Automatic​ ​Customer​ ​Classification:​ ​The​ ​First​ ​Step​ ​to Segmentation​

automatic customer classification

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?

Unbiased results

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.

Cluster analysis

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.

Looking forward

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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Machine Learning and Artificial Intelligence: What’s the difference?

difference between machine learning and artificial intelligence

Machine learning has fundamentally changed the way computers work.

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.

These fields covered diverse topics such as:

  • Language processing: Understanding human language
  • Computer vision: Identifying and handling unlabelled images
  • Genetic algorithms: A method for solving optimization problems using the same process evolution
  • Decision theory: The study of how decisions are made and why

Game Changing Shift in Focus

Machine learning is a subdiscipline of artificial intelligence that aims to give machines the ability to learn from previous experiences and use that knowledge in future interactions.

Essentially, a computer is given a pile of data and a machine learning algorithm to process it.

The algorithm sorts the data, adjusting itself after mistakes, until it can achieve the desired results with a high degree of accuracy.

Machine learning does require a data scientist to adjust the model, choose the algorithm, and subset the data.

Still, it represents a huge leap forward in teaching machines to think.

The advent of machine learning was a unifying force on AI research.

It’s wide applicability meant it could be used in many fields, and learning has become a much-desired characteristic of artificial intelligence.

There are two major types of machine learning.

In supervised learning, the algorithm begins with a training dataset of labelled input variables and output variables.

An algorithm is used to map the relationship between variables so that for any new input variable, the correct output can be predicted.

“Supervised” refers to how the training dataset is like a teacher checking the algorithm’s answers against itself.

Unsupervised learning deals with data that doesn’t come with a labelled set of outcomes for reference.

It finds patterns among blocks of data.

Cluster modelling, the most widely used unsupervised learning method, groups data points by their most relevant traits.

It’s often applied to sales data during the customer classification and segmentation process.

Other unsupervised methods include:

  • Pattern mining
  • Data mining
  • Image and object recognition
  • Sequence analysis

Machine learning in action

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.

Recognizing trends:

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.

Final notes

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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Machine Learning Vs Predictive Analytics: What’s the Difference?

In discussions about AI and its impact on business, the terms “machine learning” and “predictive analytics” are sometimes used interchangeably.

This can be misleading.

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.

Descriptive analytics

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.

Diagnostic 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.

Predictive 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.

Prescriptive analytics

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.

predictive analytics vs descriptive analytics vs diagnostic analytics vs prescriptive analytics

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.

Facial recognition

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.

Search engines

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.

Companies who adopt machine learning-powered predictive analytics gain a serious competitive edge on those who aren’t simply by being able to process more of their data. They stay one step ahead of their competition.

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?

Looking Forward

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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Find Your Best Customers Through Intelligent Customer Profiling

intelligent customer profiling

In the past, digital marketing used the same techniques as print advertising.

Marketers made their best guess about the demographics and keywords relevant to their product, measured a campaign’s success, and refined their approach from there.

Machine learning technology has changed that.

Now companies can use predictive analytics to build intelligent customer profiles, allowing them to maximize revenue by knowing which customers to target and how.

Underused Resources

The biggest problem from a digital marketing standpoint isn’t acquiring useful data.

Companies make data every day.

Sales patterns, loyalty programs, website traffic, customer volume, social media activity- all of these result in data that could provide valuable insights.

The real problem is that companies have struggled to find a way to effectively interpret their raw data into marketing direction.

More data has been generated in the last five years than in all of human history before then, yet only 0.5% is ever analyzed and put to work.

Instead, data ends up siloed within different departments of an organization.

Parts of it are used, but the bulk of a company’s data ends up aging out of usefulness in databases.

Deeper Customer Understanding

Predictive analytics can take advantage of that existing store of information by using it to build models of customers based on data, a process called intelligent customer profiling.

Although customer profiles have been a popular marketing tool for years, until now their creation was subjective.

There was no reliable way to know who was buying a product and why, so profiles tended to be limited to demographics and whatever information could be gleaned from focus groups or customer interviews.

Intelligent profiling- sometimes referred to as predictive customer intelligence– is worlds beyond that approach.

It draws on data gathered through the course of normal operations to add refined demographic and psychographic details to the customer profile.

Targeted Marketing

Having a better grasp on customers has major benefits for a company.

For starters, it provides a reality check as to who uses their services the most and why.

Is it the ideal customer from the company’s buyer persona, or is there growing popularity among a completely different demographic?

Customer profiling also serves to connect the data-gathering back end of a business with the action-focused front end.

In other words, predictive analytics is where the business value of data science lies.

It provides concrete suggestions that can steer the marketing team in the most profitable direction.

These are just a few questions intelligent profiling can answer:

  • Who is most likely to be a repeat customer?
  • What types of customers offer the highest ROI?
  • Who will respond to an email?
  • Why do consumers choose this company over its competitors?
  • What indicators suggest a customer will qualify for a program or loan?
  • Is marketing reaching the right sort of customers?

These profiles are powerful tools for the marketing department.

They can help reach full potential revenue on existing customers by revealing what matters most.

Alternately, they can reveal opportunities to target prospective customers, ones that weren’t included by the subjective buyer personas.

Intelligent profiling benefits more than the marketing department.

It guides the sales team in creating the perfect pitch for every profile type, each informed by hard data that reveals what matters most to that customer.

If you’d like to know more about how predictive analytics can empower your sales team, watch this blog.

We’ll be posting the second part of our customer profiling series next month.

Parting Thoughts

Knowledge is power, but only when it’s used.

Intelligent customer profiling is the path from inert data to actionable insights.

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Best Software Development Trends of 2017

software development trends

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

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 science

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.

Read about more industry trends from our blog. Here are the best web development trends and best mobile app development trends of 2017.

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The Best Mobile App Development Trends of 2017

mobile app development trends

One of the most important parts of being an enterprise technology leader- whether CEO, CTO/CIO, or VP of tech- is keeping an eye on emerging trends.

Technology moves fast, and that’s doubly true in an active field like mobile app development.

Knowing what features are gaining traction and which were just a passing fad can save a company hundreds of thousands in failed development projects.

With that in mind, here are 5 of the most promising mobile app development trends of 2017.

Enterprise Apps

Enterprise apps have proven their worth.

Adobe found that the average investment in mobile enterprise apps provides an average ROI of 35%.

Nearly three quarters of companies have built or updated mobile enterprise apps this year, up from 61% in 2016.

Apps are being used internally to collect maintenance data, streamline team projects, and even manage restaurant waitlists.

In fact, the demand for mobile enterprise apps in 2017 is expected to grow at least five times faster than internal IT departments can deliver.

Push Notifications

73% of consumers say that regularly getting useful information from advertisers is the most useful tool when selecting brands.

The key word is “useful”: people want short, timely notes that are relevant to their interests.

Push notifications meet this need, letting companies reach out to their customers without waiting for them to open an app.

The trend is noticeably more popular among Android users.

They’re twice as likely to open a push notification than Apple users (and twice as likely to opt into them in the first place).

Mobile App Security and Privacy

The Hewlett Packard Enterprise Mobile Application Security Report surveyed over 35,000 apps and found that over 96% failed at least one privacy test in 2016.

That’s changing fast; this year, security technologies like SSL, HTTPS, and advanced encryption are being incorporated into mobile app development as a rule instead of a special feature.

Companies are also becoming more selective with permissions as customers push back against intrusive apps.

If you are looking to tackle the rising challenge of security threats, you may want to read Balancing Speed and Security in Software Development.

Artificial Intelligence/Machine Learning Apps

Intelligent apps, or those that use AI/ML to power special features, are one of Gartner’s Top 10 Strategic Technology trends for 2017.

AI is making its way into every corner of the mobile market, from personalizing customer service to detecting fraudulent activity.

Starbucks has even announced an app that will translate verbal orders into an actual order placed at the user’s selected store.

Add that to the growing integration of personal assistant apps like Alexa and it’s easy to see why AI is a trend to watch.

If you want to learn more about AI and how it can affect your business, download our new white paper: How Businesses Can Use Data Science and AI to Gain a Competitive Advantage.

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Augmented Reality (AR)

AR has been quietly improving since it was first used in the 90s to train Air Force pilots.

Back then it required a bulky backpack and goggles, but now smartphones are pushing augmented reality into the public eye.

While most consider it to be a gaming technology, AR is increasingly relevant in e-commerce.

IKEA uses it to allow customers to visualize how furniture will fit in their homes, and apps like FaceCake helps users choose makeup shades online.


Whether it’s an enterprise app or one meant to capture the public’s eye, user engagement is key.

24% of apps are only used once in the first six months, but it takes 30 days to build a habit.

Incorporating one or more of these trends into your app can provide the value needed to make it part of your user’s daily habit.

Want to know more trends happening in this industry? Check out 2017’s list for best software development trends and best web development trends.

Are you having trouble retaining users? Contact Concepta to discuss how these trends can boost your app’s usage rates!

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