marketing
This year alone, the U.S. is projected to absorb a shortfall of 190,000 data scientists — and that’s not even counting the 1.5 million more analysts and leaders needed to make use of the information big data supplies.
This is an especially terrifying prospect in the marketing world, where data science provides the signals that let marketers know their decisions have paid off. “In the end, the analytics won’t tell you the next big creative idea,” Elea Feit, assistant professor of marketing at Drexel University, says. “It will tell you when the next big creative idea is working.”
Data scientists can use data points and trends to help strategize content, tweak content to meet demand, and measure the outcomes of the actions taken by marketers. They combine the science of statistical models with the art of creative work to go past the “gut feelings” of the “Mad Men” era and into a space where marketers can not only see a payoff today, but also a payoff tomorrow.
What You Don’t Know Can Hurt You
Having a wealth of knowledge is a huge advantage — until your knowledge surpasses others’ understanding. If people don’t know how to apply a significant piece of information, that data is useless.
This is why data science is so essential to the marketing equation. “ The most powerful data scientists are those who act as bridges between insights and people ,” says Kirill Eremenko, the founder and CEO of SuperDataScience, an online educational portal for data scientists and data science enthusiasts. “There’s a science behind analytics; however, communicating insights is an art.”
Straddling that line is important because data science insights are connected to marketing results. Marketing departments are expected to quantify their results as justification for keeping their budgets and strategies intact. Marketers handle digital information within their campaigns and collect it to improve their tactics, increasing the demand for data science.
Data science is responsible for mapping social networks and illustrating customer personas. It also identifies demographics and locations, in addition to tracking target audience responses and moods. Data science has enabled companies to customize their customer experiences. It also helps develop new approaches to long-held marketing challenges.
“Data is massively complex and comprehensive, which makes it difficult even for experts to understand,” says Eremenko. “Extracting insights is the first step, but the crucial follow-up is finding ways to communicate and contextualize those insights so they’re accessible to all.”
Application As Inspiration
If there’s a shortage of data scientists, what does this mean for marketers? Marketers have to learn how to use data science for their work on a global scale, and they need to position themselves for success, regardless of how accessible data scientists may be on any given day. 91% of senior marketers indicated that customer data was essential to making decisions. Here’s how marketing teams can take advantage of every piece of that data.
1. Break Down Departmental Silos
Data science can’t take into account data it doesn’t have. Department- or division-wide silos put up barriers where they shouldn’t exist, blocking one department from receiving data from another that could be valuable. How many times have you heard about content marketing teams having to start newsletter subscriber lists from scratch because the sales team wouldn’t share its email lists?
The same idea applies here. Find ways to allow your platforms to integrate and share data; at any rate, build systems to report data from one segment to another. Something seemingly small — such as your company Facebook page’s demographics — could influence not just your social media marketers, but also your SEO team, your affiliate marketers, even your R&D department.
2. Keep Your Streams of Data Current
Data has to be timely to be actionable — or, at the very least, it needs to include information from the past through the present to highlight patterns and trends. As big data analytics and visualization firm Zoomdata explains, real-time data analytics are the optimal option because they allow marketers to act on information as it’s happening. Streaming analytics, which occur nearly in real time, are a close second.
The focus is on fresh data so your decisions are made based on what’s best for your current market. But context is important, too. Creating entire trails or streams of data will allow your marketing team to see that a product that sold well last winter and dismally this winter may be influenced by bigger factors you’re also tracking, such as economic downturns or a declining audience segment.
3. Invest in Tools and Technologies, Particularly for Visualization
Data can only be gathered if you have the technology to do so. If you’ve been putting off investing in a data platform because you figure your team can do it manually, or you assume the information will sit in a database never to see the light, think again. Data not only showcases ROI, but it’s also ROI itself — you need numbers to justify numbers. Remember that you’re only as good as the information you have.
Visualization is an especially important tool to have in your data-gathering belt. Dynamic visualizations can simplify complex data and capture numbers in a graphic representation, which will speak more clearly to a wide swath of people. Most importantly, visualizations unlock collaborative opportunities for marketers and data scientists to discuss data together and interpret the data’s meaning for future campaigns and marketing efforts.
While we’re looking at a dearth of data scientists in the near future, that doesn’t diminish the importance of data science for marketing. If anything, it should compel marketers to set their systems up to benefit from data science and empower themselves by learning to broadly analyze data alongside data scientists. What you don’t know can hold you back — and what you do know can drive your company’s ROI.
Written by: ,
March 8, 2018
10 Tools Every Marketer Should Know About
Steve Jobs once said: "Technology is nothing. What's important is that you have a faith in people, that they're basically good and smart, and if you give them tools, they'll do wonderful things with them."
Not sure what tools Mr. Jobs had in mind but here's a list of tools that can help any marketer at any level do their jobs just that much easier and more efficiently.
1. Deluxe Logo Maker
Don’t underestimate the power of a good logo. Signs.com reports that about 94% of the world’s entire population recognizes the Coca Cola logo. You might be setting your sights a little lower than universal recognition, but a good logo is still essential for communicating your brand’s identity and values and helping it stand out from the crowd. Deluxe Logo provides multiple different price levels for custom-designed logos, ensuring you get the most out of this important branding tool.
2. Packlane
Shopify estimates that the e-commerce market will generate $4.5 trillion in annual global sales by 2021. That means a ton of companies need or are going to need packaging and shipping options for getting their product to customers. Packlane ensures that your customer’s “unboxing” experience is a pleasant and branded one. Companies can easily use Packlane’s user-friendly website to customize packaging style and size, create color schemes, and even upload original art.
3. Yotpo
Yotpo helps you leverage one of the most powerful branding tools you have: satisfied customers. ReachLocal reports that 90% of shoppers use online reviews to help them decide on a purchase, and 70% of consumers will leave a review if asked. Yotpo provides frictionless tools to help you solicit reviews from recent customers, as well as valuable information such as photos and Q&As. It automatically displays your best reviews on your website and across a wide variety of platforms, such as Facebook and Instagram. Consumers trust each other far more than they trust marketers--Yotpo uses the most of that trust.
4. Traackr
Influencer marketing is essential for digital branding. According to Marketing Profs, companies see an average of $7.65 return on each dollar spent on influencers. Influencer marketing can be informal and difficult to track, but Traackr provides a platform for monitoring influencer efficacy, discovering new influencer trends related to your topic, and establish long term relationships.
5. ReBrandly
Your marketing strategy likely includes distributing a URL, perhaps just to your homepage, or perhaps to a specific discount offer or blog post. But URLs, and especially URLs for pages within larger websites such as Facebook posts, can be ugly and off-putting for customers. Many URL shortening services will destroy your domain identity in the process, creating ambiguous links that customers find untrustworthy. Rebrandlyoffers branded domain shorteners, which means you can very briefly share a link to content while preserving your brand identity in the URL. Social Media Examiner reports that branded domains can increase click-through rates on Twitter by up to 34%.
6. Unbounce
Landing pages are essential for branding because they’re often the first real encounter a websurfer has with your company’s digital presence. The Landing Page Course advises that a dedicated landing page is essential for starting any new marketing campaign. Unbounce allows you to create aesthetically pleasing, brand-consistent landing pages for new marketing campaigns--quickly, and without having to learn any code.
7. Frontify
Your careful branding efforts can be easily undone if your branding seems inconsistent--customers might think you’re unreliable, or even assume your one great brand is, in fact, two lightweight ones. Brand consistency covers everything from your tagline to your color scheme. Pixelnomics recently recreated several famous logos in “regular” fonts, and the contrast between these recreations and actual logos will drive home that even font choice matters. With Frontify, your brand can create cloud-based, sophisticated style guides, brand portals, media and pattern libraries, and shared collaboration spaces.
8. MailChimp
Email marketing is another essential component of any branding strategy. Wordstream reports that companies can get up to $44 in returns from just one dollar spent on email marketing. MailChimp is an email automation platform that can be connected to any ecommerce website, providing custom email content and newsletters to potential customers. MailChimp’s services involve abandoned cart email reminders, beautiful email templates, and Facebook ads management.
9. WiseStamp
A simple but effective tool, WiseStamp lets your company create individual branded email signatures for your employees. Centralized control allows you to implement changes across all signatures all at once, emails appear professional and polished, and consistent branding and contact information is maintained no matter who in your company is sending an email or when they last remembered to update their signature. Popular online tutoring provider, Grad Coach, recently implemented WiseStamp and saw a 21% increase in social media traffic from email signatures, according to the MD, Derek Jansen.
10. HootSuite
HootSuite is one of the world’s most popular social media marketing platforms. It allows you to compose and schedule posts to a wide variety of platforms, such as Instagram, Facebook, and Twitter, all from one user dashboard. It can also allow your employees to coordinate social media marketing through task creation and assignment, and it creates analytic reports to help you figure out whether your social media plan is working.
Article by: ,
January 12, 2018
What is Deep Learning? Here's Everything Marketers Need to Know
The machines are here.
You may have heard rumors about artificial intelligence (AI) potentially taking over our jobs. And the question is: Should you be concerned?
In my opinion, we should be excited.
AI -- especially “deep learning” technology -- brings new opportunities and innovation in the way digital marketing, sales, and customer support are handled.
But what is deep learning? How does it work? And how can it be applied to marketing and sales in your company?
What Is Deep Learning?
Deep learning is a discipline within AI that uses algorithms mimicking the human brain. Deep learning algorithms use neural networks to learn a certain task. Neural networks consist of interconnected neurons that process data in both the human brain and computers.
Neural Networks in Advertising
Let’s assume we are an online car dealership, and we want to use real-time bidding (RTB) as a mechanism to buy ad space for our product on other websites -- for retargeting purposes.
RTB is an automated process that takes place in a short time frame of under 100 milliseconds. When a user visits a website, an advertiser is alerted, and a series of actions determines whether or not that advertiser bids for an ad display. Have a look at the image below:
Source: Periscopix
In RTB, we use software to decide if we want to bid for a certain ad -- the software will make a decision by predicting how likely the website visitor is to buy one of our products. We call that "buying propensity."
In this instance, we'll use deep learning to make this prediction. That means our RTB software will use a neural network to predict the buying propensity.
The neural network inside our RTB software consists of neurons and the connections between them. The neural network on the above image has only a handful of neurons. In reality, a digital neural network has thousands -- or even millions -- of neurons and connections.
In this scenario, we want to find out if a certain website visitor is likely to buy a car, and if we should pay for an ad to target her. The result will depend on the interests and actions of the website visitor.
To predict the buying propensity, we first choose several “features” that are key to defining this person’s digital behavior. In our example, those features will consists of which of the following four web pages were visited:
- Pricing.
- Car Configurator.
- Specifications.
- Financing.
Those features will influence the output of our neural network -- or, essentially, our conclusion. That output can have one of two values:
- The website visitor is interested in the product, or “ready to buy.” Conclusion: We should display an ad.
- The website visitor is not interested in the product, or "not ready." Conclusion: Do not show an ad.
How the Neural Network Functions
Let's have a closer look:
For each input, we use “0” or “1”.
“1” means the user has visited the webpage. The neurons in the middle will add the values of their connected neurons using weights -- or, more simply put, they define the importance of each visited webpage.
This process continues from left to right, until we reach the “output” neurons -- “ready to buy” or “not ready,” as per our earlier list.
The higher the value of the output, the higher the probability that this output is the correct one -- or the more accurately the network predicts the user’s behavior.
In this example, a website visitor looked at the Pricing and Car Configurator pages, but she skipped Specifications and Financing. Using the numerical system above, we get a “score” of 0.7, which means that there is a 70% chance this user is “ready to buy” our product.
So, if we look at our original formula, that score indicates the conclusion that we should buy the RTB ad placement.
Training of the Neural Network
Now that we know how a neural network functions, let's have a look at how to make sure our output neurons are calculated correctly, in order to make the right decision.
The challenge is to come up with the correct “weight” factors for all the connections inside the neural network, which is why it needs to be trained.
In this context, “training” means that we feed the neural network data from multiple website visitors -- things like visitor features (which web pages users have visited), as well as indicators of their eventual purchase decisions from us (which are labeled as "yes" or "no").
The neural network processes all these data, adjusting the weights of each neuron until the neural network makes appropriate calculations for each person within the training data. Once that step is done, the weights are fixed, and the neural network can more accurately predict the outcome for new website visitors.
The Future of Deep Learning
Democratization of AI
AI is quickly finding its way into marketing tools that we use every day. Take, for example, the AI-powered Chatbot builder by Motion.ai (part of HubSpot), which allows you to easily create and publish your own chatbot.
Another example is Dialogflow, a platform from Google that lets you build a chatbot for your company or service.
It certainly doesn't stop there. AI can assist with the setup of advertising campaigns, hyper-personalize emails, optimize lead scoring, categorize and escalate customer issues, and actually help you with anything that requires data processing or orchestration.
Deep learning can be applied in any area of digital marketing, provided that you have a sufficient amount of “training” data. The challenge is typically to extract data from your various marketing tools -- that's where data integration platforms like Blendr.io will be crucial in connecting your data silos when you start experimenting with deep learning and AI.
The Future: AI ... That Builds New AI
Google explains that the process of designing neural networks often takes a significant amount of time for development and experimentation, because all of the neural network layers have to be crafted by people. That's why Google invented AutoML: AI that can build new and better AI algorithms.
Imagine what that type of technology can bring to something like marketing automation, for example. The AI will be able to build additional, customized AI algorithms that will learn and automatically optimize nurturing campaigns, for example.
Though deep learning may sound complicated, it's a process that, much of the time, boils down to math. Neural networks “learn” in a manner similar to humans: by viewing many examples, and discovering the commonalities among them.
Once the neural network is trained, it can perform complex tasks and a certain level of reasoning. Deep learning and AI can be integrated into many aspects of digital marketing and sales automation. The machines are not coming -- they are already here.
Originally published January 05 2018, updated January 08 2018
Written by Niko Nelissen
photo by: H Heyerlein - unsplash
January 4, 2018
How to engage and work with Influencers [Infographic]
Photo by: Brooke Cagle, Unsplash
Infographic by: Scott Marticke
December 7, 2017
Five Trends Shaping The Future Of Customer Experience In 2018
Money talks.
Particularly when it comes to customer experience, money talks.
When it comes to money — the CEO decides. So the priorities of the CEO matters — because what the CEO focuses on grows. And generally there’s a lot of pressure on the CEO from the board to make quarterly dividends. With so much board focus on quarterly profits and growth, it’s hard for the CEO to truly focus on customer experience — which involves long-term investments, or being misunderstood by wall street for long periods of time.
The discussion of customer experience is dependent on a discussion about money — because views on money shape entire company.
A few industry leaders are taking a contradictory stance regarding money and the obsession with wall street and quarterly profits.
Co-founder and Chief Executive of BlackRock with 4.6 trillion in assets Laurence Fink recently said, “Today’s culture of quarterly earnings hysteria is totally contrary to the long-term approach we need." When he says “we need” I interpret that to mean the approach we need to make improvements in business. Fink also said in business quarterly earnings reports require executives to look back. He said, "quarterly earnings don’t articulate management’s vision for the future." Investing in customer experience requires a view into the future.
Investing in customer experience is an act of bravery, because often the returns don’t happen right away — and many boards and CEOs are not willing to wait around for that payday. And there’s truth to the fact that it’s costly and time consuming to improve customer experience. Amazon, often cited as the best in class case study for customer experience —doesn’t need to turn a profit, and possibly one of the reasons the company is able to take so many risks with its customer strategy. Amazon has been criticized in the past for its inability to make money. In the third quarter of this year Amazon boasted a profit for the 9th quarter in a row, however before that the numbers were very up and down year to year. According to Investopedia this is called a slow burning model to a sustaining business.
Paul Polman, Unilever CEO on his first day of work in 2009 announced that shareholders should no longer expect to see quarterly annual reports from the company with earnings guidance for the stock market. Polman said, “Put your money elsewhere if you don't “buy into this long-term value-creation model, which is equitable, which is shared, which is sustainable.”
The stock was worth $38 per share in 2012 and five years later is steadily increasing, and the day I wrote this it was $58 a share. If the CEO doesn’t focus on customer experience, in no way can you expect the company as a whole to improve customer experience.
Last Year 75% Of Companies Said Top Objective Was To Improve Customer Experience
Most executives think customer experience is important but that’s where it stops. In 2016 one study found that 75% of companies said their top objective was to improve customer experience.
So we can simply pack up our bags and go home right?
If you plan on going through a customer experience transformation the conversation needs to start in the boardroom. The CEO must drive the leadership conversations about the importance of pivoting to become a customer-focused company
Once you do that you can start making investments in the five areas I’ve outlined below in my annual predictions post — many of which have a technology focus. Technology isn’t everything, but it doesn’t matter if you have the most customer-focused culture in the world, if your technology experience doesn’t make customers’ lives easier and better you will lose customers.
2018 is going to be a big year for customer experience because now there’s c-level awareness that someone at the top of the company needs to be dedicated to driving it. In its fourth year, I’m happy to share with you the five topics that I believe are the most critical for 2018.
My top five predictions for 2018 include CEO involvement in the customer experience strategy, personalization, the use of data and decisioning, the customer experience cloud, and all types of augmented reality experiences for customers.
1. CEOs Get More Involved In The Customer Experience Strategy
The CEO sets the tone for your entire company. When it comes to how your company treats customers, it all starts from the top. The CEO is the leader of the organization and the person who people will look to every single day. In uncertain times, the CEO’s reaction sets the tone for the culture of the entire company. The CEO also sets the tone in their day-to-day actions by what they focus on. Is your CEO the kind that rages when the stock price drops quarter to quarter? Does the CEO impulsively lay off entire groups of employees rather than focus on transformational growth and innovation? It matters. Customer experience is directly related to employee experience, and the CEO has an influential role in palpably shaping that. Is the CEO more of a visionary or a firefighter?
The companies that have better customer experiences all have CEOs that care about customer experience and sew it into the fabric of the company.
For companies that have CEOs that don’t make it a priority, you cannot expect the customer experience to improve. Forrester reported this year that the top three challenges for a customer experience program include organizational culture (54%), organizational structures (45%), and processes (41%) and these three programs are all driven by the CEO. While most CEOs aren’t working in the contact center day to day, the companies that boast better customer experiences have CEOs that are heavily involved in the listening and feedback mechanisms at the company. The CEO knows exactly what’s happening on the ground of the company.
A CEO with an infectious attitude towards creating a powerful customer experience can lead a company towards growth and financial success. Some companies have a habit of telling employees that if there’s a problem at the company don’t look up. However this attitude makes no sense considering those at the top make all the significant decisions particularly when it comes to resources - and much of the employee experience, which shapes the customer experience is pre determined by the CEO. If the CEO invests in a customer-focused culture, then it will be felt in the customer experience. So yes employees should look up - because leadership must be accountable. While a company might have a chief customer officer, it’s the responsibility of the CEO to be the representative for the customer — to constantly guide the company —with the customer experience is the true North Star for the company.
2. Time Is Our Most Important Commodity: Personalization Aims To Respect Customers’ Time.
You ever hear your elders talk about how when they were kids they had to walk to school in the freezing snow uphill and both ways? Eventually we will tell our kids stories about the sheer number of irrelevant content we had to watch, read and generally deal with - a world they will not know. The next generation won’t watch ads on the television, they won’t be spammed offers by companies that aren’t relevant for them, and they will be able to opt out of almost everything. Why? Because being forced to ingest stuff that isn’t relevant for you is not a pleasant experience and it caters to our most important commodity; time. If you aren’t in the business of saving your customers their precious time, making their lives easier and better, you risk being disrupted by a newbie who will.Personalization is the best marketing investment you could make. People want experiences that are actually relevant for them. We know this but something gets very lost in the execution.
Customers today demand the hyper-personalization of everything. Personalization is what happens when the company leverages a deep understanding of customer preference, structured and unstructured customer data, conversations in and across all channels; when companies can preemptively anticipate a customer’s needs. Additionally augmented reality has already allowed companies to personalize and shape customer experiences.
Before personalization we need to establish the identification of the customer. In 2017 customers still repeated themselves at every turn. In-store retail is the biggest offender. In stark contrast to Stich Fix, an online retailer that offers personalized styling services and had a powerful IPO this year.
It’s possible to bring personal tailored experience to a customer with the use of technology like machine learning. We talk a lot about machine learning, but only a handful of companies are quick to leverage it in a way that improves the customer experience. One early and easy to understand example is Spotify - when you listen to a playlist and you like a song by hitting the thumbs up icon, the playlist improves in real-time. Wouldn’t it be nice if our healthcare providers, insurance companies and retail experiences were all that seamless and intuitive?
In the recent past companies have not made personalization a priority. According to Business Insider the number one barrier to personalization is too few resources dedicated to it. The second reason is lack of a clear roadmap. The companies that do embrace personalization - embrace personalization with gusto. For example Sephora has done a great job of creating personalized experiences for customers - marrying online and offline engagement strategies - using tools like augmented reality to allow customers to try on different looks via their phone. In every aspect of digital customer engagement Sephora tops the “best of” list. They have some of the most successful customer communities I’ve ever seen which drives sales. You can shop a look of another customer. They do an amazing job of marrying content with shop-able in-context products. Sephora leverages data from the beauty insider program to personalize every aspect of the customer experience.
3. Companies Embrace Decisioning And Data To Find Opportunities To Add Value To Customers’ Lives
In some scenarios, a person is better than a machine. But when it comes to combing through large amounts of data in-real time to serve up the most relevant next action to the customer, a machine is simply better at it. And combing just gets more and more arduous as we create more data. By 2020 it’s estimated we’ll produce 44 zettabytes every day. That’s equal to 44 trillion gigabytes. One gigabyte can hold the contents of enough books to cover a 30-foot-long shelf. Multiply that by 44 trillion. That’s a lot of data — which most companies cannot process fast enough. Frontline employees are generally operating with data that’s “too little, too late.” Now we can use artificial intelligence to empower our technologies to identify opportunities and experiences that are relevant for the customer. Frontline employees can in no way provide the same just-in time customer experience that decisioning can help with.
For example Sprint uses data to create better customer experiences. In 2014, Sprint had a customer churn rate of 2.3% — twice as much as its biggest competitors. The company was relying on customer experience agents — who were relying on their own judgement — to look through the data to identify how to best serve the customer. Before decisioning, the agent would look through 20 or more offers, picking the best offer while on the phone with the customer. Customers hate waiting, and often customers are waiting on the phone while employees figure things out. Whether they are on the phone with other employees, or looking through large amounts of information. While the customer waits, and quietly hates you with increasing zeal.
Sprint wanted to get away from relying on its employees to make real-time decisions. After implementing a data solution from Pegasystems (Disclosure: Pegasystems is a former client of mine), Sprint leveraged predictive and self-learning analytics to find customers at risk of churn and proactively provide personalized retention offers. Sprint reduced customer churn by 10% to historic lows, and increased its net promoter score by 40%. Additionally Sprint boosted customer upgrades by eight times, convincing 40% more customers to add a new line, and improving overall customer service agent satisfaction.
Decisioning can help create that sought after personalized customer experience.
4. The Customer Experience Cloud Gains Prominence
We’re used to hearing about the customer service cloud, or the marketing cloud, but now we’re seeing a new focus on the customer experience cloud. This has to do with how the company manages their data. Historically organizations had individual clouds for their marketing and sales programs as a way to pull together data, with each cloud holding information for just one area of the company. But the customer journey is changing, and so is how companies manage the customer journey. The big push now is the customer experience cloud, which brings together things like customer data, digital experience, and personalization to create an efficient, modern way to manage interactions with customers.
The old way of doing business with only a marketing cloud or sales cloud just isn’t enough anymore. Marketers used to have to juggle numerous tools to stay on top of what customers were thinking, how they were communicating with the brand, and what products they were interested in. Without integration, so much time was lost to inefficiency, and brands never truly had a full picture of their customers.
Customers don’t care if the person they are talking to works in marketing, sales, or IT — they just want to have a personalized interaction with the brand to build the relationship or have their problems solved.
5. Augmented Reality Makes Reality Better For Customers
I will never buy perfume on my phone because I can’t smell it through my iphone - well not yet. Part of the problem of engaging with online services is it’s not the same as the real thing. What would be possible if a person could experience a simulation of an experience? The possibilities seem endless. AR can be used in a number of ways depending on the needs of the organization. Some brands have created worlds where customers can virtually “shop” in a store and try items on their own bodies.
Other brands are using it as a way to provide a taste of a product they can order online. For example I have tried on lipstick and eye-lashes using Sephora’s Visual Artist tool on its ios app. Through augmented reality I can try on various lipsticks, blushes and even eyelashes find my eyes effortlessly. Another example is Wayfair, the ecommerce company. I’ve used Wayfair’s augmented reality feature called “Search with photo” allowing me to take a photo of any piece of furniture or item I find. Wayfair will find a similar item for me on the website. And it works. I took a photo of my office chair, uploaded it to the app and Wayfair served up similar chairs for sale on the website.
Augmented Reality gives an accurate prediction of what a customer can expect. For example when you order food off a menu an app called KabaQ you provided an augmented reality app showing highly-realistic 3D models of menu items on the user's view of their table, allowing them to see various selections from multiple angles and zoom levels.
There are so many opportunities where augmented reality can give customers a more realistic idea of what an experience will be like — or help the customer make a better decision for themselves and their family. Healthcare, retail, insurance, and hospitality are four examples of industries where augmented reality would be helpful.
2017 has been a great year for customer experience — far and wide there is much more awareness about the topic and the definition of customer experience. There is more understanding that customer experience does not equal customer service. In sum, most of what I’ve talked about here is technology-driven. That doesn’t meant that culture is not important. Culture drives the decision to take customer experience seriously. Even if you’re late to the customer experience game, it’s never too late to begin improving your programs. When you set out to make your customers’ lives easier and better, you will see your business transform. That’s a prediction I can guarantee.
View Article Here - Written by: Blake Morgan, Contributer | Forbes
Photo by Markus Spiske on Unsplash
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