaS, Mobility and Analytics are a part of the trend towards “personalization” of business models driven by IT, where segments-of-one are the goal and the power of search and selection at the hands of the customer are practically infinite. Automation and Intelligence are intuitively the next steps in this trend, and we foresee these techniques becoming ubiquitous in the next year or two.

Today, Machine Learning and AI is heralding new algorithms, applications, and frameworks bringing greater predictive accuracy and value to enterprise data. With the availability of huge data sets and the low cost of computing and storage, enterprises now are keen on exploring new technologies that can redefine their processes, bring in innovations and helps them to stay ahead of the curve. Automation is being implemented across industries, ranging from transportation and utilities and manufacturing and different sectors have their own sets of business benefits. Different industries will have to implement and apply automation in different ways to achieve the desired results.Some use cases for Automation and Intelligence transcend industry verticals- such as Chatbots for assisting with customer/ employee interactions, Advanced Search for better leverage of knowledge capital, Robotic Process Automation to integrate and streamline business processes; in more detail,for a Retail business, automation and intelligencecan help in Predictive Inventory Planning, for a Travel business it can streamline traffic patterns and congestion management. Similarly for Healthcare it can help in real time patient monitoring and prognosis, and for a Manufacturing business, it can help in predictive maintenance and improved collaborative planning.

Faiz Askari: What kind of caution or preparedness is required by enterprises to adopt these technologies?

Ramesh Subramanian: Enterprises are bound to face some challenges with Automation and Intelligence. On the one hand are the well-known risks to jobs arising out of more decisions being made automatically by machines, and improved digitization from process automation.

On the other hand, evidence suggests that early ML/AI pilots are unlikely to produce the dramatic results that technology enthusiasts predict. For example, early efforts of companies developing chatbots for Facebook’s Messenger platform saw 70% failure rates in handling user requests. Firms need to understand that this technologyrepresents a completely different paradigm and requires a well-though out, strategic approach to adopting it.

The following steps/ approach can be leveraged to effectively operationalise enterprise ML/AI programs-

  • Prepare groundwork for adoption– Machine learning based use-cases cannot be implemented overnight. Preparation for leveraging ML and AI technologies must be planned just as well as implementation of the technology itself. The business challenge needs to be articulated clearly and data required must be identified, prepared and manged on an ongoing basis. Identify the features required and engineer the architecture for building the same.
  • Build Required Capabilities- Hiring the required skills is essential for deploying sophisticated ML and AI programs. This is probably the biggest challenge today, given the high demand for such skills but severe shortage in supply. Skills that are essential for ML include- modern architecture and technology skills, data science and experienced domain skills to understand complexity and requirements of implementing an industry use- cases.
  • Plan for Deployment at scale- Too many ML/AI projects fail today when implemented at scale, even though they might have had a successful PoC. Hence the planning for deployment must take into the account the scale for implementation- it should be well defined mathematically and the ontology that is developed must be robust at scale. The algorithms need to be trained adequately by ensuring availability of large and clean data sets.

Faiz Askari: What kind of investments are required by enterprises?
Ramesh Subramanian: Automation is the way forward and investments are increasing sharply particularly by tech giants like Apple, Amazon or Google who are investing in billions in R&D and in acquiring AI based start-ups. According to a recent report there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years. However the areas are yet to be explored for commercial benefit and in a recent McKinsey survey of around 3000 businesses across the globe, business leaders are still uncertain about the benefits from their investments in AI. It is predicted that AI adoption for enterprise will remain slow in 2017.

Faiz Askari: How is Infogain helping enterprises? What kind of solutions are you bringing to the market?

Ramesh Subramanian: Infogain is at the forefront of implementing some aspects of the Automation and Intelligence technology spectrum. We help our customers automate and digitize business processes across countries and business groups, resulting in visible and significant benefits to their bottom lines.

We help our customers get a perspective on value of such technology to their businesses, identification of use-cases within their range of activities, and implementing pilots to review outcomes and caveats. This phase of ‘digital’ consulting enables our customers to plan strategically and adopt these technologies to the extent and within constraints as relevant to their businesses. While this still gives some exemplary results to our customers, such a methodical approach allows them to approach the business case without too many surprises.

We have helped our customers to make quicker and better decisions based on image analysis and monitoring- including video monitoring, in an automated manner; these customers span industries as wide as Finance, Insurance, Healthcare, Manufacturing, Travel, etc. Some of these use cases involve a combination of cutting-edge technologies, such as IoT, remote sensing and imagery, blockchains etc. alongside Automation and Intelligence to deliver value that was impossible in an earlier era.

We also actively induct partners and alliances into our portfolio, to ensure the entrepreneurial technology initiatives are made fully available to our customers.

— The writer is VP, HR, Infogain

News Originally Posted on: SME STREET


Enterprises Approach to Machine Learning

Contributed By Ramesh Subramanian, CTO, Infogain

Machine Learning—a subset of Artificial Intelligence(AI) is the latest buzzword in the technology industry which is emerging as the pathway to the future for enterprises. Machine Learning provides enterprises with the required framework, insights, and algorithms to ensure better predictive ability.

The ever-increasing usage of electronic means of interaction and commerce, as well as IoT devices has been producing an incredible volume of data and statistics which is impossible for humans to analyse manually. ML technology helps combine all the data gathered from myriad touch points for delivering useful insights to enterprises that contribute to the various strategic outcomes.

A survey conducted in 2016 by the National Business Research Institute revealed that 62 per cent enterprises will deploy AI technologies by the year 2018.It is evident that both Machine Learning and AI is becoming a vitalfacet for several burgeoning as well as eminent industries as it can offer deeper insights to businesses besides enhancing the process of decision-making.

Machine Learning allows computers to be capable of categorizing, processing, and generating data based on buying and spending patterns of customers, their feedback and interactions, their peers and social groups, and virtually anything else.

An ML-based computer algorithm can identify which customers are most likely to abandon your brand besides helping you profile their identity, buying habits, and the reasons that are making them leave your brand.While such use-cases for ML abound, enterprises are adopting ML algorithms to increase flexibility of shop floors, supply chains, collaborative partnerships, even detect the price points that consumers will prefer.

How are Enterprises Adopting Machine Learning?

More and more businesses are embracing this technology with open arms as it generates positive ROI and leads to the advancement of future products. Technology giant Google was among the first to recognise the significance of integrating ML technology; nevertheless, at the present time,deep-learning techniques have a wide appeal across industries. For example, financial institutions are using ML technology for identifying potential cases of fraudulent claims, and also for conducting a risk analysis. Similarly ML is helping healthcare providers save lives by identifying severe health issues by analysing patient data.

Today ML is controlling applications such as real-time speech translation, biometric identification system, gene mapping, web-content curation, and so on.In order to leverage Machine Learning enterprises are working in tandem with their customers to comprehend their grievances so as to make sure that their products and services address the specific needs of the customers.For example, Pinterest has been using ML technology to display more interesting content to you. Similarly, Disqus utilises ML to eliminate comment spams. Likewise, several e-commerce companies are employing ML strategies to provide their clients with the advantages of machine learning when the clients browse for products on their sites.

Enterprises that have incorporated ML technology into their business processes are mainly focusing on the following application areas—

  • Text classification and text data mining,
  • Natural language processing (NLP) that can generate reports by analysing texts and establishing connections between concepts,
  • Image recognition, image classification, image tagging,
  • Behaviour analysis and personalising customer service,
  • Mapping and recommendations,
  • Risk analysis and data security; &
  • Smart Assistant like Microsoft’s Cortana and Apple’s SIRI.

Measurable Business Outcomes of Machine Learning

One of the crucial objectives of implementing ML technology in business is to transform business objectives into asses-sable goals.Here are some measurable business outcomes of machine learning—

  • Helps enterprises in meeting measurable objectives by enhancing market share.
  • Assists businesses in determining their success by analysing the number of new and existing customers which in turn helps businesses to assess its different marketing tactics.
  • Enables businesses to measure the volume of feedback they receive from their customers or clients. This helps businesses to detect their weak points in their customer services besides allowing them to develop their overall service levels.

What kind of readiness is required for ML adoption?

In order to implement ML technology successfully, it is important for enterprises to ensure readiness across the following factors—

  • Tactical Readiness: Enterprises should define their goals precisely for ML-based solutions. To begin with, they must identify the factors they are willing to measure. At the same time, they should identify the business problems they are trying to resolve with the help of analytics.
  • Readiness for a Data-driven Culture: Most ML-based solutions need lots and lots of quality data.Enterprises should ensure that they are geared up for making data-driven decisions by building a strategic analytics culture. The company’s leadership should be willing to adopt the insights generated by the data analytics team and utilise the analytical outcome for decision-making.
  • Domain Readiness: Enterprises should build an infrastructure that can access and evaluate data effortlessly. This can be done by investing in a skilled workforce and providing training to the existing employees. Companies should also invest in analytical and ML tools besides investing in recruiting workers that are skilled enough to utilise the data. Domain readiness also entails thorough comprehension of an organisation’s business and data landscape.
  • Functional Readiness: Successfully implementing a ML solution can call for a lot of preparation, timing, proper budgeting, and well-defined goals. It is crucial for enterprises to gauge success and discover the vital performance metrics for assessing progress towards the predefined organisational objectives. Moreover, enterprises should chalk out a precise dissemination strategy so as to convey analytic insights to those who require them.

ML Technology– Helping Enterprises Turn Unstructured Data into Competitive Advantage

Respondents of a study conducted by MIT Technology Review Custom in association with Google Cloud revealed that the most important advantage of ML technology is that it enables implementersto achieve a competitive advantage. The study further states that 25 per cent of existing ML implementers believe that they have already accomplished that goal.76 per cent respondents stated that they are utilising ML for escalating sales growth.

Here are few organisational goals cited by the survey that ML adopters can achieve by implementing this technology—

  • Improved insights and data analysis;
  • Better understanding of customers;
  • Faster assessment and more prompt insight;and
  • Better internal efficiency.

What kind of initiatives is required in the adoption of Machine Learning?

  • Emphasize on Data: In order to generate accurate results, the data supplied to machine-learning algorithms should be labelled, cleansed, and organised in a proper manner. In order to transform the massive volume of unstructured data generated on a regular basis, enterprises may opt for internal data labelling and data scrubbing or they may opt for a third-party service provider. Simultaneously, enterprises should improve their ML readiness by organising and amalgamating their scattered data sources into an integrated data warehousing platform.
  • Embrace the Latest AI Tools: AI innovators are encouraging the implementation of AI technology across industries by open-sourcing machine-learning libraries and Application Programming Interface (API). For example, Google’s open-sourced library TensorFlow enables enterprises to have access to unconventional algorithms and optimised neural networks for quick deployment in an organisational setting.Enterprises can also reap benefits from cloud-based Machine Learning APIs that makes the process of computing in-house AI software simpler.
  • Develop Narrow Expertise: Though it is quite tempting to make use of ML technology in each and every business process, such a policy may exhaust enterprise resources besides reducing the progressive effect of AI advancement. Hence, businesses should give precedence to concrete artificial intelligence solutions having the greatest potential of enhancing monetary value and customer happiness. Developing narrow proficiency in a single area will go a long way in helpingenterprises utilise resources on a specific task, thus, facilitating the progression of an advanced solution for one’s business.

In today’s digital landscape, it is indispensable for businesses to incorporate machine-driven approaches.Since ML technology is playing a key role in driving an era of innovation, businesses should adopt machine learning for gaining a competitive edge in their respective industries.

— The writer is VP, HR, Infogain

News Originally Posted on: PC Quest


Changing Face of Retail Business Intelligence

Rikki Jolly
VP-Global Delivery (Retail), Infogain

Paving way for a bigger, brighter and magnanimous future, analytics and Big Data are transforming one of the largest global industries – the retail industry. The value, variety and velocity of retail data is surging by the day, making it imperative for the industry players to elevate their offerings to match the changing consumer paradigms. While consumers may be tilting between the widely growing network of e-tailing and traditional brick and mortar stores, it’s the innate charm of providing a personalized experience that still draws consumers to ground zero. However, times are e-changing and gone are the days of long-term business planning. With technology paving its way deep into the sector, it has become crucial to transform to stay in the game.

The traditional retails stores are left with no choice but to be a part of the change and make a dash for a bevy of reforms to give attract, retain and widen their consumer base. With understanding the consumer forming the basis of every business strategy, it becomes the demand of the industry to scale up data collection, analytics and its usage. A McKinsey report had suggested that retailers making use of big data analytics could increase their operating margins by as much as 60 per cent.

The tremendously competitive retail environment has made it extremely complicated to understand and win consumers. The roadblock doesn’t exist in the unavailability of data defining the consumers and their buying patterns, rather its available dime a dozen. The biggest challenge is to fathom and interpret the data procured from a multitude of channels to take informed business decisions. And this is by far the tallest challenge given the plethora of tools available analyze and report on data that may not give deep decision making insights unless rightly interpreted and aligned to the business goals.

This makes it imperative for retailers to be proactive, alert and dexterous with having customer-oriented ground staff, aided with technology to enable a customer needs friendly environment. There is a need to adapt technological disruptions and work closely on the data and analytics capabilities to survive in the ever-moving market dynamics. This agility and ability to understand the mind of the consumer gives the retailer an opportunity to gratify the consumer across all contact points and occasions, building relationships that have the potential to deliver incremental profits.

How is Big Data helping Retailers?

1.Identifying customers – Today retailers have a better way to identify the customers and offer them the right product. The customer segmentation is now much more refined and data driven based on customers transaction history, basket analysis, loyalty programs, social media interactions. It is easier for retailers to get a 360 degree view of the customer and offer them customized products based on their past preferences or what people similar to them are buying.

2.Price Optimization – Online Retailing is based on Dynamic Pricing and the price of a product depends on multiple factors from market demand, inventory, competitors pricing, whether a particular product is the seasons must have product, etc. Earlier retailers used to give end of season sale. But now based on Machine Learning, prices are adjusted real time and recommendations or offer are sent to a specific set of customers who has purchased those products or has earlier shown interest, propensity to buy those.

3.Generating Customer Loyalty – Customers today need to be treated royally, they want Retailers to understand their requirement, recommend product and services that suits them, and keep them informed at every stage of the selling cycle from booking, shipping, and product delivery to feedback. This is not an easy task for the retailers keeping in mind the varied customers they serve. Big Data Analysis can help you to recommend the right products to a customer or run targeted marketing campaigns to reach out to a specific segment. It also helps you to understand the customers’ path to purchase or their buying pattern, thus reaching out to them at each step to close the sales cycle.

4.Forecasting Demand – Predicting demand has become much more efficient now and retailers can easily find if a particular product is in demand during certain time of the year, or in a particular city or by a specific group and how to adjust the inventory. Retailers also gather a lot of data using social media to understand the changing preferences of a customers or do sentiment analysis to find whether the product is getting positive, negative or neutral feedback in the market.

5.Fraud Detection – Big Data Analytics can be effectively used to detect any fraud by analyzing data from daily transactions and activities such as purchasing, accounts payable, POS, sales projections, warehouse movements, employee shift records, returns and store-level video and audio recordings.

Enhancing Customer Experience
In the extremely competitive retail environment, the trick lies in creating an unforgettable customer environment to attract and retain customers. And effective use of technology can help in making this a reality as innovative retailers are making use of digital tools to augment the customer experience in their stores. And the ones that understand the consumer pulse are getting down to offering free Wi-Fi, cloud based POS systems among other measures, encouraging consumers to do research and make informed buying decisions, giving the retailers also an opportunity to track their shopping behavior. This preliminary insight will enable the retailer to create bespoke recommendations and multi-level reward programs. And the free Wi-Fi may turn out to be as good a crowd puller like a sale signboard!

The pace and the dexterity with which micro data is collected, gives the retailers immediate insights on the shopping trends. This analysis on the move allows them to adjust their prices and add to the lure by announcing on the spot discounts on the sales floor based on their current and previous shopping patterns. This data, often collected through interactive mobile devices in stores, provides the retailer an understanding of the buyers needs and give insights into making smarter decisions about product placement in the store.

These, small yet effective insights, gives a retailer acumen about future consumer behavior, which is a must to stay alive in the competitive retail world. This would be a far cry from the historical retailers who only scratched the surface while making use of the piles of customer data available with them. And with the social media redefining the rules of the game, understanding and leveraging these social media sentiments can give them insights into the customer behavior and intentions. The formidable power of social media that can make or break brands is forcing the retailers to transform the retail landscape dramatically with interactive and immediate communication strategies pushing away the traditional approach. Customer has indeed become the king for retailers that needs an ‘out of the way’ experience with unexpected customized and bespoke services on offer to be able to make an impression in the bevy of lures already preoccupying their minds.

News Originally Posted on: ET Retail


Intelligence and Automation – The Rise of Another Disruptive Era

By Mr. Ramesh Subramanian, Chief Technology Officer, Infogain.

This is a sentiment that echoes across the industry corridors nowadays. Technological advancements and digital transformations are drastically changing the way we do business today. Technologies like Big Data, Internet of Things (IoT), Artificial Intelligence/ Machine Learning (AI/ ML) are the new buzzwords of today, transforming user experience, engaging the consumer, analyzing patterns and enabling proactive and real-time response. On the one hand technology innovations are challenging existing business models like never before. Businesses are having to re-think everything that they do today from the “new” consumer perspective, lest they risk their own survival in the digital age. While on the other hand, technology has become a lever for agile and forward thinking enterprises to race ahead of the pack and create a differentiation within their respective markets by building smart, intuitive solutions.

“Information technology is at the core of how you do your business and how your business model itself evolves”. Satya Nadella

The focus today is on speed, flexibility, and consumer choice more than ever as traditional firms are having to contend with frequent comparisons to the likes of Google, Amazon, and Facebook, even in the enterprise space.

Intelligence and automation combined can change the way business is done across all sectors of the economy. Intelligent automation systems sense and synthesize vast amounts of information can automate entire processes or workflows, learning and adapting as they go and give consumers an experience that is highly customized to their tastes and preferences. Artificial Intelligence (AI) takes personalization to the next level as AI, in the form of Machine Learning (ML), can predict and personalize answers to customers’ queries and requests. This trend enables marketers to have one-on-one customers’ conversations by applying data points from social media posts, browsing and purchasing histories, and past customer interactions to get a complete view of every customer. This enables us to predict the next sale or marketing opportunity for each customer and automates multiple manual steps to improve the customer’s experience. An early example of such technology is the deployment of “shopping assistants” or “chatbots” of varying nature by almost all retailing companies. Chatbots record interactions with individual customers, the various outcomes, the suggestions, and responses, etc. Big Data repositories supply other information such as clickstreams from all interactions so far, past track records, public information, etc. Knowledge Management and ML can then allow such chatbots to update their behavior so that the next interaction incorporates the sum total of all past knowledge and a prediction for future outcomes based on immediate and individual conversation. Such chatbots can “understand” and respond to human queries intelligently helping them to solve problems, some before they have even arisen.

Ray Kurzweil, the famous American author, computer scientist, inventor, and futurist said that Artificial intelligence will reach human levels by around 2029. Machines are becoming more intelligent and capable each day in performing tasks that need narrow as well as broad intelligence. Traditionally machines were envisaged to automate tasks that involved a great deal of repetition or standardized, hazardous tasks with great quality and less management overheads to deliver better outcomes. Machines today can manage information, connect with vast data sources, analyze and pull out the right information/ insights with minimum human intervention. Software can learn and intelligently interpolate and extrapolate data. More importantly, it can learn and adapt.

A report released by Business Insider states that by the year 2020, about 34 billion devices will be connected to the internet with apps and software that will collect data from customers like geo location, search history, browsing habits etc. but all in un-intrusive ways, thus, eliminating the burden of data capture that creates privacy concerns. Big industry players like Google, Apple, Facebook, Microsoft, Amazon, IBM, etc., are paving the way by embracing advanced technologies like AI, ML as well as their Digital Assistants such as Siri, Cortana, Alexa, etc. With the involvement of intelligent systems like robots in the workplace, businesses will soon witness a multi-faceted shift. It is inevitable that as the intelligent systems including robots and IoT devices become all pervasive, the connection between humans and smart systems will affect the ecosystem intensely. Amazon recently launched the first ‘self-driving’ grocery store, Amazon Go with no checkout lines, in Seattle in early 2017. Walmart, is experimenting with robot shopping carts. Walmart also said it was testing whether drones could help check the inventory in its stores. And Elon Musk is teaching robots new tricks through Virtual Reality lessons!

With all the brave forecasts and hype, there are still some very old and extremely pertinent caveats for the new age. The experience of IBM’s Watson at a cancer cure hospital in Texas is a case in point- the rules of prioritizing business value and good governance have never been more important.

According to experts, the robot-based workforce will take over 30 percent of jobs by 2025. Consequently, some jobs will be lost, while some new jobs will be created. All these examples paint a truly fantastic picture of where intelligence and automation can take us in the future. It also brings into question what traditional firms in each of these sectors need to do in order to cope with such technological revolution.

The best way to survive in these ever-changing digitization trends is to constantly improve the customer experience through innovation. One thing is clear that with intelligent software and hardware and their synthesis into automation the company of future will look very different- but to survive and thrive, it will still need the same emphasis on process, governance, and alignment to strategy.

News Originally Posted on: PC Quest