As a Business Development AVP Tanjon Pagar, 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 Business Network & planning can make your business and products more popular.
For your profession as Business Development AVP Tanjon Pagar 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.
How To Create A Brand Strategy: Unique Digitizing Services
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.
In this article we're going to discuss the tricky aspect of marketing strategy when applying for a patent.
Getting a patent is a tricky process under normal circumstances. Under laws of the United States a company or person is entitled to a patent unless the invention was on sale in the country for more than one year prior to the application date of the patent. This applies to both sales and offers of sales. Therefore, companies conducting marketing campaigns must be careful not to destroy their patent rights. In a perfect world, application for a patent should be filed before any sales begin. But then that would hurt the company's bottom line because that ultimately puts profits on hold. In a competitive marketplace this could spell disaster for the company.
Therefore, it is important for a company to understand just what it is that starts the one year clock ticking. In other words what can they do and what can't they do in order to avoid their product being put on the timer?
In order to answer that question we have to understand what exactly, according to law, starts the clock running. There are basically two conditions. The first one is that the invention must be ready for patenting at the time of the sale. If it can be shown that the inventor had sufficient drawings that would enable another person to use the invention then this would satisfy the first criteria.
The second criteria is that there has actually been an offer for sale. In other words, the inventor or company that owns the invention approaches another company and offers to sell them the invention. This can either be in the form of a letter to the other company or in an actual physical meeting between the two companies. Usually the meeting follows a letter.
In the form of a letter the owner of the invention will usually draw up a letter stating that they have such and such an invention and go on to say that they feel this is something that would enhance their business. In the letter they would describe what the invention does and how it would help them. They would then ask the other company to get back to them if interested.
When it comes to the meeting the inventor will bring drawings of his invention and present them to the company interested in acquiring the invention. Maybe the inventor even has a working prototype he can show them. This is always a plus. Companies actually like to see that the invention they are interested in works.
Where the law comes in, and this is where inventors can delay the clock, is that the following items do not fall within the two criteria. Solicitation of customer pricing information from distributors and sales representatives; publication of preliminary data sheets and promotional information on invention features; communications to sales representatives; sales representatives providing customers with preliminary data sheets; and sales representatives' requests for customer samples.
Therefore, an inventor can engage in any of the above activities and NOT start the one year clock running. This allows the inventor to get as much preliminary leg work done for his patent without actually "technically" starting the process.
This is important information for any inventor to have if he is trying to gain as much ground in his quest for a patent as possible.
Emerging Technologies In Supply Chain Management
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 Business Development AVP Tanjon Pagar, 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.