As a Marketing Vice President Redhill, 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 Supply Chain & planning can make your business and products more popular.
For your profession as Marketing Vice President Redhill 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 Best Branding Strategy - Make A Real Connection
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.
Market segmentation is widely defined as being a complex process consisting in two main phases:
- identification of broad, large markets
- segmentation of these markets in order to select the most appropriate target markets and develop Marketing mixes accordingly.
Everyone within the Marketing world knows and speaks of segmentation yet not many truly understand its underlying mechanics, thus failure is just around the corner. What causes this? It has been documented that most marketers fail the segmentation exam and start with a narrow mind and a bunch of misconceptions such as "all teenagers are rebels", "all elderly women buy the same cosmetics brands" and so on. There are many dimensions to be considered, and uncovering them is certainly an exercise of creativity.
The most widely employed model of market segmentation comprises 7 steps, each of them designed to encourage the marketer to come with a creative approach.
STEP 1: Identify and name the broad market
You have to have figured out by this moment what broad market your business aims at. If your company is already on a market, this can be a starting point; more options are available for a new business but resources would normally be a little limited.
The biggest challenge is to find the right balance for your business: use your experience, knowledge and common sense to estimate if the market you have just identified earlier is not too narrow or too broad for you.
STEP 2: Identify and make an inventory of potential customers' needs
This step pushes the creativity challenge even farther, since it can be compared to a brainstorming session.
What you have to figure out is what needs the consumers from the broad market identified earlier might have. The more possible needs you can come up with, the better.
Got yourself stuck in this stage of segmentation? Try to put yourself into the shoes of your potential customers: why would they buy your product, what could possibly trigger a buying decision? Answering these questions can help you list most needs of potential customers on a given product market.
STEP 3: Formulate narrower markets
McCarthy and Perreault suggest forming sub-markets around what you would call your "typical customer", then aggregate similar people into this segment, on the condition to be able to satisfy their needs using the same Marketing mix.
Start building a column with dimensions of the major need you try to cover: this will make it easier for you to decide if a given person should be included in the first segment or you should form a new segment. Also create a list of people-related features, demographics included, for each narrow market you form - a further step will ask you to name them.
There is no exact formula on how to form narrow markets: use your best judgement and experience. Do not avoid asking opinions even from non-Marketing professionals, as different people can have different opinions and you can usually count on at least those items most people agree on.
STEP 4: Identify the determining dimensions
Carefully review the list resulted form the previous step. You should have by now a list of need dimensions for each market segment: try to identify those that carry a determining power.
Reviewing the needs and attitudes of those you included within each market segment can help you figure out the determining dimensions.
STEP 5: Name possible segment markets
You have identified the determining dimensions of your market segments, now review them one by one and give them an appropriate name.
A good way of naming these markets is to rely on the most important determining dimension.
STEP 6: Evaluate the behavior of market segments
Once you are done naming each market segment, allow time to consider what other aspects you know about them. It is important for a marketer to understand market behavior and what triggers it. You might notice that, while most segments have similar needs, they're still different needs: understanding the difference and acting upon it is the key to achieve success using competitive offerings.
STEP 7: Estimate the size of each market segment
Each segment identified, named and studied during the previous stages should finally be given an estimate size, even if, for lack of data, it is only a rough estimate.
Estimates of market segments will come in handy later, by offering a support for sales forecasts and help plan the Marketing mix: the more data we can gather at this moment, the easier further planning and strategy will be.
These were the steps to segment a market, briefly presented. If performed correctly and thoroughly, you should now be able to have a glimpse of how to build Marketing mixes for each market segment.
This 7 steps approach to market segmentation is very simple and practical and works for most marketers. However, if you are curious about other methods and want to experiment, you should take a look at computer-aided techniques, such as clustering and positioning.
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Through business relationships and experience sharing in confidential settings for Marketing Vice President Redhill, 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.