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.
Category Archives: Learn Salesforce
Have you ever sat wondering if you had followed up with everyone you were supposed to talk to today? Or managed a team and wondered if they we’re following through? These questions plague most sales driven businesses daily, but to Addiction Treatment Centers this could mean that someone’s life is on the line.
It is extremely notable that the amount of customer data is expanding with each passing moment. One can’t simply physically investigate information and know each pattern or test numerous theories. Taking care of and dealing with this colossal data is no less than a challenge which stresses on the significance of analytics further. Acknowledging this need to be of immense importance, Salesforce recently uncovered its most progressive CRM platform called Einstein Analytics. The analytics has opened ways to a few stunning elements for CRM clients, empowering them to utilize advance analytics powered by Artificial Intelligence.
The proportion of data that a business is developing at a rapid pace, and recognizing each pattern and relationship, and to test each speculation, utilizing traditional manual situated techniques is currently beside unimaginable but Einstein analytics resolves client’s dilemma by stimulatingsales, improving client service and optimizing their marketing campaigns. Overall, the Insights offered by Einstein allow executives to make decision 38 % faster which data scientists took days or even weeks to achieve.
When the user enters a query, the tool uses Artificial Intelligence (AI) to throw logically important outcomes, pinpointing what is occurring, the purpose for such an event, and expectation on what is probably going to occur next. Einstein utilizes machine learning to identify factual patterns and designs, and convey the same to users in a significantly more far reaching, reliable and contrasted way than what data scientists can ever give. The engine consequently examines billions of data combinations, and prescribes the best game-plan, assessing the in all probability situation, and the upsides and downsides of each possible action. Users may penetrate down to the most state of the art information, reveal bits of knowledge, team up and take instant action from any device.
Salesforce Einstein Analytics goes above and beyond, and is a direction framework, which utilizes past information and mediates it with current circumstances and future conceivable outcomes, to anticipate patterns.
Consider the case of a sales executive out to meet his quarterly numbers. When he uses Salesforce Einstein Analytics, he gets comprehensive and in-depth insights on a customer, competitor and pipeline data, and recommendations – Let us suppose Mark is his customer and he had set up a meeting with Mark to close the sales 3 days earlier. He assumes Mark to be loyal but Mark is talking to the competitors very often. Taking him for a coffee may be a good idea or he needs to bring in three or so managers to close the deal. Here is what you should be doing and so on.
Using Einstein Dynamics, each Sales executive gets a dynamic dashboard, complete with all the required functionalities, for example, activity tracking, account whitespace, benchmarking, and more, offering them powerful insights required to perform successfully and close more deals. Having identified a new opportunity or uncovered a trend, the user may create a task, change a close date or share insights on any device, all seamlessly from a single pane, without bothering with spreadsheets and other tools.
Einstein Discovery is a tool launched concurrently with Einstein Analytics, and superbly supplements the latter. It just takes couple of minutes for Einstein Discovery to get outcome from insights from millions of data combinations. It makes precise predictions by instantly analyzing massive amounts of data and mining out crucial patterns. “Further, Einstein Discovery generates explanations, answers, and recommendations that are easy to understand and implement by the users. This is done with slide presentations that are generated automatically, containing visualizations and key points.” (http://www.algoworks.com/blog/salesforce-einstein-analytics/, 2017)
To learn new exciting analytics, salesforce offers a package of 12 modules – “Analytics trailblazers” fun way to learn salesforce. With these smart analytics, Independent software vendors (ISVs) can now create apps using analytics app designer, visual data preparation, and outside data connectors to build their own custom analytics apps on the Salesforce platform, ensuring they have all the relevant metrics they require, and omitting any superfluous details for their business. AppExchange has around 20 analytics apps from ISVs
So, this is how you can become a data scientist in a fun and smart way!!!!!
Since a long time, engineers have been striving to make machines perform tasks that human beings do; which has led to birth of the field of machine learning. Understanding the language humans speak, constitutes a vital part of this field. This field of computer science which deals with human-machine interactions, especially concerned with computer programs which can process natural language efficiently, is known as Natural Language Processing, mostly referred to by the abbreviation NLP.
NLP sits at the intersection of computer science, artificial intelligence and computational linguistics. “By utilizing Natural Language Processing algorithms, developers can organize and structure textual data to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.” (En.wikipedia.org, 2017)
Natural Language Processing is characterized as a hard problem in computer science since human language is rarely precise, or plainly spoken. To understand human language, one must not only understand the words but their meaning & context and how they interconnect to form meaning. The vagueness and ambiguous nature of human language makes it difficult to learn for computers while being easy to learn for humans.
Components of NLP
There are two components of NLP which are listed as follows:
- Natural Language Understanding(NLU)
This includes understanding the different aspects of the language and mapping the input text in natural language to useful representations. This is the harder of the two components since this section has to deal with the ambiguity & complexity of the language. There are mainly three levels of ambiguity which are as follows:
- Word-level or Lexical Ambiguity
- Syntax Level or Parsing Ambiguity
- Referential Ambiguity
- Natural Language Generation(NLG)
As evident from the name, NLG is the process of producing or generating meaningful phrases and sentences in the form of natural language. It involves text planning, sentence planning and text realization.
Syntax: It refers to arrangement of words which form a sentence. It also involves determination of structural role of each word in the sentence.
Phonology: It is the study of organizing sounds systematically.
Morphology: It is study of how words are constructed using primitive meaningful units.
Semantics: It deals with the meaning of words and how they can be joined/combined to form meaningful sentences.
Discourse: This determines how the immediately preceding sentence can affect the interpretation of the next sentence.
Pragmatics: This deals with how the interpretation of a sentence changes according to the situation.
What can developers use NLP algorithms for?
- Summarizing blocks of text to extract the meaningful information from the given text, ignoring the remaining non-relevant text
- Understanding the input and generating the output in Chatbots
- Deriving the sentiment of a piece of text using Sentiment analysis
- Break up large text into simpler tokens such as sentences or words
Some Open Source NLP Libraries
- Apache OpenNLP
It is a Java based machine learning toolkit provided by Apache, that supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection and coreference resolution. OpenNLP also includes maximum entropy and perceptron based machine learning. It provides built-in Java classes for each functionality as well a command line interface for testing the pre-built agents.
- Natural Language Toolkit(NLTK)
It is a platform for building Python programs to read and process human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.
- Stanford CoreNLP
Stanford CoreNLP provides a set of human language technology tools. It can give the base forms of words, their parts of speech, mark up the structure of sentences in terms of phrases and syntactic dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract or open-class relations between entity mentions, get the quotes people said, etc.
MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. Apart from classification, MALLET includes tools for sequence tagging for applications such as named-entity extraction from text. Algorithms include Hidden Markov Models, Maximum Entropy Markov Models, and Conditional Random Fields.
These are few of the many open source libraries and toolkits available for development on Natural Language Processing which can be utilized by developers in their applications.
In conclusion, Natural Language Processing is an important part of the artificial intelligence field and needs to be given importance if someone wants to master the trade of Machine Learning or Artificial Intelligence.
Customer data is the soul of an organization. Quality data is priceless and acts as a driver of high productivity and good decision making. Managing Quantity and Quality parallelly has always been a challenge. Same is the case with data now-a days, as the data volumes grew rapidly, the data quality went down, posing a serious challenge in maintaining the sanctity of databases. This challenge of avoiding data duplicity and keeping the orgs clean is faced by almost every company and although it may seem like a trivial issue, the impact these duplicates have on businesses cannot be looked down upon.
All the marketing and service activities revolve around the customer database that a company has.
Duplicates prevailing in a system means:
- Increased cost for marketing campaigns.
- Bombarding the same customers with same information multiple times, eventually degrading the brand image in the minds of customers.
- Reduced Operational Efficiency.
- Confusion among the sales reps while processing requests.
- Bad service experience encountered by the clients.
- Bad Data leads to Badly informed business decisions.
- Increased challenges in maintaining the database or moving the current system to a new one.
What causes these duplicates:
- Human error – Sales Rep entering duplicates
- Multiple sources capturing the same information
- No instrument to restrict duplicates from entering the system
Some pointers on how you can keep your Salesforce org clean:
- Think of all the ways the customer data enters your CRM system: Manual customer data Entry by Sales Reps, bulk customer data upload, Automated Lead Sources like web to lead etc. and ensure that you have them all covered to avoid duplicates entering your CRM system.
- Keep a mandatory field on each Object which handles your customer data, one which is relevant to your business operation like a phone number or an email on Lead. For Standard fields have your tech. team enable Duplicate Rules & Matching rules to avoid duplicates formation and for Custom fields check ‘do not allow duplicates’ so that Salesforce won’t accept duplicity against that field on the Object and sales rep won’t be able to create duplicate records.
- If your mandatory field is a picklist field, ensure that you enable restricted picklist for that field as Salesforce allow values coming in from external systems other than the field picklist values also.
- Monitor the data that your CRM system is getting from multiple sources to keep an eye if anything wrong is happening around your data so to handle the situation in time. Generally, companies don’t realize this until they have loads of dirty data existing in their CRM systems and then try to recuperate from the damage.
- Ensure that you have clean data while uploading data in bulk into your system.
- Define all your lead sources clearly and distinctly, this can avoid a lot of confusion when leads start to pour-in from multiple channels.
- Make sure your Sales Reps fill all the necessary customer details in the form and instruct them not to save a half-filled form.
- Cloning the records should not be a general habit of the team, this leads to human error of saving the cloned records without making relevant changes and thus creating duplicates. You can even disable the Clone button from the layout if you want to.
- Use standard Salesforce Reports for tracking duplicates entering your system (if any) are essential to your business or not.
- With large volumes of data coming in your system, some duplicates are anyway bound to enter your database, one way or the other. Use a deduplication tool, this can really make your life easy by keeping your Salesforce org clean of duplicates.
Check out Advitya – The Perfect Duplicate management app for Salesforce. Advitya solves all your data duplicity problems and can save you time and effort in keeping your org clean.
Keeping your Salesforce Org clean is not a one-man job or a onetime activity. It calls for a collective effort and is an ongoing process, you strive to keep your Database clean and perfect which eventually gives you returns in terms of smooth Operations and Profits.
All the best in keeping your Org. clean!!
Workflow is an automated process to implement the business logic, that evaluates the record at the time of creation or update. It is one of the most powerful tools of Salesforce.
Workflow becomes indispensable to organizations for maximizing efficiency in their business processes. It allows tracking of processes.
What is REST: –
REST is Representational State Transfer which helps two systems to talk to each other with a common programmatic language. Rest is a successful replacement for HTTP.
Integration in Salesforce can be done in the following steps: –
2) Call out.
3) Web services.
What is an EventBrite?
An EventBrite is an app which syncs event data into salesforce. It is a free app which help the event organizers to import data from their event to the salesforce. Event data is created through ticketing platform is synced to salesforce contact list and promo code can be sent back to the Eventbrite.
Contact Roles in Salesforce.
I observed in many salesforce communities that lot of salesforce people facing challenges in associating one contact with multiple accounts, cases, Contracts or Opportunities.
In salesforce architecture Contact has a Standard Account field, which means a Contact can be associate with on account.
However we can create few more Custom lookup fields on Contact to associate few more accounts, but that will not be a good approach to achieve this requirement if you want to associate one contact to n number of accounts, cases or Opportunities.
Macros are very powerful and interesting component of salesforce. Macros are like set of computer instructions that executes to automate some task that task can be to assign value in a field, to automatically send an email, to update any field, selecting an email template and more. All instructions that we create using macros get execute on a single click. Macros are mostly use with case feed layout basically to update case and to send quick response to user about any update of case raised by that user.