The Importance of User-Generated Content (UGC) and Listening to the Customer

 As the importance of establishing an ever-expanding digital corporate presence to build, enhance, and improve the customer experience for both B2C and B2B customers has gained momentum, companies are realizing the growing importance of what is known as User Generated Content (UGC).

Consumers trust authentic, unpaid recommendations from real customers more than any other type of content.

UGC consists of content such as text, videos, images, and reviews that are generated by real customers, influencers, and independent individuals rather than by the brands themselves. It is important to note that any modifications made to this content should only aim to enhance clarity, conciseness, or formality without altering the original message or quotes. This content focuses on customer experiences, such as reviews, testimonials, case studies, guest posts, comments in online communities and forums, collaborative webinars, podcasts, hosted events, social media posts, and PR campaigns, as well as partner, distributor, and vendor promotions can be utilized in numerous ways to educate both new and current customers about the potential brand experience.

UGC is clear evidence of direct customer feedback, often unsolicited. It is the voice of the customer in its purest form. The value and impact of UGC are even greater in eCommerce settings where this content is widely understood to be a primary driver for conversions and purchase motivation.

In the B2B context, UGC is more than just reviews and case studies, and should be considered to be "any content others create related to your business".

UGC is important in modern digital marketing for many reasons, as summarized below:

• Authenticity: UGC is a more authentic and experiential form of content than corporate content because it is created by customers, free from artificial embellishments or supervision by brands. Consumers tend to trust UGC more than traditional advertising, and it serves as a contemporary variation of word-of-mouth marketing, a force that has always played a significant role in influencing consumer purchasing decisions.
• Social Proof: UGC offers social proof that impacts the buyer's journey. It builds consumer confidence and is an extremely efficient strategy for a brand to influence its audience and convert them into customers. In simpler terms, social proof is the equivalent of a reference in a B2B setting or someone else's stamp of approval. UGC also facilitates community-building, which can result in greater loyalty and advocacy.
• Unlimited Authentic and Unfiltered Content: UGC offers brands unrestricted, genuine, and unedited content to improve brand awareness and strengthen brand reputation. Brands that implement UGC show their willingness to engage in a two-way discussion, fostering more trusted and engaged relationships with consumers.
• Cost-Effective: Generating marketing content can be a time-consuming and expensive process for an enterprise, which is why UGC is quickly becoming a critical component of digital marketing campaigns.
• Increased Engagement and conversions: User engagement increases due to user-generated content, which is directly correlated with conversions. User-generated content validates and legitimizes your marketing message, leading to an increased likelihood of user conversion and higher sales.

While some marketers still believe that branded content is more trustworthy or preferable to user-generated content, research suggests otherwise. Customers consider authentic user-generated content (UGC) the most trustworthy content in both B2C and B2B contexts.

UGC has many benefits for businesses. Authentic and uncensored content can establish trust and credibility, as customers are more likely to believe and engage with content from peers and independent observers than from the brand itself. 

Today, most customers are cautious of claims of superiority made by brands and actively seek information from like-minded customers and independent observers to better understand the product or service during the buyer and customer journey.

Additionally, it is a cost-effective way for a business to create trusted content that can favorably influence engagement and build stronger relationships with customers at various stages in the buyer and customer journeys.

Furthermore, UGC provides valuable insights into customers' experiences and perspectives and enables the enterprise to engage with customers more deeply and effectively. Statistics show that consumers find UGC 9.8x more impactful than influencer content, and 79% of people say UGC highly impacts their purchasing decisions. Some of the most recent research also confirms that consumers rank authentic UGC as the most trustworthy content in their buyer journey.

Here are some recent statistics from reputable sources on the value and impact of UGC:

• 64% of consumers agree that when a brand they like and use re-shares content by customers, they are more likely to share content about the brand or its products.
• 76% of consumers have purchased a product because of someone else’s recommendation before.
• 72% of consumers believe that reviews and testimonials submitted by customers are more credible than the brand talking about their products.
• A study by Bazaarvoice showed that websites with UGC can see an increase of 29% in web conversions, a 20% increase in return visitors, and a 90% increase in time spent on-site.
• Research by BrightLocal indicated that 79.69% of consumers look at ratings and reviews before making a purchase.
• 6 in 10 marketers report that their audience engages more with UGC in marketing and communications channels than branded content.
• 75.78% of consumers have used social media to search for or discover products, brands, and experiences. 
• Three-quarters or more of travelers were active on at least one social media platform in 2019.
• Cost-per-click has been seen to decrease by 50% with the addition of user-generated content in social media ads.
• The majority of millennials, 66%, book their travel trips using their smartphone. A higher majority, 74%, said that they use their smartphone for research related to their travels. Again the most trusted content tends to be UGC and peer commentary on travel experience.

These statistics show that User Generated Content (UGC) is a valuable tool for marketers to establish trust, engagement, and loyalty with their audiences. Engaging with UGC helps marketers listen to their customers, understand their needs, and collaborate with them as co-marketers to create more compelling content. This engagement strategy enables marketers to attract new customers, foster brand loyalty, and increase customer satisfaction.

However, research indicates that many businesses still struggle to comprehend, utilize, and harness the potential of fast-moving, high-impact UGC content. Furthermore, most marketing organizations remain focused on developing and disseminating brand messages, rather than actively monitoring and engaging with the ongoing stream of customer feedback across social media and the internet.

The Translation Challenge & Perspective

As can be expected, the volume of user-generated data is constantly increasing in the modern era, and the challenge for the modern enterprise is to track it in all its most relevant variants and to set up translation production processes for the most important and relevant content.

According to World Economic Forum estimations, by 2025, the amount of data created by humans each day will be about 463 exabytes (one exabyte is equal to one billion gigabytes). As of 2021, we produce over 500 million tweets, ~300 billion emails, and 4 million gigabytes of Facebook data every single day.

While this data has primarily focused on G7 economies in the past, it is expected to shift significantly as economic growth continues to surge in the Global South and South Asia over the next two decades. As a result, global business leaders must master the skills to listen, share, communicate, translate, and comprehend various content streams in an expanding array of languages. The languages that hold the utmost relevance at present may not retain the same level of significance in the upcoming decades.

This will require that leading global businesses will enable and be capable of being multilingual along all of the following content dimensions:

Social Media Content: As social media grows into a better search engine, it’s up to marketers to create searchable content. Many buyers request user-generated content along their buying journey and this should be easily accessible as they peruse and investigate your site. Here are some examples of B2B use of social media as a digital marketing channel.

Multilingual Email Content: Personalized email content that enables quick and effortless retrieval of User Generated Content (UGC) and reviews, and prompts customers to share their feedback for future content development.

Digital Advertising: There is a clear trend towards more video/audio content, along with a strong preference for access to genuine user-generated reviews, forums, and discussions.

Web Content: Customers crave reviews from others with similar needs. The inclusion of visual reviews on your website and product pages, in addition to user-generated content, can create the feedback loop necessary to satisfy your audience's desires.

Brand Content: Branded content mixed with relevant and specific user-generated content addressing evaluation issues raised by many customers is crucial. However, numerous consumers only consult it after they have already satisfied themselves with other customer opinion data. While consumers often consult other customer opinions before turning to UGC, buyers are 4-6 times more likely to purchase from purpose-driven companies that they advocate for through UGC and word-of-mouth referrals. Moreover, the addition of UGC in social media ads has been shown to decrease cost-per-click by 50%. 6 out of 10 marketers report that their audience more frequently engages with user-generated content (UGC) in marketing and communications channels than with branded content.

The truth is that today, the #1 marketing channel used by most companies is social media and the brand's website is the second most used marketing channel, especially in B2C settings.

Measuring the success of a UGC campaign involves tracking key performance indicators (KPIs) that align with overall business goals. These can vary by language and can thus help to identify the most and least receptive markets. Here are some KPIs and metrics to consider when evaluating the success of a UGC campaign:

1. Engagement Metrics: Monitor likes, comments, shares, and clicks to understand the impact of UGC on audience engagement.
2. Reach and Impressions: Measure the number of people who see your UGC and the total number of times it's displayed.
3. UGC Volume: Track the total number of user-generated posts, reviews, or other content forms associated with your brand.
4. Conversion Rates: Analyze how UGC influences customer behavior, such as driving traffic to your website, increasing sales, or prompting sign-ups for newsletters.
5. Content Performance Metrics: Track metrics tied to specific goals, pieces of content, or distribution channels, such as impressions, reach, engagement, clicks, conversions, sales, revenue, or customer loyalty.
6. ROI Calculation: Consider factors like content creation costs, revenue spent on paid social ads, the value of your visual content library, cost per click (CPC), and overall conversions when calculating the ROI of your UGC campaign

To be able to participate effectively in the global market an enterprise will need not only the most streamlined and efficient translation production capabilities but also have infrastructure and processes that continually improve and adapts to changing customer requirements.

This is precisely the solution that has been developed by Translated for any global enterprise to be able to undertake this content deluge challenge successfully. This is a solution and a technology that has been developed in close collaboration with clients who have focused on serving customers who have expressed a preference for having multilingual content access at scale, particularly for more dynamic real-time UGC which inform evaluation and purchase decisions.

Unveiling Hyper Adaptive ModernMT

Translated recently announced a new model of ModernMT, its adaptive machine translation (MT) system. The new model, called Hyper Adaptive, enables companies to translate billions of words at ultra-fast speeds without compromising quality. It is domain-specific and designed for use cases such as translating user-generated content, datasets for multilingual large language models, and web content for data mining activities.

In recent years, companies have approached Translated with requests to leverage the accuracy of ModernMT's adaptive MT system to quickly translate specialized, unique content and high volumes of ongoing content. While a generic adaptive MT model can handle the request to some extent, it is not designed to translate millions of words per minute in a specific domain.

Hyper Adaptive solves this issue by using sophisticated compression techniques and training the MT model for specific use cases based on the customer's previous translations and translation memories (TMs) to ensure high-quality performance even at a scale of many billions of words a month.

The resulting highly specialized MT model is much smaller and more efficient than a generic adaptive model and can process content at ultra-fast speeds, in as little as 50ms for a typical sentence. An example to clarify the performance capability at Translated's dedicated data centers: it can translate the entire English Wikipedia (4.4 billion words) into another language in less than a day (3 million words per minute). By training directly using customer data, the Hyper Adaptive model achieves translation accuracy equal to or better than state-of-the-art custom adaptive MT models.

Often, when very high throughput is required, MT systems will need to make compromises on output quality. Typically there is a trade-off between quality and throughput. In contrast, this solution helps companies maintain high quality even when translating massive volumes of content at ultra-high speeds.

In some specific use cases, such as dynamically changing user-generated content, combining the dynamically learning adaptive MT model with ongoing professional translator corrective feedback can further improve the quality of the MT output over time. 

Even though the model is optimized throughput speed, the model is still adaptive, and thus, it continues to improve after initial training through ongoing corrective feedback and the addition of new TMs delivered to match the company's style.

As the demand for agile global enterprises scales to translating billions of words a month, solutions like Hyper Adaptive ModernMT allow continuous improvement daily yet can easily translate billions of words of relevant UGC into over 200 languages every day.

We designed the Hyper Adaptive model to enable the translation of content that has never been translated before. Its language coverage allows companies to reach over 99% of the world's population in their own language. Hyper Adaptive is one more step towards global understanding.

Marco Trombetti – Translated CEO

Integration and Costs

Like all other ModernMT models, the Hyper Adaptive model can be integrated into the translation workflow via API. Costs vary depending on the use case, the amount of data to be translated, and the amount and quality of existing translations and TMs. Existing Translated customers can contact their account manager to get a new service quote.

Thanks to the Hyper Adaptive model, user-generated content on Airbnb has reached an unprecedented level of quality, greatly improving the experience for our user base. The real-time, high-quality translation of UGC has helped Airbnb foster a stronger sense of community among our hosts and guests, which has had a tremendous impact on our business.

Salvo Giammarresi – Head of Localization at Airbnb

Chatbot Pitfalls and Solid Advice on Getting it Right

According to Gartner research, it is estimated that 60.5 million Americans use a virtual assistant at least once a month. If this trend continues, chatbots will likely power 85 percent of all customer service interactions by the year 2020. 

It is said that chatbots are the new FAQ. Every second, 40,000 search queries are made online worldwide. That adds up to 3.5 billion per day or 1.2 trillion each year. That’s a lot of people looking for a lot of answers. If chatbots are built right they definitely enhance the customer experience and make it easier for a customer to find the content they need. Technical support is the most common type of chatbot content. Chatbots provide a bridge between technical documentation teams and customer support call centers. Salesforce’s Chief Digital Evangelist, @ValaAfshar predicts that “The line-of-business that is most likely to embrace AI first will be the customer service – typically the most process-oriented and technology savvy organization within most companies.” AI here refers to NLP and NLU enabled content structures.

From my vantage point at SDL, it has become increasingly clear that content really matters. The modern digital customer journey is a journey that is marked and defined by interaction with different content.  The conversational interaction is needed everywhere, not just with Alexa and other voice-based virtual assistants. Imagine searching for technical support content and finding 20 large PDFs that may contain the answer to your question. Wouldn't it be useful to have the ability to zero in on the right content within these PDFs through a series of clarifying questions?  The real question, then, is not where does the content for chatbots come from, but rather how do we prepare, organize and structure that content so the chatbots can use it? This superior delivery of the right content to many customer questions can only happen if you have a proper content organization model. At SDL we call this GCOM and it involves content architecture, organization and process to make it happen.

Source:5 ways chatbots are revolutionizing knowledge management

 Read the full post here.

However, it is very easy to build chatbots that are frustrating, useless and inefficient. We have all experienced websites where the only question the IVA (interactive virtual assistant or chatbot) is able to ask is "Hey dude, do you wanna buy my s*&t?" Chatbots are purely a reflection of the capability, fastidiousness and patience of the person who created them; and how many user needs and inputs they were able to anticipate. When done badly, which is very often at this stage of evolution, chatbots will kill customer service.

This is not so different from DIY machine translation 10 years ago. Many really bad systems were built and forced upon poor unsuspecting translators. Like MT, this too will take competence and skill and it is wise to work with experts.

Digit’s Ethan Bloch sums up the general consensus: “I’m not even sure if we can say ‘chatbots are dead,’ because I don’t even know if they were ever alive.”  

Our guest post by Ultan O'Broin is a look at how to do the right things the right way to make chatbots that are indeed useful in providing the right content to the right people and provides insight from the design and UX perspective in particular.  While the content organization issues are discussed often, it is worth a review and is well summarized in this article. Ultan has several other posts on Medium on the subject of chatbots and is worth a closer look.

What excites me the most is that once you have the IVA working properly and actually useful, it then makes sense to make it multilingual with optimized machine translation. But first things first.

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Ultan O’Broin looks at how the Jobs To Be Done framework and simple build approaches for the minimum viable product can counter chatbot product management failure and fatigue.

Guerrilla testing of Snapchat Spectacles at Hook Head in Wexford, Ireland. We can try out products in the wild, but are we as focused on the job we’re hiring our product or service to do?

 

One of the compelling hopes for chatbots was that utter power of an agency to increase digital participation in a natural way, partly through reducing app fatigue. Ironically, I now believe we have reached the stage of chatbot fatigue.

Just like everyone was “doing” a smartwatch solution two years ago, suddenly chatbots are the tech “game changer” du jour.

And botification is being done just as badly as the smartwatch overkill.

Chatbot Game Chancers

There’s bot rot out there. The problem is often there is no design thinking about whether a new or existing job is worth botifying or not.

I have seen some criminal chatbots out there. From a barista training chatbot aimed at millennials that delivered a 50 page PDF manual in response to the utterance “how can I be a barista?” to that fitness chatbot telling me that my nearest Parkrun was 8,000 kilometres away, the assumption being that anyone asking was within a 300 kilometres radius of an Irish location. I was in San Francisco at the time, where there is a local run.

None of the arrivistes responsible for such rotbots have asked: “Why is this person hiring this chatbot to do this job?”

The problem is the wrong chatbots are being built, wrong. Fundamentally, begin by asking, “Why would anybody hire my chatbot to do this job?”

This article is about designing the right chatbot, right. But the profound principles to do so can be applied to any app or service.

The “User” and the Damage Done

First, the term “user” must go from the design conversation! User? WTF!

I hate that term “user”; only the usability community and illegal drug business uses it. Let’s reclaim “user” as a role in real life with a real job (unpaid or otherwise) to do. So, our “user” becomes a sales rep, a technician, a concert-goer, a mother, a parent, a patient, and so on.

But how do we find such people, especially if you’re a small startup or innovator? The answer is to think smart, get out on the street, watch real people, and ask them about what they’re doing and encourage them to articulate reasons they would use a chatbot, app, or service instead.

 

Job Profile Persecution

Forget about the job profile approach to identify your “users”.

Those unwieldy tl;dr profile documents of job titles, descriptions, qualifications, experience, org chart position, motivations, IT expertise, and so on. These tomes owe more to recruitment agencies and HR departments than to design thinking and product management.

Profiles list many tasks that the role performs, not just the focussed critical 80/20 reason why your conversational UI might really be a game changer and around which a killer solution can be built.

A job profile is not a real person. In the user experience business, it’s a statement for the prosecution of the mediocrity of design.

 

Don’t Take It Persona-ally, But…

Personas are next to go onto that “user” bonfire of the inanities.

Personas are stereotypical people, dumbed down versions of “a day in the life”, complete with stock photographs, typical responsibilities and tasks, personal characteristics, motivations, and tools.

Again, a persona is not a real digital adopter, but a distant idealisation, performing a non-prioritized list of tasks. These persona peeps may use many types of technology too; not good for innovation disruption by understanding why they might switch or integrate with your innovation.

There’s a reason the word “stereotype” usually comes with a negative connotation.

 

Get Close and Personal. Swipe Right for The Right Design

Instead of “user” personas and profiles, move closer to the customer, and discover what they do.

How? Great chatbot design begins with a contextual conversation.

Take a walk in their shoes. Try a little fun UX ethnography, guerrilla research, and the “Wizard of Oz” technique to identify that killer question or job that might inspire someone to continually turn to a conversational UI to actually do something.

Research and design a chatbot that resonates emotionally and culturally, as well as functionally for your region. Check out Hofstede’s country comparison dimensions to help you shape that experience.

For example, if we were building a chatbot for fitness enthusiasts we might hit the local gym session schedule, post to a Facebook fitness group or fitness app discussion section, or engage on Twitter or Instagram with an appropriate hashtag.

Use a deep-listening, “tell me more about that”, approach.

Original “The Wizard of Oz” movie poster. From Wikipedia; image in the public domain.

Amazon, in the run-up to the French launch of its Echo, for example, introduced Alexa to employees in its French fulfillment centres who interacted with the emergent voice assistant to help the voice chatbot learn French, cultural nuances and behaviours, and how to respond.

The launch of the French version of the Amazon Echo was preceded by real people to learn the language, cultural norms, and how they actually behaved!

Fundamentally, focus on the person’s behaviour, and not on what they say or think they want. Watch their actions. Think of this as a Lean product management approach, a way to quickly design and build a solution to determine the certainty about its value. As Eric Ries says:

“The minimum viable product (MVP) is that version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort.”

I encourage you to read Eric’s book, “The Lean Startup” and apply the thinking to chatbot product management and design.

What Features to Design?

Why do drivers of cars buy milkshakes at drive-in takeaways? There could be many reasons. By way of Harvard professor Clay Christensen’s (and others) Jobs To Be Done framework, it was found that people don’t buy and use things because they fit a persona or user profile.

Instead, people hire a service or product to do a job for them. In our milkshake example, the drink was hired because the drivers were bored and in danger of nodding off on a long journey.

So, use the Jobs To Be Done (JTBD) approach for designing the right thing, right!

Again, read (or listen to the podcast) about Clay’s theory of disruptive innovation and Google JTBD for more.

Think of it this way, as mentioned on the startup Intercom’s blog: JTBD allows you to focus on the essential product feature, and to generate a user story about why it’s needed. The story plot line structure for a product story, using the JTBD method, would be something like:

“When X happens, I want to Y, so I can Z.”

An example from the sales rep job world might be:

“When new sales collateral comes online, I want an SMS on my phone, so I can take it on the road with me.”

Designing using JTBD enables a template approach, listing the job to be done, the role, needs, and expected job outcome. We can add outcome performance goals in there too, as a way of testing and proving ROI.

Here’s a one-page enterprise JTBD example you can turn into a template and use yourself: 

A JTBD template completed for a mobile sales rep. You can use this template approach to frame the job to be done and as a way to shape a story about the job itself.

In our example, a sales rep on the road might hire a chatbot to enter new sales prospect details rapidly without using a special device, easily capture meeting locations and images of the opportunity, and share that data in SaaS for work later. She might want to do in 5 seconds. So, a voice-driven chatbot using smartphone out-of-the-box features such as the camera and GPS capability seems like a good opportunity for a chatbot solution.

Using the JTBD approach also helps shape and communicate the job story. A story untold is not a story. Here’s your storytelling formula: SUCCESS — Simplicity, Unexpectedness, Concreteness, Credibility, Emotion, Story.

Phrase your job story well; using bullet points is fine. As John Dewey put it:

“A problem well put is half solved.”

You can tell you JTBD story to customers, product managers, developers, investors, designers, or any stakeholder (although C-level management will probably just want an executive picture on the return on investment for their budget spend).

Competition is Fierce, from Unexpected Combatants

Remember: There is competition with other tools for hiring your product or service.

In the case of our sales rep, she will still carry a notebook and pen — it helps recall — sticky/Post-it notes, an Apple iPad, a Windows laptop, and a Samsung smartphone, maybe.

This hire competition is at the job level, it’s not about the category the tool fits into. As Intercom points out, Microsoft Skype, for example, addresses the same purpose as an airline seat for a business trip— communicating with colleagues.

Chatbots compete against other tools and methodologies on the job hire level in many arenas — communicating, scheduling engagements, ordering, onboarding of employees, managing things to do, marketing, educating, entertaining, finding simple solutions and fixes — and must do so without special training, equipment, and so on.

But fundamentally, the JTDB approach really means the end of design as many people have come to think they know it. Design now becomes about the job the chatbot is being hired to perform by a human.

tl;dr? OK, Summary

So, to design and build the right product right, remember these six simple points.

 

Build the Right Thing

1. Use the JTBD framework: Why is this chatbot (or other product or service) being hired to do the job by this person? Watch their actual job behaviour, ask, and listen.

 

Build It Right

2. Get to the MVP stage fast. Apply proven UI principles for chatbots such as creating prompts and messages with a conversational style, shape a personality, pick use cases based on speed, simplicity, pain alleviation, and that requires no special device or training.

3. Have a clear primary job to be done. What is the 80/20 effort of the job? Avoid corner cases, nice-to-haves, and “what ifs?”.

Consider Microsoft Excel; a super-popular desktop spreadsheet and now service-offering, packed with features. But how much of that functionality do you use and how often? Probably 20% of the features or less to do 80% of the jobs you need Microsoft Excel for. There are other use case examples from the enterprise to get you thinking along those lines for chatbots.

4. Sketch and wireframe your solution first. Balsamiq, for example, offers great digital solutions, and there are now conversational UI specific options. But all you really need to start is a pen, paper, and ideas. You do not need to be an artist.

Using wireframes means you can also apply familiar UX design patterns, making for productive development. Document any open questions, use an Agile backlog and try collaborative, integrated cloud tools like Slack, or whatever suits your context of work, to agree your sketch with stakeholders.

Balsamiq wireframing stencils. There are awesome tools for designers, developers, product managers to sketch and agree on that chatbot JTBD

 5. Use an accelerator design kit or platform with suitable backend AI and NLP capability to innovate fast. Leverage usability heuristics, and use real text and voice scripts in your designs — in the language of the hirer— and iterate with the key stakeholders to agree on a chatbot solution before you code anything. This agreement eliminates surprises later!

Smartly.ai, a platform for creating conversational UI chatbots for SaaS, across different devices.

6. Don’t dribblise your design. With chatbots and conversational interactions stay true to the idea that no UI is the best UI of all. The UX design toolkit is now API calls, AI, NLP, ML, integrations with services and regular device features (GPS, SMS, camera, and the microphone, for example). Design platforms and kits often provide any UI widgets if and when needed (for maps, attachments, avatars, and so on).

No UI is the best UI to create that killer new user experience.

 

Job Satisfaction? Test It!

Finally, do remember to test your innovation.

UI heuristics and baked-in platform usability can do some heavy lifting to prove your idea’s value, but for JTBD the most reliable testing of having solved a design problem are tests that focus on real tasks by real people doing real jobs.

Plan to test your innovation before going live and after, and iterate if needed. Keep focused on the JTBD. If that is wrong, then no amount of fancy visual UI design is going to fix it and improve switching or adoption from another app. As Seymour Chwast puts it:

“If you dig a hole and it’s in the wrong place, digging it deeper isn’t going to help.”

Remember, people are hiring your chatbot to do a job. Hiring. In the age of the cloud, it’s easy to switch chatbots, apps, and services because of a subscription model.

There’s competition for that apps, services, and chatbot job hire. So, be competitive!

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Ultan O’Broin (@ultan) is a digital transformation and user experience consultant working with startups and the STEAM community, specialising in conversational UI and PaaS4SaaS. With over 20 years of product development and pre-sales design thinking outreach with major IT companies in the U.S. and Europe, he also speaks and blogs about UX and technology.

Watch out for ConverCon 2018 in Dublin, Ireland in September 2018 (previously reviewed), an event with chatbots at its heart. I might see you there to chat in person about the JTBD of your chatbot!

https://www.linkedin.com/in/ultanobroin/

All images and screen captures in this article are by Ultan O’Broin. Copyright or trademark ownership vested in any screen capture is acknowledged, where applicable.

Why MT Matters and its Role in Digital Transformation

We live in an era where there is more information available to a digitally savvy human than has ever been possible in the history of mankind as we know it. The volume growth implications are so significant and substantial that it is worth considering some contextual facts to get a proper understanding of this fact.

The Encyclopedia Britannica announced in 2012 that after 244 years, dozens of editions, and more than 7M sets sold, no new editions would be printed. The 32 volumes of the 2010 installment, it turns out, were the last edition of this great publication. The primary cause for this was the increasing use and relevance of the Wikipedia (and other digital alternatives) which would cover 2,670+ Encyclopedia Britannica sized volumes if it were to be actually printed. While some may argue that the Wikipedia is less reliable, this contrast is quite astonishing and clearly, there is more information.

This explosion of content is even more astounding if we survey the larger information landscape which reveals the following fact described in the graphic below.

This explosion, however, presents special challenges to the modern enterprise which now needs to assimilate, digest and determine what is relevant and what is not. In this age of digital disruption, those who fail in doing this becoming increasingly irrelevant, as we have seen in the retail industry in particular. Once iconic brands now fall by the wayside, and will quietly disappear e.g. Sears, Toys R Us, and many more. Studies by experts suggest that many more companies, across many industries, will disappear because they fail to understand the changes in values and priorities inherent in this content explosion and the related digital disruption it causes.

The behavior of the modern customer has changed and is now much more affected by freely flowing content. In fact in many B2C and even B2B scenarios we see that the modern customer may conduct the whole customer journey without ever talking to a salesperson. Studies show that as much as 67% of the buyer’s journey is conducted digitally (though some put a different spin on that statistic), and customer behavior driven by content that they discover. It can be said that in the modern era, companies that provide relevant, high-quality content succeed, and those that don’t, become irrelevant. The following graphic shows the many stages at which relevant customer content is required to persuade and develop a deeper engagement with a potential customer, and then maintain an ongoing relationship with an enterprise or a brand after a customer relationship has been established.

Now, consider doing this across the many languages that are needed to engage and connect with the global customer, and we see the crying need for not just translation of mandated packaging materials, but translation capabilities that can support the constant and always updating content that may influence a buyer in their evaluation and decision-making process. And, also provide adequate information support in the post-purchase phase of the relationship.

While historically corporate marketing had a great degree of control, today most consumers distrust or at least prefer additional sources of this kind of product messaging, and many would rather trust the shared customer experience of fellow consumers. The value of business content increasingly has a very short shelf-life and thus traditional (slow and expensive) translation approaches are increasingly questioned for information that may have little or no value after six months.  In actual fact, the fastest growing type of content is actually user-generated content (UGC) that is found in blogs, FB, YouTube, Twitter and community forums. It is estimated by IDC that 70% of the content on the web is UGC and much of that is very pertinent and useful to enterprises to understand trends and customers better. This content is now influencing consumer behavior all over the world and is often referred to as word-of-mouth marketing (WOMM). Consumer reviews are often more trusted than “corporate marketing-speak” and even “expert” reviews which are often funded by the same corporations. We all have experienced Amazon, travel sites, C-Net and other user rating sites. It is useful for both global consumers and global enterprises to make this multilingual.

 It is estimated that as many as 600 billion words a day are translated by computers today, across the various MT (machine translation) portals. This dwarfs what the localization and professional business translation industry does by a factor of more than 99X! Recent reports suggest that a new MT developer, Alibaba, does as much as 200 billion words a day alone on their various eCommerce platforms. This brings the total MT word tally up to almost 800 billion words a day. Clearly, global customers need specific information that may not be available in their native tongues, and they will use MT to get at least a gist of what they need to understand. While many continue to moan about the imperfection of MT quality at a human linguistic quality assessment level, we have already reached a point in human history where the substantial bulk of language translation being consumed on the planet today is being done by computers.

 Global customers who research products and services, can and will get many disparate sources of information, that is not controlled by an enterprise to make their evaluations on whether to buy a product or not. MT will allow them to get access to non-native language content, and instantly obtain a translation that is “good enough” to support a personal evaluation process.

In our modern times, we’re experiencing a state of unprecedented connectivity thanks to technology. However, we’re still living under the shadow of the Tower of Babel in terms of global human communication ease. Language remains a barrier to business and marketing. Even though technological devices can quickly and easily connect, humans from different parts of the world often can’t. And traditional translation service offerings simply cannot scale to the real translation needs of the modern enterprise without leveraging technology in a substantial and competent way.

There are many kinds of business translation applications where MT just makes sense, and it would be foolish to even attempt these kinds of projects without competent MT technology as a foundation. Usually, this is because these applications have some combination of the following factors:

• Very large volume of source content that simply could NOT be translated without MT in any useful time frame
• Rapid turnaround requirement (days, hours or minutes) for the content to have any value to the content consumers
• A user tolerance for lower quality translations, at least in early stages of information review
• To enable information and document triage when dealing with large document collections and help to identify highest priority content from a large mass of undifferentiated content. This process also helps to identify the most important and relevant documents to send to higher quality human translation processes.
• Translation Cost prohibitions (usually related to volume)

One can find this combination of requirements in several customer communications oriented functions like providing technical support knowledge-base, eCommerce product listings, customer service/support, and customer experience reviews for all kinds of products and service experiences. However, in an increasingly digital world, we see that the need to be able to process large volumes of business content will only grow, and the need to identify what is most relevant and valuable for ongoing international business mission needs is becoming a critical success-enabling technology requirement.

Competently deployed machine translation technology, that is properly integrated with relevant content flows to enhance customer experience, solves high-value business problems that further and enhance any and all global business initiatives.

So if we are to summarize these key trends we see the following:

• A content explosion that makes huge amounts of information available to global customers to help them understand products and services on a scale that has never been seen before. Much of this content is out of the control of the modern enterprise but yet it can deeply influence the behavior of potential and existing customers of the enterprise. Understanding what is most relevant and important is also becoming an increasingly more valuable skill.
• An era where content is increasingly your best salesperson and customers everywhere will use digital content to make purchase decisions. For an enterprise to be relevant in the modern era they will need to be present at every stage of the buyer and customer journey with relevant and high-value content. Those that provide the best digital experience (DX) with relevant content will thrive and prosper, and those that do not will struggle and fail.
• The modern global customer expects to get as much content and information in his language as his counterparts across the world. MT technology use will continue to accelerate to support these needs to make relevant content visible in a timely and efficient manner. MT technology will continue to improve as the smartest researchers in the world continue to focus on it.

"Mass machine translation is not a translation of a work, per se, but it is rather, a liberation of the constraints of language in the discovery of knowledge."  

                                                                                      Peter Brantley

Thus, in this era of digital disruption, content-driven customer engagement, and B2C relationship building, where global customers want access to the same content that their English speaking counterparts have, what is the leadership at a modern enterprise to do?

In reviewing the needs for the day and the skills that really matter for the future increasingly point to three things:

1. An understanding of what is relevant content within the deluge that every enterprise today faces. The need is to increase relevant communication not just make any random content more available.
2. An alignment of the content development and management strategies, with efficient and optimized translation processes that enable the enterprise to quickly reach a global audience. Content creation needs to be aligned with content transformation (translation) and delivery strategies.
3. An understanding of the new AI-based emerging technologies that will help the enterprise to rapidly evolve to producing relevant content and establish a global digital presence so that the relevant content is delivered to the right customers at the right time.

 In a respected paper published in the Harvard Business Review, the authors point out three building blocks that can help drive a modern enterprise into building a market leadership position in this age of digital disruption. The building blocks are summarized in the graphic below.

And for those who look carefully, we can see that MT has now reached a point of being a critical and strategic technology to assist in this digital transformation.  MT enables the communication that enables and underlies product innovation from global teams, helps an enterprise to understand and communicate more effectively with customers across the world, and, also enables efficient delivery of highly relevant multilingual content across the world to build market leadership.