Predictive Analytics

The success of any organization depends on its vision and its ability to shape the future course of action with unmatched accuracy and timeliness. Wouldn’t it be great if this prediction part is made efficient, easy and streamlined for your employees which could ultimately lead to better fortunes!!

Predictive Analytics is one such technique that holds the potential to rise your business up-ahead from the market competition. Continue reading

Posted in Artificial Intelligence, Machine Learning. Tagged with , , , .

Machine Learning with Python

Machine Learning and Artificial Intelligence are considered as an integral part of future technologies.

Artificial Intelligence is an area focused on developing intelligent machines that work and react like humans. To achieve this Artificial Intelligence considers all the traits that can help achieve the feat, these traits include perception, learning, and planning. Machine learning on the other hand focuses on development of programs in such a way that systems can access data and use it to learn for themselves Artificial Intelligence focuses on making machines smart i.e. react as the situation demands whereas machine learning is based on providing machines access to data, making them learn themselves which makes their decisions learned rather than smart.

For purview of our topic lets focus on Machine Learning now. Continue reading

Posted in Artificial Intelligence, Machine Learning, Salesforce AI, Salesforce Machine Learning. Tagged with , , , .

Machine Learning vs. Artificial Intelligence-The identical twins or are they really?

Since you are reading this, I assume you are aware of, or at least have heard about Machine Learning and Artificial Intelligence. Being two of the hottest buzzwords in the industry right now, these are often used interchangeably leading to some confusion. However, these two have different meanings and applications. The two terms are very strongly related though, as they share a containership relationship between them where the former is a subset of the later. Lets dive deep into these topics and try to find the reason for this confusion and related solutions.

Why this confusion?

The main culprit behind this confusion is the interchangeable use of these two terms and the limited knowledge of the subject among the developer as well as the user community. Artificial intelligence is heavily dependent on machine learning,

Machine Learning

Machine Learning and AI confusion

which has led to the perception that both terms refer to the same thing. This confusion has spread like wildfire in the industry and only people who are experts in this field, know the clear distinction among these terms.

Artificial Intelligence-The Big Brother

Artificial Intelligence is the intelligence demonstrated by machines which emulates a human like thinking and behavior, allowing them to make their own decisions in real life situations. Going by the computer science definition, AI is referred to as the study of intelligent agents, which are devices that perceive their environment and take actions accordingly in order to maximum fulfillment of their goals. These agents mimic certain cognitive functions, which humans relate with the human mind, like problem solving and learning. AI, traditionally, attempts to solve problems such as Reasoning, Knowledge Representation, Learning, Planning, Natural Language Processing etc. Generating an intelligent agent which can think like humans is the long-term goal since it makes use of all the former techniques mentioned.

Now, there are two ways in which the intelligent agents can achieve this, the first one being by using a set of if/else

Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning

statements which provide the answer for each problem statement. This is a very orthodox, ineffective and tedious way of doing this. Another way is to use Machine Learning which is more flexible & dynamic, being able to learn from the data it processes and improve upon the results incrementally in real time.

Due to disagreements on any established paradigm to be used in machine learning, there is still no fixed approach which works effectively. Some of the popular approaches are as follows:

  • Cybernetics and brain simulation
  • Cognitive Simulation
  • Statistical Learning

Machine Learning-An Overview

Machine Learning is a field of artificial intelligence which makes use of statistical techniques and functions to give a computer the ability to learn itself from the provided data, without the need of any explicit programming. This act of the machine system learning by itself progressively improve its performance for a specific task. The heart of machine learning models lies in the dataset which is being used to train the model as well as the algorithms which operate on the data. These algorithms learn from the provide data and make predictions, overcoming static and fixed programming instructions by employing data-driven decisions through a model which it builds from the training data given.

There are two learning types in machine learning- supervised learning and unsupervised learning. The former takes a well labeled data set to operate upon and makes deductions based on it. The later one takes in raw, unlabeled data and finds patterns in the data based on which new data predictions are made.

Closely related to the field of Computational Statistics, machine learning also focuses on making predictions though the use of computers. Mathematical optimization is also tightly coupled with ML since it provides the theory, methods and application domains to the field. Machine Learning is also used in the field of Data Analytics to generate complex models and algorithms which lend themselves to prediction; commercially this is referred to as Predictive Analytics. These models help data scientists, researchers, engineers and experts, to produce reliable results, repeatedly and uncover hidden insights within the data

There are N number of algorithms present which can be utilized to solve your machine learning problem. Each has its own strengths and weaknesses and its fit depends completely on the dataset and the use case. Some of the popular algorithms/methods being used are Decision tree learning, Artificial Neural networks, Inductive Logic Programming, Deep Learning, Bayesian Networks and many more.

Conclusion

Well, this confusion, however small it is, must be cleared since this might lead to problems down the line as the scale of artificial intelligence and machine learning increases. In large and critical implementations, using them interchangeable should be unacceptable and the community should be made more aware about these concepts and their differences. You can reach us for all your AI needs since we, being the AI & ML experts, can help you design a custom solution for your machine learning problem.

References

Posted in Artificial Intelligence, Machine Learning, Salesforce AI, Salesforce Einstein, Salesforce Machine Learning. Tagged with , , , .

Use TransmogrifAI to jumpstart Salesforce machine learning

Salesforce released TransmogrifAI, a machine learning library written in Scala that runs on top of Spark. This can be potentially deployed on any cloud such as Heroku/PostgreSQL platform. What all is involved in TransmogrifAI?

  • Language: Scala
  • Underlying engine: Apache Spark data processing engine
  • Deployment platform: A standalone local machine or cloud platform like Heroku

Let us explore a bit more about these new players in the scene and whether they will align with our need to build robust machine learning models. The entry barrier to using the TransmogrifAI library is likely to be the new tech stack that a typical Salesforce developer needs to scale up to. Continue reading

Posted in Salesforce AI, Salesforce Einstein, Salesforce Machine Learning. Tagged with , , .

Salesforce DevOps (CI/CD) InfoGraphic

Start your journey to Salesforce DevOps with this infographic that captures introduction to different aspects of DevOps in Salesforce platform. It compares different approaches to Salesforce DevOps and finally has a deep-dive on Salesforce DX as well. Be it a free tool or a commercial one, do exercise caution while choosing any of the approaches – since it takes 6-12 months investment to stabilize a DevOps approach.

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What is driving the need for Salesforce DevOps?

Salesforce DevOps is picking up steam with the recent focus on source driven development on Salesforce platform, particularly through increasing adoption of continuous integration and continuous delivery (CI/CD) using Salesforce DX. What contributed to this shift from ‘Changesets’ and ANT migration tool approaches? Following are some crucial factors:

Movement to Lightning

The shift to Lightning experience meant lot more control to developers. There is a lot more code to be written and hence to be preserved for future reference. While the approach of solving using configurations and settings is still predominant in a CRM implementation, there is definitely lot more custom code required if one is building a partner or customer community portal. Continue reading

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Playing with the Sentiments-a blog on Sentiment Analysis

People have always had an interest in what other people think, or what opinion they hold. Since the inception of the internet, increasing numbers of people are using websites and social media platform for expressing their opinion. Due to platforms such as Facebook, Twitter etc., it has become feasible to analyze and extract the public opinion on a certain topic, news story, product, or brand. Opinions that are mined from such services can be valuable. Data mined from these sources can be analyzed and presented accordingly to easily identify the online mood (positive, negative or neutral). This allows individuals or business to be proactive as opposed to reactive when a negative conversational thread is emerging. Alternatively, positive sentiments can be leveraged to identify product advocates as well to shape the business strategy by seeing the parts of the strategy that are working.

Salesforce Sentiment Analysis

Sentiment Analysis

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Posted in Learn Salesforce, salesforce certified, salesforce consultant, Salesforce Einstein, Uncategorized. Tagged with , , , .

7 Ways Technology is Changing the Face of Treatment Centers

salesforce technology

Technology has provided healthcare many breakthroughs in recent years and Salesforce has been at the forefront of that. So if this is the case why are most treatment centers still so far behind. Everyday people are scouring the internet trying to find a great place to recover from the diseases of Mental Health and Addiction. It’s estimated that over 20 million Americans over the age of 12 have an addiction. Many treatment centers lack the basic tools needed to be able to serve these patients with the care they need. As a consulting partner for Salesforce we have seen technology changing the face of Treatment Centers. Technology is providing treatment centers with the advanced tools needed to navigate the ever changing landscape. We’re going to outline 7 ways that technology is changing the face of treatment centers.

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1. The addition of a CRM or Patient Relationship Management platform to the contact center is the number one way to better serving your patients. We have seen organizations taking notes on note pads or using excel to track patient calls. This is a major problem, in my experience working in a very busy Contact Center even your very best admissions representative will miss a follow up. Also in my experience, as a recovering alcoholic with almost 7 years of sobriety, I know that when I was ready to get help patience was not one of my strongest areas. So timely follow ups and the ability to take a call from beginning to end without hanging up the phone has resulted in a 35% increase in patient admissions based on case studies we have conducted.
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Posted in Agile, Salesforce, salesforce administrator, Salesforce Challenges, Salesforce cloud Implementation, salesforce community implementation, salesforce consultant, salesforce customization, salesforce development, salesforce for healthcare, salesforce for medical clinics, salesforce for NonProfit, salesforce for small business, salesforce integration, Salesforce Support Packages, salesforce Tips & Tricks, Salesforce.com. Tagged with , , , , , , , .

Salesforce for Wineries: The Customized Winery and Spirits CRM

Any business trying to grow and increase their client base needs a customer relationship management (CRM) software program. It’s just a simple fact that even the smallest businesses can’t avoid for very long before keeping track of clients starts to consume the entire day. Wineries are no exception.

The wine industry has a tendency to be reactive with their wine sales after they build a client relationship. The wine industry needs to have a CRM that empowers wineries to take action proactively in their marketing efforts. Salesforce has this ability through the use of marketing automation, data analytics, and AI. Salesforce can use your winery’s historical data of purchases made by clients to predict what products that client may be interested in. This allows wineries to target specific customers with specific content.

Salesforce For Wineries

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Thinking About Marketing Automation For Higher Ed?

How can we put our name in front of someone right when they are thinking about committing to a brand? This is the question we ask ourselves in marketing. The best marketing campaigns deliver a coordinated high-quality experience across every channel and with every prospect interaction. Marketing automation tools from Salesforce bolster an organization’s ability to deliver this syndicated message. Higher education organizations are thinking more and more about how to attract news students through marketing as the space becomes increasingly competitive. Marketing automation is a dream for any organization. Salesforce has made it possible for higher education institutions, and Mirketa Inc is able to implement that possibility with you.

Marketing Automation Tools

The Salesforce marketing cloud unlocks incredible recruiting possibilities for the higher education space. At a high level, marketing cloud uses data collected about prospects to then market back to prospects in the way that makes the most sense to each prospect and higher education organization. Salesforce has the ability for organizations to track the activity of prospective students on their website. Let’s say for example that a recruiter has met a bright student at a recruiting event at a local high school and this student agrees to give the recruiter a few pieces of administrative data like their name and email. The recruiter would then go back and enter this student as a prospective student as a prospect in their Salesforce. From that moment forward the recruiter would be able to see what sort of things the student was clicking on while visiting the school website, what they were searching, and how often they came back to visit.

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