YML is ecstatic to be partnering with Tom Siebel and the C3.ai team to produce category defining customer experience design work. C3 is the leader in how to bring digital transformation across massive organizations, and we believe this work will be transformative for their brand.

Read more about the partnership here.

By Shayna Stewart

BONUS MATERIAL: Who's winning at customer-centric strategies? Discover the strategies of leading brands.

Loyalty: the holy grail of all company metrics.

This makes sense -- the more people who continue to purchase, the more profitable a brand is.

However, loyalty as a strategy is a self-serving strategy that removes people’s needs and desires from the equation altogether.

What brands should focus on is creating people-centric strategies that provide long term intrinsic value in order to generate loyalty.

When brands use the word loyalty, they are typically referring to one of two things:

  1. A points system offered to repeat customer that results in discounted prices
  2. A business goal that refers to keeping people coming back to make a purchase

These constructs of loyalty are now outdated and the progression away from “loyalty” as a strategy has been happening for sometime.

Those that are leaders in loyalty are aware that loyalty as a strategy is dead, but the construct of loyalty that a person may have to your brand is very real.

In this article, we dive deep into why loyalty as a strategy can hurt your business and does not actually promote loyalty from real people, and how you can get started on a people-centric strategy to reconnect with your customers.

Table of contents

  1. Why loyalty points erode loyalty
  2. Loyalty as a strategy focuses on the past, not the future
  3. Loyalty as a strategy is not a people-centric strategy
  4. How to get started on a people-centric strategy

1 / Loyalty points erode loyalty

Brands are competing on consumer experience as opposed to pricing.

Historically the way brands captured market share, and what most of us learned in our economics books, is to reduce the price of your product in order to trigger demand for your product.

What this traditional framework does not account for is delivering upon a superior consumer experience.

In fact, we are seeing that people are willing to spend even more money for the exact same product with brands that have a superior consumer experience, which is an extreme deviation from our 101 economics books.

If you think that offering people discounts will sway them to buy from you compared to a competitor that has a superior experience, you will be sorely mistaken.

Intuitively, the price of a product is not the first thing you think of when thinking of superior brands - because pricing is table stakes these days. People just expect fair pricing for the value that is being provided previous to the purchase, and post-purchase.

The reasons the winners are winning is not because of a cheaper price, but because they provide superior connected digital and in-store experiences throughout their customers’ journey.

This concept is not new:

B. Joseph Pine II and James H. Gilmore published the first inkling of this concept back in 1997!

Below is how they framed up creating superior experiences as opposed to competing based on price for making goods or delivering services.

Source: Harward Business Review

There’s a new breed of companies that have changed the way consumers interact with their category altogether, all of which build their products with consumers' needs in mind first and the business second. This category is not represented in the framework above.

Think Uber, Etsy, Ebay, Amazon, any mobile banking app.

These brands and technologies have revolutionized their respective markets so much that they have shifted consumer expectations, which captured market demand without pricing playing a role. In these cases, the pricing strategy in terms of point systems or discounts came after they have proven the intrinsic value from their product.

Telling people that your product is cheaper is not a good long term strategy. Discounting your price via rewards points or continuous sales will erode the consumers perspective of your brand:

You become the “cheap” brand.

2 / Loyalty as a strategy focuses on the past, not the future

Loyalty as a strategy does not promote proactive optimizations to the product.

If return visitors are on the rise, then what is there to change?

People’s expectations are constantly changing. A simple UI improvement in an unrelated industry could become the standard for all industries (think the Like button from Facebook, which single-handedly changed review ratings forever).

The brands who are doing it right, know that they are never safe and therefore should always improve their digital presence.

They spend a significant amount of money doing so too.

Jeff Bezos recently said “Amazon is not too big to fail”. This is coming from the CEO of the brand that spends the most money on research and development in the tech industry.

If you feel you are in a safe climate where your return visitors are increasing, know that your success will not stay for long without the continuous optimization from your product teams.

In case you are experiencing a decrease in return visitors, loyalty tactics that typically will benefit your product will actually counteract increasing return visitors.

Counteractive Loyalty Tactic #1: Double down on core users

This tactic is great, particularly for new to market products, but in the case you have been available to the market for a while, this is counteractive to your long term growth.

Focusing solely on optimizing the product to core users will emphasize features that were already showcasing value to a small group of users instead of understanding what value to showcase to capture a more expansive return audience.

Counteractive Loyalty Tactic #2: Increase conversion for new users

At surface level this makes sense, you are trying to provide value to a new user by ensuring they have access to the product or service you are trying to sell.

This tactic does not improve loyalty long term if your product or service is not showcasing the value.

Think of Netflix:

they increase new user conversion rate by offering a free one-month subscription. Imagine that their product doesn’t showcase the value of paying for it in the first month, they would see a complete drop off in return visitors past that month.

If your product is not showcasing value to the users, then a conversion isn’t valuable to the business in the first place. In addition, this is a highly gameable metric as it’s possible to increase the conversion rate by offering steep discounts.

Which actually promotes a degradation in the brand perception.

Counteractive Loyalty Tactic #3: Spend more on marketing

Reaching new audiences is always a goal, but if your return visitors are declining, this tactic won’t fix anything; it will just decrease your marketing efficiency.

This doesn’t fix the problem because it does not address the product enhancements that need to happen in order to create the stop gap.

Andrew Chen summarizes this nicely, saying that you have to be customer-friendly, platform-friendly and product-friendly before you start on a marketing strategy.

Otherwise, this tactic is just throwing money at the problem without addressing the problem head on.

If you find yourself optimizing your product when the trends are not going your way, you are optimizing from a losing spot in terms of customer expectations. This is what a loyalty-centric strategy will produce.

To get ahead of this you need to adopt a people-centric strategy:

A people-centric strategy will allow for your product pivot as soon as the market changes and if you're lucky, create the pivot in customer expectations.

3 / Loyalty as a strategy is not a people-centric strategy

The key is to not focus on “loyalty” per se but to focus on the intrinsic motivations of why someone would want to come back; In other words, adopt a people-centric strategy.

Intrinsic motivations describe an innate reason for a person to perform some action: they are doing something because it feels good to do it.

Think about studying hard for an exam.

Some people may do it just for the A, which is an extrinsic reward. But some people may do it because they are generally interested in learning about the topic.

The latter would be an example of an intrinsic motivation, where someone does something because they just want to, no reward needed.

Designing around intrinsic motivations is where brands must spend all of their time because, ultimately, that will lead to a person's’ long-term engagement aka loyalty.

Can you remember the last time that you told yourself or your friends that you are going to be loyal to a brand?

No, you can’t because people don’t think like that.

The way you think to yourself about why purchasing or using a brand again is usually about:

  • Was that easy for me to use?
  • Did the product do what it said it did?
  • Was I able to contact service reps easily?

If the consumer answers yes to these questions, then the brand is successfully answering to intrinsic motivations (note: none of which have to do with price).

Unlocking a great people-centric strategy means you are leaning into the custom intrinsic motivations of why people would love your product.

4 / How to get started on a people-centric strategy

If you find yourself in this losing battle of loyalty as a strategy, there’s still hope.

Adopting and acting on a people-centric approach will enable you to pivot their digital product as quickly as consumer expectations change.

Here’s what you have to do:

First, get the team together to hypothesize more broadly around what is motivating someone to seek this product out.

Secondly, you must think through what are all of the questions that someone may have when understanding if this product is right for them.

This will help ensure that all of the content is succinctly and accurately answering the questions that someone needs answers to prior to purchase.

Lastly, the brand must make sure that as the consumer is qualifying, purchasing and engaging post-purchase that each step is delivering upon exactly what the person wants to achieve.

If you frame up your strategy with this framework you can quickly identify areas that consumers are most interested in and understand why.

This is the difference in being proactive vs. reactive where you understand what behaviors predict repeat visits as opposed to just monitoring if repeat visits are up or down.

Start thinking in terms of how people think.

If you do that your strategies will supersede market expectations, which will get you your desired outcome in the end, which is a growing group of long-term customers.

Want to see the people-centric approach in action? Check out how brands using people-centric approaches created wins and losses in their industries.

WHITE PAPER FREE DOWNLOAD: How are the most prolific companies using AI to deliver a better customer experience?

Last year, PWC named AI as one of the eight essential technologies in business and 38% of businesses employed AI in its systems.

That percentage is expected to grow to 62% this year.

IDC estimates that the AI market will grow to be more than $47 billion by 2020, and Gartner predicts “more than 40% of all data analytics projects will relate to an aspect of customer experience.”

Let's see how customer experience and Artificial Intelligence are blending together to deliver superior customer needs and increase customer satisfaction in the years to come.

Table of contents

  1. Are we delivering empathetic customer experiences?
  2. The shifting battleground: CX
  3. AI and the zettabytes of data
  4. Six types of AI engines helping brands create empathetic CX
  5. The next steps for brands

Are we delivering empathetic customer experiences?

Ever lost or misplaced your phone? Or how about the time when your computer or hard drive crashed and there were key files and data you hadn’t transferred yet to the cloud?

Remember how distraught and helpless you felt?

Now imagine if the customer service person you come in contact with was an imperious know-it-all? Imagine how that experience would add to the mental and emotional strain you already felt. Imagine how you would perceive that brand moving forward? In moments like these, it’s true that you want someone to fix your problem, but more importantly, you want someone to listen and acknowledge your distress.

Apple understands this all too well. The company includes an empathy guide in its training manual for frontline workers providing customers with device support in Apple retail locations.

Source: How To Be a Genius: This Is Apple's Secret Employee Training Manual

The training manual teaches Genius Bar employees how to assess what the customer needs based on their body language, and even suggests phrases to use depending on the customer’s specific needs at the time, like “I can appreciate how you feel…” which is a suggested phrase listed in the manual.

Today, nailing the consumer experience means going above and beyond, taking the time to understand customers, and applying insights to every aspect of the business, from new product development to call center training to designing a comprehensive user experience. The only way that businesses can do this effectively is by having actual empathy for the customer experience.

Here, we define empathy as a brand’s ability to experience their own product or service from the point of view of their customer.

There is no current shortage of brands claiming to deliver empathetic customer experiences.

But have we as an industry actually delivered?

According to Bain & Co., 80 percent of companies say they deliver 'superior' customer service — however, only eight percent of people think these same companies deliver 'superior' customer service.

The shifting battleground: CX

These harsh realities have made customer experience the new battleground at the top of business agendas today.

But while budgets and platitudes may continue to focus on CX inside boardrooms, the speed of technological change and the ephemeral wants and needs of consumers make this a quickly moving target.

And it’s easy to miss.

Consider how we got here: the modern manufacturing age — roughly between 1900 and 1960 — was marked by few pools of capital able to fund and maintain factories. As a result, the marketplace saw little legitimate competition, and an oligarchy of industrialists owned much of the global supply chain.

Source: Forrester

Starting in 1960 there were transformative changes to the industry, highlighted by globalization, deregulation and free trade deals that made it possible to manufacture goods more cheaply in other parts of the world. Even though customer experience were still important, it was price and distribution that were the real decision-makers for customers.

Then, in the 1990s, information and technology became readily available and accessible to the average consumer. This Information Age brought with it a shift in power dynamics, from sellers to buyers as customers now had information to easily compare brands.

The customer experience became a part of a business’s product and service — part of research & development, content marketing, public relations, social media, mobile presence and usability, and website design.

Today’s connected life — there are 4.3 billion mobile phone users worldwidewill see mobile experiences as the key touchpoint for businesses. Modern consumers want a seamless and integrated digital customer experience that ties into various devices and screens.

Source: Accenture

That means everything in the customer experience, from targeting to messaging needs an empathetic approach. Accenture found out that 44 percent of customers are “frustrated when companies fail to deliver relevant, personalized shopping experiences”.

Consumers today are increasingly less forgiving after a bad experience:

According to a study by WOW Local Marketing, 52 percent are less likely to engage with a company after a bad mobile experience — that includes everything from poor design, missing content, and even slow loading times. In Right Now Technologies’ 2011 Customer Experience Impact report, nine out of every 10 customers said they would walk away after a poor customer experience to conduct business with a competitor instead.

But it’s not all dire news. Studies have shown that customers will pay more for a better experience. After all, how many of us have paid for a more expensive airline ticket because we prefer the experience we have with a particular airline? Study after study show that good customer experience will boost even your stock value:

CX is an opportunity.

Source: The Customer Experience ROI Study

Businesses that truly understand the demand for empathetic design in the customer experience know that they can’t do it with human help alone. There’s just no way for humans to deliver against these expectations. Forward-thinking companies are turning to machine learning and massive data to make better decisions on their customers’ behalf.

According to IDC’s 2016 Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide, the market for cognitive/AI solutions is expected to experience an annual growth rate of 55.1 percent between 2016 and 2020.

Using the powerful combination of AI and data means businesses have a better chance of giving customers exactly what they want — and even preemptively anticipate their needs before they’re even aware.

It’s this kind of empathy from brands that deepens trust and keeps customers coming back.

AI and the zettabytes of data

Think about how much data is produced everyday, from photos to videos to songs to text messages. The total amount of data in the world was 4.4 zettabytes in 2013 and that number is expected to rise to an astronomical 44 zettabytes by 2020.

To put this in perspective, one zettabyte is equivalent to 44 trillion gigabytes and to break that down even further, one gigabyte is enough data for the books needed to fill a

30-foot shelf. That’s a lot of collected data.

So how can companies make sense of all the information, especially considering most of it is unstructured?

They can’t. Not with human employees in real-time anyway.

In short, this means that brands that stand out during the customer journey have to turn to artificial intelligence to personalize experiences by identifying areas that are relevant to individual customers.

If empathy is all about understanding and being aware of and sensitive to the experience of another, then empathetic customer experiences should be focused on recognition and responsiveness.

Six types of AI engines helping brands create empathetic CX

Empathic thinking requires thinking the right systems in place so that you can proactively respond and resolve issues that come up at the speed of human expectation. While only a few brands employ empathic design well, the ones that do should serve as examples for the rest of the business world.

Below, we’ve listed six examples of AI engines that help brands transform the customer experience through empathetic designs.

WHITE PAPER FREE DOWNLOAD: How the most prolific companies in the world are using AI to deliver a better customer experience

1 / Recommendation engines

Recommendation engines are probably the most common form of machine learning and currently used largely in online retail and media industries and rely on algorithms based on the customer’s past behaviors and patterns. Like receiving a gift from a friend who really knows you. Brands implementing recommendation engines connect the massive data they collect to personalize every aspect of the customer experience.

Perhaps the most influential recommendation engine in the world today is YouTube’s Recommended Videos on their infamous right rail. Of the more than ONE BILLION hours of Youtube we consume, fully 70 percent of it comes from these algorithmic modules. That’s a lot of cat videos, man.

Source: YouTube's AI is the puppet master over most of what you watch

In the future, we could see recommendation engines more prevalent in industries with vast amount of data like healthcare, where AI can help personalize care by taking into account patient history, lifestyle information, medical records, and more. Algorithmically recommended treatment courses, or specific prescriptions are not out of the question.

2 / Predictive Searches

Predictive Searches allow web visitors to get results faster by automatically populating results while the user is still typing. Typically, there is a drop-down list that pops up during the search, which guides users to potential results. When thinking of predictive searches, most of us think of Google’ Autocomplete invented in 2004. Then, in 2010, Google’s search technology expanded to include Instant Search, which uses machine-learning to predict what it thinks you will type, simultaneously streaming results for those predictions in real-time.

Many brands have since adopted Google’s original technology, but there are some drawbacks. In 2017, Google announced that it would drop instant search as its default setting given most searches are now done on mobile, and loading results for predictive searches on a limited screen ends up being a poor user experience.

A Google spokesperson said at the time:

“We launched Google Instant back in 2010 with the goal to provide users with the information they need as quickly as possible, even as they typed their searches on desktop devices. Since then, many more of our searches happen on mobile, with very different input and interaction and screen constraints. With this in mind, we have decided to remove Google Instant, so we can focus on ways to make Search even faster and more fluid on all devices.”

Even after developing an empathetic design — Instant Search — Google continues to find ways to be even more empathetic by thinking about how the product can be fast and fluid on all devices, for all users!

3 / Virtual Assistants

Virtual Assistants, like Amazon’s Alexa, Microsoft’s Cortana, Apple’s Siri, and  Google Assistant, range from chatbots to more advanced systems that are changing what customer engagement looks like.

  • Google Duplex is a new capability of Google Assistant that can make calls on your behalf and book your next hair salon appointment or table at your favorite restaurant for you. And the person on the other side of the phone won’t even notice she’s talking with a bot.

Source: Google Duplex: An AI System for Accomplishing Real-World Tasks Over the Phone

Virtual assistants are definitely a growing niche, so why have only 10 percent of enterprises in the U.S. employed a virtual agent? Mainly because there are still a lot of challenges that can interfere with the digital customer experience. For instance, while virtual assistants are improving greatly with natural language, most of them are still far behind humans when it comes to understanding slang, typos, misspellings, or complex grammar.

4 / Natural language processing

Natural language processing is AI that can process massive amounts of natural language data and can therefore, understand human speech the way it is spoken. When thinking of NLP, an Amazon Echo often comes to mind with its voice recognition, but NLP’s technology holds promise in a lot of industries, including healthcare where it can help with faster diagnosis by finding patterns in a physician’s unstructured notes. Think of how many lives can be saved with AI mining our health records!

Think about Skype Translator, that can understand several languages at the same time, in real-time, which can encourage conversations between people who speak different languages.

5 / Sentiment Analysis

Sentiment Analysis evaluates voice inflections to determine the emotions, attitudes, and opinion in normal human conversation to determine what’s actually being said.

Vibe is a product created by Tokyo-based software company AIR that can scan conversations on workplace communication tool Slack to determine team morale. The product analyzes keywords and emojis used during conversations and places the team’s mood into five emotions

At YML, we used sentiment analysis to understand which company is loved the most: Uber or Lyft?

Source: Uber vs Lyft – Who is loved more? A deep dive analysis using Google’s Sentiment Analysis API

6 / Computer vision

Computer vision uses machine learning models to teach computers to see things the way humans see them.

A company doing a great job is Uru, that uses computer-vision to find spaces in videos where native advertisements can appear to create a non-obtrusive, uninterrupted, more organic experience for users.

For instance, the company’s algorithm identifies spaces, like a blank wall or the board of a snowboarder, where ads or brands graphics can appear. The startup has caught the attention of the industry’s top accelerators and investors.

The next steps for brands

The rise of the internet has provided us with various ways to communicate and interact. Even brands now communicate with their consumers through multiple platforms and channels, so it makes sense that customers expect you to “get” them.

After all, customers know brands are tracking, personalizing, and optimizing every step along the customer journey, so why shouldn’t they be more empathetic to CX? With so much competition out there, the only way a brand can have a competitive advantage is by maintaining an obsession with customer experience.

Once a brand has determined that they want to create empathetic experiences, they need to then do two things: (1) focus on results, and (2) focus on small wins.

When focusing on results, brands should identify the tasks they want to tackle first, then determine the optimal technology to help them accomplish those tasks.

This can get a little tricky with various AI technologies competing in the market, from machine learning, chatbots, virtual assistants, robotics, natural language processing (NLP), and much more: in order to achieve business goals, businesses need to think about the big picture, then work backward.

For instance, if your customers want 24/7 support, then it might be worth it to invest in a chatbot experience so customers aren’t waiting to get through to a human employee. Or if customer satisfaction seems to be an issue, consider a technology that can take the data you already have and connect them with what customers may need, based on their individual preferences. Customer satisfaction will surely improve if businesses present relevant recommendations rather than spewing out random offers.

Finally, brands need to focus on small wins if they want a chance at any of the big wins.

How do you know which small win you’d like to tackle first? Identify the low-hanging fruit. What complaints are you getting the most from customers? Tackle that area first.

As soon as you’ve identified the problem, it might be tempting to use AI to solve dozens of issues right away. This is not the best way to start improving customer retention quickly. Instead, if you want to make the most impact, you’ll want to tackle what’s easy to measure and achievable first.

In other words, think big, start small. What small wins can you achieve in the next few weeks or months?

AND NOW LEARN MORE: How are the most prolific companies using AI to deliver a better customer experience?


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