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Self-driving cars run on machine learning algorithms.
Natural language processing and image recognition software use these algorithms.
In the financial world, stock trading and fraud detection are carried out by using machine learning algorithms.
How Machine Learning Works? Machine learning uses three types of techniques. These techniques train a model and predict outputs based on the following:
Supervised Machine Learning
: It consists of input variables (x) and an output variable (Y). A supervised algorithm then uses the training data to map inputs to the desired output. It is called supervised learning because it requires human interference in making predictions on the training data. The algorithm makes a number of iterations to get the acceptable level of output. Supervised machine learning is used in classification and regression problems. Some of the common applications of this technique include:
Linear/ Logistic regression
Discriminant Analysis
Support vector machines (SVM)
Random forest
k-Nearest Neighbors
Naive Bayes
Unsupervised Machine Learning
: In unsupervised machine learning there is no outcome variable. The algorithm models the data using input variables and presents the structure based on the same. Unsupervised machine learning is used in forming classification (clusters) and associations in data. Examples of this technique include:
K-means and Hierarchical clustering
Neural Networks
Gaussian Mixture
Apriori algorithm for association rule mining
Reinforcement Learning
: In this technique, the machine produces programs, called agents, through a process of learning and evolving. The agent learns from past consequences of its actions and selects the best possible solution through trial and error learning. Applications of this technique include Hidden Markov models. When to Get Started with Machine Learning? Machine learning aids in solving business problems involving a large amount of data. In order to use machine learning, organizations need to have scalable data preparation capabilities. The machine learning algorithms quickly produce models that can analyze complex data, and deliver faster and accurate insights. With these models, data analysts/scientists can identify profitable opportunities and mitigate potential risks. However, organizations should first need to choose the right technique and algorithm to make the best use of machine learning. Machine Learning by Industries Machine learning is proving its worth in many industries globally. It significantly drives efficiency; deliver customer value and helps in gaining actionable insights. Some of the key sectors that embrace machine learning religiously include:
Financial Services
: The financial services industry was one of the first sectors to implement artificial intelligence in business decision-making. Fraud detection, face recognition, compliance are carried out meticulously through machine learning with a large amount of structured and unstructured data.
Retail
: Machine learning helps retailers to increase sales and customer engagement through predictive analytics such as market basket analysis, item recommendations, analyzing buyer sentiment, ad scoring, and identifying new markets, among others.
Healthcare
: Machine learning offers an array of benefits to patients and healthcare providers. It is used in discovering a correlation between patient behavior and disease. Use of biometric sensors is saving lives of millions of patients globally. Machine learning is extremely crucial in clinical trials as it helps to know if the treatment would be safe and effective. The Outlook Machine learning heralds a significant potential for the growth of humans and the economy. According to a market research firm, the machine learning as a service market (MLAAS) is estimated to grow from US$613.4m in 2023 to US$3,755m by 2023, at a CAGR of 43.7%. Machine learning will radically transform processes and make our lives and businesses efficient. It will reduce the need for human interventions and has fascinating implications for the global industries. So, are we ready for it?
We are living in the era of technological transformation that is bringing about changes in the way we take decisions. As big data is becoming pervasive across all the industries, use of machines to find patterns and predict future is gaining a lot of prominence in the market. Machine learning is a method of data analysis which automates the process of model building. The algorithms use computational techniques to generate insights that help organizations make better decisions. Machine learning is used in different areas in real-time business situations. Here are a few widely used examples of machine learning applications you must be familiar with:Machine learning uses three types of techniques. These techniques train a model and predict outputs based on the following:: It consists of input variables (x) and an output variable (Y). A supervised algorithm then uses the training data to map inputs to the desired output. It is called supervised learning because it requires human interference in making predictions on the training data. The algorithm makes a number of iterations to get the acceptable level of output. Supervised machine learning is used in classification and regression problems. Some of the common applications of this technique include:: In unsupervised machine learning there is no outcome variable. The algorithm models the data using input variables and presents the structure based on the same. Unsupervised machine learning is used in forming classification (clusters) and associations in data. Examples of this technique include:: In this technique, the machine produces programs, called agents, through a process of learning and evolving. The agent learns from past consequences of its actions and selects the best possible solution through trial and error learning. Applications of this technique include Hidden Markov models.Machine learning aids in solving business problems involving a large amount of data. In order to use machine learning, organizations need to have scalable data preparation capabilities. The machine learning algorithms quickly produce models that can analyze complex data, and deliver faster and accurate insights. With these models, data analysts/scientists can identify profitable opportunities and mitigate potential risks. However, organizations should first need to choose the right technique and algorithm to make the best use of machine learning.Machine learning is proving its worth in many industries globally. It significantly drives efficiency; deliver customer value and helps in gaining actionable insights. Some of the key sectors that embrace machine learning religiously include:: The financial services industry was one of the first sectors to implement artificial intelligence in business decision-making. Fraud detection, face recognition, compliance are carried out meticulously through machine learning with a large amount of structured and unstructured data.: Machine learning helps retailers to increase sales and customer engagement through predictive analytics such as market basket analysis, item recommendations, analyzing buyer sentiment, ad scoring, and identifying new markets, among others.: Machine learning offers an array of benefits to patients and healthcare providers. It is used in discovering a correlation between patient behavior and disease. Use of biometric sensors is saving lives of millions of patients globally. Machine learning is extremely crucial in clinical trials as it helps to know if the treatment would be safe and effective.Machine learning heralds a significant potential for the growth of humans and the economy. According to a market research firm, the machine learning as a service market (MLAAS) is estimated to grow from US$613.4m in 2023 to US$3,755m by 2023, at a CAGR of 43.7%. Machine learning will radically transform processes and make our lives and businesses efficient. It will reduce the need for human interventions and has fascinating implications for the global industries. So, are we ready for it?
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Fedramp Certification: What Is It, Why It Matters, And Who Has It
FedRAMP stands for the “Federal Risk and Authorization Management Program.” Find out if you need to become FedRAMP authorized.
Hacked celebrity camera rolls. State-based cyberespionage. And everything in between. Data security has a huge range of applications. And it’s a major concern for everyone who uses or supplies cloud-based services.
When government data is involved, those concerns can reach the level of national security. That’s why the U.S. government requires all cloud services used by federal agencies to meet a meticulous set of security standards known as FedRAMP.
So just what is FedRAMP, and what does it entail? You’re in the right place to find out.
Bonus: Read the step-by-step social media strategy guide with pro tips on how to grow your social media presence.
What is FedRAMP?
FedRAMP stands for the “Federal Risk and Authorization Management Program.” It standardizes security assessment and authorization for cloud products and services used by U.S. federal agencies.
The goal is to make sure federal data is consistently protected at a high level in the cloud.
Getting FedRAMP authorization is serious business. The level of security required is mandated by law. There are 14 applicable laws and regulations, along with 19 standards and guidance documents. It’s one of the most rigorous software-as-a-service certifications in the world.
Here’s a quick introduction:
FedRAMP has been around since 2012. That’s when cloud technologies really began to replace outdated tethered software solutions. It was born from the U.S. government’s “Cloud First” strategy. That strategy required agencies to look at cloud-based solutions as a first choice.
Before FedRAMP, cloud service providers had to prepare an authorization package for each agency they wanted to work with. The requirements were not consistent. And there was a lot of duplicate effort for both providers and agencies.
FedRAMP introduced consistency and streamlined the process.
Now, evaluations and requirements are standardized. Multiple government agencies can reuse the provider’s FedRAMP authorization security package.
Initial FedRAMP uptake was slow. Only 20 cloud service offerings were authorized in the first four years. But the pace has really picked up since 2023, and there are now 204 FedRAMP authorized cloud products.
Source: FedRAMP
FedRAMP is controlled by a Joint Authorization Board (JAB). The board is made up of representatives from:
the Department of Homeland Security
the General Services Administration, and
the Department of Defense.
The program is endorsed by the U.S. government Federal Chief Information Officers Council.
Why is FedRAMP certification important?
All cloud services holding federal data require FedRAMP authorization. So, if you want to work with the federal government, FedRAMP authorization is an important part of your security plan.
FedRAMP is important because it ensures consistency in the security of the government’s cloud services—and because it ensures consistency in evaluating and monitoring that security. It provides one set of standards for all government agencies and all cloud providers.
Cloud service providers that are FedRAMP authorized are listed in the FedRAMP Marketplace. This marketplace is the first place government agencies look when they want to source a new cloud-based solution. It’s much easier and faster for an agency to use a product that’s already authorized than to start the authorization process with a new vendor.
So, a listing in the FedRAMP marketplace makes you much more likely to get additional business from government agencies. But it can also improve your profile in the private sector.
That’s because the FedRAMP marketplace is visible to the public. Any private sector company can scroll through the list of FedRAMP authorized solutions.
It’s a great resource when they’re looking to source a secure cloud product or service.
FedRAMP authorization can make any client more confident about the security protocols. It represents an ongoing commitment to meeting the highest security standards.
FedRAMP authorization significantly boosts your security credibility beyond the FedRAMP Marketplace, too. You can share your FedRAMP authorization on social media and on your website.
The truth is that most of your clients probably don’t know what FedRAMP is. They don’t care whether you’re authorized or not. But for those large clients who do understand FedRAMP – in both the public and private sectors – lack of authorization may be a deal-breaker.
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What does it take to be FedRAMP certified?
There are two different ways to become FedRAMP authorized.
1. Joint Authorization Board (JAB) Provisional Authority to OperateIn this process, the JAB issues a provisional authorization. That lets agencies know the risk has been reviewed.
It’s an important first approval. But any agency that wants to use the service still has to issue their own Authority to Operate.
This process is best suited for cloud services providers with high or moderate risk. (We’ll dive into risk levels in the next section.)
Here’s a visual overview of the JAB process:
Source: FedRAMP
2. Agency Authority to OperateIn this process, the cloud services provider establishes a relationship with a specific federal agency. That agency is involved throughout the process. If the process is successful, the agency issues an Authority to Operate letter.
Source: FedRAMP
Steps to FedRAMP authorization
No matter which type of authorization you pursue, FedRAMP authorization involves four main steps:
Package development. First, there’s an authorization kick-off meeting. Then the provider completes a System Security Plan. Next, a FedRAMP-approved third-party assessment organization develops a Security Assessment Plan.
Assessment. The assessment organization submits a Security Assessment report. The provider creates a Plan of Action & Milestones.
Authorization. The JAB or authorizing agency decides whether the risk as described is acceptable. If yes, they submit an Authority to Operate letter to the FedRAMP project management office. The provider is then listed in the FedRAMP Marketplace.
Monitoring. The provider sends monthly security monitoring deliverables to each agency using the service.
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FedRAMP authorization best practices
The process of achieving FedRAMP authorization can be tough. But it’s in the best interest of everyone involved for cloud service providers to succeed once they start the authorization process.
To help, FedRAMP interviewed several small businesses and start-ups about lessons learned during authorization. Here are their seven best tips for successfully navigating the authorization process:
Understand how your product maps to FedRAMP – including a gap analysis.
Get organizational buy-in and commitment – including from the executive team and technical teams.
Find an agency partner – one that is using your product or is committed to doing so.
Spend time accurately defining your boundary. That includes:
internal components
connections to external services, and
the flow of information and metadata.
Think of FedRAMP as a continuous program, rather than just a project with a start and end date. Services must be continuously monitored.
Carefully consider your authorization approach. Multiple products may require multiple authorizations.
The FedRAMP PMO is a valuable resource. They can answer technical questions and help you plan your strategy.
FedRAMP offers templates to help cloud service providers prepare for FedRAMP compliance.
What are the categories of FedRAMP compliance?
FedRAMP offers four impact levels for services with different kinds of risk. They’re based on the potential impacts of a security breach in three different areas.
Confidentiality: Protections for privacy and proprietary information.
Integrity: Protections against modification or destruction of information.
Availability: Timely and reliable access to data.
The first three impact levels are based on Federal Information Processing Standard (FIPS) 199 from the National Institute of Standards and Technology (NIST). The fourth is based on NIST Special Publication 800-37. The impact levels are:
Low-Impact Software-as-a-Service (LI-SaaS), based on 36 controls. For “systems that are low risk for uses like collaboration tools, project management applications, and tools that help develop open-source code.” This category is also known as FedRAMP Tailored.
Does the service operate in a cloud environment?
Is the cloud service fully operational?
Is the cloud service a Software as a Service (SaaS), as defined by NIST SP 800-145, The NIST Definition of Cloud Computing?
The cloud service does not contain personally identifiable information (PII), except as needed to provide a login capability (username, password and email address)?
Is the cloud service low-security-impact, as defined by FIPS PUB 199, Standards for Security Categorization of Federal Information and Information Systems?
Is the cloud service hosted within a FedRAMP-authorized Platform as a Service (PaaS) or Infrastructure as a Service (IaaS), or is the CSP providing the underlying cloud infrastructure?
Keep in mind that achieving FedRAMP compliance is not a one-off task. Remember the Monitoring stage of FedRAMP authorization? That means you’ll need to submit regular security audits to ensure you stay FedRAMP compliant.
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Examples of FedRAMP-certified products
There are many types of FedRAMP-authorized products and services. Here are a few examples from cloud service providers you know and may already use yourself.
HootsuiteAs of March 2023, Hootsuite is an officially FedRAMP-authorized social media management dashboard. A number of major government agencies, including The US Department of the Interior, the Department of State, and FEMA use Hootsuite’s software to achieve a wide range of federally-related objectives.
Former CEO of Hootsuite, Tom Keiser, said of the official designation:
“With the world relying more heavily on social networks for communication, community, and global e-commerce, it’s more important than ever to ensure our security practices are constantly evolving to meet a rigorous set of standards. With our FedRAMP ATO, the US Federal Government, and all Hootsuite customers, can feel confident that we are constantly improving on our security practices.”
Read more about how Hootsuite is the #1 trusted social media management tool for government agencies or book a free demo (no commitments necessary).
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Amazon Web ServicesThere are two AWS listings in the FedRAMP Marketplace. AWS GovCloud is authorized at the High level. AWS US East/West is authorized at the Moderate level.
— AWS for Government (@AWS_Gov) October 18, 2023
AWS GovCloud has a whopping 292 authorizations. AWS US East/West has 250 authorizations. That’s far more than any other listing in the FedRAMP Marketplace.
Adobe AnalyticsAdobe Analytics was authorized in 2023. It is used by the Centers for Disease Control and Prevention and the Department of Health and Human Services. It’s authorized at the LI-SaaS level.
Adobe actually has several products authorized at the LI-SaaS level. (Like Adobe Campaign and Adobe Document Cloud.) They also have a couple of products authorized at the Moderate level:
Adobe Connect Managed Services
Adobe Experience Manager Managed Services.
Adobe is currently in the process of moving from FedRAMP Tailored authorization to FedRAMP Moderate authorization for Adobe Sign.
— AdobeSecurity (@AdobeSecurity) August 12, 2023
Remember that it’s the service, not the service provider, that gets authorization. Like Adobe, you might have to pursue multiple authorizations if you offer more than one cloud-based solution.
SlackAuthorized in May of this year, Slack has 21 FedRAMP authorizations. The product is authorized at the Moderate level. It’s used by agencies including:
the Centers for Disease Control and Protection,
the Federal Communications Commission, and
the National Science Foundation.
— Slack (@SlackHQ) August 13, 2023
Slack originally received FedRAMP Tailored authorization. Then, they pursued Moderate authorization by partnering with the Department of Veterans Affairs.
Slack makes sure to call attention to the security benefits of this authorization for private sector clients on its website:
“This latest authorization translates to a more secure experience for Slack customers, including private-sector businesses that don’t require a FedRAMP-authorized environment. All customers using Slack’s commercial offerings can benefit from the heightened security measures required to achieve FedRAMP certification.”
Trello Enterprise CloudTrello was just granted Li-SaaS authorization in September. Trello is so far used only by the General Services Administration. But the company is looking to change that, as seen in their social posts about their new FedRAMP status:
— Trello by Atlassian (@trello) October 12, 2023
ZendeskAlso authorized in May, Zendesk is used by:
the Department of Energy,
the Federal Housing Finance Agency
the FHFA Office of the Inspector General, and
the General Services Administration.
The Zendesk Customer Support and Help Desk Platform has Li-Saas authorization.
— Mikkel Svane (@mikkelsvane) May 22, 2023
FedRAMP for social media management
Hootsuite is FedRAMP authorized. Government agencies can now easily work with the global leader in social media management to engage with citizens, manage crisis communications, and deliver services and information via social media.
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What Is Machine Learning? Its Utilize
In accordance with some 2023 survey, 49 per cent of businesses are already utilizing machine learning how to boost their conventional business processes. In the smallest startup to the largest multinational company, just about any firm can benefit from integrating machine learning to its company workflow.
This guide is for you.
What is Machine Learning and How People Use Machine LearningIn its core, machine learning is your endeavour of earning computers smarter without explicitly instructing them how to act. It does so by identifying patterns in data–particularly helpful for varied, high-dimensional data like graphics and individual health records.
Supervised LearningIn supervised learning, the system has access to some training dataset which is composed such as data points along with their labels.
By way of instance, assume the machine is provided with a picture, in addition to the job of recognizing if a cat is within the picture. The training dataset would then include a succession of pictures, together with a notice for everyone that denotes if the image includes a cat.
Also read: 9 Best Cybersecurity Companies in the World
Unsupervised learningUnlike supervised learning, unsupervised learning doesn’t offer the machine using some training dataset. Rather, the machine has to require unlabeled, unstructured information and discover the structure inherent in this info.
Reinforcement LearningIn reinforcement learning, the system tries to locate the perfect action to consider while being put at a set of distinct situations. These activities may have short-term and long-term effects, requiring the student to detect these connections.
The idea of reinforcement learning borrows greatly from psychology experiments on animals, like birds and rats, where the creature seeks to acquire a reward like food without explicitly knowing how to get it. Likewise, reinforcement learning tries to educate the machine that the set of activities that will cause a positive or negative outcome. Without being explicitly educated, the system learns on its behaviours that cause a punishment or reward.
“Reinforcement learning is really crucial for applications like self-driving cars which are complicated to model”.
The “reward” could be considered as a prosperous trip between two places, and also the “punishment” is any crash or reckless driving which places somebody at risk.
The Basic Difference Between ML and AITo people unfamiliar with all the conditions, “machine learning” and “artificial intelligence” may look like the exact same idea. In reality, machine learning is a subcategory of artificial intelligence and a specific approach to creating machines smarter. Early campaigns in artificial intelligence tried to specify explicit logical principles by which machines must act; nonetheless, these jobs had mixed success. As opposed to using specialists to specify lines of reasoning, the system itself depends on vast amounts of information and various experiences to be intelligent on its own.
Also read:
Best CRM software for 2023
How People Use Machine Learning or its Limitation“Machine learning has altered numerous industries, it underlies the tech on your smartphone from virtual assistants such as Siri to forecasting traffic patterns using Google Maps”
Based on AI specialist Andrew Ng, machine learning will probably be very good at jobs that human beings could achieve at a second or not. Little, repetitive actions can readily be automated with machine learning, developing machine learning company time, effort, and cash.
The capability of machine learning units to manage high-dimensional information is very beneficial for companies. AI-enhanced applications can do things like finding patterns in consumer accessibility information and correctly forecasting customer retention, and which might otherwise be impossible to get a human to perform. But, machine learning also includes its very own set of constraints. For one, it is only great for specific kinds of use cases, which means that your employees will not be replaced using a robot workforce anytime soon. Additionally, machine learning could be vulnerable to individual biases that are found in the training dataset. The information that you just train the versions on ought to be large and agent so as to find the best results and prevent overfitting.
In the not too distant future, techniques like deep neural networks enable machines not just to classify and audience information, but to create new content depending on the training data. By way of instance, Neural Networks may perform tasks like shifting art styles between pictures, so that even the funniest picture of your cat may seem just like a Van Gogh painting.
Final IdeasWith popular curiosity and use instances just continuing to grow, the future of machine learning appears bright indeed. To learn more about how it is possible to apply how to create your machine learning company more efficient and effective, reach us out now.
What Is The Future Of Salesforce?
Salesforce is the best cloud-based customer relationship management (CRM) solution. Its features let you manage customer relationships, make custom software, and connect your business with others.
Salesforce is a great example of how Software as a Service (SaaS) based on a multi-tenant architecture can be useful. It has features like API connectivity, configuration, flexibility, and platform compatibility.
Salesforce can help you build and keep strong customer relationships. It is vital for the success of your business in this age of rapidly changing technology and fierce competition. One can look at the results of their campaigns and other ways to promote them from every angle.
This market-leading CRM platform is in the cloud. So businesses can use many tools and apps to learn more about their customers and better serve them.
SalesForceDotCom (SFDC) has grown a lot in recent years. The Salesforce suite of products is one of the most interesting Cloud platforms.
In any case, what is in store for Salesforce? Can we expect the Salesforce ecosystem to grow quickly so it eventually takes over the corporate software market?
What Plans Does Salesforce Have to Grow?As was already said, the company’s Service Cloud and Marketing Cloud will get a new data science module. In Service Cloud, users can automate how they help customers, and in Marketing Cloud, they can use data to make their marketing more accurate and focused.
The new products from Salesforce will help the company grow at the same rate it is now. In 2023, the company started to use Lightning CRM. With Salesforce Lightning, the company has made its sales force more efficient. They can also give businesses smart, analytical data that has helped drive sales. The company says that Salesforce Lightning is the future of CRM software because it is more useful than its first CRM product.
Adding services like data science will help keep customers happy in the long run. With the release of Salesforce Lightning and all its new features, clients who didn’t care about Salesforce will start to.
If Salesforce keeps spending money to improve its SaaS offering, it will stay the market leader for a long time. But as more industries start using Salesforce, companies will realize they need CRM solutions that are made for their industry. Organizations must keep track of more specific and useful information in every field. Still, the many features of the cloud-based platform make it easy to give each customer a customized service.
Top Developments in Salesforce Right NowHere are the top five trends in Salesforce that will affect your job prospects.
Cloud-based AdvertisingIf you don’t know who is visiting your website, you can’t market it. Also, giving people customized content will not help you figure out what they want if you don’t know what they want.
So, Salesforce made the Marketing Cloud to help with being customer-focused. This platform can be used for multi-channel project management, mapping the customer journey, pre- and post-project analytics, customer engagement, integrating social media, and managing data.
Slack-First Customer-Centered 360Since Salesforce bought Slack in 2023, it will be the company’s main CRM platform. It is a response to the fact that more and more companies are using remote workers and hybrid office layouts.
The platform helps your company’s sales, salesforce marketing, and service departments work together. It gives users an even experience across channels.
Health 2.0 on the Salesforce CloudSalesforce got into the healthcare market with the release of Health Cloud. With the help of the CRM platform, many healthcare institutions and professionals were able to cut down on unnecessary work and give patients more personalized care.
A year after the COVID-19 outbreak, Salesforce changed its Health Cloud to help hospitals and patients more.
HyperforceSalesforce can grow faster because of Hyperforce. The scalability and efficiency of the public cloud and the faster integration to deploy new features will help it grow rapidly.
They are still in the early stages of getting Hyperforce out there. Salesforce’s CDP is only available in the United States and Germany. However, India and Australia can use the company’s core services. Since Salesforce wants to grow into new countries by the end of 2023, we should learn more about Hyperforce in the coming months.
Integrating Data and SystemsCustomer 360 works with MuleSoft Inc.’s cloud-based integration platform. It gives users more meaningful, customized experiences and helps businesses make better decisions. With the new updates, it will be possible to manage data unification and authorization, fully segment audiences, and do much more.
It might take some time to get rid of data silos and give users a consistent experience across all channels. But if Salesforce’s development strategy puts integration first, the ROI of CRM tools in the future may be better than expected.
ConclusionIBM, SAP, and Microsoft are all competitors in a field where business software is becoming more important and is used by many people.
One of the best things about the new Salesforce Lightning is that you can turn your dashboards into “opportunity boards” that show you how all open deals are doing. Now, salespeople can move deals as easily as if they were digital Post-It notes. Also, the contextual hover feature lets you learn more about a customer’s situation without going to a different site.
What Is Salesforce Commerce Cloud And The Benefits Of Using It?
With the different Salesforce commerce clouds, companies can create integrated procurement processes to align them between Marketing and Sales. Table of content
What is Salesforce Commerce Cloud?
Benefits of Salesforce Commerce Cloud
Customer acquisition integrating Pardot, Social Studio, Service Cloud, Sales Cloud and Marketing Cloud
Considerations for Optimizing Customer Engagement Using the Salesforce Ecosystem
Conclusion
What is Salesforce Commerce Cloud?With the latest generation of artificial intelligence integrated into Salesforce Commerce Cloud, you will be able to customize the order in which your products appear, predict the purchasing behaviour of your customers, suggest promotions and discounts, etc., allowing you to take intelligent actions for your strategy.
Benefits of SalesforceCommerce Cloud
Boost e-commerce with the world’s number 1 CRM.
Boost every interaction with artificial intelligence, the best applications, and tools to personalize your e-commerce. Connect the customer experience by sharing all your data in one integrated platform.
Get personalized information from each client.
Improve your conversion rate by creating personalized shopping experiences. Get more real information about each of your clients with the integration of the areas of sales, services, marketing, commerce, and IT.
Customer acquisition integrating Salesforce CloudWith the different Salesforce clouds, companies can create integrated procurement processes to align them between Marketing and Sales. This entails having an operating model and governance of marketing with sales, which requires alignment between all areas that impact the customer experience: marketing, sales, IT, marketing agencies, community management and customer service, including internal or external call centres.
Considerations for Optimizing Customer Engagement Using the Salesforce EcosystemIn order to integrate the different Salesforce clouds, the following important considerations must be considered to implement customer acquisition processes.
Data Quality: Have a data profiling on the most important customer data: Name, Surname, Telephone numbers, Emails, Addresses, and Country. Have data quality rules between all clouds to keep it at a high percent. Tools on the AppExchange can help with data quality management.
Customer Data Model across all clouds: Identification of Consumers vs Businesses using Person Accounts (B2C), Business Accounts (B2B) or other methods. The hierarchy between contacts and deals using roles and types of relationships. Standardize the different types of communications: objects to maintain multiple emails, telephones, addresses, and other contact methods. For example, you can use JavaScript in Pardot (Landing Pages), AMPScript in Cloud Pages of the Marketing Cloud, and business rules in Sales Cloud or Service Cloud to manage data quality.
Duplicate Management: Use of duplicate rules to identify duplicate contacts between clouds based on the data model. Aside from the Salesforce duplicate functionality, tools on the AppExchange can assist with handling duplicates.
Contact Preferences: have the contact preferences synchronization (Opt-In / Opt-Out) between the different clouds. Pardot and Marketing Cloud have their processes for managing Opt-In / Opt-Out in their different preference centers and their synchronization methods with the Sales Cloud.
Management of different brands: Pardot and Marketing Cloud offer the functionality of Business Units and the Sales Cloud (or Service Cloud) has options to manage divisions, record types, and hierarchies of permissions and roles of the company to be able to have in a single instance multiple brands or business units. Social Studio uses Workspaces instead of the Business Units concept. They also have to consider whether different email and Internet domains are needed for each brand or business unit.
Have a Campaign structure and governance:
Social Studio does not have a campaign concept. Marketing Cloud has the functionality to synchronize campaigns via Data Streams with Sales Cloud for use in Journeys, but it is not like the synchronization between Pardot and Sales Cloud that synchronizes everything in the campaign using “Connected Campaigns”. With the use of Sales Cloud campaigns via integration and manual processes, a campaign structure and governance can be managed using approvals, hierarchies, costs, dates, metrics, and any other type of campaign criteria. The important thing is to be able to have a campaign government for its planning, development, execution, and measurement.
Use of Campaign Members: With the use of “Campaign Members” in the Sales Cloud you can measure the “performance” of the campaigns for the contact, account, and opportunity. Even though the opportunities can have a primary campaign, multiple campaigns can be related (Campaign Influence) and thus know that other types of campaigns supported the sale.
Synchronization model between Pardot and Sales Cloud: Not every contact that enters via a campaign to Pardot must synchronize with Sales Cloud. You must have scored and nurturing rules so that when a contact is a Marketing Qualified Contact and synchronizes with Sales Cloud. Likewise, not every contact in Sales Cloud needs to sync with Pardot. Via selective permission rules between Pardot and Sales Cloud, it must be defined which contact synchronizes to Pardot.
ConclusionEnhance the capability of your marketing department and marketing team by integrating Salesforce marketing cloud into their operational processes. We can assist you with the integration of all Salesforce solutions so that you can take your business to the next level. Enjoy all the benefits of the Salesforce marketing cloud.
Author Bio:What Is The Lambda Function In Python And Why Do We Need It?
In this article, we will learn the lambda function in Python and why we need it and see some practical examples of the lambda function.
What is the lambda function in Python?Lambda Function, often known as an ‘Anonymous Function,’ is the same as a normal Python function except that it can be defined without a name. The def keyword is used to define normal functions, while the lambda keyword is used to define anonymous functions. They are, however, limited to a single line of expression. They, like regular functions, can accept several parameters.
Syntax lambda arguments: expression
This function accepts any number of inputs but only evaluates and returns one expression.
Lambda functions can be used wherever function objects are necessary.
You must remember that lambda functions are syntactically limited to a single expression.
Aside from other types of expressions in functions, it has a variety of purposes in specific domains of programming.
Why do we need a Lambda Function?
When compared to a normal Python function written using the def keyword, lambda functions require fewer lines of code. However, this is not quite true because functions defined using def can be defined in a single line. But, def functions are usually defined on more than one line.
They are typically employed when a function is required for a shorter period (temporary), often to be utilized inside another function such as filter, map, or reduce.
You can define a function and call it immediately at the end of the definition using the lambda function. This is not possible with def functions.
Simple Example of Python Lambda Function Example # input string inputString = 'TUTORIALSpoint' # converting the given input string to lowercase and reversing it # with the lambda function reverse_lower = lambda inputString: inputString.lower()[::-1] print(reverse_lower(inputString)) OutputOn execution, the above program will generate the following output −
tniopslairotut Using Lambda Function in condition checking Example # Formatting number to 2 decimal places using lambda function formatNum = lambda n: f"{n:e}" if isinstance(n, int) else f"{n:,.2f}" print("Int formatting:", formatNum(1000)) print("float formatting:", formatNum(5555.4895412)) OutputOn execution, the above program will generate the following output −
Int formatting: 1.000000e+03 float formatting: 5,555.49 What is the difference between Lambda functions and def-defined functions? Example # creating a function that returns the square root of # the number passed to it def square(x): return x*x # using lambda function that returns the square root of # the number passed lambda_square = lambda x: x*x # printing the square root of the number by passing the # random number to the above-defined square function with the def keyword print("Square of the number using the function with 'def' keyword:", square(4)) # printing the square root of the number by passing the # random number to the above lambda_square function with lambda keyword print("Square of the number using the function with 'lambda' keyword:", lambda_square(4)) OutputOn execution, the above program will generate the following output −
Square of the number using the function with 'def' keyword: 16 Square of the number using the function with 'lambda' keyword: 16As shown in the preceding example, the square() and lambda_square () functions work identically and as expected. Let’s take a closer look at the example and find out the difference between them −
Using lambda function Without Using the lambda function
Single-line statements that return some value are supported. Allows for any number of lines within a function block.
Excellent for doing small operations or data manipulations. This is useful in cases where multiple lines of code are required.
Reduces the code readability
Python lambda function Practical Uses ExampleUsing Lambda Function with List Comprehension
is_odd_list = [lambda arg=y: arg * 5 for y in range(1, 10)] # looping on each lambda function and calling the function # for getting the multiplied value for i in is_odd_list: print(i()) OutputOn execution, the above program will generate the following output −
5 10 15 20 25 30 35 40 45On each iteration of the list comprehension, a new lambda function with the default parameter y is created (where y is the current item in the iteration). Later, within the for loop, we use i() to call the same function object with the default argument and obtain the required value. As a result, is_odd_list saves a list of lambda function objects.
ExampleUsing Lambda Function with if-else conditional statements
# using lambda function to find the maximum number among both the numbers print(find_maximum(6, 3)) OutputOn execution, the above program will generate the following output −
6 ExampleUsing Lambda Function with Multiple statements
inputList = [[5,2,8],[2, 9, 12],[10, 4, 2, 7]] # sorting the given each sublist using lambda function sorted_list = lambda k: (sorted(e) for e in k) # getting the second-largest element second_largest = lambda k, p : [x[len(x)-2] for x in p(k)] output = second_largest(inputList, sorted_list) # printing the second largest element print(output) OutputOn execution, the above program will generate the following output −
[5, 9, 7] Python lambda function with filter() Example inputList = [3, 5, 10, 7, 24, 6, 1, 12, 8, 4] # getting the even numbers from the input list # using lambda and filter functions evenList = list(filter(lambda n: (n % 2 == 0), inputList)) # priting the even numbers from the input list print("Even numbers from the input list:", evenList) OutputOn execution, the above program will generate the following output −
Even numbers from the input list: [10, 24, 6, 12, 8, 4] Python lambda function with map()Python’s map() function accepts a function and a list as arguments. The function is called with a lambda function and a list, and it returns a new list containing all of the lambda-changed items returned by that function for each item.
ExampleUsing lambda and the map() functions to convert all the list elements to lowercase
# input list inputList = ['HELLO', 'TUTORIALSpoint', 'PyTHoN', 'codeS'] # converting all the input list elements to lowercase using lower() # with the lambda() and map() functions and returning the result list lowercaseList = list(map(lambda animal: animal.lower(), inputList)) # printing the resultant list print("Converting all the input list elements to lowercase:n", lowercaseList) OutputOn execution, the above program will generate the following output −
Converting all the input list elements to lowercase: ['hello', 'tutorialspoint', 'python', 'codes'] ConclusionIn this tutorial, we learned in-depth the lambda function in Python, with numerous examples. We also learned the difference between the lambda function and the def function.
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