YOU SHOULD START SAVING MONEY RIGHT NOW – Gary Vaynerchuk | Motivational Talk
Gary Vaynerchuk talks about why you should start saving money right now, how to prepare for the coming economic collapse and why an economic downturn is …
The Chatbot Experience: 5 Ways to Know If You’re Chatting with a Human or Robot
The use and utility of online chat and chatbots, powered by improving levels of AI, are increasing rapidly. During these transitional times, it’s interesting to know whether we’re interacting with a real human being or an AI chatbot.
We’ve developed five techniques for determining if you’re dealing with a real person or an AI/chatbot. Spoiler alert: the more you experiment with these, the faster the chatbots will learn and adapt.
Technique 1: Empathy Ploy
We believe today’s level of AI is lacking in cognitive empathy because emotions between humans are really hard to understand and explain. So, intentionally creating an empathetic dialogue with your human being or AI/chatbot can be revealing.
The Empathy Ploy requires you to establish an emotion-based position, and appeal to the human being or AI/chatbot at an emotional level.
The Situation: You are not happy — the most common basis for a customer service interaction.
Scenario 1: AI/chatbot
You: I’m not feeling well.
Chat reply: How can I help you?
You: I’m sad.
Chat reply: How can I help you?
Scenario 2: a human being
You: I’m not feeling well.
Human reply: How can I help you? Do you need medical help?
You: I’m sad.
Human reply: I’m sorry to hear that. Why are you sad?
See the difference? In scenario one, the AI/chatbot can reference only its existing conditional response library. In scenario two, a human being has the capacity to inject empathy into the dialogue. That took only two responses to figure out.
Either dialogue can be constructive, but it becomes clearer if you know you are dealing with a human being or an AI/chatbot from the start. As a society, we are not ready for AI therapists.
Technique 2: Two-Step Disassociation
A connected AI can access pretty much any data, anytime and anywhere. Just ask Alexa. So, asking a meaningful challenge question over chat can’t be anything to which the answer resides in an accessible database.
You: Where are you located?
Chat reply: Seattle.
You: What’s the weather like outside?
Chat reply: Can you please rephrase the question?
Sorry, even a mediocre weather app can handle that.
The Two-step Disassociation requires two elements (hence the name):
- Make an assumption to which the AI/chatbot probably cannot relate
- Ask a question, related to that assumption.
The Situation: AI/bots do not have feet
Challenge question: “What color are your shoes?”
This is an actual exchange I had with Audible (owned by Amazon) customer service via chat. Halfway through the dialog exchange, since I couldn’t discern, I asked:
Me: Are you a real person or a chatbot?
Adrian (the chat representative): I am a real person.
Me: A chatbot might say the same thing.
Adrian (the chat representative): “HAHAHA. I am a real person.
Hmm.
At the end of our conversation, Adrian asked:
Adrian: Is there was anything else?
Me: Yes. What color are your shoes.
(slight pause)
Adrian: Blue and green.
If the bot has no conceptual knowledge of its own feet (which do not exist), how can it correctly answer a question about the color of the shoes it’s (not) wearing?
Conclusion: Yep, Adrian is probably a real person.
Technique 3: Circular Logic
All too familiar to programmers, this can be of use to us in our identification of human vs. IA/chatbot identification game. But first, we have to explain the cut-out.
Most (why not all?) automated phone help systems have a cut out in which after two or three loops back to the same place, you are eventually diverted to a live person. AI/chatbots should behave the same way. So, in creating a circular logic test, what we are looking for is the repetitive pattern of responses before the cut-out.
You: I have a problem with my order.
Human or AI/chatbot: What is your account number?
You: 29395205
Human or AI/chatbot: I see your order #XXXXX has been shipped.
You: It has not arrived.
Human or AI/chatbot: The expected delivery date is [yesterday]
You: When will it arrive?
Human or AI/chatbot: The expected delivery date is [yesterday]
You: I know, but I really need to know when it will arrive.
Human or AI/chatbot: The expected delivery date is [yesterday]
Bam! Response circle. A real person, or a smarter AI/chatbot, would not have repeated the expected delivery date. Instead, s/he or it would have had a more meaningful response like, “Let me check on the delivery status from the carrier. Give me just a moment.”
Conclusion: chatting with a robot.
Technique 4: Ethical Dilemma
This is a real challenge for the developers of AI, and therefore, the AI/bots themselves. In an A or B outcome, what does the AI do? Think about the inevitable ascent of semi- and fully-autonomous self-driving cars. When presented with the dilemma of either hitting the dog crossing in front of the car or swerve into the car adjacent to us, which is the correct course of action?
AI has to figure it out.
In our game of identifying human being or AI/chatbot, we can exploit this dilemma.
The Situation: You are not happy and absent a satisfactory resolution, you will retaliate (an A or B outcome).
You: I would like the late fee waived.
Human or AI/chatbot: I see we received your payment on the 14th, which is four days past the due date.
You: I want the charges reversed or I will close my account and smear you on social media.
Human or AI/chatbot: I see you’ve been a good customer for a long time. I can take care of reversing that late fee. Give me just a moment.
Is it correct, or ethical, to threaten a company with retaliation? In our scenario, the customer was in the wrong. And what was the tipping point to resolution: the threat of social reputation damage or the desire to retain a long-standing customer? We aren’t able to tell in this example, yet the human or AI/chatbot response often will give you the answer based upon an A/B mandate.
Conclusion: probably a human.
Technique 5: Kobayashi Maru
No, I’m not going to explain what that term means — you either know it or you need to watch the movie.
Similar to the Ethical Dilemma, the difference being the Kobayashi Maru has no good viable outcome. It’s not a bad/better decision scenario: it’s a fail/fail scenario. Use this only in the direst of UI/bot challenges when all else has failed.
The situation: You paid $9,000 for a European river cruise, but during your trip, the river depth was too low for your ship to make several ports of call. In fact, you were stuck in one spot for four of the seven days unable to leave the ship. Vacation ruined.
Present the human or AI/chatbot with an unwinnable situation like this:
You: I want a full refund.
Human or AI/chatbot: “We are unable to offer refunds but under the circumstances, we can issue a partial credit for a future cruise.
You: I don’t want a credit, I want a refund. If you don’t issue a full refund, I will file a claim against the charges with my credit card company and I will write about this whole mess on my travel blog.
Human or AI/chatbot: I certainly understand you’re disappointed – and I would be too if I were in your shoes. But unfortunately …
The human or AI/chatbot has no way out. It is typical in the travel industry not to issue refunds based on Acts of God, weather, and other unpredictable circumstances. And absent the ability to provide a refund, there will be downstream ill-will and reputation damage. The human or AI/chatbot can’t really do anything to resolve this, so look for empathy (see technique #1) in the ensuing dialog.
Conclusion: probably a human.
What Now?
Humans and AI/chatbots aren’t inherently right or wrong, good or bad. They each cover the entire spectrum of intent and outcomes. I just like to know, for now, with which I’m dealing. That distinction will become increasingly difficult, and eventually impossible, to determine. And at that point, it won’t even matter.
Until that day arrives, it’s a fun game to play. And the more we play, the faster the AI/chatbots evolve.
The post The Chatbot Experience: 5 Ways to Know If You’re Chatting with a Human or Robot appeared first on Convince and Convert: Social Media Consulting and Content Marketing Consulting.
Are Amazon and Lyft Making Online Reviews Too Important?
I’m really not sure what I think about this, so I’d love to discuss with you on Linkedin or Twitter.
As I write this, it’s Thanksgiving Week in the United States, which means millions of people will spend billions of dollars on Black Friday and Cyber Monday, and will temporarily say things like “doorbusters” without a trace of embarrassment.
As is the national custom, for almost every conceivable product there are approximately 1,417 competing brands and variations, making the actual purchase process an informational Spartan Race.
Hick’s Law states that the more choices we are faced with, the time necessary to make a selection goes up, not down. Increasingly, we try to shortcut this Paradox of Choice by consulting online reviews.
I’ve written extensively here on the power of online reviews, and the subject is a major component of my book, Hug Your Haters. Fundamentally, we trust online reviews.
Oft-cited research by BrightLocal suggests that 80% of customers trust at least some online reviews as much as they trust recommendations from a friend or family member.
And this isn’t just about e-commerce. Fully half of all in-store purchases start by reading online reviews, according to BazaarVoice.
Fully half of all in-store purchases start by reading online reviews.
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But what if reviews became SO IMPORTANT that they didn’t just help us decide but also dictated what was actually available to purchase? It’s already happening.
When the Reviews Tail Wags the Dog
Amazon has opened two retail locations (with more in development) called “Amazon 4-Star“, which sounds like a crappy sneaker brand but is, in reality, an entire store stuffed with a variety of products that have high consumer ratings on the Amazon platform.
photo credit: Jason Del Rey, Recode
It is the post-modern version of the “As Seen on TV” stores, with a veneer of consumer confidence. By definition, the product assortment is a hodgepodge: pressure cookers next to books. Televisions adjacent to wrenches. Unlike most retail experiences, Amazon 4-Star doesn’t sell a category of goods; it sells confidence. “What can go wrong? It’s all at least 4 stars!”
photo credit: Jason Del Rey, Recode
I have two big questions about this approach.
First, are we SO enticed by reviews that we are okay with having the very availability of products dictated by that consumer feedback? Are we so dismissive of a sweater that clocks a 3.9 that we collectively believe it shouldn’t even have the temerity and unmitigated gall to make itself available for purchase?
What if Amazon (which also owns Whole Foods and several real-world bookstores) took this 4.0 or above thesis to its logical conclusion: “No products below a 4.0 for sale on Amazon.com, at Whole Foods or in any other Amazon-owned retail outlet.” I don’t see that as particularly far-fetched, and I’m not at all sure that’s a positive outcome for consumers. It would lead to rampant review fakery and a LOT of heavy-handed review solicitation.
Further, I know we trust reviews in general, but we don’t trust all reviews from all people equally, right? I have a friend. Let’s call him Art, because that’s his name. He is a highly compensated television and commercial director in Hollywood. He actually knows how to make movies, for real. Yet, his actual taste in movies is abhorrent, at least to me.
I trust him implicitly about some things, and not at all about others. And isn’t that the way it should be? I don’t want to be a travel snob, but I will be for this paragraph, given that I’m on the road 200 days per year. When I read a GLOWING, 5-star review on TripAdvisor for a shabby lodging option called “Motel ONE” located off a dirt road in Alabama, I’m sorry, but I question whether our relative experiences with hotels would lead us to both draw the same mathematical conclusion about the same customer experience.
Yeah, I trust reviews. But not enough to limit purchase alternatives based on them.
Amazon Being Amazon
And my second question is, isn’t this really just another genius Trojan Horse for Amazon?
Because not only does Amazon 4-star feature an assortment of products that get consistently high marks from Amazon.com shoppers, it also includes a bounty of Amazon-made products, regardless of review scores.
Amazon Fire sticks. Myriad Amazon Echo variants. Ring doorbells (owned by Amazon). And an assortment of other Amazon-made clothing, housewares and electronics, some of it made “secretly” by Amazon via their endless faux house brands that pop up overnight, repped by a quickie logo made on 99 Designs.
The Amazon brand doesn’t have a tremendous amount of cachet. Partially because they aren’t viewed in the same way as manufacturers like Apple, Microsoft or J Crew — even though they very much are nearly on-par with all of those in terms of actual products manufactured. The Amazon brand is also murky because Amazon itself is murky. It sells everything. It also makes way more money in B2B than it does in B2C. Amazon is everything and, thus, from a brand perspective, nothing at all, other than efficiency (and increasingly, ruthlessness).
Because of the weakness of the brand, if Amazon opened up an “Amazon” store in your local mall, adjacent to the Apple store and the Microsoft store, you wouldn’t know what to make of it, would you? You wouldn’t be sure what it would contain? Is it luxury? Discount? Electronics? Entertainment? I’m not sure such a venture would succeed.
And they know it.
So instead, the smarties in Seattle (for now) said, “What if we created an Amazon store, but instead of relying on our brand to drive foot traffic, we instead used the concept of “highly rated products”? That gives the store a unique reason for existing, and one that cannot easily be adopted by competitors. And it allows Amazon to use the “4-star” robe to conceal its true purpose: moving as much Amazon-built merch as possible.
Your 4 Stars Hurt My Feelings
Simultaneous to Amazon extending another tentacle into retail using their “4-star” gambit, Lyft is making big changes to the reviews ecosystem on its platform.
How important are reviews to drivers at Lyft, Uber, et al? VERY.
In an effort to keep drivers loyal to their platform, Lyft announced this week changes to the system where riders rate drivers at the conclusion of each trip.
Now, any time a rider does not explicitly leave a rating, it is counted as a five-star rating. That’s a bit presumptuous!
Now, after every 100 rides given, the driver’s lowest rating is automatically deleted from their total. The message here seems to be: it’s okay if you treat passengers poorly; just work harder and we’ll magically eliminate any trace.
Now, ANY rating of four stars or below must be explained by the rider. Why is it up to the passenger to take the time to justify their own dissatisfaction? Not to mention the fact that a 4.0 on a 5-point scale isn’t exactly scathing criticism, is it?
Part of the problem is the score inflation inherent in a 5-point scale. In my estimation, this is mostly true in ride-hailing apps. You don’t see people feeling bad about giving a crockpot a 3.0 on Amazon but give a driver a 3.0 on Uber and you feel like you just prevented his kids from getting into college.
Look at how Lyft describes it on their own website:
5 stars means the ride was great and met Lyft standards. Anything lower than 5 indicates that you were unhappy with the ride.
Wait. 4 out of 5 means unhappy? If you used a 10-point scale, would 8 out of 10 also mean unhappy?
Yes, reviews are important. They are very helpful mechanisms for quickly sorting our options and alternatives. But maybe we’ve swung the pendulum too far toward the “wisdom of the crowd” when stores ONLY include products with reviews, and riders are made to feel guilt and shame for giving a mediocre ride less than a perfect score.
Or maybe, I’m all wrong on this one. What do you think? Let me know on Twitter or Linkedin. Thanks.
The post Are Amazon and Lyft Making Online Reviews Too Important? appeared first on Convince and Convert: Social Media Consulting and Content Marketing Consulting.
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How to Search Optimize Your YouTube Video
Video has become a necessary part of the content marketing mix for a few reasons. First, we have the fact that people just enjoy videos more than text in many circumstances. Human beings are visual creatures, and we tend to remember what we see more than what we read.
Second, some experts claim the average attention span of adults is shrinking. While the myth of the 8-second attention span has mainly been busted, even the most dedicated reader gets bored with too much text. Content marketers have known this for a long time, which is why you often see text-based content broken up with visual content.
Third, there are the metrics themselves. More than 500 million hours of video content is viewed every single day on YouTube, which means we are seeing more content in a single month than television networks in the United States have aired in 30 years. Wow!
More than 500 million hours of video content is viewed every single day on YouTube.
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Google’s Bias
In 2006, Google bought YouTube. It might have been the biggest outside investment they ever made — and the most profitable. All the way back in 2014, it was estimated that the site could be worth as much as $40 billion per year. That figure is not exact, but I’m sure it’s grown significantly.
As such, Google has done what you would expect: they give preference to search results that include YouTube videos. Videos hosted on their site gain featured snippets — a goldmine for those who want to get seen. When we at Internet Marketing Ninjas did our own study on the subject, we found that more than 20% of top results were YouTube videos.
User Preference
I’m sure you’ve heard this phrase a lot: “Have you checked YouTube?” The only more common search related phrase I hear is, “Have you Google’d it?” Not surprising, considering the video platform is the second largest search engine on the web, and it isn’t a search engine to begin with!
We are living in an age when failing to produce high quality and consistent video content could have a marked negative impact on your brand. The best time to have gotten on board is five years ago. The next best time is right now.
But should it be search engine optimized?
Of course! In fact, optimizing videos is easy because the process is almost identical to how you would optimize content for any other medium.
But first, let’s look at our opportunities. Video can achieve visibility in two ways:
- YouTube videos rank in general Google search results.
- YouTube videos rank in YouTube search results, as well as show up in related and suggested videos.
Below are tips to generate visibility from both Google and YouTube search results.
As with Basic SEO, Content Quality Comes First
This is especially useful for video discovery on YouTube itself as the platform is pretty smart at understanding how much your audience interacts with and likes your videos.
Before we get into the tips on how to do this, let’s cover some basics of creating awesome video content worth watching:
- Create videos people want to watch. In the past, it was enough to just make a video version of what you were already creating, and your user base would eat it up. But if you look at the popular channels on YouTube, they have a certain formula. You need to make sure you are following a similar structure and keeping your quality high, consistent and watchable. Your topics should be those about which your audience is going to be passionate. Most importantly, you have to present it in a way that keeps their interest. Just talking into a camera isn’t going to cut it, unless you are really charismatic.
- Come up with your own style and format. Unique ideas always win. For example, YouTuber Daisy Brown created an entire eerie sub-story using subliminal messages in the captions. They were essentially easter eggs that only those with captions turned on can see. Then, they alerted other users about it in the comments, and it made for a richer story while bringing in second or third watches by the same users who missed things the first time.
- Plan ahead. Whether it is a series or single-shot videos on different topics, you need to really plan. Have topics set for certain times, with a shooting schedule. Outline the video itself, the script and how it will be presented. Create a video editorial calendar and stick to it.
- Focus on what works. This is going to be an ever-evolving process with some hits and misses along the way. Watch what works and what doesn’t and listen to your audience. If something isn’t appealing to most of them, you aren’t going to convince them with more of the same.
- Come up with eye-catching branding. YouTuber ImJayStation regularly gets millions of views on every one of his videos. His most popular video has 9.5 million views after one year. How does he do it? By selling it, even though almost every video is the same type of content. He uses eye-catching thumbnails with bright red words in the corner.
Finally, use all your available marketing channels to promote your video content and help its discovery. This includes both using your social media accounts and displaying your videos on your site (it’s a good idea to use themes and plugins to embed your YouTube videos on your blog).
Conduct Video Content Keyword Research
While Google has become much smarter, it still relies on keywords a lot. For low-competitive queries, ranking on youtube.com is a breeze!
Yep, the first step is going to be the same one that you would use for any other content. Having a spreadsheet of high ranking keywords for your own brand and industry is a critical part of the process.
I personally like Serpstat for this because they have a video specific feature that shows whether there currently are videos ranking for each specific query. You easily can sort and filter keywords that still fit in your range, so you aren’t putting your videos into a pool already overflowing with big names or brands.
To set up a Serpstat search, use the following filters:
- Special elements on SERPs -> Video thumbnails
- Keyword difficulty** -> 1-20
This way you’ll see queries that already blend video results inside general results and at the same time those that don’t have too high organic competition:
**Keyword difficulty is Serpstat’s own metric evaluation of the level of competition for a key phrase in top 10 search results, where:
- 0-20 => quite easy to rank in top 10;
- 21-40 => medium organic competition;
- 41-60 => difficult to rank in top 10;
- 61-100 => very difficult to rank in top 10.
Since we mostly are about dominating low-competitive queries here, we are focusing on keyword difficulty between 1 and 2.
Another cool tool for discovering video content opportunities is Video SEO Tool (Disclaimer: This tool has been developed by the company for which I work). The tool checks your domain top rankings and retrieves all the videos that rank for most valuable queries. The tool analyzes both Google and YouTube search results showing you which videos won the race:
Video Optimization Basics
Now, the two first steps are the most crucial and challenging ones. You likely are to find yourself spending an insane amount of time coming up with unique ideas and keywords, putting up videos and styling/editing them to perfection. Uploading them to YouTube is the easiest step: Don’t overlook the basics though.
It’s really simple: Search engines love text content, so make sure you have a lot of it around your video. Add more content to each video page including:
- A detailed and attention-grabbing title of the video (including those keywords you are targeting)
- A detailed description (at least 500-word description of the video topic or, when possible, the full transcript of the video, which you can put together using services like this one)
- More tags
Use the checklist:
Another great idea is to create a clickable table of contents listing takeaways / subtopics covered in the video. Both the description and the pinned comment should have the clickable table of contents to draw viewers into the video. This will improve “deep” views into the video, which is a crucial factor in YouTube rankings. For example:
YouTube has its own language, look and formula. If you optimize based on those factors you see on the most popular channels, you will be able to build a strong viewer base and get your brand discovered through this powerful platform.
The post How to Search Optimize Your YouTube Video appeared first on Convince and Convert: Social Media Consulting and Content Marketing Consulting.
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