Infosys experts share their views on how digital is significantly impacting enterprises and consumers by redefining experiences, simplifying processes and pushing collaborative innovation to new levels

October 15, 2018

Machine Learning Implementation in CRM

Customer Relationship Management (CRM) is the most common platform used in every industry across the world to manage customer relationship and interaction in order to provide best service to their customers and improvise their business. The volume of data streams in CRM applications would be very high as it captures growing data in several stages of the CRM life cycle such as marketing, Sales and Customer service stages.

Most of the CRM packages in the present market provide only to generate reports and BI charts with the available support of their system (out of the box support), these reports and graphical representations can be generated from various tables and columns of CRM database, in addition it also provides graphical representation in the form of pie, bar charts and comes with capability to illustrate various trending information. There is always a need for manual intervention, may be sales expert or external BI tools would need to be employed for forecasting the business and that too, I daresay with only about 50% probability that forecast and actuals would match. Now, it's time to switch to Machine Learning (ML) to improvise your business with rapid and closer predictions to handle large amount of data ranging from Terabytes to Exabytes.

While ML is now proving that it can help predict future prospects based on past data, CRM merely focuses on past and present data and provides insights about customer and sales patterns. However, Machine Learning does continuous learning and provides real-time insights also it provides recommendations over customer, sales and prospects for the best outcomes.

Let's see, how traditional system works and how this can be improvised with ML. For instance, consider the following data from opportunity records,


The above set of data (table-1), probably won't give any insights for the reason behind loss of the business even with existing customer, now look at the below table (table-2). It would give some inference for winning and losing business by just adding more meaningful columns from other tables. This kind of intelligent and real-time data extracts from various tables can be generated and reported in present CRM system with available tools and techniques and this could be extended to some graphical representation. If you are looking even beyond such reports, you need to look no further than ML.


Make a prediction with machine learning algorithm

Now, let's understand about Machine Learning and how this can be used in CRM world. For convenience, lets convert the string into 0s and 1s in table-2 and will apply simple decision tree algorithm to understand machine learning prediction here, then the data representation becomes as given below format (Table-3) and a model (algorithm) to be trained with training data(features) to predict upcoming opportunity record in the CRM system i.e. whether the opportunity has just created in the system is potential or not? (Hot or Cold Deal). We will use Python in this example.


The following Python code snippet illustrates the model and decision tree is a classifier used to predict the input data.



Let's feed the input to the model and see the result, input is just created opportunity record



Now the prediction is completed and we have received a data-driven result. There are even optimization techniques to improve the results to be more accurate. The more we train the model with training data, the more accuracy would be obtained.

As ML always deals with large volume of data, it would definitely help your business with more accurate results when compared to BI tools or any other solutions available. May be in near future, CRM systems will come with out of the box feature in-built Machine learning classifiers for future forecast, in fact all this may be available as a package as part of your Cloud platform.

Apart from insights over customer, sales trend and opportunities and its outcomes, Machine Learning can also be used for automation in customer service, provides suggestion over sales and service, chatbots, feedback etc.

We will discuss more about CRM and Machine Learning in subsequent blogs along with some case study.

January 23, 2018

Uncover the potential of NLP in SCM & EAM

What is NLP?

NLP (Natural Language Processing) is a combination of ML (Machine Language) & AI (Artificial Intelligence) and is also referred as Computational Linguistics, which enable humans to interact with machines and data through text/voice based natural conversations. These interactions are typically enabled through Chatbots or voice enabled devices. We have been interacting with computers in the language they understand, but NLP platforms enable computers to understand human language. NLP frameworks with the help of AI/ML, can process structured (e.g., tables) or unstructured data (e.g., news feeds, social media, mails) for needs such as sentiment analysis.

Continue reading "Uncover the potential of NLP in SCM & EAM" »

September 18, 2017

Microservices in the Insurance Industry - Part II

...continued from Part I

Maturity Level in an organization for MSA      

When MSA is being introduced, it is important to be prepared to handle the additional complexities and realize the benefits. Below are some of the key competencies that are considered essential in an organization for effective implementation of MSA:

  • Containerization and Automated provisioning: One of the key drivers behind MSA is that services can be deployed and scaled independently, based on the demand. Also, MSA demands reliable and scalable infrastructure. For this to happen seamlessly, there is a need for containerization (e.g. Docker), and the provisioning of resources (servers, virtual machines, containers etc.) should be automated as much as possible. This can be achieved by the effective adoption of one of the various available cloud platforms (e.g. Azure, Cloud Foundry, AWS) for computing resources.
  • Monitoring: When MSA is adopted, one can expect a large number of services in the target state. By its very nature, one can expect very frequent changes and deployments of different services. In such a scenario, chances of any of the services not working as expected increase. To enable to quickly troubleshoot, pinpoint and fix an issue, it is important to invest in robust monitoring of all the services. Also, since transactions could span multiple services, the logging mechanism should ensure complete traceability.
  • Automated Deployment: Whether it is introducing new features, or fixing bugs, one can expect a large number of deployments within a short span of time in the MSA world, in comparison to monolithic systems. This calls for the ability to rapidly deploy changes, and hence tools and process to automate deployments as much as possible.
  • Agile and DevOps Culture: Rapid rolling out of new features, fixes and deployments requires Agile development and DevOps culture.
  • Event Driven Architecture, Eventual Consistency etc.: With MSA, data is expected to be private to every service. This requires a big change in mindset with respect to data consistency and the way systems are designed and developed. With a large number of services, interactions between different services is expected to be asynchronous, and it is important for the organization to embrace the concepts of Event Driven Architecture and Eventual Consistency.

E.g.: Consider a Policy Administration System that takes an underwriter through the various stages of a Policy - Submission, Quote, Bind, Issue etc. After successful completion of every stage of the workflow, it is necessary to update the status of the policy in the master data. This would be simpler in a monolithic system with a single database, since each action can have direct access to, and update all the necessary data within a single transaction. However, in the case of MSA, for instance, the Issue Service may not have direct access to the Policy master data. So, after the successful execution of the Issue service, an event may have to be published to a message broker with the necessary details, which will be subscribed to by the Policy Service, to update the status of the Policy in the Master Data. This is not an ACID transaction; here the data belonging to different services is expected to become eventually consistent.

MSA sample use case.png

  • API Management, Gateway, Security etc.: For a monolithic service, interactions between the client and the service, as well as security implementation tend to be simpler. However, with a large number of microservices, this would become complex. Hence, for instance, instead of every service in a MSA implementing security separately, an API Gateway may implement centralized security. Similarly, instead of the UI making a large number of calls to the services, an Edge Service may do the necessary orchestration and reduce the chattiness of interactions between client and services.
  • Service Discovery: In a MSA scenario, new instances of services can be launched on demand, moved to different computing resources, and so on. For a client to be able to easily and correctly connect to the target service, a Service Discovery (e.g. Eureka) solution is essential.
  • Resilience to Failure: When a monolith fails, the entire system fails. One of the key goals for adopting MSA is that when a monolith is split into a large number of small services, the failure of one of them should not bring down the entire system. But, if the services are too dependent on each other and not built to withstand failure of one or more individual services, it will defeat the purpose of MSA. Hence, it is necessary to design services keeping fallback procedures (e.g. using Circuit breaker) etc. in mind, to make the entire system resilient to failure.


Typical use cases in the Insurance Industry

In the Insurance industry, if we take the example of a Policy Administration System, some of the key functionalities include:

      • Account management
      • Intake of applications through various channels
      • Submission
      • Quote
      • Bind
      • Bill
      • Issue
      • Rating
      • Document Processing etc.


Before one dives into MSA, it is important to assess the overhead and risks involved, as well as the potential benefits. There are some key implementation considerations that need to be kept in mind:

    • Service Granularity: When a service has low granularity, it is less agile, but simpler with fewer interactions. On the other hand, when a service is highly granular, it is highly agile, but is more complex with high external interactions. The goal is to keep services small enough to stay focused and big enough to add value.
    • Data Ownership: For a service to be truly independent, it should have its own private data. Any communication with other services should be over exposed public interfaces.


Where MSA can work:

Some functionalities can be more easily segregated into independent services than others. For e.g., Account/ Customer management, Rating, Billing, Issue etc. are functionalities that can become truly independent microservices, if the underlying data models are well designed and interactions with other services can be minimized.


Where pure MSA may not be beneficial:

However, if there are functionalities (e.g. Submission and Quote) where the underlying data is very closely knit with each other and data consistency is very important, pure MSA can be a challenge. The overhead of managing data consistency and communications between such services can make it too complex and risky, thereby outweighing the benefits of MSA.

In such cases, it may be more beneficial to initially have granular services for each functionality along with a Shared Database. Later on, if required, there could be a remodeling of the database, to have separate databases for each service.


Other use cases for MSA:

There could be certain functionalities (e.g. document processing) that are highly resource intensive. Such functionalities are good candidates for microservices, since they can be independently deployed and scaled as per needs, thereby avoiding adverse impact on the performance of the rest of the application.

MSA - Transformation.png


The transformation from monoliths to microservices will help an organization improve its agility, speed-to-market and efficiency, and thereby compete better with its peers. However, there are no hard and fast rules for this transformation. What worked for one may not necessarily work for another organization.

Organizations that are pioneers in MSA faced a different set of challenges. Insurance is a different domain, with a more complex domain model. Transformation to MSA here will have a different set of challenges. It necessitates a major change in the mindset of all stakeholders - embracing concepts such as Domain Driven Design, Eventual Consistency etc. Systems will have to be designed accordingly, striking a balance between simplicity and agility.

Also, there are many pre-requisites to the adoption of MSA - some of the prominent ones being the capabilities in Automated Provisioning, Continuous Delivery, Monitoring, Agile Development and DevOps culture. Though an organization may start small with a single service, one has to eventually develop all these competencies before the widespread adoption of MSA.


Microservices in the Insurance Industry - Part I

Recent Digitization Trends, and where MSA fits

In the increasingly competitive Insurance industry, speed-to-market, ability to innovate, efficiency of processes, and agility of systems are supremely important for any Insurer to stay ahead of the competition.

Legacy applications in the insurance industry have typically posed multiple challenges in realizing the goals of the Insurer. A typical legacy application would consist of a monolithic architecture where, a large number of functionalities are packaged under one roof. Though monolithic architecture has its own advantages, it also poses a number of challenges. Some of the key challenges include:

    • Since, different components/ functionalities cannot be deployed independently, it slows down the development and deployment process for everyone, thereby greatly affecting the speed-to-market.
    • A failure in one part/ component will affect the entire monolith.
    • Different functionalities within a single package may have widely varying computing resource requirements. There may be a need to scale different components independently. That is not possible in a monolithic architecture.
    • Since everything is packaged under one roof, there is bound to be resistance to change the existing technology stack, even if certain functionalities can be handled better by other stacks.
    • Loading a huge code-base in the IDE will affect developer productivity.


Steps are being taken/ contemplated by most Insurers to modernize their IT systems and address these challenges. Some of the key trends include:

    • Cloud adoption: With speed-to-market being one of the primary concerns, cloud adoption helps automate and drastically reduce the time to procure IT infrastructure, provides enhanced and simplified IT management and maintenance capabilities, better reliability, and reduces costs.
    • Agile and DevOps: Agile and DevOps help faster delivery of features, more stable operating environments, improved communication and collaboration, and more time to innovate.
    • Microservices: If an application has a monolithic architecture, it does not help achieve the full benefits of Cloud adoption and DevOps. This is where Microservices Architecture fits in. When a microservices-based architecture is adopted, the benefits realized include:
      • Helping truly leverage Agile Methodology with smaller, independent teams.
      • Increased speed-to-market and agility, with shorter build, test and deploy cycles, which helps the faster rollout of newer versions of a service.
      • Ability to scale specific functionalities, independent of others.
      • Better reliability, since failures are more isolated.
      • Better availability due to greatly reduced/ zero downtimes during rollout of newer versions.
      • Greater choice of technology stacks, since microservices are loosely coupled.


MSA in the Insurance Industry: Scenarios, Approaches, & Pre-requisites

A commonly expected scenario in the Insurance industry is one where, a monolithic legacy application that has served the business needs of a company for years, needs to be modernized.
The transformation from a monolithic architecture towards a Microservices Architecture is supposed to ensure more independence/ autonomy, speed-to-market etc. However, the transformation will not be the same for every organization/ domain. What worked in one organization/domain may not work for another.

Big Bang or Incremental?
Given the fact that microservices introduce more complexities, and there could be many unknowns, things could go wrong in a Big Bang approach.
Hence, it is important to start small and take key learnings forward, as one incrementally migrates all functionalities to MSA.

Incremental Approach
In this incremental approach, whenever any new functionality is needed, it is developed as a separate, independent service, instead of adding to the existing monolith. Also, from the existing monolith, modules are extracted into independent services, one at a time. While the refactoring is done one new service at a time, the newly created services co-exist with the existing monolith until the monolith keeps shrinking and is ultimately, fully replaced.
To make this process simpler, it is recommended to refactor the code within the monolith into loosely coupled modules first.

Starting Small
In this incremental approach, it is important to choose the right module/ functionality to start with.
1. Choose a module that is easy to extract. This will help one implement it quick and also document best practices etc. for the other services that will be implemented.
2. The focus can next be on modules that offer the most business value as separate microservices. Examples include:

  • Modules that change very frequently - ensures that only this service needs to be redeployed after every change.
  • Modules that have significantly different scalability or other computing resource requirement

3. As newer services are added, it is important develop the competencies (elaborated later) that are essential for adopting MSA.

Data Ownership, Consistency etc.

One of the most challenging parts of building microservices is the ownership of the underlying data. The domain model in the Insurance industry is more complicated than the ones where microservices were first implemented. Hence, it is important to identify the most appropriate bounded contexts while developing microservices. 
In the traditional monolithic architecture, ensuring data consistency is far simpler since the entire database belongs to one monolith. But, to reap all the benefits of MSA, every service needs to be independent, with its own private data. This introduces a lot of additional complexity when it comes to managing data consistency across multiple services.
This requires a change in the mindset, to embrace concepts such as Domain Driven DesignEvent Driven ArchitectureEventual consistencyResilience from Failure etc. Also, this might necessitate a remodeling of the underlying data model, to be able to adapt to the new architecture.


.....To be continued in part II

August 15, 2017

Integrated Blockchain with API & Microservices

Industry leaders, economists, scientists and researchers have called out the most promising change agent that will impact day-to-day businesses and even our lives to be Blockchain. The impact will be more than any other technologies like AI, robotics, IoT, Driverless cars or Passenger spaceships that continuously make to the news stable. Knowing this, numerous FinTechs, Venture Capitalists, and Fortune 1000 firms are aggressively investing and researching to become the early adopters and gain advantage of this nascent technology.

Bottom-line, as Don Tapscott a leading technology author conveys “Blockchain is the revolution to bring the Internet 2.0 (Internet of Value) and it will become as ubiquitous or more as the current internet (Internet of Information)

Continue reading "Integrated Blockchain with API & Microservices" »

July 18, 2017

Chatbots: Transforming Customer Experience

Author: Jitendra Jain | Senior Technology Architect | HILife-ADG (Architecture & Design Group)

Chatbots- What is it?

Chatbots are special kind of smartly designed artificial intelligence enabled computer programs to simulate or mimic conversation with human users over the Internet. Chatbot are also known as chat robot, means a robotic system who can answer different questions. In pure technical terms a chatbot is also referred like a service engine, derived by some logical rules and fixed set of predefined pathways that a user or customer interact with via a chat interface. This service could be well integrated inside any major chat product like (Facebook Messenger, Slack, Telegram, Text Messages, etc.)

Chatbots can transform the way you interact with the internet from a series of self-initiated tasks to a resembled type of conversation. Now chatbots are not just a possibility, chatbots are already starting to significantly change everything we do know about customer communication. Chatbots are now also seen as one of the promising way of Digital engagement. A lot of business enterprises have understood the fact that they can increase their business revenue by offering qualitative chatbot to provide digital user experience to customers.

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July 1, 2017

The New UX using VR, AR and Touch less gestures

Author: Arshad Sarfarz Ariff, Technology Architect


UX in web applications were always driven by mouse and keyboards. Similarly, UX in mobile applications were always driven by touch screens coupled with gestures. But advancements in the field of virtual reality, augmented reality and touch less gestures are gearing up to challenge the status quo. AR and VR has been listed in Gartner report titled - "Top 10 Strategic Technology Trends 2017". Gartner has predicted that AR, VR and mixed reality (MR) solutions will be evaluated and adopted in 20% of large-enterprise businesses by 2019.

Continue reading "The New UX using VR, AR and Touch less gestures" »

June 21, 2017

Locating Marketing Moorings in the Digital World






It's quite interesting how enterprise orientations have changed from efficient manufacturing to global competitiveness to finally being more simplistic, and 'customer centric' i.e. intensely Customer oriented.

This evolution, if we look at, is neither radical nor abrupt. The world moved on from the concept of 'Manufacturers' & 'Consumers' in the pre and post-world war era of 'isolated focus on manufacturing efficiently, with cheaper resources, closer to 'raw material sources' and 'consuming them in another part of world or profiting from them in a completely different part of the world' to a world which became even more resourceful and adaptive- where everyone could manufacture and sell anywhere and the differentiations in profit making or market expansion was to come from:

  • Quality (this is when the Japanese found quality of national importance)

  • Intelligent localized market targeting strategies (Globalization being theme)

  • To the now- intensively aggressive customer centricity (digital enabled targeting and product positioning)

    How fast, how close one is to understanding its customer or finding new ones, and how quick they are to react on the customer data determines not just the immediate bottom lines but long term business strategy and enterprise viability.

    The focus was on 'providing' for 'pre-conceived needs or demands' - the world neither had the assurance of global trade rules' to foster healthy market practices nor the wherewithal of knowing global needs, global consumers, their preferences, paying capacity or the means to find and factor what decision parameters customers, in a diverse and expansive markets, are applying for deciding on manufacturers fate.

    A simple look at a scenario, for instance, how a beverage or a FMCG companies did marketing in pre- second world war, post second world war and post the internet evolution era would give interesting insights into:

  • How this space evolved- Today what shapes this activity and a rationale forecast of how this is likely to bring new, unheard dimensions into marketing and create a paradigm shift, already we see this happening in many areas, in how enterprises are going to use insights (data), agility (ability to not reactively but proactively adapt to what customer may want in real-time) and tailored experiences - to differentiate themselves. It will be na├»ve to suggest that product features, product qualities etc. will seize to evolve and only customer experience will differentiate in isolation- but it will be the customer experience and centricity of customer which will lead changes in various product and enterprise strategies - at a much faster speed


  • This marketing and customer experience orientation is almost certain to be the single biggest factor determining enterprise success, success of almost all of enterprise function and to a great extent determine enterprise viability and its sustenance altogether

    So a beverage company which in early 20th century found a need to have accessible packaged drink for people and runs "awareness" campaigns changes to - finding local flavors, local branding and extrapolates positioning in line with regional, local nuances in the 1960's and 70's to a more holistic customer-led product creation, customer oriented product delivery and branding infused with specified segment targeting (youth, executives, kids, sports, health conscious etc.) and building ability in its product positioning and marketing strategies to enable that.

    In the coming days, Marketing - which if we quote the Guru- Philip Kotler defining this as- "the science and art of exploring, creating, and delivering value to satisfy the needs of a target market at a profit" is going to be something like this- "the techniques by which you lead customers' thoughts, needs & desires to your offerings, use customer behavior & insights to extend customer aspirations and creating new markets and having a unique positioning to fulfill those"

    And the techniques and the pace of marketing evolution is going to be extremely fast-tracked by technologies like big data, GPS, augmented reality, real-time decisioning ability, to real-time enterprise process orchestration for experience creation across factors covering pricing, product delivery, product core features, post sales experience management and leverage for further customer targeting. This space is going to see innumerable shocking, disruptive shifts - complicating what seems like a simple sale to customer- bringing a huge set of dimensions on which to differentiate an enterprise - in a nutshell:

  • The ability to proactively listen, absolutely precisely engage and enabling an individualized interaction with customer defines what enterprises will do for its product strategy, their manufacturing process, pricing strategies, quality nuances, its brand positioning strategies and not the other way round

  • Today enterprises are listening to client aspirations and juxtaposing their branding to those- before customers move onto something which suits their personas better

    In addition to the differentiation and customer experience & aspiration led, there are certain environmental, global changes as below, that are bringing completely new dimension to marketing today:

  • Democratization of supply, and demand: Today the supply chain, distribution or fulfilment cycle is part of marketing strategy. These are no more fixed, and affect not just organization strategy but influence customer choices and buying behavior. A J-I-T offer for a premium mobile device for someone in the U.S can be very different from someone in India, because of fulfilment cost and availability factors. Similarly, a dealer in India can transpose it's marketing strategy at real-time if an relevant accessory for that device is available or can be procured from a nearby market

  • Virtual and augmented reality: This is another powerful change; the extent of the impact is still not fully assimilated. Customers will no more go by the asynchronous isolated content but will experience, know much finer nuances of their purchase than what was ever possible. Augmented realities would force marketers to think ahead of future needs and ensure they meet expectations that far ahead

  • Customer led promotions, user generated content mapping: Customer is no more a passive entity in marketing cycle, they are part of marketing chain. They form a potent channel of marketing and leveraging customer delight & satisfaction for accentuating brand value is going to be a force multiplier with completely non-linear ROIs possibility

  • Extreme data sciences creating unique, live- opportunities via Patterns, trends & projections: with close to four billion people and more than those many number of digital touchpoints, content & data is indeed the King and the ability to make sense of that data using BI, AI or machine learning is likely to create unforeseeable dimension & possibilities, presenting marketers with new segments, markets and personas with intelligence at their disposal which will help devise products suiting customer aspiration & experience (a big change from experience & marketing coming after the product strategy)


    The world has changed in terms of fulfilling customer needs, giving better experience (what they called marketing), the rate of change has been exponentially increasing with passage of time. Marketing today is trying to keep pace with this change and this is a journey than destination- robotics, machine learning, greater democratization of buyer-supplier relationship is going to keep pushing at these changes with greater force than ever and it will be an interesting attempt to keep track of where this heads to.


June 19, 2017

'THUNDER' Power in Salesforce (IoT)

'THUNDER' Power in Salesforce (IoT)


Quadrant (Magic) leader in CRM -Salesforce is scaling up on the latest Buzzword - Internet of Things (IoT). IoT is and will be affecting the world through its human free transfer of large quantum of data over the network.


What a better name than 'THUNDER' being christened to power the leader in this space for Salesforce. Thunder is a phenomenally scalable, real time event processing engine.


Below are the 3 key tenants on which Thunder is envisaged:



What will it allow: CONNECTIVITY!!


With the Power of Thunder, Salesforce equips the organizations to connect and capture tsunami of data from every source -be it Hand held devices, sensors if any, websites, PoS, social media or any other channel of interaction or data capture. The biggest source of data generation is Smartphone followed by social media. As of now there are exabytes of data being created daily with little or no use. This data 'dump' first needs to be connected and captured.


Data assimilation: Organizations need to not only capture these data threads but have the ability to traverse through data, search, analyze as well be able to present in meaningful format for deriving benefits. With the help of tools - in house and external, the large volumes of data captured from multiple sources can be traversed to understand patterns and significant events and allow for real time action (triggers).


Informed and outcome oriented interaction: The ultimate aim is to take the right decision to affect outcomes. Once the data is converted to meaningful and actionable intelligence, users can leverage salesforce to proactively reach out to their prospects and customers for intelligent interactions and ensuring a focused and informed decision making. This is about moving from system of records to systems of intelligence.


Multiple cloud based analytical tools are in play empowering business users to derive actionable intelligence on click of a button--real time. Since devices and sensors will be key in this equation, machine learning and algorithms start gaining importance in the intelligent decision cycle. Organizations will thus start banking on artificial intelligence to reduce if not eliminate human intervention.


In this field of technology the quest is to move from unaware / unknown to being proactive and ultimately predictive. Being predictive through juggling of data, deriving actionable intelligence and outcome oriented action will be key in times to come.




May 24, 2017

Improving the Win ratio through Salesforce AI - Einstein

Improving the Win ratio through Salesforce AI: Einstein


World leader in CRM space, Salesforce has continuously evolved. From an early mover in digital cloud space it is now focusing on Artificial Intelligence (AI) to ensure a more meaningful data convergence.

Einstein is being designed with a focus on ensuring that the users are provided a focused set of data - Leads, Opportunities and Insights into decision making and Improve ROI. Einstein is designed to go through and learn from the data that is available including online feeds and provide focused data sets for improved decision making. Salesforce users will now have more bandwidth to focus on what they do best rather than trying to decipher through multitude of data and manual slicing and dicing to arrive at actionable data sets.


Some of the key aspects of this robust AI incorporated into Salesforce are:


1.       Lead Scoring

2.       Opportunity Insights

3.       Account Insights

4.       Activity Capture

5.       Follow ups.


Key features are pictorially depicted below:


Infosys being a Platinum partner of Salesforce is uniquely positioned to be onboard this latest offering and help its clients in improving their sales cycle time thereby enhanced ROI on the CRM front.

Continue reading "Improving the Win ratio through Salesforce AI - Einstein" »