Rising above all the cacophony of today’s disruptions sweeping across organizations and countries, adaptive learning technology stands out above all the rest. Adaptive learning is an innovative, albeit expensive way to address the problems of costs, retention, and customer success, especially in digital businesses where this technology promises to be most useful.
At the point when asked what adaptive learning is, who is using it, with what success, and at what risk, you will find many marketing leaders reconsider, especially those in large organizations that would be best served by this technology.
Why did the marketer get off the trampoline?
He was worried about his bounce rate.
Adaptive Learning Outcomes
Adaptive Learning in Marketing automation is about introducing the right product or the right communication to the right customer at the right time through the right channel to satisfy the customer’s evolving demands. However, most existing marketing practices and academic research focuses on methods to select the most profitable customers for a scheduled intervention.
The results of a global study demonstrate an organization’s innovativeness affects the firm’s performance. A company’s learning‐orientation influences firm’s innovativeness. An organization’s market‐orientation impacts company’s learning orientation. A firm’s learning‐orientation mediates the relationship between company’s market‐orientation and innovativeness.
The interrelationships among a company’s market orientation, learning orientation, and innovativeness are some important research area for investigators in the field of management, strategy, and marketing. However, most of the studies were conducted in large enterprises in developed countries while ignoring small and medium‐sized business (SMEs) in general, and in developing countries in particular.
Adaptive Learning’s correlation with Marketing
Marketing knowledge and expertise are a critical corporate resource for carrying out strategic decision making that supports marketing functions. Intelligent marketing systems can offer a way for marketing managers to share knowledge and expertise.
Such sharing could help improve the finances and effectiveness of the marketing function. Traditional marketing systems are limited in their decision support capabilities. Unlike traditional systems, an intelligent marketing system incorporates the use of a knowledge base of marketing strategies.
Marketing managers make decisions about products, brands, advertising, promotions, price, and distribution channels, based on deep knowledge about customers. The outcomes of marketing decisions are dependent on the behavior of other factors such as competitors, suppliers, and resellers.
Marketing decision making not only refers to tactical marketing media mix (the well-known 4Ps), but also to strategic issues, such as product development and innovation and long-term decisions with respect to positioning, segmentation, expansion, and growth.
Advancements in Machine Learning (ML) has created many opportunities to build Bots and automation for proactive marketing decisions. Just as SEO, programmatic buying, and other new technologies have revolutionized certain aspects of the marketing industry, ML figures to change the whole process, from how we handle simple marketing tasks to how we approach the larger task of telling brand stories.
First, though, it’s important to dispel common misunderstandings. ML isn’t the same as artificial intelligence. Instead of developing the capabilities to rival or even surpass human intellect, Machine Learning is more about optimizing certain problem-solving processes.
So those marketing automation tools that are promoting their AI capabilities, actually are not aware of what exactly they are doing. It is more of a marketing buzz word than a real-time automation capability.
History of Machine Learning
Machine Learning is a very advanced tool. But it’s a technology that will change everything because of how quickly it can work, and how much complexity it can handle. Data is critical to an effective adaptive learning strategy, which is where it fits perfectly with today’s digital marketing strategy.
Digital marketing’s best opportunities all flow from data, and marketers will quickly discover that their ability to leverage ML technology will only be as good as the data they have driving those insights.
While marketers may or may not realize it, crude versions of ML have been used for years. And innovators are gradually building more sophisticated digital strategies around the Machine Learning technology available today. The impact has been powerful, but it’s nothing compared to what’s Retainly has been able to achieve.
How many marketers does it take to screw in a light bulb?
None – they’ve automated it.
Machine Learning is not necessarily a new technology—it’s just something that has evolved over time, and gained considerable new strengths in recent years. Spell-check tools have long used basic ML principles to identify spelling and grammatical errors. The tools weren’t always perfect, but they used basic data sets to recognize potential errors.
More recent examples include online product recommendations, which use data to make algorithmic suggestions to consumers. Google’s search browser also routinely features examples of ML. Not only do search results themselves flex the power of machine-centric problem-solving to understand the intent and speak to the searcher’s pain points, but Google routinely uses ML technology to make sense of search queries.
As an example, when those queries are littered with spelling errors Google still prompts the correct search string. Retainly uses sophisticated Machine Learning to provide real-time and proactive decision automation to the modern marketer.Adaptive Learning is no more a subject for a PHD thesis alone. Retainly has brought it within the reach of the… Click To Tweet
What is Machine Learning?
Machine learning is a study combining science, statistics and computer coding that aims to make predictive actions based on patterns discovered in data. As against the rule-based decision systems, which follow an explicit set of instructions known by the developers in advance, machine learning algorithms are designed to analyze data and discover patterns that people cannot find by themselves.
Machine learning leverages the massive power and objectivity of computers to see things in big data that slow and biased humans cannot. Then use those insights to determine how new data can be used to accurately predict cognitive results.
How Machine Learning works
In most of the machine learning techniques, the method of learning is similar to how humans learn in the real world. The training stage is where the machine makes all sorts of mistakes or error while trying to predict and it constantly tries to reduce errors between the actual and predicted values.
The best part of it all is that it mathematically boils down to a simple optimization requirement for minimizing errors.
Machine learning helps marketers segment customers, predict churn, forecast customer LTV and effectively personalize messaging.
How Machine Learning Helps Marketers?
Machine learning and pattern recognition can help marketers in various ways. One of the biggest challenges facing marketers is how to personalize messaging to individual prospects and customers so that it most strongly resonates with the recipient.
The results of successful, highly-relevant, and real-time marketing communications include increased customer loyalty, engagement, and spending.
Without machine learning, it is simply too difficult to compile and process the huge amounts of data coming from multiple sources (e.g., purchase behavior, website visit flow, mobile app usage and responses to previous campaigns) required to predict what marketing offers and incentives will be most effective for each individual customer.
When all of this data is made available to computers programmed to perform data mining and machine learning, very accurate next best action predictions are made.
Why can’t a lead date a religious marketer?
Because she’ll always be trying to convert him.
More areas in which a machine learning application can help marketers include:
- Customer segmentation – Machine learning customer segmentation models are very effective at extracting small, homogeneous groups of customers with similar behaviors and preferences. Successful customer segmentation is a critical tool in every marketer’s toolbox. Retainly uses this to build dynamic customer personas.
- Customer churn prediction – By discovering patterns in the data generated by many customers who churned in the past, churn prediction machine learning forecasting can accurately predict which current customers are at a high risk of churning. This allows marketers to engage in proactive churn prevention, an important way to increase revenues.
- Customer lifetime value forecasting – Marketing machine learning systems are an excellent way to predict the customer lifetime value (LTV) of existing customers. LTV is a valuable tool for segmenting customers, and for measuring the future value of a business and predicting growth.
Implementing Machine Learning in Marketing
Pattern recognition and machine learning software have come a long way since their early days in the 1960s. New algorithms and technologies are constantly emerging, suggesting new possibilities and applications.
Despite this, most marketers are not using any form of machine learning in their day-to-day efforts because it remains a complex field, requiring the involvement of data scientists and developers.
As an outcome, viable executions of machine learning calculations in advertising stay past the compass of some little and medium-sized organizations.
Nonetheless, particular applications grew particularly to address advertising challenges – and to be simple for advertisers to utilize – are currently accessible for littler organizations with unobtrusive spending plans.
This is a distinct advantage for keen advertisers since machine learning can dispense with the mystery required in a number of the most difficult and significant parts of information driven promoting.
Deep learning, the newcomer in machine learning have recently even outperformed humans in terms of recognizing complex objects in images. What differentiates machine learning is that it is not the usual programming that we are taught in school, where we instruct the machine how to respond to a specific set of rules(algorithm) for every instance of input it gets from the environment, but rather it tries to replicate the human intelligence which extracts the useful patterns or signals from the huge amount of data scattered with noise.
This is similar to the method proposed by Jeff Hawkins to create the true artificial intelligence in his classic book ‘On Intelligence’.
But these sophisticated automation tools are only as good as the data they’re fed. For your company to truly benefit from machine learning, you need to make sure that you’re using good, clean and relevant data from the get-go. Here are some key areas to focus on when getting started.
Positive and negative goals
A marketing campaign starts with a business priority that needs addressing. Once you have clarified the basic elements of who, what, when, where and why, and fed this information into your software, you need to define positive and negative goals.
Machine learning is essentially problem-solving technology that uses data to find patterns that the human mind and eye cannot. The more definitive the information, the better the results.
So, if your business need is to convert more website visitors to customers and increase revenue via the online check-out function, then your goals are quite straightforward: a purchase is positive and no purchase is negative.
Machine learning allows for more sophisticated analysis and can direct a far more personalized marketing campaign to achieve specific results. You could, for example, want to understand which elements of your campaign are attracting the biggest spenders and which are putting them off.
Goals can, therefore, be defined according to time and engagement: an email opened or a minute or more spent browsing the website can be seen as positive, an email deleted or less than 60 seconds spent browsing is negative.
Establish these metrics within your marketing software and it will learn from how your customers engage with your campaign – and deliver the right results.
Machine learning is advancing so rapidly that treating each customer as their own individual segment is not an unlikely possibility in the near future. As it stands today, audience segmentation methods use demographic data, buying behaviors and engagement-based intelligence to group audiences and deliver as personalized a customer experience as possible.
Machine learning can enhance this by identifying patterns, problems and future trends within these segments with even greater precision.
The more relevant the foundation of existing data, the more exact your marketing efforts will be. Within one seemingly homogenous segment, machine learning can distinguish loyal customers from fickle buyers, and time-wasters from genuinely interested prospects.
The software can then refine its algorithm according to each type. This will allow you to focus your resources on the most lucrative audiences with highly personalized communications that push each interaction closer to a sale.
Through machine learning, marketing teams can not only help drive more revenue but also save money by correlating the quantum of resources spent with the return on investment.
If a specific segment of your database is taking up a lot of time with no results, you can choose to lose their custom and focus your efforts where it matters most – on your existing customers.
Content and Distribution channels
How do you reach your customers? Your company may currently email a monthly newsletter out to its database, but how effective is it if both new and old customers are receiving the same content and only half read their emails?
Machine learning can assess the value of your marketing channels as well as the content you are pushing, and help you tailor both for specific audiences.
If for example, you know that a certain segment of your database prefers to receive marketing information in bite-sized chunks over social media, it’s no good emailing them long brochures.
You can filter the intelligence down even further by identifying patterns such as preferred time of contact or change in buying habits and refine your strategy accordingly.
Learn more about Drip Marketing Automation.
Start Using Machine Learning in your Marketing Campaigns Today!
It’s not realistic to expect marketing teams to become IT specialists, but as they adopt a more horizontal role across businesses, they do need to know how to get the most out of their marketing technologies. Ultimately, the cleaner and more relevant your data, the more effectively your marketing software can learn. Regardless of how intelligent the machine is, its results are only as smart as the marketer feeding it. Adaptive Learning is not only for CocaCola. Retainly has brought affordable technology within the reach of Small and Medium businesses as well. Start using Retainly Marketing Automation today.