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Artificial Intelligence In CRM Customer Relationship 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.
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.
An Overview of Proper Supply Chain Management For Your Business
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Keeping a watchful eye on technical innovation is vital to develop a clear vision for the future of any business. But effective strategies for success depend on managers and executives avoiding hidden blind spots and investment decisions that obscure the way forward. Last year, according to World Economic Forum figures, private sector global spending on digitizing business operations exceeded $1.2 trillion dollars, yet just 5% of executives reported being satisfied with the results. In most industries the transition from analog to digital is one of the biggest challenges facing business leaders today. There are 8 common mistakes executives make.
Finding the best way: As with most human activity, planning is everything. The digitization process is a unique opportunity for executives to take a good hard look at their enterprise and ask some important questions:
What digital activities are already underway?
What will the industry look like in 5, 10 or 20 years?
What strategies can the company employ to succeed in a digital future?
What is the end goal of the transition from analog to digital?
Understanding where the business is attempting to go should help avoid some of the following bumps and wrong turns in the journey. Most of the common mistakes executives make with the digitization process relate to investment. Nearsighted investments focus too heavily on the short term, giving insufficient consideration to an organization’s long-term needs. While, farsighted investments focus on future needs with scant attention given to immediate development, which undermines current performance and impacts future goals.
Even when the current and future needs of a business are given equal consideration blind spots can occur, as parts of the business are overlooked by investment and turn into points of weakness that disrupt overall performance. Putting a coherent strategy in place directs funding to areas of the business most in need. As well as scheduling where and when to invest, this strategy prevents executives making “scattershot” small investments without an overall funding plan.
Mind your own business As each organization is unique, no two paths to a digital future are the same. The structure of a business can influence its digitization journey, with heavily centralized companies at risk of suffering from a rigid chain of imposing policy from on high. Similarly, command structures that encourage parts of the business to operate as independent units, or islands, can duplicate investments which also duplicate costs. Every six months the management should ask these questions:
How the digitization of work affects us all?
Why a futuristic digital healthcare system, might not be out of reach?
How can we build a workforce for our digital future?
Enabling change Aside from investment decisions, another common area where mistakes are made relates to the balance of resources and their application. A company’s data, technology, operating model and talent either work to enable digital progress or hinder it. Some companies focus too heavily on building up these enablers, without considering if additional staff, technology and data capacity add value to the business. Whereas, the digital transformation of other companies suffer from a lack of resources to accommodate spending on new business applications.
The new digital reality Image: WEF The pace of technological change is impacting the business and social worlds faster than ever before. A new digital reality is emerging where 85% of customer interaction will take place without humans and where 65% of today’s young will grow up and work in industries or jobs that don’t yet exist. Companies that successfully bridge the gap from analog to digital are in prime position to fully embrace the opportunities offered by a digital future.
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.