As a Chief Information Officer Downtown, 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 Digitization Challenges & planning can make your business and products more popular.
For your profession as Chief Information Officer Downtown 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.
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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.
The management of supply chains is constantly developing due to momentous changes such as the Internet, E-commerce and the globalisation of supply chains. Its success often relies on rapid, accurate and efficient handling of data. The trend towards lean and agile distribution channels and the growth of Fourth Party Logistic Providers (4PLs) within the supply chain industry requires significant organisation and management. The efficient control of these activities requires supply chain knowledge, operational information and importantly, timely and accurate data to support the decision making process. Essentially, effective and efficient data acquisition techniques are required.
RFID is a generic term for technologies that use radio waves to communicate the identity of individual items over an air interface. RFID works similarly to bar code technology in that an item has to be interrogated by a scanner or reader for it to be identified. Barcodes, however, have one significant downfall, they require line-of-site technology. That means the scanner has to see the barcode to read it, which usually means items have to be manually oriented towards the scanner for it to be read. Conversely, RFID does not require line-of-site and can be read as long as the item is within range of the reader.
RFID is now being considered as an integral link in E-Commerce environments. The technology in theory should enhance and complement Electronic Data Interchanges (EDIs) to facilitate quick response and the generation of exception reports. This should allow real time information to be transmitted to partners within the supply chain supporting the decision-making process. Ultimately RFID should provide immediacy of data right down to individual item level identification. This can help bridge the gap between the customer, the order and order fulfilment process to the satisfaction of the customer. This means that it can enable the enhanced responsiveness expected within an E-Business environment.
The supply of on-demand barcode label printers currently represents one of the most widely used AIDC technologies (technologies such as: barcodes, smart cards, magnetic stripes on credit cards, optical character recognition etc) in supply chain applications (e.g. EPOS, warehouse and inventory management). Due to mandates set by influential leaders in the retail and defence industries, barcode label printers with RFID enabled capabilities present a real opportunity for companies to develop and extend their product portfolios by providing products which will enable companies to meet compliance objectives. Opportunities also exist to provide printers for those companies faced with compliance for when usage and acceptance of the technology becomes more prevalent. An entire new market segment will have emerged, requiring a widespread ongoing supply of printers, peripheral equipment and consumables.
Bar code systems Bar code systems include the symbologies that encode data to be optically read, printing technologies that produce the machine-readable symbols and scanners and decoders that capture the visual images of the symbologies and convert them into computercompatible digital data. Barcode scanning reduces errors associated with manual data handling, and produces visibility to aid supply chain management. A significant benefit of bar codes is that they are extremely cheap to produce and provide an efficient means of item identification. Unfortunately, according to some sources, bar codes are proving increasingly inadequate in a growing number of applications. Bar coding is an optical technology, which introduces constraints regarding orientation of the product (invariably requiring human intervention) and cleanliness of labels and scanners for fast efficient data collection. Bar codes can be easily copied and so become an easy target for counterfeiting. In addition, standard barcodes have low storage capacity, cannot be reprogrammed and only identify the manufacturer and product and not the unique item. Industry bodies indicate that bar code systems are now a mature technology with limited potential for further growth.
RFID is emerging as a complementary technology to help overcome some of the drawbacks associated with bar code technology. RFID systems consist of transponders (tags), which are made up of a microchip with a coiled antenna and an interrogator (reader) with an antenna. The tags are attached to the items to be identified and the RFID readers communicate with the tags via electromagnetic waves. RFID middleware (software) provides the interface for communication between the interrogator and existing company databases and information management systems. RFID is a term used to describe any identification device that can be sensed at a distance by radio frequencies with few problems of obstruction and mis-orientation. The devices are often referred to as 'RFID tags' or 'Smart Labels'.
In its most basic form, a smart label consists of an ultra- thin RFID tag often referred to as an inlay. Inlays for smart labels are available in the 13.56 MHz, 860 to 930 MHz and 2.45 GHz frequency ranges. The inlays are embedded in label material, which is printed with human-readable text, graphics and bar codes (passive smart label). The printed data both supplements and backs up the information that is programmed into the tag. An evolutionary product to passive smart label technology is the smart active label (SAL). SALs can be defined using the same definition of smart labels above, but for one clear distinction, the inclusion of an integral power source. This distinguishing characteristic allows SALs to provide enhanced functionality over passive RFID smart labels including sensory, processing, display and locating capabilities. Smart labels are typically used for disposable applications and are not as durable as permanent RFID tags, which can be encased in materials to withstand harsh environments. Although one company suggests that the label material can be developed to withstand environmental conditions and that appropriate adhesive can ensure the label lasts the required duration.
Smart labels are referred to as smart because of their flexible capabilities provided by the RFID tag embedded in the label. The tag can be programmed and/or updated in the field allowing the same label to be reused serving multiple needs and disparate applications. Subsequently, the label is no longer static as a bar code label, but dynamic in its capability when equipped with RFID. Supporters of RFID suggest benefits which include: cost savings through automating the check-out process, a reduction in labour associated with performing inventory counts; improved theft prevention and increased authenticity control, a reduction in inventory holding cost, diversions and improved product availability. Unfortunately, an exact description of how the benefits are attainable in practice has often remained vague. The main criticisms on RFID technology are that it is too expensive and that it is unlikely that the investment will pay off. It is also argued that RFID is an over-marketed, hyped technology and that existing bar code based systems already provide most of the needed functionality.
Strategy - Brand Identity Guru Tips On Strategic Planning
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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.