Customer segmentation is a critical part of identifying your best customers, but you can’t do it until you know more about them.
That’s where automatic customer classification comes into play.
This article will explain the distinction between classification and segmentation, outline the core concepts of classification, and highlight the actual business benefits of automatic customer classification.
Classification Vs. Segmentation
In simple terms, segmentation is applied to the results of classification.
Segmentation can’t happen without having some characteristics to use, and classification is pointless if the information is not put to use.
Customer classification is the act of seeking out and identifying common traits in a group of customers.
It answers a broad question: what is similar about these people and their purchasing habits?
Segmentation takes that a step further by subdividing customers according to those similarities.
It answers a more focused questions: what is the most useful way to group these people based on the commonalities found during classification?
Automatic classification involves using an algorithm to sort customers as data about them becomes available.
When done right, it incorporates all data resources regardless of whether a person thinks the information may be relevant.
Customer data is thus drawn from the silos where it tends to collect and put to work.
There are undeniable benefits to using automatic classification methods.
When a person does classification – even when setting up a series of filters – they can only filter by what they think might be relevant.
People typically end up using demographics as differentiating factors (age, family structure, income, residence).
Using diagnostic analytics, an algorithm can find unexpected points of similarity that better predicts customer behavior and potential lifetime value.
Algorithms have no preset bias about how people of various socioeconomic brackets or regions behave.
The labels they generate are based solely on patterns found within the given dataset.
They might reveal unexpected commonalities in high-value customers such as path to purchase, lifestyle factors, similarities gleaned from touchpoint analysis (how recently the customer interacted with the brand before purchase), or other factors that are hard for human analysts to detect.
A common method of customer classification is cluster analysis, also known as cluster modeling or cluster-weighted modeling.
Cluster analysis gathers data points into clusters based on both their similarity to each other and their difference from other clusters.
Some of the more popular clustering algorithms are:
K-Means clustering: Clusters data points together based on Euclidean distance. The amount of clusters is determined organically in response to the data.
Hierarchical clustering: Creates a ranked group of clusters. It either begins with all data points in their own clusters and moves up to pull together similar points or starts with all data in one cluster and moves down, breaking out data points that are no longer similar to each other until each is in its own cluster.
DBSCAN: This is a very common clustering algorithm based on distinguishing coherent clusters from outliers. It also doesn’t need to be fed the number of clusters before sorting.
Most of the time, several techniques will be combined to realistically represent the data.
Benefits of automatic customer classification
Automatic classification can handle more data than human analyst.
It’s faster and more accurate, making the best possible use of a company’s customer data.
Letting machine learning determine what characteristics actually impact value uncovers useful information about the customer base.
These insights suggest ideas for future products and services or areas where the company can improve to widen their appeal.
Finally, automatic customer classification informs a highly precise customer profile.
Better profiles lead to a more personalized buying experience, where customers are treated as individuals with different needs instead of being presented with generic offerings.
With buying experience fast becoming the leading differentiating factor among brands, understanding who customers are is of paramount importance.
Automatic customer classification is the first step on the path to a closer, more profitable connection with customers.
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Because machine learning is one of the most well-known evolutions of artificial intelligence, it’s natural that the two terms are often used interchangeably by non-engineers.
However, they aren’t quite the same thing.
The distinction is mainly hierarchical, but keeping it in mind helps to better understand the practical applications of both.
Read on for a discussion of the differences between these concepts and a glimpse of what’s coming in the future.
What is Artificial Intelligence?
Artificial intelligence is one of the fastest growing yet least understood tech trends.
Some people fear AI will take jobs from humans or lead to Hollywood-style robot wars.
The reality is that artificial intelligence is both less fanciful and more intriguing than fiction suggests.
Instead of rendering human workers obsolete, it gives them tools to become more effective and frees them to pursue more highly skilled tasks.
The concept is very broad.
Artificial intelligence strives to create machines that can behave in “intelligent” ways.
Given a large subset of data, AI could make its own decisions about relevance and priority rather than relying on subroutines.
AI-driven processes don’t need predetermined guidelines for every possible situation.
They have the ability to judge situations and take the most reasonable action without needing human oversight.
This is a major departure from non-AI programs.
Even the most highly refined logical algorithm can’t account for the millions of tiny elements involved in everyday tasks.
Consider email sorting.
There are very sophisticated algorithms used in evaluating whether a particular email matters to the account holder, yet dozens of mass advertising messages find their way to the inbox.
Too many variables affect the outcome: sender, content, past interactions, and even date. In its ideal state, artificial intelligence could scan a message’s content and combine that with metadata and other factors to create a “living inbox” where the most relevant emails are always listed first.
AI was divided into specific subfields for most of its history.
Each application was treated as a different subject, and there was little interaction between subdisciplines.
Machine learning has an astounding variety of end applications.
There are too many to describe them all, but they can be broken down into a few functions.
Distinguishing relevant features (Classification):
Machine learning finds patterns within data as well as areas where there are no consistent similarities.
These patterns informs an assessment of the relative importance of the data.
It used to take years for a human worker to sort and identify the relevant features of a disordered dataset.
Machine learning “shakes out” these features in a fraction of that time.
Machine learning excels at recognizing trends in data based on relevant features.
It predicts the classification of incoming data according to past outcomes.
This method- using a model to predict future events- is called time series forecasting, and it has powerful implications for business.
Companies can use insight gained through machine learning to prepare for future disruptions, adjust their supply chain in response to anticipated increases in demand, and decide where to focus new campaigns.
Model selection/Fine-tuning parameters:
For any given artificial intelligence process there are millions (sometimes billions) of factors that affect the process’ operation.
Small changes in these factors can increase or reduce the accuracy of an algorithm’s outcome.
There are too many for a human to manually adjust.
Trying to choose the perfect setting for each would take years.
Machine learning techniques can be used to find the optimal setting for each involved variable.
The Limitations of Machine Learning
Machine Learning isn’t a perfect solution for every problem, of course.
As game-changing as the technology is, it hasn’t advanced to a fully autonomous level yet.
There are limitations to how it can be used.
Machine learning requires a lot of data.Its nature means that machine learning works best on vast amounts of data.The more data is fed through the algorithm, the more refined it will become.That leads to faster processing and higher accuracy.
Gathering and structuring enough of the right sort of data could present a challenge; at least half of a data scientist’s time is preparing data for machine learning.
This is more of a statistics problem than a machine learning problem, and there’s a lot of labelled training data available for most purposes.
Most algorithms need to be trained for their intended use.With the exception of Neural Networks and similarly versatile examples, machine learning algorithms have to be directed to a specific application.While the core model may be reusable, experience gained in filtering spam isn’t very useful for image clustering.Refining an algorithm takes time, too.
Machine learning requires lengthy offline training before reaching the point where it adds value.
Machine learning systems are hard to test and debug.To describe machine learning as complicated would be a massive understatement.As a consequence machine learning systems hard to assess and maintain.Traditional software can be tested for functionality using Boolean-based logic (“This program works as expected”), but engineers use degrees of success when evaluating machine learning (“This algorithm produced 85% accurate results and has improved from the last test by 10%”).
As an interesting wrinkle, it isn’t always possible to be absolutely sure whether machine learning has produced the “correct” result.
Its results are often more indicative of what most people would say rather than what is actually true.
Google’s Director of Research Peter Norvig explains the dilemma: “For some problems, we just don’t know what the truth is.
So, how do you train a machine-learning algorithm on data for which there are no set results?”
What else is out there?
It’s hard to draw a line between machine learning and other artificial intelligence fields like computer vision or natural language processing.
Machine learning has become such a useful way of approaching AI that it’s often incorporated into other applications.
Essentially, artificial intelligence systems that can learn from their mistakes and new data involve machine learning.
There are systems that exhibit “intelligence” without learning on their own.
An example would be an expert system – software programmed to function as an expert in a specific domain.
Expert systems use rules, probabilistic reasoning, and logic to reach conclusions rather than relying on past experience.
They’re capable of providing advice, solving problems, demonstrating processes and explaining their logic, and predicting results.
They have trouble working around gaps in its knowledge base, however, and don’t learn or refine themselves.
The Future of Machine Learning
Just as machine learning is the natural successor to artificial intelligence, Deep Learning is on the cutting edge of machine learning.
It’s the next logical step.
Deep Learning deals with neural networks – algorithms designed to mimic the function of the human brain.
These aren’t the primitive neural networks of the 90s, though.
Scale is of paramount importance.
Deep Learning neural networks are huge, fed with as much data and spread across as many machines as possible.
The more layers and data incorporated into the network, the more accurate the results.
There’s a lot of hardware and training time involved in bringing them to a functional maturity.
In essence, Deep Learning is machine learning on an epic scale.
Distinguishing artificial intelligence from machine learning is like differentiating between automobiles and electric cars.
It’s a matter of succession and inclusivity.
In other words: all machine learning is artificial intelligence, but not all artificial intelligence is machine learning.
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There is a strong relationship between the two (the first is a technique often used to do the second) but they are distinctly different concepts.
Let’s explore each term, where they diverge, and how they work in synergy within a business context.
Laying the Foundations
Machine learning is an artificial intelligence technique where algorithms are given data and asked to process it without predetermined rules.
Machine learning algorithms use what they learn from their mistakes to improve future performance.
Data feeds machine learning; the results are most accurate when the machine has access to massive amounts of it to refine its algorithm.
There are two general types of machine learning: supervised and unsupervised.
Supervised: A training dataset is provided to tell the machine what kind of output is desired. The labelled data gives information on the parameters of the desired categories and lets the algorithm decide how to tell them apart. Supervised learning can be used to teach an algorithm to distinguish spam mail from normal correspondence.
Unsupervised: In this type of learning, no training data is provided. The algorithm analyzes a body of data for patterns or common elements. Large amounts of unstructured data can then be sorted and categorized. Unsupervised learning is used in intelligent profiling to find similarities between a company’s most valuable customers.
Predictive analytics is the analysis of historical information (as well as existing external data) to find patterns.
These patterns are used to make informed predictions about future events.
It’s an area of study, not a specific technology, and it existed long before artificial intelligence. Alan Turing applied it to decode encrypted German messages during World War II.
As a general rule, any attempt to quantify the possible future based on past events is encompassed by predictive analytics. A number of alternate techniques are still common in business.
For example, using sophisticated mathematical & statistical models to evaluate data provides excellent results.
Differentiating predictive analytics from some closely related practices offers a better understanding of the field and where it falls on the analytical spectrum.
It describes past activity and the current state of things. It breaks the raw story of “what happened” or “what is happening” down into quantifiable data that can be used to better understand a situation.
Charting a marketing campaign’s performance in real time is an exercise in descriptive analytics.
It determines why an event happened the way it did, screening out unrelated data and assigning relevance to each component.
It can uncover previously unexpected contributing factors. Principle components analysis is a form of diagnostic analytics.
It attempts to forecast the most likely scenarios by comparing current conditions to historical data and placing the results in a modern context.
It’s often used in sales lead scoring, where leads are assigned priority based on the past value of similar customers.
It provides suggestions for future decisions by evaluating the possible outcome of several courses of action.
While not widely adopted, the healthcare industry has shown interest in using it to manage the treatment of patients with multiple medical conditions.
Sometimes one issue should be addressed before another for the best result. Predictive analytics weighs thousands of factors to recommend an optimal schedule of treatment.
Related, but Not the Same
Because predictive analytics is one of the most common enterprise applications of machine learning, they’re understood by casual users to mean the same thing.
It’s true that machine learning is an excellent means of forming predictions from data.
Classification and regression are strengths of supervised learning, and unsupervised learning can find relationships within enormous databases of unstructured data.
Machine learning is much bigger than predictive analytics, though.
There’s a broad spectrum of business use cases that fall outside the predictive umbrella.
Supervised algorithms have been distinguishing between humans and animals or picking faces out of larger images for some time.
Now, they can identify specific people regardless of body position or lighting.
This is one of the more mature uses of machine learning, used for everything from password authentication to automated security monitoring.
Natural language processing
Natural language processing, or NLP, processes normal linguistic patterns without demanding specific phrasing or keywords.
It’s the technology driving the meteoric rise of chatbots. Among other uses, chatbots give companies the ability to provide consistent entry-level customer service at all hours, no matter where the user is in the world.
Managing user-generated content
User-generated content is both an asset and a risk. It’s a core piece of the business model for social media platforms, but it’s hard to manage in any useful volume.
Some of it is low quality and should be ranked lower in search results regardless of its associated keywords. Some content violates community standards and shouldn’t be accepted at all.
Sorting, ranking, and labelling unstructured data like forum comments, videos, and social media posts would be incredibly difficult without machine learning algorithms.
When a person types a phrase into a search engine, a number of rankings happen between clicking “ok” and receiving a page of links.
The initial results are ranked in terms of properties like technical match, contextual relevance, location, sentiment, and personal search history.
While the average search returns millions of potential matches, only 10% of users will go farther than the first page. Machine learning helps search engines put the most helpful results on that all-important first page.
Also, there are other ways to do predictive analytics. As discussed earlier, it’s more an end goal than a specific technique.
Methods other than machine learning are still in use around the world. Forecasting based on an autoregressive integrated moving average (ARIMA) model is reliable enough to be used in modern logistics.
One recent usage of an ARIMA model was a 2016 study aimed at understanding and streamlining shipping traffic between the Far East and Northern Europe.
Machine learning could have produced similar results, but the ARIMA model gave a sufficiently clear picture for logistical planning.
A Dynamic Pairing
Despite the differences, it makes sense that predictive analytics and machine learning are often found together. Predictive analytics is one of the newest and most exciting applications for machine learning at an enterprise level.
One reason for the interest is the sheer volume of data involved in operating a business.
Sales numbers, production processes, inventory control, website activity, social media- there’s far too much data to process in a timely manner without artificial intelligence strategies.
Data is only useful when it results in actionable insights. Machine learning provides those insights with growing reliability.
These companies create more efficient marketing strategies, are better prepared to act on time-sensitive opportunities, and often see fraud risks and security threats far enough ahead to limit the potential damage.
Even in cases where statistical methods of predictive analytics can be applied, machine learning has advantages.
Other techniques are limited to considering factors the user identifies, but machine learning algorithms don’t need to be told what’s important.
They find patterns that may only be visible in the aggregate. It’s a highly efficient way to do predictive analytics, too.
Fewer humans need to be involved in processing machine learning results, making them less prone to error in general.
Real Word Usage
With more convenient and cost-effective cloud computing on the rise, machine learning is poised to become the business world’s favorite way to do predictive analytics.
The technologies can already be found in many areas of operation.
Refining marketing strategies: Which activities have the highest ROI? Which activities don’t produce appreciable results?
Customer segmentation: Who are your customers? How are they alike or different?
Optimizing inventory/ordering systems: How much inventory should be kept on hand at this specific time? When will demand increase or decrease?
Predictive pricing: Where is the “sweet spot” between reasonable profit and customer satisfaction? When should it change in response to external events?
Recommendation engines: Based on past activity, which future activities will a specific customer enjoy? What kind of recommendations will inspire increased engagement?
Artificial intelligence and machine learning have been trending upwards in use for some time now.
Besides the undeniable cool factor, they satisfy the need for personalized service delivered more efficiently.
There will always be a place for other predictive analytics methods, but as business problems grow larger to fit into the global marketplace those other methods become awkwardly labor-intensive or inaccurate.
Machine learning can adjust itself to match a project’s scale. This flexibility makes it a necessary part of an executive’s digital tool box.
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Developers have been pushing two goals this year: efficiency and customization.
The most popular trends have been those that either streamline the development cycle or offer features tailored to the changing needs of end users.
Mobile technology is beginning to take center stage for developers (you can read about our top mobile trends here) but there are some exciting trends brewing in the general software community, as well.
Here’s a closer look at the ones drawing the most attention:
Consumers have heavy expectations of customer service.
When contacting a company via the in-site messaging service or social media, nearly half expect a response within an hour.
A third want a reply in thirty minutes.
Rather than maintain a twenty-four-hour customer service staff, companies are adopting chatbots and chatbot-enhanced technology to keep customers happy after hours.
Chatbots employ Natural Language Processing (NLP) to let programs understand free form questions and reply in natural speech patterns.
Gartner predicts that by 2020 customers will manage 85% of their brand interactions without speaking to a human.
Macy’s has programmed their On Call feature to provide some more custom services: personalized product recommendations, directions to items within a store, and responses to common shopping questions.
Platform as a Service (PaaS)
PaaS is a cloud-based solution for developing, operating, and managing applications.
PaaS providers supply both hardware and software services to developers, who can log in via a web browser and begin building with a minimum of setup.
Some experts predicted it would fizzle out due to concerns over security risks and lack of developer control, but it’s been enjoying a resurgence in popularity in 2017.
Open source databases like MySQL and MongoDB have long been popular because of the lower price point.
Now software built on other open source technologies is increasingly in demand for other reasons: scalability and innovation.
The exploding collection of available plug-ins lets companies create software that meets their exact needs.
With technology changing at such a rapid pace, agile methodologies are more important than ever.
Agile emphasizes responding to change on an ongoing basis, listening to client feedback, and delivering many small portions of a project as they’re finished rather than holding everything until the end.
This year Agile is being praised for one byproduct of iterative development: security.
Bugs which might cause vulnerabilities in the finished software can be fixed after every mini-deliverable instead of waiting for the end.
Doing so keeps the development timeline short while improving the quality of the final product.
Automation has been edging onto the development scene for a while.
Automated actions are often confused for intelligent software, though unlike AI programs automation doesn’t use past experience to refine its algorithms.
It involves maintaining a list of processes which are triggered by specific conditions.
Some common processes are spam filtering, offering to resolve an error message rather than simply alert to the error, sorting new customers, and sending follow-up emails after a comment or complaint.
Though it lacks the responsive nature of a chatbot, automation does give end users an additional suite of partially customizable features.
Artificial Intelligence/Machine Learning
With cloud computing and storage becoming widely affordable, experimenting with artificial intelligence has never been easier.
This year the focus is on using AI to improve the customer experience.
Consumers want personal service around the clock, and AI is a cost-effective way to provide that service regardless of time zone.
Look for AI and ML-powered customer-facing features like online assistants, website design tools, facial recognition, and recommendation engines.
Data is being created at a faster rate than ever before, and companies are looking for solutions to make sense of their data.
Those solutions may be custom software, embedded analytics, testing procedures, or simple process changes.
Whatever the form, data science is becoming an essential part of software development.
Experts predict the drive for customization will continue through the end of the year and beyond, so we can expect more emphasis on features like chatbots, intelligent programs, and automation in the coming months.