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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.
There’s been plenty of headlines about AI and machine learning being the future of retail marketing. The concept itself can seem overwhelming and out of reach for retailers. However, more marketing organizations are gearing up to add machine learning capabilities sooner than you may think. This article is all about what machine learning marketing is and how it can improve your customer experience.
What is Machine Learning?
84% of marketing organizations are implementing or expanding AI and machine learning in 2018!
If most organizations are implementing it, then what is it? Machine learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world integrations.
There’s a lot of academic research and forums around the concept of machine learning. For merchants though, machine learning is giving customer data to computer systems so it can analyze the data and automatically learn and improve. Machine learning marketing then is using these types of technologies to provide better services and experiences to your customers. Think of products like Siri, Amazon Echo or services like Facebook’s retargeting ads. No matter how machine learning or AI is being used, it all comes down utilizing accurate customer data.
Benefits of Machine Learning for Commerce
Why are merchants using machine learning and AI? The answer is bettering customer experiences!
75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10%. Customers no longer shop and buy on price and quality alone. They’re looking for intuitive customer experiences that make purchasing easy, convenient, and personalized to their needs. That’s a tall order for merchants, but machine learning marketing is a way to get there.
Here are some of the major benefits of implementing machine learning for your business that help improve the customer experience:
Real-time Marketing across Digital Platforms
Marketing is all about getting the right message to the right person at the right time. Machine learning is now making that a reality for merchants. AI systems have unparalleled level of responsivity when it comes to analyzing customer data and then delivering. For example, technology utilizes a customer’s web history to deliver fast and accurate content based on a customer’s interests.
According to a recent reports, two-thirds of consumers are more likely to buy from a retailer that recognizes them by name, recommends options based on past purchases, OR knows their purchase history. Machine learning can handle your Big Data so you can utilize it to better know your customers. Your customers will actually feel like you know them, not just that you’re selling to them.
Service or Support
A lot of merchants struggle with providing quick and helpful customer support when something goes wrong. With machine learning, you can automate parts of your customer service to ensure quicker response times and offer 24×7 support. As an example, many companies are already using chatbots for part of the process on both their websites and mobile apps to answer easy customer questions.
Future Product Development
If you know your customers better, you also understand their needs. Customer data from machine learning is also valuable for future product development. You can identify customer needs easier and tweak or create new products that you’ll know your customers will love.
Factors Affecting Marketing Strategy New Innovations
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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.