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Old Spice Launches 2h Invisible YouTube Film

This is perhaps Old Spice’s weirdest experiment yet. And it’s called “Invisible World”, a full feature length 2 hour online film where you can’t see a thing, all designed to promote their new Invisible Spray deodorant. Reminder: You see absolutely nothing in this film, a few words here and there, a glitch of colour randomly… […]

Digital Buzz Blog

#SocialSkim: Facebook Rolls Out ‘Watch,’ LinkedIn’s New Audience Network: 10 Stories This Week

Facebook’s YouTube competitor Watch goes live; LinkedIn’s major new Audience Network tool; WhatsApp gets serious with a business app; where Snapchat still beats Instagram; the app Millennials say is more indispensable than Facebook or Instagram; much more… Read the full article at MarketingProfs
MarketingProfs Daily: Content

5 Lies You Tell Yourself About Your Analytics (And How to Fix It)

Consulting data is good.

But being a slave to data is not.

There is such a thing as being too data-obsessed. Confirmation bias pops up. And you miss the good, albeit, intangible stuff that comes along with your efforts.

The solution is to uncover those biases and misunderstandings that lead you astray.

It’s not easy. Or even intuitive. But it’s the only way to avoid these five analytics blinders.

Here’s how it strikes when you least expect it.

Here’s why you fall for it.

And here’s how to avoid it by bringing in other types of feedback and analysis.

Lie #1. Your “Conversions” Are Flawless

You’ve got three AdWords campaigns.

  • The first brings in zero leads on $ 78 bucks spent.
  • The second brings in one at a cost of $ 135.31.
  • The third brings in two at $ 143.28 per lead.

Nine times out of ten, the campaign with more “conversions” is declared the winner.

But what do you really, truly, know about this scenario?

Which campaign is actually performing the best? Which is putting the most money back into your pocket?

There’s simply no way to tell at this point.

First and foremost, these “conversions” are leads — not closed customers.

Second, they might be for different products or services. So different average order values or LTVs come into play.

Third, this is nowhere close to statistical significance. For example, the third campaign has the most leads because you’ve spent the most money on it.

Not because it’s “better.”

What if you simply spend the same amount on the first two? What if you let them both get to around the same ~$ 150/per mark?

See what I mean?

Too many “what ifs” for my taste.

Yet this is exactly what happens inside any marketing department. The same end result pops up after each client or superior meeting.

Everyone points to the third campaign. It gets the adulation. It gets the increased budget. It gets the additional staff and resources.

So it becomes a self-fulfilling prophecy.

One solution to figure all this out is closed loop analysis.

Ideally, your goal is to match up the customer’s information (name, email, phone, credit card) to the lead data you’re seeing inside Google Analytics.

Haha — just kidding.

That would mean you were gathering Personally Identifiable Information, which is a big no-no in Google Analytics.

Do it and they’ll delete your account right away.

The simplest alternative is to just use a tool that gives you this power, without jeopardizing your data. Hint, hint.

Lie #2. Your “Top” Traffic Sources

What are your top sources of traffic?

A quick glance inside Google Analytics usually tells you (1) organic search and (2) direct. Maybe a little (3) referrals thrown in for good measure if you got some press last month.

Here’s the problem.

Two of those three are legit. The other is not.

The problem is that your direct traffic isn’t, in reality, all that “direct.”

Technically, this should be the number of people typing in your website URL to the address bar and hitting “Enter.”

Instead, it’s a healthy mix of email, social media, and good ol’ organic search.

The bigger the site, the bigger this problem usually is.

For example, The Atlantic couldn’t account for or explain how 25% of their visitors came to their site.

One of the biggest publishers in the world. One of the most respected. Who gets paid based on the number of visitors and page views they get. Has no idea how a quarter of their traffic is getting to their site.

That ain’t good.

But how can you really tell where people are coming from, if most analytics programs can’t tell you with any degree of accuracy?

For instance, let’s say your new, fantastic-looking email campaign is about to go out.

It’s been given the green light. “Legal” gives you the A-OK.

But wait! You didn’t tag the promo links correctly.

Now, you’ve spent all that time on a campaign that won’t have anything to show for it, because the traffic you get will now end up in the dumpster pile officially known as “Direct traffic.”

This isn’t just an email. It affects each and every social message, press mention, and blog post referral, too.

It can even affect your organic search traffic.

Groupon found this out the hard way. Literally. By completely de-indexing themselves for a few hours.

What did those crazy couponers find? That nearly 60% of their direct traffic was actually coming from organic search.


But don’t freak out just yet. There are solutions here.

First, you can use Google’s UTM builder to make sure you are properly tagging your links. This means any and everything you have control over.

Manually tag them before they head out the door, or copy & paste into a lightweight app like Terminus.

If you’ve got long, cumbersome URL, you can be pretty sure that any traffic to that page didn’t come from Direct traffic.

People aren’t going to remember it. Which means they aren’t going to just spontaneously type it in.

Instead, these peeps probably came from another place, like an organic search or email.

However, in the same breath, you can probably consider homepage traffic to be legitimate Direct.

So create a segment based on these URLs and traffic sources to pinpoint “Dark Traffic” in its tracks. And prevent it from ruining your data in the future.

Lie #3. Top of the Funnel Performance = Results

Yes, we want traffic.

Yes, we want pageviews.

They make us feel all warm and fuzzy and proud. Like our hard work isn’t going unnoticed.

But they should not be the end-all, be-all.

Use them to see how you’re doing over last month. But don’t misunderstand numbers to be the Holy Grail, either.

Like this, for instance:

Looking at only this, you walk away feeling like a boss for all the numbers you’ve racked up. Seriously, I can’t even count that high.

But what about when you consider the bounce and exit rates for each of those pages? Are people staying? No? Color you embarrassed.

Are you still so excited by your thousands of pageviews if most of them left immediately?

Bounce rates are real. And you’ve gotta consider them when you are looking at your metrics.

They mean that people haven’t had the chance to interact with your soft micro conversions. They haven’t had a chance to activate.

So take a look at the big picture.

Are your blog posts and site pulling people in, but not making them stay?

This isn’t a horrible problem to have, because it’s a problem you can pinpoint.

The traffic is there. They just don’t really like what they are seeing once they get to your site. Which you can fix.

First, set up some events to get a better idea of what’s happening on your pages. Then, make sure you have actionable goals that will allow for movement you can track.

Or use the Kissmetrics’ Customer Engagement Automation tool to analyze what people are actually doing on your site and with your products. Then, you can interact with behavior-based messaging to keep them around longer. Or keep them coming back for more.

That way, you can increase conversions, engagement, and retention without the guesswork.

kissmetrics populations

Just always remember that numbers don’t tell the whole story. Use them with a grain of salt and a little bit of context.

Lie #4. Deceptive A/B “Wins”

I’m just going to be honest with you. Those A/B testing “wins” you just got? Don’t always have the best track record.

I’m sorry to be so harsh right off the bat. Sometimes the truth hurts.

What’s even more worrisome? Oftentimes, tests will look like they have succeeded. But that’s not always the case (or at least, not the whole picture).

Start with Google Analytics content experiments, instead.

You can use it to contrast your varying pages to see if there are any sizeable adjustments that cause positive changes.

Instead, it allows you to compare different page variations to see which ‘bigger’ changes result in improvements. Maybe this works a little better because it adds an extra letter– it’s an A/B/N test.


The problem with this test is when you get a little too grab-happy.

You can quickly and easily remove fields to get better results, for instance. A simple reduction of three fields will increase your conversions by 11%.

Or, you can take away specific conversion-busters like the need to add a credit card for a non-paying trial. Sure, this will up your “conversions.”

But remember how far that got you a few lies ago?

That credit card field you took away? It was a huge indicator for which of your customers will eventually buy. 50% of people who put in their credit card will end up converting. While only 15% will of those who don’t enter a credit card will.

And we’re talkin’, conversions-conversions here. Like, bottom-of-the-funnel, paying customer conversions.

Context is key when you are looking at analytics.

Don’t test landing pages or simple changes to fields while only evaluating the top of the funnel. Make sure you dig in to see how the changes affect the rest of the customer experience and journey.

To do that, use the Funnel Report so you can see exactly how top-of-the-funnel changes are impacting bottom-of-the-funnel sales.

Lie #5. Your Channel Source Attribution

A Forrester Research study years ago found that 33% of all transactions of all transactions happened after new customers had gone through more than one touchpoint.

That number jumps to 48% when considering repeat customers.

The same report showed that paid search is the highest source of conversions.

Is it, though?

Or is it just the last point most commonly used before a sale?

Just because it’s the last one, doesn’t mean it’s the only one.

What other marketing tactics are working to increase growth? Forrester went on to declare that while email works for repeat conversions, social media brings in less than 1% of sales.

Ok. Then how do you explain SpearmintLOVE?

You know, the freaking baby blog that boosted their revenue by 991% in year using Facebook and Instagram.

The only reason I know about them? Because my wife has bought clothes from them. After discovering them on Facebook and Instagram.

One, simple Google graph puts this myth to bed. Fast.

If you look at the left side, or “assist interactions,” you’ll see that social channels will put new products in front of people.

As you move toward the middle, customers get more information about products and options using search. At the end, they’re on their way to the website.

Notice all the possible interaction options here. It’s not just the last-touch that brought the customer to the website. They can take many steps to get there.

Google Analytics has a few different attribution options built-in to help you change how conversions are assigned.

Image Source

These include:

  • Last Non-Direct Click: This will overlook Direct clicks and go to the channel used right before.
  • First Interaction: This uses the social or advertisement that got them to the website.
  • Linear: Here, each channel that a customer used before purchasing will get equal attribution.
  • Time Decay: This will consider the channel that was used immediately before conversion, rather than channels used in the past day/week/month.
  • Position: This model gives priority to the first and last channel used before conversion. Anything in the middle gets less attribution.

The depressing part, though?

There’s no right answer here. The attribution model you pick largely depends on your sales cycle, your customers, and even what specific objective you’re trying to figure out.

For example, if you’re spending a ton on ads, you might want to see how the First Interaction looks. Especially when using social ads that often bring people into your ecosystem for the very first time.

In other cases? It would be a terrible choice.

The trick is to know what you’re solving for, first. Then working backwards.


Data is important. It’s huge.


But, be careful.

Google Analytics is a marvelous, cost effective, game-changing tool.

However, it’s been known to lie a little from time-to-time. (Yes, we’re still talking about Google Analytics here.)

Remember that conversion results aren’t always spot on. Direct traffic data might not be correct. Vanity metrics aren’t everything. A/B results can fire off false positives. And last touch isn’t everything.

Uncovering biases is never fun.

But it’s the key to creating campaigns that actually achieve results.

Without just blowing a lot of hot air.

About the Author: Brad Smith is the founder of Codeless, a B2B content creation company. Frequent contributor to Kissmetrics, Unbounce, WordStream, AdEspresso, Search Engine Journal, Autopilot, and more.

The Kissmetrics Marketing Blog

Embedding Google Data Studio Visualizations

Embedding Google Data Studio Visualizations

Last year I wrote about the Marvel vs. DC war on the big screen. It was super fun to merge two of my passions (data visualization and comics) in one piece. It started with my curiosity to understand what all those movies are amounting to, and I think it helped me prove a point: Marvel is kinda winning 🙂

One of the things that annoyed me was that I had to link to the interactive visualization, readers couldn’t see the amazing charts in my article (!) – so I ended up including static screenshots with some insights explained through text. While some people clicked through to play with the data, I suspect many just read the piece and went away, which is suboptimal – when I publish a story, my goal is to allow readers to interact with it quickly and effectively.

I am extremely excited that now Google Data Studio allows users to embed reports in any online environment, which empowers us to create an improved experience for telling stories with data. This feature will be an essential tool for data journalists and analysts to effectively share insights with their audiences.

A year has passed since I did the Marvel vs. DC visualization, so I thought it was time to update it (5 new movies!) and share some insights on how to use Data Studio report embedding to create effective data stories.

Enable embedding

The first step to embed reports is a pretty important one: enable embedding! This is quite simple to do:

  1. Open the report and click on File (top left)
  2. Click on Embed report
  3. Check Enable embedding and choose the width and height of your iframe (screenshot below)

Google data studio enable embedding

Please note that the embedding will work only for people that have access to the report. If the report is supposed to be publicly available, make sure that you make it viewable to everyone. If the report should be seen only to people in a group, then make sure to update your sharing settings accordingly. Read more about sharing reports on this help center article.

But how do you make sure you are choosing the right sizes? Read on…

Choosing the right visualization sizes

Needless to say, people access websites in all possible device categories and platforms, and we have little control over that. But we do have control over how we display information in different screens. The first obvious recommendation (and hopefully all the Interweb agrees with me) – make your website responsive! I am assuming you have already done that.

On Online Behavior, the content area is 640px wide, so the choice is pretty obvious when Data Studio asks me the width I want for my iframe – make sure you know the width of the content area where the iframe will be embedded. Also, since you want the visualizations to resize as the page responds to the screen size, set your Display mode to Fit to width (option available on Page settings).

Without further ado, here is the full Marvel vs. DC visualization v2!

I personally think the full dataviz looks pretty good when reading on a desktop, I kept it clean and short. However, as your screen size decreases, even though the report iframe will resize the image, it will eventually get too small to read. In addition, I often like to develop my stories intertwining charts and text to make it more digestible. So here is an alternative to embedding the whole thing…

Breaking down your dataviz into digestible insights

As I mentioned, sometimes you want to show one chart at a time. In this case, you might want to create separate versions of your visualization. Below I broke down the full dataviz into small chunks. Note that you will find three different pages in the iframe below, one per chart (see navigation in the bottom of the report)

Right now, you can’t embed only one page, which means that if you want to show a specific chart that lives on page 2 of a report you would need to create a new report, but that’s a piece of cake 🙂

I am looking forward to seeing all the great visualizations that will be created and embedded throughout the web – why not partner with our data to create insightful stories? Let’s make our blogs and newspapers more interesting to read 🙂 Happy embedding!

BONUS: Data Studio is the referee in the Marvel vs. DC fight!

As I was working on my dataviz, I asked my 10yo son (also a comic enthusiast) to create something that I could use to represent it. He created the collage / drawing below, I think it is an amazing visual description of my work 🙂

Data Studio referee

Google data studio enable embedding
Data Studio referee

Online Behavior – Marketing Measurement & Optimization

The Hypothesis and the Modern-Day Marketer

On the surface, the words “hypothesis” and “marketing” seem like they would never be in the same sentence, let alone the same paragraph. Hypotheses are for scientists with fancy lab coats on, right? I totally understand this perspective because, unless A/B Testing CRO (conversion rate optimization) is part of your company’s culture, you may ask yourself, “Where would I ever use a hypothesis in my daily activities?” To this question, I would answer “EVERYWHERE.”

By everywhere, I don’t just mean for your marketing collateral but also for any change within your company. For example, I oversee the operations of our Research Partnerships, and when making a team structure change earlier this year, I created a hypothesis that over the next few months I will prove or disprove. Here’s a modified version of this hypothesis:

IF we centralize partnership launches BY creating a dedicated Research Partner launch team and by building out processes that systematically focus on the most critical business objectives from the start, we WILL increase efficiency and effectiveness (and ultimately successful Partnerships) BECAUSE our Research Partners place high value on efforts that achieve their most critical business objectives first.

Can your offers and messaging be optimized?

The American Marketing Association defines marketing as “the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large.”

So, if you are reading this blog right now and your job function reflects the above definition even in the slightest, can you answer one question for me: Do you have 100% confidence that each offer and message you create and deliver to your customers is the best it could possibly be?

If you answered yes, then our country desperately needs you. Please apply here: https://www.usa.gov/government-jobs.

If you answered no, then you are like most of us (and Socrates, by the way, i.e., “I know that I know nothing”) and you should have hypothesis creation operationalized into your process, because why wouldn’t you want to test message A versus message B to a small audience before sending an email out to your entire list?

How to create a marketing hypothesis

So first, let’s discuss what a hypothesis is and how to create one.

While there are several useful definitions of the word hypothesis, in Session 05: Developing an Effective Hypothesis, University of Florida’s graduate course MMC 5422, Flint McGlaughlin proposes the following definition as a useful starting point in behavioral testing: “A hypothesis is a supposition or proposed statement made on the basis of limited evidence that can be supported or refuted and is used as a starting point for further investigation.”

Now that we know what we are looking for, we need a tool to help us get there. In Session 04, Crafting a Powerful Research Question of this same course, McGlaughlin reveals that this tool is the MECLABS Discovery Triad, a conceptual thinking tool that leads to the creation of an effective hypothesis — the “h” in the center of the triad represents the hypothesis.

Before creating a hypothesis, the scientists at MECLABS Institute use this Discovery Triad to complete the following steps for all of our Research Partners:

  1. We uncover the business objective (or business question) driving the effort. Typically, we find two patterns regarding the business objective. First, it is broader in scope than a research question that would be suitable for an experiment. Second, this objective takes the form of a question, which typically starts with the interrogative “How.” For example, “How do I get more leads?” “How do I drive more traffic?” or “How do I increase clickthrough rate (CTR)?” My business objective in the examples above was, “How do I create a more valuable research partnership from the perspective of the research partner?”
  1. Now that we are focused on an objective, we ask a series of “What” questions. For example, “What is happening on this webpage?” or “Where are visitors to page A going if they do not make it to page B?” Essentially, we are looking to understand what the data can tell us about the behavior of the prospective customer. This series of “What” questions should encompass both quantitative (e.g., on-page clicks, next page visits, etc.) and qualitative questions. (e.g., What is the customer’s experience on this page?)
  1. We ask a question which starts with the interrogative “Why.” A “Why” question enables us to make a series of educated guesses as to why the customer is behaving in a certain way. For example, “Why are 75% of visitors not clicking the ‘Continue to Checkout’ button?” “Why are 20% of shoppers not adding the blue widget to their cart?” or “Why are only 5% of visitors starting the free trial from this page?” To answer “Why” questions, the research scientists at MECLABS apply the patented Conversion Heuristic to the page:
    1. What can we remove, add or change to minimize perceived cost?
    2. What can we remove, add or change to intensify perceived value?
    3. What can we remove, add or change to leverage motivation?

  1. We ask a second, more refined, “How” question (research question) that identifies the best testing objective. For example, if your business question was, “How do we sell more blue widgets?” and during the “What” stage, you analyzed your entire funnel, discovering that the biggest performance leak is on your blue widget product page, then your Research Question could be something like, “How do we increase CTR from the blue widget product page to the shopping cart checkout page?”

Essentially, a powerful Research Question focuses your broader Business Question around a specific subject of research. After all, there are many ways to sell more blue widgets but only a handful of possible ways to sell more blue widgets from the product page.

The four components of a hypothesis

With a powerful Research Question created, you are now ready to develop a series of hypotheses that will help you discover how to express your offer to achieve more of your desired customer behavior using the MECLABS Four-step Hypothesis Development Framework:

  1. The IF statement. This is your summary description of the “mental lever” your primary change will pull. In fact, the mental lever is usually a cognitive shift you are trying to achieve in the mind of your prospective customers. For example, “IF we emphasize the savings they receive” or “IF we bring clarity to the product page.”
  1. The BY statement. This statement lists the variable(s) or set of variables (variable cluster) you are testing. This statement typically involves the words “add, remove or change.” For example, “BY removing unnecessary calls-to-action (CTAs)” or “BY adding a relevant testimonial and removing the video player.” (Tip: This statement should not contain detailed design requirements. That next level of precision occurs when you develop treatments or articulate your hypothesis.)
  1. The WILL statement. This should be the easiest statement to compose because it is the desired result you hope to achieve from the changes you are proposing. For example, “We WILL increase clickthrough rate,” or “We WILL increase the number of video views.” (Tip: This statement should tightly align with the Test Question.)
  1. The BECAUSE statement. While last in order of appearance, this statement is the most critical as it’s what connects your work deeply into your customer’s being. By that I mean, the metric identified in your WILL statement either increased or decreased because the change you made resonated or did not resonate in the mind of your customer. For example, “BECAUSE prospective customers were still searching for the best deal, and every distraction made them think that there’s a better deal still out there,” or “BECAUSE prospective customers clearly see the savings they receive.” (Tip: Your BECAUSE statement should be centered around a single customer insight that, through testing, adds to your broader customer theory.)

So, if you put all this together, you have:

IF we bring clarity to the product page BY removing unnecessary CTAs, we WILL increase clickthrough rate BECAUSE prospective customers were still searching for the best deal, and every distraction made them think that there’s a better deal still out there.

Just like I used the Discovery Triad and Four-Step Hypothesis Development Frameworks for a team structure change, these processes can be used for any type of collateral you are “creating, communicating, delivering.” Even if you are not A/B testing and just making changes or updates, it’s a valuable exercise to go through to ensure you are not just constantly throwing spaghetti on the wall, but rather, systematically thinking through your changes and how they impact the customer.

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The post The Hypothesis and the Modern-Day Marketer appeared first on MarketingExperiments.


Your angry tweets may require libel insurance

(Bloomberg) — Courtney Love spent almost six years in litigation, accused of libeling her former attorney in a Twitter post that was visible for less than 10 minutes. She paid a reported $ 780,000 in settlements as a result of two other defamation suits, both stemming from Twitter missives Love wrote about designer Dawn Simorangkir. “Twitter should ban my mother,” her daughter, Frances Bean Cobain, once said. Love, an actress, musician and the widow of late Nirvana frontman Kurt Cobain, inherited the band’s publishing rights. She can afford to take on defamation lawsuits. You probably can’t. Given how much of our lives is spent venting on social media, especially in the age of Trump, the…

This story continues at The Next Web
Social Media – The Next Web

First steps with LUIS, the Language Understanding Intelligent Service

There already are several articles on my blog around the subject of the Microsoft Cognitive Services. One that’s still missing, is LUIS – the Language Understanding Intelligent Service. So today, I’ll give you a brief introduction about LUIS so you’ll be able to know what it is and what it can do. As a demo, we’re trying to teach an AI to act like a restaurant where we want to order some food. Hello LUIS!

What is LUIS?

LUIS is part of the Microsoft Cognitive Services which means that it’s part of an extremely powerful yet easy set of APIs developers can use in order to tap into the massive power of machine learning. As the name suggests, LUIS is specifically made to understand language. Although it might look simple at first, it’s actually a lot harder than you might think. Sentences and utterances can be completely different, although they mean exactly the same. Consider the following examples:

  • I want to order food
  • Bring me a hamburger
  • I want to place an order

The sentences look totally different, they have the same intent: order some food. Normally, you would try to parse each sentence (maybe through the usage of regular expressions), but that’ll cost you a lot of effort and you’ll probably won’t get it right. LUIS will help you with this problem, since it’s able to understand all the intents and handle accordingly.

Creating your first LUIS model

In order to do so, simply head over to LUIS.ai and navigate to My Apps. Select New App to start. Although the name “App” might be a little bit confusing here, since we’re essentially going to create a LUIS Model. LUIS supports a couple of different languages, so make sure you select the correct language before moving on.

Intents and Entities

Now that we have our LUIS model, we can start training. As stated at the beginning of the article, every utterance comes down an intent, the action you’re trying to achieve. Navigate to Intents, Add Intent and give it a name (in my example: OrderFood). Now start in typing Utterances that’ll be used to start your intent. In other words, simply write down a couple of sentences you would expect people to say.

LUIS Intents

But wait, do I need to add another utterance for each type of food that I sell? Luckily, you won’t need to do that. Simply select the word that’s variable (in my case the word hamburger) and add it as an Entity. LUIS can now be trained to understand different kind of foods when they are placed in the same kind of utterance.

LUIS Entities

Don’t forget to Save all your changes before moving on.

Train and test

Once you got your intents and entities in place, head over to Train & Test. This page will allow you to train LUIS to learn your intents, utterances and entities. Simply press the Train Application-button and let LUIS do it’s magic.

Now, we can use the Interactive Testing to check how the model behaves. Simply test some utterances, change some words and check how LUIS responds. The most importing thing here is the Top scoring intent, since that’s the intent that LUIS will assign as a matched utterance. Also note that LUIS changes words to the assigned entity automatically when it recognizes it.

Train LUIS

In this example above, I’ve used the following utterance: i want to eat a hotdog. When I created OrderFood intent in the previous step, I have never used exact “i want to eat a” phrase. Also, I’ve never used the word hotdog. Still, LUIS is able to recognise this sentence as an OrderFood and even parses the hotdog as the correct entity. All because the system is learning from all kind of phrases and recognizes entities itself.

(Re)training through the API

In order to get your automated build integrated with LUIS, you’ll need to be able to call it’s Programmatic API. There are several methods available in order to do so. This will im- or export the LUIS model as you wish so you won’t be able to do so through the portal. Take note all these methods require a Ocp-Apim-Subscription-Key in the Request header, which holds your subscription key. These methods are most commonly used to (re)train the model.

  • Export Application – Exports a LUIS application to JSON format
  • Import Application – Imports an application to LUIS, the application’s JSON should be included in in the request body.
  • Add Batch Labels – Adds a batch of labeled examples to the specified application
  • Train – Gets the trained model predictions for the input example

You can use a tool like Postman or the API testing Console from Cognitive Services to call the API. Here you’ll find an example of Postman calling the Export Application on the API (note the {appId} and {key} have been removed in this example).

LUIS Postman

An exported model will be a JSON-file. It contains all the data from the LUIS model. Here’s an example of a (trimmed down) version from my RestaurantLuisModel.

 {     "luis_schema_version": "2.1.0",     "versionId": "0.1",     "name": "RestaurantLuisModel",     "desc": "",     "culture": "en-us",     "intents": [ {             "name": "OrderFood"         } ],     "entities": [ {             "name": "food"         } ],     // Removed data     "utterances": [ {             "text": "i would like to order a hamburger",             "intent": "OrderFood",             "entities": [ {                     "entity": "food",                     "startPos": 24,                     "endPos": 32 } ]         }, {             "text": "i want to place an order",             "intent": "OrderFood",             "entities": []         }     ] } 


LUIS makes is fairly easy to understand a language and identify Intents with utterances and Entities. Since it’s a system that you can train, you can gradually make it better and learn from the past. Although not discussed in this article, LUIS even has the possibility to check Suggested Utterances making your LUIS Model even better. Take note that not all supported languages have Prebuilt entity support. This might cause LUIS to act differently than you would expect.

In my next article, I’ll dive into the possibility to integrate LUIS with the Microsoft Bot Framework in order to create a smart chat bot. This combination is extremely powerful when you’re creating a chat bot that uses natural language. Stay tuned!

Want to learn more about this subject?
Join my “Weaving Cognitive and Azure Services“-presentation at TechDaysNL 2017!

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How to Set Up a Creative Facebook Cover Video

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– Your Guide to the Social Media Jungle

No, Your Brand Isn’t Too Big for Thought Leadership

No, Your Brand Isn't Too Big for Thought Leadership

As we get older, we grow out of many things: tricycles, swing sets, sandboxes, and even Legos. We expect it to happen because it makes sense.

What doesn’t make sense is when a company thinks it’s outgrown the value of thought leadership. Not only can well-positioned thought leadership, fostered by strategic content marketing, expand any business’s branding and sales, but it can also solidify a foundation of trust, education, longevity, and awareness.

Content Rules for Businesses Small and Large

Startups and small- to mid-sized enterprises tend to lean heavily toward thought leadership, and almost half of executives think it makes them more competitive. They’re right, of course: Content allows businesses to communicate expertly with the public in a quick and cost-effective manner.

Seventy percent of consumers get information about businesses and product recommendations by reading articles—not by sifting through advertisements—and the average American spends about 490 minutes of each day immersed in digital media. In other words, thought leadership is a huge opportunity for you to get your brand and your ideas in front of millions of people.

The issue comes when the organization grows. Scaling naturally decreases flexibility, making thought leadership more complex. It’s easier for consumers to see the human side of a small mom-and-pop shop than a giant international corporation.

One company that’s bucking the trend and hitting gold with thought leadership is outdoor recreation company REI. As a retailer, its name is globally recognized, but it has suffered online due to heightened competition. To try to woo customers and compete in the online market, it created niche content that provides value to its customers. The company promotes itself as the place to learn about outdoor activities, which helps drive consumer confidence and sales.

The key to using thought leadership effectively as a large business is to remain authentic. You don’t want your customers to see a faceless corporate entity; you want to appear relatable, genuine, and, above all, a go-to resource for valuable insights.

Developing a Thought Leadership Agenda

When creating a thought leadership strategy, you need to start with a concrete agenda. Creating content as a large company requires a different approach than small businesses might utilize, and it’s important to keep a few things in mind so your strategy is cohesive, effective, and authentic:

1. Consider what your audience wants to hear.

In order to create content, you need to know your readers. Develop target customer personas, going into as much depth as you can until you have a good grasp on the problems they’re facing and how you can use your expertise to solve them.

This is also a great opportunity to get your entire team involved in the content creation process. Every employee can bring something unique to the table, and those diverse insights will help customers see the human side of your company.

The most authentic marketing strategies are audience-centric, meeting your customers where they are to deliver the message they need to hear at that moment. Solving problems for them will create a bond with your customers and give them a reason to come back to you when they’re facing another problem.

2. Don’t forget about your brand promise.

For young startups, a brand promise is all you have to convince customers that you’re worth their time and money. But as your company grows, it can sometimes become more difficult to uphold that promise. It’s sometimes easy to get so caught up in your daily activities that you begin to lose focus on what that promise was in the first place.

A thought leadership strategy, however, is the perfect opportunity to ensure you haven’t lost sight of that promise. Customers will know when you’re trying to fool them, and they won’t hesitate to call out a piece of content that contradicts your brand promise. Consistency is key to creating a successful brand image, and it involves regular checkups to ensure your message remains true to your company values.

3. Create concrete guidelines.

Your content needs to have standards to create a cohesive strategy across your various channels. And while each thought leader should be sharing her own thoughts and ideas in her own voice and style, everyone should be conforming to a specific set of guidelines so that the overall message remains consistent.

Organization should also be a key component of your guidelines. A simple checklist of concepts, keywords, or links that each piece of content should contain will ensure that your content is consistent—even when you have multiple thought leaders.

In addition, an editorial calendar is crucial to keep everything organized and allow all content creators the chance to collaborate and brainstorm. There are many tools available to keep your content organized, from the basic Google Calendar to blog management tools like WordPress to more advanced resources like Kapost and Trello.

Meeting customers where they are drops their resistance and opens the door to genuine relationships.
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4. Take advantage of influencers.

Content creation doesn’t come naturally to many people, and large organizations, in particular, often struggle with content marketing. In fact, one study found that 81 percent of CMOs believe their businesses struggle when coming up with new ideas for thought leadership content.

This is where content curation and influencers come into play. When you’re open to sharing content from aligned companies and individuals, you can expand your reach and enhance your narrative.

The key word here is “aligned.” If you choose to work with influencers to help you create or promote your content, make sure they’re doing it because they truly believe in your brand—not just for the paycheck. This will give your content a more genuine and authentic tone because it’s coming from someone whose mission meshes with your brand’s.

For example, Capitol Records recently partnered with Olay and Mode to create a few behind-the-scenes videos with Michelle Jubelirer, COO of Capitol Music Group and a powerful female role model in the music industry. The videos featured Olay products and gave young women advice about living their best lives while balancing their careers with parenthood and self-care, and they went viral—garnering more than 10 million views. They were so powerful because they were authentic and emotional, and they gave viewers something they wanted from brands and people they trusted.

While scaling companies can expect to someday outgrow their workspaces, vendors, and perhaps even clients, they are never too large to incorporate smart thought leadership strategies into their sales and marketing mix. Meeting your customers where they are drops their resistance and opens the door to amazing, profitable, and genuine relationships.

Get a weekly dose of the trends and insights you need to keep you ON top, from Jay Baer at Convince & Convert. Sign up for the Convince & Convert ON email newsletter.

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