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Artifical intelligence

Artificial Intelligence, 21st Century Growth, and Creating a Better Customer Experience

Artificial Intelligence has long been a source of fascination for mankind; the idea of machines with the power to think like humans is both incredible and confounding. There are many technical concepts that make up the science of AI, but its main goal is easy to summarize: the objective of AI creation is to build intelligent computer programs. The world of science fiction has hijacked the concept of AI, convincing many that artificial intelligence aims to create lifelike robots. No, AI science is not out to create a race of droids, it is determined to build machines that are useful to humans and the increasingly complex businesses that we conduct. In fact, AI systems are primarily used by major corporations for the purposes of sales, service, and marketing.

Ready to move beyond what you think you know about artificial intelligence and learn about its true applications? Read on!

Artificial Intelligence: The Basics

The most basic definition of artificial intelligence is this: AI is the science and engineering dedicated to creating intelligent machines. Now intelligence, in this sense, refers to the ability to successfully compute data to achieve meaningful goals. It should not be confused with the concept of IQ as it rarely aims to simulate human intelligence and cannot be measured at a comparable rate of development. Though some IQ test questions can be helpful in the development of AI programs they are generally programmed to be highly specialized in one or two particular areas of computation. AI’s ultimate aim is definitely to achieve human-level intelligence, but only in the sense that a program has the ability to problem solve and adapt in order to attain goals as well as humans.

Artificial intelligence research began after World War II and has been consistently researched ever since. It is an ever-changing field, constantly updated and always benefiting from advancing technology. Capabilities that are currently classified as AI are as follows: speech recognition, playing strategic games at high levels, data interpretation, and now, self-driving cars. The progress of AI is practically a constant as new developments and faster technologies come into play.

Measuring AI: The Turing Test and Beyond

The English mathematician, Alan Turing, is thought to be the first to study the concept of artificial intelligence. He gave a lecture on the subject in 1947 and seems to have advocated AI research through computer programs as opposed to building new, untested machines. Turing also developed a set of conditions which are even now used to test the intelligence of a program. What is now known as the Turing Test argues that a machine that can successfully pretend to be human should be considered intelligent.

However, it should be noted that this one-sided test is rather outdated as much of our current AI technology does not focus on imitating human behavior. Much of the AI technology of today relies on the concept of machine learning and focuses on high level computations that are often beyond typical human capabilities. As a result, measuring artificial intelligence with this system has become less pertinent as a requisite to claiming intelligence.

The Brains: Machine Learning

Machine learning is at the heart of AI research today. Where previous iterations of artificial intelligence were comprised of computer programming tricks, machine learning truly captures the ability to model after the mind. Machine learning is what allows machines to sense, analyze, and learn from the external world. This kind of technology is applied in fraudulent bank alerts, your smartphone’s ability to recognize your voice, and even what items you’re most likely to be interested in purchasing on Amazon.

The techniques and tools that are applied in machine learning truly give a program the ability to think by creating algorithms based on mountains of collected data. Predictive analytics and pattern recognition make up the bulk of our 21st century AI applications. This technology helps our devices and businesses become smarter by helping us make better decisions with faster, more accurate information. AI is an augmentation of what we already know or understand not something that seeks to replace us. It is a technology that can – and already does – make life easier.

AI and Your Business

So how can AI and machine learning be applied to the growth of your business or creating a better customer experience? Easy. Feed a program data and learn from the predictive results and analytics it gives back. Truly, there are so many applications for this kind of technology. It has been used in education, finance, and medicine with great results, becoming more accurate with greater data stores and guiding human strategies by carefully arranging and analyzing said data.

Below, are a few bullet points highlighting the areas in which a firm can benefit from AI technology.

  • Sales – Through customer demographics and buying patterns AI can determine your best potential leads. Harley-Davidson recently introduced AI to its business strategy and reports that it drives 40% of its sales in New York.

 

  • Marketing – Analytics and predictive programming can help you deliver targeted marketing that delivers the next product, content, or offer you want you current and future customers to see. AI can also determine when the best time for engagement is, sending messages directly to customers for direct engagement.

 

  • Service – AI has the ability to handle your customer service needs by predicting questions and complaints while following strictly programmed parameters. Chatbots can engage your customers and help them navigate your business in order to keep customer satisfaction high.

Artificial intelligence is changing the face of business in almost every industry. By adding to our already vast human abilities and knowledge AI is helping us move forward in countless ways. The forecasting power of AI can grow your business and create a more succinct customer experience. And the best part: with continuing advances in computer technology and AI research, we’re just getting started.

Business concept, businessman working with smartphone. Architectural plans on the

Customer Journey Analytics: How to Engage Quicker and Win More Customer-Driven Brand Loyalty

Customer Journey Analytics: How to Engage Quicker and Win More Customer-Driven Brand Loyalty

 

When you can manage the customer experience in a more creative and strategic way, consumers will have good thoughts about your brand when they are in research mode and during the time they make their purchases. When you can provide them with more useful and inspiring information, this helps you win over new customers and has a positive impact on customer-driven brand loyalty. Searching online and asking specific questions is a key experience for a consumer and is not just about product-related information. Customer journey analytics help reach out to people during these searches because they help you understand what is behind these customers’ actions and what motivates them towards making their buying decisions.

When you have a full picture instead of just a snapshot of who your customers really are, and can see patterns in how they behave, this greater insight will allow you to know where they are (physically) when they research online; what times they like to do this; and, the devices and channels they prefer. Using big data to help you understand the ‘why’ behind this behavior will help you develop marketing strategies that will have a greater impact on their final decisions. Did you know that a customer completes roughly 60% of his/her decision journey before they even connect with your brand? Here’s how using more in-depth analysis will help.

What Are Customer Journey Analytics?

Web analytics have been around for a long time and help us understand what the customer is doing while they are on our website, but these customers now have different devices and channels they use and are not dedicated to using just one. When they do research, compare prices and make their purchases online or in a physical store, you have to be there with the right information, at that time, so you can close on that deal. Journey analytics combine big data technology with advanced statistics so you can create a positive buying experience for them and help verify the decisions they make. When you can identify data across different customers, channels and touch points, all at the same time, you can make highly informed business decisions quickly.

More In-Depth Data Gathering

Customer journey analytics help gather data from different channels like webpages or social media sites, so you can review those metrics and get a better idea of the types of interactions that happen there. This search data is so valuable to your business because you can see what customers talk about, what they click on when they visit and what their actual needs are. This is especially true on social media sites where reviews can determine whether or not your existing website is providing the right information buyers need.

ReviewTrackers.com tells us that reviews can affect brand reputation in two different ways. 4 in 5 consumers will reverse their purchase decisions based on negative reviews and positive reviews will push them towards paying even more for products and services than they originally planned. This is why it’s so important to gather this data and connect with these new buyers at the start of their buying journey. At this point, they are usually looking for educational material, customer reviews and testimonials to see what’s out there, and as they interact with your brand; this is a critical time to capture, understand and engage with them as they move along their decision journey.

Know Where Your Customers Are

Customer journey analytics help link together a single customer view, across different channels and devices, so you can understand how your customers interact with your brand. This helps you follow in your customers’ footsteps, gives you more knowledge about where they actually are and what they use to access information throughout their entire life cycle. This requires an investment in the right type of analytics that gives your business the flexibility it needs to have better response times in an ever-changing mobile world.

When you can deliver relevant content in the formats your buyers prefer, which can change during the buying process, this keeps them engaged because they know the information they receive is consistent. By doing this, you generate more customer-driven brand loyalty and people will want to come back, buy other products and services and give positive reviews to all their friends. Information, provided at the right time and through the right channel, is the difference between having an engaged user, an unsatisfied one and a transaction that drives business value.

Put That Data into Action

After gathering and reviewing this data, you have to act on this information to achieve real business results. Emailmonday.com reports that 66% of smart phone users turn to their phones to look up something they saw in a TV commercial. While 91% of consumers, they say, check their email at least once a day which is how you can attract those buyers who are in the early stages of their journey. Gartner Marketing Researchers also tell us that by 2017, 89% of businesses will compete mainly on customer experience alone. This is because they know how important it is to maximize customer satisfaction in order to realize more success.

Consumers today don’t see their interactions with a brand on different channels as separate experiences. They see each interaction as just one step within a journey towards their ultimate buying decision. Whether it’s on the web, in an app or in a physical store, everything they do in the buying process connects them to you in some way. They don’t just buy the product–they buy the brand, the experience and have positive emotions when they feel as if they can depend on you. Your ultimate goal is to win their loyalty by providing a better customer experience and meeting their needs better than anyone else. By incorporating customer journey analytics into your business plan, this helps you understand what motivates them and inspires more creative marketing strategies.

Behavioral Analytics Help Companies Gain a More Nuanced, Detailed and Accurate Picture of Changing Market Trends

 

Why is it so important to have a way to predict human behavior? This is the way we win customers, elections and our ongoing battle with disease. With politics, it can help explain the behavior of a group, an organization or a nation. In healthcare, it is extremely important because this predictive activity will generate answers to questions that have an impact on saving more lives. In the world of business and advertising, it is important to our economical health because using these predictions can drive commerce and help businesses, entrepreneurs and their communities grow and succeed. When organizations can operate more effectively and determine when to make the right offers; to the right customer; at the right time, this gives them a more nuanced, detailed and accurate picture of the marketplace so they can respond better to any changing trends. How then does behavioral analytics play a part in this?

It’s The Way Successful Companies Do Business

Amazon knows what books you like, Netflix knows which shows you watch and your credit card company lets you know when your spending patterns change. How do they do this? It’s all because they use customer behavior to analyze and transform it into valuable insights that improve customer service. When these companies use behavioral or customer analytics in their decision-making processes, this helps with improving sales, market optimization, inventory planning, fraud detection and other factors that give them a competitive edge. These companies are so successful in analyzing customer behavior because they have a strategic plan for using these analytics to clarify their customer profiling. When you can gain an accurate understanding of which products and customer types are most profitable, this is because you have a plan that aligns with using analytics across the entire organization.

In order to measure the results of their analytical efforts more effectively, these businesses use the insights they gain through behavioral analysis so they actually make a difference, and are more actionable approaches to better customer management. What this means is that they realize their customers are always trying to reach out and connect with the types of products and services they sell. So, they use these analytics for research, data insight, context and predictive modeling to better understand and connect with their regular customers and to find better ways to attract potential ones as well. However, they realize that you can only accomplish this when there is total commitment from all the stakeholders in your company to make more data-driven decisions. For that to happen, leaders must have a willingness to invest the time and energy it takes to make sure that all managers across the organization have the right tools that align with the company’s data-driven mission.

Where Exactly Will Behavioral Analytics Help?

The major strategic benefit lies in the ability to analyze data using predictive models so you can gain insights into future customer buying behavior. There is also:

More Personalization

Personalization is when you can give your customers what they need, when then need it. This requires you to recognize what your audience segments are and an insight into their unique desires and preferences. As you know, customers have varying tastes, shopping styles and communication preferences, but how do you find an accurate way of knowing what they are? Behavioral analytics will allow you to segment targeted customers into unique groups made up of people who share common characteristics. This will allow you speak to them specifically in your marketing campaigns and you can serve content and messaging that is highly relevant to them. You can then offer customer experiences that are more engaging and improve your product offerings. This segmentation helps you uncover the 4 Ps of better Customer understanding:

  • Propensity – How likely will each segment buy my products?
  • Potential – What is the lifetime value from each customer I win over?
  • Profile – Who exactly are my customers; what do they like; and how can I reach out to them?
  • Preferences – Which of my products will they prefer?

 

Real-Time Campaign Monitoring

There is a wide variety of behavioral data that you can collect during marketing campaigns so you can better understand a prospect’s interest and their ultimate buying intent. Retailers use these analytics everyday so they can closely track customer paths across the entire sales channel. When used effectively, this allows them to sell many products and makes their customers keep coming back to buy more. Knowing when, where, how frequently and which transaction types their customers use (including in brick-and-mortar stores) lets them analyze and use this data to figure out which products their marketing teams need to focus on. When you can view, analyze and begin to understand user behavior in this more updated way, you can increase your outbound campaign volume and refocus them in a way so you will achieve the best possible results.

This is crucial for gaining deeper insights into how your audience is engaging with your brand and can help when you need to make marketing decisions quicker. A tweet mentioning your product, a news story or even a weather report can change trends in customer demand. Taking advantage of these opportunities by automating your marketing with real-time behavioral analytics, will allow you to capture new and repeat business in a more effective way. This is because you can align your messaging with their behavior and adapt your marketing strategies based on what’s happening right there in that moment.

Companies Who Know the Benefit of Behavioral Analytics

Amazon & Netflix

Dave Hastings, who is Netflix’s director of product analytics, says that over the years, both Netflix and Amazon have accumulated extensive data by analyzing every moment of a customer’s journey. With this analysis, they’ve managed to build a digital strategy that lasts.

CVS

CVS uses predictive behavioral analytics to understand which patients are likely more nonadherent or who don’t realize the dangers of partial treatment. This not only helps patients but the companies who develop medical products as well.

Delta Airlines

Akhil Anumolu, Manager of e-Commerce Optimization & Personalization at Delta Airlines was voted as the Digital Analytics Association’s 2016 Rising Star. This is because of his experience with using data and analytics, coupled with optimization & personalization, to improve both customer experience and revenue.

Wal-Mart, Target & Macy’s

These retail giants use behavioral analytics to delve deeper into their customer data to uncover insights and opportunities. By focusing on areas such as consumers’ preferred payment methods, they can better measure their goods in both online and offline sales.

Companies who use behavioral analytics in their business practices can more effectively collect, manage and use this valuable data for strategic purposes. When you use this information for a competitive advantage, you will have the necessary tool that allows you to make the right offers; to the right customer segments; at the right time. Having more clarity in customer profiling and knowing who bought what, when and through which channels, is essential in gaining an accurate understanding of which products and customer types are most profitable to your business. This knowledge of why customers act as they do, is what you need in a battle plan that leads to more success.

Data Science & Cluster Analytics: Profiling Customer Behavior and Other Patterns

You’ve no doubt worked with business data tools at some point in your business life, though realized one thing during use: The data wasn’t offering discernible patterns for more thorough insight. A major reason is, raw data hasn’t gone through enough processing to glean any reliable information. While some attempt processing, all it does is lead to a term that doesn’t always help: Cooked data.

With bad data a concern for companies in a time when analytics are extremely important for survival, you need other technologies to see all angles. Most importantly, you need something better defining customer behavior.

In other words, you should study data patterns, and raw data isn’t going to do this. A sub-segment of machine learning called cluster analytics is a major solution you’ve perhaps ignored in recent years.

If you’re new to cluster analytics and machine learning in general, it helps cluster elements in data that look similar to one another. It “learns” patterns in the data you accumulate and even helps distinguish which clusters of information aren’t alike.

While this is basic, you need to know more about what cluster analytics do so you can start personalizing your services and marketing strategies.

Understanding Cluster Analysis Methods

Despite this machine learning method continuing to evolve, it currently has three different methods top companies use to see customer patterns.

The first has a bit of a complex name: Agglomerative Method. In this process, you start with data in separate clusters. Then the program combines all data looking alike into a single cluster. Afterward, you choose the optimum number of clusters from the options given.

On the opposite end, you have the Divisive Method, which is almost self-explanatory. More precisely, it’s the Agglomerative Method in reverse. This helps remove non-alike data out of a single cluster for more clarity in your metrics.

You may prefer using non-hierarchical clustering methods for larger data sets. Some examples of this includeCanopy Clustering, which is a much faster method of grouping things together. It uses an algorithm to better sort data points looking the most alike.

However, you also have K-Means Clustering, which sometimes comes after Canopy Clustering. K-Means groups data sets together by numerical means for better organization.

So what things can you discover using cluster analytics? Let’s look at the insights possible and how much customer data you’ll receive if you have multiple stores across the region.

Average Customer Spend

If you have numerous business locations throughout Canada, cluster analytics helps sort out what customers spend throughout your market. Thanks to accumulating matching data, you’ll see how much customers spend every quarter in your stores. Plus, you’ll discover what they’re spending their money on.

These two data points alone are going to help you make smarter business decisions along your journey. You won’t have to go on generalities or second-guesses before making a strategic business move.

Average Customer Behavior

Digging deeper into your cluster analytics platform, you’ll start to discover basic patterns in customer behavior. Doing this is the most important of all, because it gives you a credible look into the future. Through these data sets, you’ll know the real reasons why customers buy what they do and determine what might make them occasionally change their minds.

You’ll save money and time in promoting products you think are going to sell, yet patterns show varying buying fickleness. Cluster analytics can even pinpoint why a certain product doesn’t sell based on customer personalities and pain points.

Helping Your Marketing Efforts

Don’t think cluster analytics only become important to your sales team. Using these metrics for your marketing team are just as essential. It helps greatly in market segmentation, which those in marketing need for better demographic targeting.

For market positioning, it’s very useful as well, and this includes better identifying test markets for your products. Clusters helps gather data on social media channels and where your customer base most likely hangs out. Now you can identify where your demographics are online and go there to start communicating with them.

All of this is an example in how machine learning turns your sales and marketing efforts into more of a personalized experience.

Machine Learning Works Silently in the Background

A recent blog on Oracle said it best when they described machine learning as being an invisible marketing force. It’s a good way to describe what it does while still helping you understand the customers you want to target.

The more sophisticated machine learning becomes, the more data it’s assembling for accurate customer portraits. With the online world now containing almost 25 years of e-commerce, machine learning can now take all this data and analyze millions of different people to give you a vivid customer persona.

It also helps if you’ve had customers for a while since it takes from what new and loyal buyers did in the recent past. Even better is machine learning now takes from online and offline sources. Never forget how offline data equally helps better define customers and leads to more transparency.

Cluster analytics now give you more purpose behind this data and assembles it within reasonable time frames. As such, you don’t have to wait to make valuable decisions. In a far more competitive business world, you can’t afford to wait and not make a move to one up a competitor.

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