Mobile Advertising Meets Streaming Media

woman using smart phone with virtual data surrounding her hands closeup

Streaming is quickly becoming a trend in mobile advertising.

Mobile Advertising Meets Streaming Media

Streaming media, from its onset, has been able to deliver captive audiences. It has also always had the ability to be used to target parameters built around your audience’s interest. Nothing much about the world of streaming media has changed except the quality and technological advancements that have made it better.

This conversation has less to do with streaming media and more to do with its sudden rise and culture-driven success. So what has changed to create this epic rise in usage? What is the impetus behind this culture shift that has empowered the growing popularity of streaming media?

Mobile Advertising Meets Streaming Media

Sometimes it just takes some time for the perfect relationship to develop. Mobile advertising isn’t necessarily new and streaming has been around for just as long, in fact, it isn’t as if these two are strangers. Streaming to mobile devices has been around long enough now that the technologies that supports streaming mobile options are as good as it gets.

One infographic reported an “87% increase in mobile advertising over the last five years.”

Advertising via mobile devices and using streaming media to fuel these efforts are now a practice that is becoming commonplace. This was the first step in the rise of popularity we are seeing in using streaming mobile advertising options.

The World Meets a New Generation

The “baby boomer” generation truly shouldered the advent of technology and the internet. Now it is time for the next generation to carry that forward. This is precisely a primary reason why we are witnessing a shift in how people are getting their information, where they are getting their entertainment and how they communicate.

How people are getting their information and news today, is often through a mobile device. Where people are getting information and entertainment is through more interest-based programming. How that information and entertainment is being transmitted is often through streaming media and more often everyday. This is the influence of Millennials and it is only beginning.

Although the biggest factor driving the growing popularity of streaming media is the generational demand, streaming media itself affords many benefits.

The Benefits of Streaming Media

In addition to being the ideal vehicle for delivering programmatic entertainment and information, there are many other reasons to use streaming media.

Affordable Advertising

The investment in traditional TV advertising is declining while more TV alternatives than ever before are appearing at a record pace. HBO Now, Hulu, Netflix, or YouTube are channels likely to be the most valuable to this fast growing contingent of “Content Connoisseurs.”

Building Brand Awareness

While the benefits of advertising at a fraction of the cost in comparison to traditional TV and radio advertising, streaming media also offers long-term benefits. Through the various streaming platforms, effectively using influence and building brand awareness has never been easier. Benefits like these continue to provide an ROI well beyond the numbers.

The Facts About Streaming Media

Speaking of numbers, there is plenty of support and reason based on the statistics to believe that, not only has streaming media arrived, it is here to stay. One recent story reported these findings…

“…programmatic video grew by an exponential 155 percent and now accounts for more than 45 percent of total online video ad spend.”

and…

“Mobile continues to be the “most” programmatic format, with 65 percent of mobile ad spend traded….”

Streaming video is how the next wave of consumers are going to be reached and it is going to be how traditional TV viewers eventually get their programming. Add to this the mobile-everything world we live in and it becomes evident, streaming media and mobile are the hottest new tandem in advertising. Contact us to find out more about this new media landscape and how streaming media and mobile can work together for you.

Brand equity

Customer Personalization: Building Relevancy, Loyalty, and Revenue

Personalization is more than just putting your recipients’ first name in the subject line of an email address. Truly personalized customer experiences can set you apart from your competition, and help your business develop fruitful long-term relationships with your audience.

True customer personalization is not easy. But executed correctly, and built on tangible audience intelligence and research, it can help you build and improve brand relevancy, loyalty, and revenue.

The Essence of Customer Personalization

We’re long removed from mass markets in which brands simply produce a one-size-fits-all product and surround it with a compelling marketing message. Going in 2017, personalization is key to success.

Every member of your target audience has unique needs, wants, and preferences. They may fit into overarching themes, but still diverge in significant ways. A single stay-at-home person will look for very different features in a new vehicle than a business professional will need for their daily commute.

Naturally, only personalized product development and messaging can equally address both of these needs. Do it right, and you’ll build a loyal customer base through relevant messaging and product development that brings in consistent revenue.

Building Message and Product Relevancy

The more you know about your audience, the better you can personalize your strategy. Account-based marketing is built on the fact that each potential client needs and deserves a customized promotional and sales strategy optimized for their unique buyer’s journey. True customization embraces the same concept.

Brands are beginning to recognize the importance of placing potential customers at the center of your messaging strategy. Caveat emptor is no longer a popular phrase; in fact, in 2017, it’s a sure way to failure.

Personalization, on the other hand, can accomplish increased relevance for each of your customers. And the results, especially as they relate to creating a more relevant messaging and product strategy, are both tangible and significant.

A 2015 survey among marketers found that personalized marketing plays a significant part in driving brand engagement and conversions. Similarly, 94% of businesses in a 2013 survey considered website personalization to be crucial to their business success.

Especially in e-commerce, but ultimately across industries, personalization is key. A 2015 survey of 500 online shoppers found that 3 out of 4 retail emails are irrelevant to consumers’ current needs and preferences. Simply blasting out a promotional email to your entire contact database, in other words, will not lead to success.

Improving Brand Loyalty

Not surprisingly, personalization after the sale can also make a difference in engaging your customers and increasing brand loyalty. In the end, driving repeat purchases depends on two variables: satisfaction with the product, and a perceived brand relationship. Effective personalization can play a significant part in influencing both.

Personalization and Product Satisfaction

As this Forbes article points out, allowing your audience to customize their products speaks directly to their preference:

A Bain survey of more than 1,000 online shoppers found that while less than 10% have tried customization options, 25% to 30% are interested in doing so. While it is hard to gauge the overall potential of customization, if 25% of online sales of footwear were customized, that would equate to a market of $2 billion per year.

It’s a natural effect. If you allow your audience to customize their product, they’ll be more likely to appreciate the result. Because they take ownership in the final product they purchase, they have a stake in seeing it succeed.

In addition, product customization allows your customers to build products specifically designed for their needs. No brand, especially on a larger scale, can reliably offer customized product options for every single potential customer. Customization at the point of sale, then, adds an extra element of personalization that would otherwise be impossible to achieve.

Personalization and Brand Relationship

Whereas product customization is at the core of customer satisfaction, message personalization is the driving force in building a sustainable relationship between brand and customers. The more effectively you can speak to individual customers, the more likely you will be to gain their appreciation and drive them toward a repeat sale.

Numerous studies have shown that personalized messaging results in an uptick in metrics that range from email to app installs. Again, it’s a common sense conclusion: the more relevant you can make your message, the more likely your audience is to respond to it.

After the sale, of course, the impact of that type of message personalization is magnified. Now, you have a large scale of information about each customer that you can use for your messaging relevancy. The type of product bought can result in follow-ups relevant to that product, while the personal information like a birthday can be used for customer-specific promotions.

The result, naturally, is a relationship between customer and brand that ultimately drives loyalty.

Driving Sustainable Revenue

Providing a more relevant customer experience drives loyalty. Repeat purchases, in turn, increase your sales significantly. In fact, 82% of companies agree that it’s cheaper to convince an existing customer to buy from you again than it is to acquire a new customer.

It’s not a stretch, then, to conclude that customer personalization can be a significant tool in helping you build and improve your revenue in a sustainable way.

A 2015 report by VentureBeat provides evidence for that point. A 2014 article by Adobe confirmed as much: if 9 of 10 consumers state that personalization has an impact on their purchasing decision, it’s no surprise that 8 of 10 brands embracing the concept have seen their revenue rise as a result.

Personalization matters, both for first-time customers and repeat purchases. Shared values build brand relationships, and it’s on the brand to both find out what values you share with each customer, and communicate these values in an effective, sustainable, personalized manner.

How to Build Personalization Through Actionable Analytics

Why should you personalize and customize your marketing and product strategy? Given the strategy’s impact on your messaging relevance, brand loyalty, and revenue, the answer is clear. That leaves one, final question: how can you build an effective customer personalization strategy?

The short answer: effective, actionable analytics. To be successful, personalization requires an effective method to not just collect data about your audience, but organize and prioritize that data in a way that can help you gain actual, valuable insights about your audience’s needs and desires.

It’s no surprise that brands who excel in customer personalization also embrace big data. At the same time, the second step – data analysis – is just as important. Without it, you risk ending up in a swamp full of irrelevant data. In that case, finding the data points that actually allow you to personalize your strategy and speak to your audience is akin to the proverbial needle in a haystack.

Find the insights that make your potential and current customers tick, and you have a starting point. Then, design a messaging strategy around these insights to ensure positive, sustainable customer personalization.

For example, you may find that in your industry, customers express very different needs and desires around a general ‘improve my life’ pain point. The more you can narrow down these needs, the better marketing strategy you can build to ensure that each of these needs is adequately addressed in personal communication with your audience.

Through modern analytics and marketing solutions, you can build this type of personalization on a mass scale while still ensuring enough human touches to encourage sustainable brand relationships. Customer personalization, at its heart, is just that: a relationship with your brand that, when successful, builds relevancy, loyalty, and revenue.

Neurons

Neural Networks in AI: Contextual Personalization for Enhanced Business Value

Artificial intelligence grows incredibly more sophisticated every year, and one of the most interesting developments is Neural Network Intelligence. If you thought AI has already turned a corner on mimicking human intelligence, Artificial Neural Networks (or ANN’s) might soon make this happen faster. The point is to imitate more rational thinking and deductive reasoning capabilities.

Up until recently, trying to replicate complex functions of a human brain wasn’t possible in AI programs. Things have started to advance quickly, and it’s time to learn about what this means in AI to enhance your business’s value.

In many cases, it can lead to more personalized experiences for your customers to help them and help you make smarter business decisions.

Overall, this progresses your ability to streamline your business’s commerce structure, hence leading to improved business aspects that bring higher revenue.

Before you get there, though, you need to learn about the technological and scientific aspects behind Neural Network Intelligence. It look more and more like a human, including learning through continued user interactions. In this case, it works similar to machine learning where it builds up superior intelligence over time.

Let’s look at how neural networks work and how you can apply this to bring more contextual personalization to customer experiences.

The Science Behind Artificial Neural Networks

When you delve into the science behind “ANNs”, the intention is to recreate brain connections using silicon and wires. Thanks to new advancements, AI recently transformed enough to build something resembling neurons and dendrites.

This occurs by creating multiple nodes, mimicking how neurons work. Just like neuron links, nodes have output as well, otherwise known as node values. Each of these connections have a weight, or an integer number controlling the signal. The weight in each connection can become adjusted based on the node output’s quality.

The topology behind this fall into two basic categories: FreeForward and FeedBack. For the former, it’s used strictly for pattern generation, recognition, or classification. In the latter, you’re creating feedback loops. In other words, it’s where you get into neural patterns to help you make better business decisions.

Since you’re bringing machine learning into this concept, you’ll frequently see neural networks use several different learning strategies. Supervised learning is more for pattern recognition, but unsupervised learning uses clusters to help find hidden patterns.

Reinforcement learning goes on observation, and that’s where Neural Networks truly shine to change how you create personalized experiences.

Processing Information in Real-Time

You’ve likely read a lot about real-time tools and how incredibly useful they are to make faster business decisions. AI now plays a major part in this thanks to Neural Networks. The latter uses human brain functions to learn through processing information in real-time so it becomes “smarter” with more user interaction.

This continues to improve and adjusts to any changes based on what a user prefers. For instance, if a user has specific preferences, the AI program is going to alter itself to suit a customer’s buying habits and whims. Any volatile behavior allows adjustments based on sudden changes in customer preferences.

What this does is bring recommendations on how you should approach communication with your customers. In metaphorical terms, it’s AI acting as an all-thinking oracle giving real-time results on how to personalize the marketing and buying experience.

In all, this replicates the feel of customers interacting with a well-trained sales associate. Instead, it’s done entirely online to give a customer the ultimate buying experience tailored just for them.

The problem is, many companies continue to use outdated forms of AI that don’t completely look at the customer as an individual.

AI Platforms Looking at Population and Probability

To show how fast AI changes, many businesses still use an older version of AI using recommendations via study of populations and probability. A couple of years ago, this was the best choice to personalize customer relationships. While better than no personalization at all, it still didn’t dig deep enough into analyzing individual buyers.

The focus was more on past behaviors as a whole, which was a good introduction for what AI could do for businesses. Also going by probability, it only gave a partial picture of what a customer might or might not do.

Having AI think like a human brain allows it to think more abstractly and fully understand consumer complexity. No one person is alike, and each customer is going to have their own pain points to integrate into your personal approaches.

Another weakness of older AI is it didn’t effectively accommodate new product lines in your business. Cutting this out of the recommendation schema created mass blind spots to product catalog performance. The only solution was to add it manually, and this led to downtime and lost revenue.

The Business Value Impact of Neural Networks in AI

You’ll find significant evidence showing personal one-on-one experiences are a vital part of today’s commerce structure. Regardless, many marketing analysts note that personalizing experiences can backfire if you don’t make it relevant to a customer’s life.

This is where Neural Network AI is going to help bring major business value by further understanding customer likes, dislikes, and intentions.

In the end, you’ll be able to increase more sales per customer, increase customer retention, create more loyalty, help your shopping cart conversions, and improve customer retention value.

Artificial Intelligence (AI) & Analytics: Enablers of Personalization & Brand Loyalty

Analytics and AI are a powerful technology package and individually, are two enablers of personalization and brand loyalty today. This is why marketers have to react now in order to figure out how they can best use them to their advantage. Older approaches towards strategic decision-making using promotional response curves, marketing mix modeling and multivariate statistics, are what businesses have relied on for years; and, they worked. Today, however, with so many stakeholders, messages and channels interacting with each other online and in the cloud; relying on outdated tracking methods is not enough and won’t keep you competitive. Artificial intelligence and machine learning based analytics are what’s happening in the marketing arena today. Companies who are giants in the business world are already using this combination to stay competitive. This newer technology is helping them make decisions that are smarter and faster than others. I’m talking about–Google, Facebook, Snapchat, Netflix, Amazon and Uber–just to name a few.

Innovations in technology have made it so that businesses have to change their tactics and communicate with customers in real-time. They want immediate answers and with communication being so flexible and personal these days; it’s also making consumers’ expectations and behavior flexible as well. Now, they expect businesses to understand their needs and provide relevant and desirable information, products, services and solutions right away. If you don’t interact with them immediately, this is a time where people will sometimes just go look for these answers elsewhere. They have access to hundreds of other options. Mabbly.com tells us that with searches now originating more from virtual assistants like Siri and Cortana, instead of search engines, marketers also have to know how this voice search is affecting their online presence and how this will affect their existing SEO strategy.

Without a more sophisticated process that helps you intervene so you can adjust accordingly and better fit these changing trends, you won’t be able to match financial goals with accurate marketing decisions. AI sorts through and gets rid of any unnecessary data so you can focus on the specific information you need to help your business stay relevant. AI really is your saving grace when it comes to these decision-making situations because most businesses don’t have the time or staff necessary to sort through all the social interactions their customers can initiate. There are business tools available that provide sentiment analytics, paired with AI, so it is possible for companies to take more useful data from this massive amount of online interactions. They can then use this information to recognize behavioral patterns in their customers, and formulate appropriate marketing plans that will maximize sales and increase brand loyalty.

How does AI actually work?

Artificial Intelligence is a field of Computer Science where they provide machines with the ability to perform rational tasks. Machine Learning is a field of AI where it’s all about pattern recognition. Various algorithms are used over a huge set of data to predict the future. Machine Learning is data driven and data oriented which makes this so effective. When brands have this easier way to process and understand millions of interactions with each customer; those interactions can then be used to understand each customer as an individual. Here are some examples:

Customer Service

AI is already helping in this area by clearing away some of the more routine elements of customer service. For example, chatbots or chatter robots, which are a type of conversational agent, are used to answer standard questions and even make recommendations for restaurants, gifts, services, etc.

Personalized Recommendations

In these situations, AI analyzes huge amounts of data–both from the customer and from similar accounts–and predictive analytics then make educated guesses about customer behavior.

User Experiences and Interfaces

Apps are intuitive devices and AI takes them one step further. VB Live tells us that Flok (one of the first loyalty apps with over 100,000 clients in the U.S. and Canada today) found out that when AI is in control of their push marketing, instead of a real person, this actually works better; 3.8 times better as a matter of fact!

Intelligent analytics work hand in hand with AI to help us understand the reasoning behind the answers, predictions and recommendations our customers get. This is because the one thing that AI cannot provide alone, is insight. In an article on Computerworld’s website, Kris Hammond tells us that “No one would ever work with a person that just spits out answers and then walks away.” How can we expect any less from our machines? Analytics provide the storytelling capabilities that are necessary for more clarity and go beyond just what the numerical output of an AI processed dataset tells us. This combination of technologies working together, allows a brand to boost customer engagement and loyalty more effectively because they can actually see and understand the reasoning behind this data.

Artificial Intelligence or (AI), has actually been around since 1956. Back then, this was probably unbelievable and a concept the general public couldn’t understand. Now, it’s a reality for everyone. These days, we have virtual chatbots with personalized images, recommendations and insight they get from customer data. People who heard about this in the 1950s, probably would not believe how far this has come. Marketing Week tells us the relationship between men and machines is constantly changing and that it’s just a matter of time before AI will be an everyday element in daily customer service interaction. As you can see, AI & Analytics are a powerful technology package and will determine how effective your marketing strategies will be in the future.

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.

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