Category Archives: Salesforce Machine Learning

Recurrent Neural Network with Long Short-Term Memory

What is a Neuron?

In Biological term, Neurons is the unit of nervous system which is responsible for flow of message in the form of electrical impulse in Human brain. So, Neuron is responsible for Human intelligence. But, in Today’s scenario it is used in Artificial Intelligence as well. Recurrent Neural Network (RNN) is a class of Artificial neural network in which connections between the neurons form a directed graph, or in simpler words, having a self-loop in the hidden layers. This helps RNNs to utilize the previous state of the hidden neurons to learn current state. Along with the current input, RNNs utilize the information they have learnt previously. Among all the neural networks, they are the only one with an internal memory. A usual RNN has a short-term memory. Because of their internal memory, RNN are able to remember things.

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Posted in Application Architecture, Salesforce AI, salesforce development, Salesforce Einstein, Salesforce Einstein, salesforce for healthcare, Salesforce Machine Learning. Tagged with , , , , .

Sales Forecasting

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The term ‘Forecasting’ by far is coined by the words ‘fore’ & ‘casting’, meaning predicting in advance… First Thoughts… are we trying to play GOD… Predicting in advance??

Oh no… That’s not it…

The best we do is act logical and have some stats to back that logic which results in a logical explanation to things.

Business Decision making has been a challenge for executives. No doubt, business decisions making needs analytical experience and that gut feeling, but that too is to be backed by some numbers and statistics. Based on the available data, forecasts simply tell what the future trend is going to be. As a result, executives have something to hold on to while taking significant business-related decisions because ‘numbers never lie’. But getting those numbers is not as easy a task as it sounds. These high-quality forecasts take a lot of time and effort due to which the demand for these forecasts is not usually met by the analytical team.

Companies based on their forecasted figures follow a certain educated guess which rarely hits on target, so as they say, ‘no forecasting model is perfect’ and it is because of the number of assumptions one must take into building a certain model. In sales institutions, the concept of demand and supply is driven by sales forecasting. Based on the historical trends and keeping in mind a ton of other factors like seasonality, cyclicity, periodicity etc., forecasting is done to reflect the best possible marketplace conditions. These factors are incorporated to include randomness and uncertainty giving the forecasting model a life-like scenario. To predict profit, one must know the number of products an organization is going to produce and sell in the next year and the price each of that product would fetch to the company. This prediction depends on the economic scenario of the coming ‘N’ number of months which will eventually decide the customer’s behavior and their buying patterns. All this cannot be known accurately beforehand while creating the forecasting plan. These are some of the assumptions one has to take while creating a forecasting model. That’s why forecasts are inaccurate, but this shouldn’t stop you from using them. Learning even a small amount using these forecasting models can give you an edge over your competition. Forecasts are not the means itself to excel in your business, they are just a benchmark for you to follow and reach a certain level.

As Oscar Wilde correctly said, “A good forecaster is not smarter than everyone else, he merely has his ignorance better organized”. There will always be gaps in these forecasting models as these are just a means for us to simplify a complicated problem. The best we can do is to judiciously use these forecasts for our advantage to back our gut instinct while taking a business decision.

Every organization uses one or the other analytical forecasting tool keeping in mind their key business factors. They plan for the next ‘N’ number of months and work accordingly to achieve and go beyond that plan. People like what is simple and that’s what our team of business consultants, technologists and data scientists did for you, they worked on the same problem statement and came up with DelphiA Sales forecasting and Predictive Analytics application for Salesforce CRM.

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Delphi intuitively uses collective organizational knowledge to analyze and predict forecasted values of opportunities. The complex algorithms predict when an opportunity in your pipeline will convert into an order and forecast’s the value of your Opportunities, Accounts and Sales Reps.

Delphi works on Machine Learning to produce quality forecasts.

Based on your Organization’s historical data Delphi analyzes the business trend and shows the forecasted output accordingly.

Delphi uses complex Machine Learning algorithms to give you a zero-hassle holistic view of your salesforce org in just a CLICK!! Delphi prioritizes your opportunity by generation opportunity score and recommends Sales Rep assignment for those Opportunities, helping you to allocate your resources and prioritize your work. It also tells when the opportunities are going to be closed and the forecasted amount that they might fetch when they are closed.

 

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Try DELPHI for free here!!

In all, Delphi is a forecasting tool which ensures that you are not left just only with your gut feeling while taking a crucial business decision. Now you do have a skilled analytical companion with you.

Thanks for reading!!

Posted in Analytics, Forecasting, Monte Carlo Simulation, Prediction, Sales, Salesforce, Salesforce AI, Salesforce Einstein, Salesforce Machine Learning. Tagged with , , , , , , , , .

Artificial Intelligence powered HR Automation in Workplace

Artificial intelligence (AI) has been changing our lives for decades, but today its presence is bigger than ever before. AI has powered HR automation. Human resource processes plays vital role in every company. Whether it’s recruitment, onboarding process, training, learning & development. Artificial Intelligence is entering the HR space, and very soon everything that can be automated, will be automated.

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HR automation is the process of enhancing the efficiency of the HR department by freeing employees from tedious manual tasks and allowing them to focus on complex tasks like decision making and strategizing. By automating standard and repetitive HR activities, organizations can reduce the cost and time they spend on manual HR planning and processing. Through HR automation, organizations can design, streamline, integrate, and deploy necessary services swiftly at a considerably lower cost. If implemented right HR Automation tools shows real effectiveness to be more productive, freeing us of our most mundane and time-consuming tasks so that HR can drive more value at work. Few of latest HR automation tools which are AI based:

  • HR Chatbots – HR chatbots are changing the way routine HR processes and functions are done. One such tool that has shown immense potential and is pegged to revolutionise the way businesses interact with customers and employees are chatbots. Chatbots are artificial intelligence programs that conduct conversations. HR Chatbots acts as Help desk.
  • E-recruitment – Another big hit automation tool for hiring process used by HR. The E-Recruitment, also known as Online Recruitment, is the process of hiring the potential candidates for the vacant job positions, using the electronic resources, specially the internet.
  • HR Analytics – Smart automation systems excel at wading through deep pools of data to pull out important insights.
  • E-Learning – Now a days through Artificial Intelligence tools, e-learning has created a big platform not only in business environment but also in education industry.
  • Virtual Onboarding – Onboarding is vital part of any HR processes and for Multinational organization companies are who are hiring hundreds of people across the globe throughout the year, creating an onboarding program that is delivered in a virtual manner is critical. It enables the new hire to access the information WHEN they need it and in a way that is engaging and easy to consume.
  • Gamification – Gamification is the usage of game-thinking and game mechanics in non-game scenarios such as business environment and processes, specifically in recruitment, training & development and motivation; in order to enhance employee engagement.

The Benefits of HR Process Automation

Through HR automation, organizations can design, streamline, integrate, and deploy necessary services swiftly at a considerably lower cost. If implemented right, HR automation can reap indispensable benefits. Here are the benefits of HR automation:

  • Improve productivity due to quick processing and data sharing.
  • Reduce employee turnover due to heightened employee engagement.
  • Slash down storage and printing costs associated with paper-based processing.
  • Stay free from compliance risk or policy violations.
  • Enhance organizational growth through efficient hiring at optimal operational cost.
  • Drop in data entry errors and misplaced/lost documents.
  • Make intelligent business decisions with insightful reports.
  • Collaborate with other stakeholders to hire, train, and retain skilled employees.
  • More time to analyse HR data to make intelligent business decisions.

 

Posted in Agile, AGILE Tools, Artificial Intelligence, Machine Learning, Process Change Management, Salesforce, Salesforce AI, Salesforce Einstein, Salesforce Einstein, Salesforce Machine Learning, Team Collaboration.

The Future For Analytics, Data And What It Can Do For Businesses?

Times are changing; this has been a term in use since the rapid progress of humanity since the 18th century. Today, it can be used even on a daily basis. Technology remains the cornerstone through which humanity evolves, it is now embedded in every single part of our lives today and imagining a future without it would be quite challenging. This is why going forward, there is always going to be a discussion about its impact and how it will change as time comes.

Data is something which humans have been able to collect very efficiently over the past few decades. In recent times, the collection process and speeds have ramped up significantly. This also means that, in the world of analytics, anything and everything will have correlations. Here are some ways data will impact us:

  • Transparency: With more and more amounts of data being made available to the public, it is only a matter of time before we use it for every single task in and around the house. Today the insights provided by these numbers and metrics can only be picked up by data scientists, but the future is not too far away when even the common man would be able to infer from the said data. Its widespread adoption for all spheres of industry and business will become even more rapid as time progresses.
  • Marketing: There will be an increased personalization of marketing. As data flows, it allows companies to select and narrow audiences. With an increase in the sheer numbers and information, it would be a matter of time before marketing strategies will become highly targeted and become personal. This also might seem like an invasion of privacy, and as things go about, there will be solutions which address this issue as it arises.
  • Collaborative Vertical Solutions: As we head into the imagination and digital age, there will be an increase in digital transformation. Collaborative tools will improve through the sheer insight that data can provide. Templates, workflows, and platforms such as Salesforce Service Cloud specifically created for highly regulated industries such as finance and healthcare can contribute well to the success of businesses in a world that is connected.
  • Proactive customer service or reactive: This dynamic change comes as a result of the influx of information through analytics. Things such as connected devices, AI and more; companies and businesses can respond and resolve problems at the origin. This can be done without the consumer even acknowledging the issue. The impact it can have in consumer satisfaction is simply unparalleled.
  • Responsible AI: Ethics and values are all clauses which are under intense scrutiny when it comes to artificial intelligence. There is a question of accountability when it comes to AI creation and usage. There is an onus on the current developers and data scientists to create AI which is responsible. There is forced accountability being put on developers, thus there is an assurance that systems created henceforth will be responsible.

These are just small glimpses into what the future holds for data analytics and AI.

Posted in Artificial Intelligence, Sales Cloud, Salesforce AI, Salesforce cloud Implementation, Salesforce Einstein, Service Cloud.

Machine Learning with Python

Machine Learning and Artificial Intelligence are considered as an integral part of the 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 learnt 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 user community. Artificial intelligence is heavily dependent on machine learning,

Machine Learning and AI confusion

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.   Continue reading

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 , , .