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Marketing Strategy - Better Change Now Before You Have To
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.
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.
Marketing Strategy - What It Is, And Why It Is So Important ?
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Artificial intelligence is going to change everything we do in advertising and marketing, but not in the way we think. The truth is that if used correctly, RPA software and intelligent machine learning can give companies and agencies the power to provide extraordinary experiences for customers. The kind of campaigns that move the customer on an emotional level.
After all, that is the key to a loyal customer base. The people who come back again and again because they know on a gut level that a business understands them. Steve Jobs saw this after a calligraphy class inspired his design for the iconic mac fonts. Creativity and intelligent automation seem like the furthest concepts from one another, but in fact, they are intrinsically connected.
We are currently drowning in a sea of data. This data contains valuable information about consumer preferences, their likes, and dislikes. The key to creating something that consumers truly want. Even giants, such as holding companies dedicate a massive amount of resources to crunching the numbers.
Combining AI with creativity can open up a whole new field of marketing and advertising. There are three ways this can take shape, and they are all interconnected.
Targeted Experiences - when you add AI to the marketing mix, it opens up a whole new category in the funnel. This means curated experiences for every different type of customer in the market. Capturing Millenials and Baby Boomers with the same campaign, using powerful messaging that appeal to each group. This isn't the stuff of tomorrow. Many agencies are already deploying AI technologies to their advantage and producing creative that works across the board. According to Entrepreneur, AI will help companies target customers more accurately and place budget dollars where they belong.
Tighter Budgets - speaking of dollars, the analytical power of AI software will help solve one of the most age-old problems in advertising. Funding campaigns that deliver on ROI and help companies take calculated risks that pay off. Marketing and creative wants the budgets to be higher, and businesses want to cut costs. There is no "right" or "wrong" party here. A large part of advertising is trial and error, but that means wasted money. However, when businesses and agencies use intelligent machine learning software to analyze customer data, a lot of the guesswork goes out the window. This creates a positive feedback loop, where money can flow to the projects that need it and build richer marketing experiences.
A Marriage of Creative and Data - any marketing manager worth their salt knows that the best creative is made possible by data and analytics. Machine learning algorithms are making this symbiotic loop stronger. They perform elaborate functions without slowing down the customer experience. This allows creative teams to get fast feedback, giving businesses time to change their approach and become agile. Instead of waiting for analysis to determine if a campaign is resonating, with MLA's, companies can get results almost in real time.
Don Draper would have killed for the kind of value AI can add to creative. It's a chance to leave the guesswork behind and make more impactful campaigns. Like it or not, advertising and marketing are just one of the many fields AI is going to change.
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.