As a Business Development Tools Yishun, 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 Strategy & planning can make your business and products more popular.
For your profession as Business Development Tools Yishun 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.
The Impact Of RFID and the Supply Chain Management
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.
To keep ahead in a fast-moving industry, leading Asia-Pacific fashion retailer Bossini needed to have better visibility into its business and operational processes through the synchronization of real-time data across multiple orders, shipments and stock-keeping units.
By leveraging the Enterprise Service Bus (ESB), Bossini is able to speed up the deployment of new applications and processes and bring new partners on board much more quickly.
The intuitive development environment of the webMethods ESB has enabled Bossini's programmers to easily, and quickly, build the large number of interfaces required to meet its complex integration requirements.
Upon transforming to a Service Oriented Architecture (SOA), Bossini could synchronize real-time data from multiple orders, shipments and stock-keeping units (SKUs) across its Point-of-Sale (POS) systems and Warehouse Management System (WMS) inventory.
• Better visibility into real-time processes with dynamic synchronization of data between point of sale and warehouse management systems
• Additional business capabilities, e.g. late shipment and sales performance monitoring via dashboards/SMS and stock-on-hand monitoring for all markets.
• Factory order allocation reduced from three days to one
• Finance month-end closing slashed from 25 days to seven
• Developer and IT productivity increased by up to 30 percent through user-friendly intuitive interface and re-use of programming objects from the webMethods ESB.
A standards-based solution
With the webMethods Enterprise Service Bus, Bossini is able to take full advantage of the standards-based interfaces to integrate with their existing systems and utilize service orchestration to connect their existing systems to their processes. This allows Bossini to speed up the deployment of new applications and processes and bring new partners on board much more quickly.
Subsequent changes are also easier to implement, and the company enjoys cost savings resulting from the re-use of the service components. "The webMethods ESB enables us to achieve real-time interoperability across numerous disparate applications, including independence from the constraints of their operating systems and hardware platforms," said Mr. Andrew Ling, Director of IT and Supply Chain of the Bossini Group.
Of equal importance is the platform's flexibility and ease of use. "This was crucial in our ability to gain competitive advantage in the fickle world of apparel retailing," said Ling. "In our decision, we also took into account the faster adoption, lower training costs and quicker return-on-investment enabled by the very intuitive design of the webMethods ESB."
Is Predictive Intelligence the Frontier of B2B Marketing?
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 Tools Yishun, 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.