As a IT Consultant Functional CBP, its very critical for you to understand developing brand strategy is extremely critical. The most important asset your company has is its brand. Quite simply, it drives the direction of your business. So you should definitely have a well thought out brand strategy in place.
Increasing competition in business develops similar products with good quality from different manufacturers. But only an effective, innovative and Agile Supply Chain & planning can make your business and products more popular.
For your profession as IT Consultant Functional CBP it becomes your responsibility to stay connected with like-minded supporting industry experts who can guide and help you deal with your day to day work issues.
A Comparative Marketing Strategy Analysis Between Starbucks and Caffe Nero
If you are entrepreneurial in nature owning a business is very exciting adventure. It can also be the most difficult thing for you to get into if you are not prepared.
According to John McCarthy, who is the father of Artificial Intelligence, an AI is "The science and designing of making intelligent machines, especially intelligent PC programs".
Artificial intelligence is a way of making a computer robot or a software think intelligently same as an intelligent human thinks. Artificial Intelligence (AI) is the concept of having machines "think like humans".
AI has a huge effect on your life. Whether you are aware or not, it has already influenced your life style and it is very much likely to grow in coming years.
Here are some examples of AI that you are already using in your daily life:
• Your personal assistant Siri - It is an intelligent digital personal assistant on various platform (Windows, Android, and iOS). It provides you an assistance whenever you ask for it using your voice.
• Smart cars - Google's self-driving car, and Tesla's "auto-pilot" feature are two examples of Artificial Intelligence.
• Recommended products or Purchase prediction - Large retailers like Amazon, recommend you the products, send coupons to you, offer discounts, target advertisements on the basis of the shopping you earlier had by a predictive analytics algorithm.
• Music and movie recommendation services - Pandora, and Netflix recommend music and movies based on the interest you've expressed and judgements you have made in the past.
Other simple examples of AI influencing our daily life are:
- Facebook provides recommended photo tags, using face recognition.
- Amazon provides recommended products, using machine learning algorithms.
- Waze (a GPS and maps app) optimal routes, all at the click of a button.
- Spotify knows my music preferences and curates personalized playlists for me.
As per Marc Benioff, AI is going to impact corporate world, employees will be faster, smarter and more productive. It will learn from the data. Ultimately, it will understand what customers want before even they know and it could be a game-changer in the CRM industry.
Salesforce already bought productivity, and machine learning startups RelatedIQ, Metamind, and Tempo AI in 2014.
AI (Artificial Intelligence) in salesforce is not about time-travelling robots trying to kill us, or evil machines using humans as batteries in giant factories. Here we are not talking about some summer blockbusters, we are talking about the salesforce AI which will make your daily experience smarter, by embedding daily predictive intelligence into your apps.
So, what is AI?
AI is not killer robots; it is killer technology.
Artificial Intelligence (AI) is the concept of having machines "think like humans" - in other words, perform tasks like reasoning, planning, learning, and understanding language.
Customer focused AI: Salesforce Einstein
Salesforce is focusing on creating a platform for solving the customer problems across Sales, Service, Marketing and IT in a completely new way by using Salesforce Einstein.
Salesforce Einstein is built into the core of the Salesforce Platform. It enables anyone to use clicks or code to build AI-powered apps.
With Salesforce Einstein, we can have answer of these type of questions:
- Are you sure that you are servicing your customers by the right client?
- Are you sure that your customers are getting services on the right channel?
- Is it correct to say that you are offering the right item to the right customer at the right time?
- Is it correct to say that you are using the right channel for marketing your products at perfect time with best substance?
Salesforce Einstein is your data scientist
Einstein is like having your own data scientist dedicated to bringing AI to every customer relationship. It learns from all your data - CRM data, email, calendar, social, ERP, and IoT - and delivers predictions and recommendations in context of what you're trying to do.
AI has the ability to transform CRM using Salesforce Einstein
- Sales people can spend more time in visiting customers, not in entering data in CRM.
- Sales people can now better understand the customer requirement and when they need it.
- Sales people can close deals faster by predicting the next step for every customer.
- A service agent could suggest a solution to the customer even before he asked for it.
- Service agent can offer cross-sell at the right time to the right customer.
- Marketing user can easily reach to the right customer at the right time.
- Marketing user know who could be the best audience for each campaign.
- He can easily identify the customer requirement so he delivers the perfect content to every customer.
Salesforce Einstein enables everyone to discover new ways, predict outcomes so help in decision making, recommend next steps, and automates most of your activities so that you can spend most of your time in building strong relationship with customers rather than making entries in system.
What will AI give me that I didn't already have?
Predictive scoring -Predictive lead scoring gives each sales lead a score representing the likelihood it will convert into an opportunity. You also get the reasons
behind the score - for instance the lead source, the industry, or some other factor is an especially strong indicator that a lead will or won't convert.
Forecasting -AI can also be used to predict the future value of something, like a stock portfolio or a real estate investment. If you're a sales manager, AI can predict your quarterly bookings and let you know ahead of time whether or not your team is on track to meet its quota.
Recommendations - Anyone who shops online knows that AI makes suggestions for retail purchases, but it can also make smart recommendations for any other product or service category from business software to tax consulting to cargo containers. And AI can also recommend things other than products - for instance, which white paper you should email a prospect in order to optimize your chance to close a deal.
Who can use AI in the enterprise
Anyone in organization can easily use AI to analyze their data, predict and plan next steps, and automate their tasks and decisions. With Einstein's comprehensive AI for CRM:
• Sales can anticipate next opportunities and exceed customer expectations by knowing what a customer needs before the customer does
• Service can deliver proactive service by anticipating cases and resolving issues before they become problems
• Marketing can create predictive journeys and personalize customer experiences like never before
• IT can embed intelligence everywhere and create smarter apps for employees and customers
What is Machine Learning
Machine learning is the core driver of AI. It's the concept of having computers learn from data with minimal programming.
3 Surprising Ways Artificial Intelligence Can Push Marketing and Advertising to the Next Level
With the support of our professional business network, you get the opportunity to exchange experience and knowledge at a top professional level, and to strengthen and develop your own skills within your management and specialist areas.
Through business relationships and experience sharing in confidential settings for IT Consultant Functional CBP, we strive to create personal and business value for all our network peers.
Hollywood paints a grim picture of a future populated by intelligent machines. Terminator; A Space Odyssey, The Matrix and countless other films show us that machines are angry, they’re evil and, if given the opportunity, they will not hesitate to overthrow the human race. Films like these serve as cautionary tales about what could happen if machines gain consciousness (or some semblance of). But in order for that to happen humans need to teach machines to think for themselves. This may sound like science fiction but it’s an actual discipline known as machine learning.
Still in its infancy, machine learning systems are being applied to everything from filtering spam emails, to suggesting the next series to binge-watch and even matching up folks looking for love.
For digital marketers, machine learning may be especially helpful in getting products or services in front of the right prospects, rather than blanket-marketing to everyone and adding to the constant noise that is modern advertising. Machine learning will also be key to predicting customer churn and attribution: two thorns in many digital marketers’ sides.
Despite machine learning’s positive impact on the digital marketing field, there are questions about job security and ethics that cannot be swept under the rug. Will marketing become so automated that professional marketers become obsolete? Is there potential for machine learning systems to do harm, whether by targeting vulnerable prospects or manipulating people’s emotions?
These aren’t just rhetorical questions. They get to the heart of what the future of marketing will look like — and what role marketers will play in it.
What is Machine Learning?
You can think of machine learning as using a computer or mathematics to make predictions or see patterns in data. At the end of the day, you’re really just trying to either predict something or see patterns, and then you’re just using the fact that a computer is really fast at calculating.
You may not know it, but you likely interact with machine learning systems on a daily basis. Have you ever been sucked into a Netflix wormhole prompted by recommended titles? Or used Facebook’s facial recognition tool when uploading and tagging an image? These are both examples of machine learning in action. They use the data you input (by rating shows, tagging friends, etc.) to produce better and more accurate suggestions over time.
Other examples of machine learning include spell check, spam filtering even internet dating - yes, machine learning has made its way into the love lives of many, matching up singles using complicated algorithms that take into consideration personality traits and interests.
How Machine Learning Works?
While it may seem like witchcraft to the layperson, running in the background of every machine learning system we encounter is a human-built machine that would have gone through countless iterations to develop.
Facebook’s facial recognition tool, which can recognize your face with 98% accuracy, took several years of research and development to produce what is regarded as cutting-edge machine learning.
So how exactly does machine learning work? Spoiler alert: it’s complicated. So without going into too much detail, here’s an introduction to machine learning, starting with the two basic techniques.
Supervised learning systems rely upon humans to label the incoming data - at least to begin with - in order for the systems to better predict how to classify future input data. Gmail’s spam filter is a great example of this. When you label incoming mail as either spam or not spam, you’re not only cleaning up your inbox, you’re also training Gmail’s filter (a machine learning system) to identify what you consider to be spam (or not spam) in the future.
According to Tommy, this type of machine learning can be likened to the relationship between a parent and a young child. When a child does something positive they’re rewarded. Likewise, when “[a machine] gets it right - like it makes a good prediction - you kind of give it a little pat on the back and you say good job.”Like any child (or person for that matter), the system ends up trying to maximize the positive reinforcement, thus getting better and better at predicting.
Unsupervised learning systems use unlabeled incoming data, which is then organized into clusters based on similarities and differences in the data. Whereas supervised learning relies upon environmental feedback, unsupervised learning has no environmental feedback.
The Power of Machine Learning
A lot of what machine learning can do is yet to be explored, but the main benefit is its ability to wade through and sort data far more quickly and efficiently than any human could, no matter how clever. Tommy is currently experimenting with an unsupervised learning system that clusters landing pages with similar features. Whereas one person could go through a few hundred pages in a day, this model can run through 300,000 pages in 20 minutes.
Machine Learning and the Digital Marketer
As data becomes the foundation for more and more marketing decisions, digital marketers have been tasked with sorting through an unprecedented amount of data. This process usually involves hours of digging through analytics, collecting data points from marketing campaigns that span several months. And while focusing on data analysis and post-mortems is incredibly valuable, doing so takes a significant amount of time and resources away from future marketing initiatives.
As advancements in technology scale exponentially, the divide between teams that do and those that don’t will become more apparent. Those that don’t evolve will stumble and those that embrace data will grow — this is where machine learning can help.
That being said, machine learning isn’t something digital marketers can implement themselves after reading a quick tutorial. It’s more comparable to having a Ferrari in your driveway when you don’t know how to drive standard or maybe you can’t even drive at all.
Until the day when implementing a machine learning system is just a YouTube video away, digital marketers could benefit from keeping a close eye on the companies that are incorporating machine learning into their products, and assessing whether they can help with their department’s pain points. So how are marketers currently implementing machine learning to make decisions based on data rather than gut instinct? There are many many new niches in marketing that are becoming more automated.
Networking has always been considered a powerful tool for improving business prospects, advancing a career, and developing ideas. Other than some brief, structured events, networking has been mostly informal and inexpensive in comparison to cost they otherwise spend on different channels. But membership is growing in many formal, long-term networking groups, and so is the price tag.
Our groups are not groups for generating sales leads, nor are they places where individuals can drop-in to gain quick advice on an immediate challenge. Members also sign a confidentiality agreement and benefits from the guided mentoring to help each other.
These groups include an experienced facilitator and use a structured discussion method to ensure appropriate participation.