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Marketing Strategies - Making Indirect Marketing Work for You
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.
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In the last century, the world saw a massive revolution of innovation.
Beyond modern marvels such as digital advancements and the evolution of the smartphone, artificial intelligence is gradually changing society and how people navigate their lives. Machine learning is gradually being integrated into nearly every aspect of life.
It's already used in machine translation, email spam filters, ATM check depositing and facial recognition - and that's just what an average person uses day-to-day.
Predictive intelligence is making businesses more efficient, effective and successful. B2B companies deploying predictive intelligence for marketing activities are closer to the holy grail of understanding each individual customer - and personalizing all content to their needs and interests.
Technology not far from artificial intelligence is making a significant impact on the marketing industry. In fact, 86% of marketing executives have already indicated they have seen a positive return on investment in marketing technology and predictive analytics. The future of B2B marketing will focus on predictive analysis and intelligence, and have a major impact on lead scoring and content targeting.
The Transformation of Lead Scoring
Lead scoring is essentially a points system used to determine where your prospects are in the buying journey. The idea is to look at customers uniquely for a better understanding of what they looking for, what you can provide them with - and if they're likely to make a purchase.
Manually scoring leads, with this helpful guide, can be an excellent introduction to the strategy of fully comprehending customers. Assigning this responsibility to your B2B marketing team brings consistency, reliability and focus to a personalization approach.
Beyond manual lead scoring lies predictive lead scoring. This is a proactive way to accelerate the sales process by determining which customers are ideal based on past behaviors and purchasing history.
This takes into account other technologies, such as CRM or marketing automation, and demographic information to predict whom sales and marketing should be nurturing closely. Still done semi-manually, this method uses the insight from traditional lead scoring and blends it with modern ways of working.
In terms of the future of B2B marketing, predictive lead scoring using predictive intelligence is yet one step further. This is even more accurate than basic lead scoring, because of its correlation between patterns discovered in both a company's first-party data and general third-party trends.
It has also become the standard for most companies, especially technology-based businesses. A 2014 study revealed 90% of users agree predictive lead scoring provides more value than traditional approaches. The comprehensive nature of looking at customers holistically and integrating that insight into how you communicate with them can fast track your marketing efforts.
Given that artificial intelligence can predict the status of hundreds of prospects in a matter of minutes, marketers have everything to gain by using this technology.
A recent Gartner study concluded that predictive intelligence is a must-have for B2B marketing leaders. Just as marketing automation is being adopted widely within the marketing industry, predictive lead scoring is likely to follow.
The immediacy of reaching customers, understanding their needs and effectively determining their value to your company has created a necessary place for predictive intelligence in lead scoring.
The Power of Personalized Content Targeting
Predictive intelligence, an important component of predictive analytics, is also critical in learning which pieces of content to target to which customers. After predictive lead scoring reveals where each customer is and might be headed in the buying journey, you can glean insights from predictive analytics for establishing the tone, material and style of content each prospect will respond to most fervently.
An algorithm that determines the factors influencing a prospect can also pull the appropriate content. Just as you would send additional white papers to a manually-scored lead with interest in more in-depth material, this algorithm identifies the many customers to whom whitepapers would apply.
Sending the right content is just as important as creating it in the first place. Predictive analytics also leads to informed idea generation and content development.
Using predictive analytics in your content marketing takes careful consideration, but can be done successfully if you know the right data points to use and what to integrate into your existing strategy.
Seeing what content receives the most engagement and is most worthwhile to your prospects helps you tailor future content to those interests. Even with predictive analytics on your side to help you gain incredibly beneficial insights, it still takes a human to use the insight wisely and proactively.
Marketing professionals who work based on data, emotions and customer connections are the whole package in targeting content most effectively.
A.I. and the Future of B2B Marketing
Although artificial intelligence is not quite at the point of thinking, processing and completing tasks at the speed of a human brain, developments in the science of machine learning are getting closer to a complete takeover of this technology.
The existing uses of artificial intelligence within marketing is a good indication that the future of B2B marketing is bright - and that lead scoring and content targeting will be perfected as the technology matures.
With an already efficient system of analyzing data from thousands of sources to make sense of a single customer, predictive intelligence is making it possible for even small B2B companies to grow at rapid rates and expand their potential faster than traditional methods.
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.