Tag Archives: automation

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CRO Hero: Claire Peña, Growth Marketing Manager at Splunk

CRO Heroes

Admittedly, Conversion Rate Optimization is not the most sexy term in the marketing world – but if you’ve ever run an A/B test where the variant won by a landslide, or made a website design change that led to a significant increase in product purchases, you know firsthand how exciting and powerful CRO can be in action. Marketers who specialize in conversion rate optimization are often a rare mix of analytical and creative; tactical, and intuitive. They need to get inside a customer’s head, but they also need to dive deep into data. Often, CRO professionals are tasked with: Reducing…

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CRO Hero: Claire Peña, Growth Marketing Manager at Splunk

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What Happens After The Conversion? How To Optimize Your Marketing Campaigns For Higher Quality Leads

How excited would you be if you doubled the number of leads your marketing campaign was generating in less than a month? What if you found out that the improvement wasn’t an improvement at all, because as lead quantity went up, lead quality was going down? That’s exactly what happened with a campaign I ran once. I can assure you – it’s not fun! One survey of B2B marketers found that their #1 and #2 challenges were generating high quality leads and converting leads into customers: Your Landing Page Conversion Rate Is Only Half Of The Story Converting visitors to leads…

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What Happens After The Conversion? How To Optimize Your Marketing Campaigns For Higher Quality Leads

The Rise Of Intelligent Conversational UI




The Rise Of Intelligent Conversational UI

Burke Holland



For a long time, we’ve thought of interfaces strictly in a visual sense: buttons, dropdown lists, sliders, carousels (please no more carousels). But now we are staring into a future composed not just of visual interfaces, but of conversational ones as well. Microsoft alone reports that three thousand new bots are built every week on their bot framework. Every. Week.

The importance of Conversational UI cannot be understated, even if some of us wish it wasn’t happening.

The most important advancement in Conversational UI has been Natural Language Processing (NLP). This is the field of computing that deals not with deciphering the exact words that a user said, but with parsing out of it their actual intent. If the bot is the interface, NLP is the brain. In this article, we’re going to take a look at why NLP is so important, and how you (yes, you!) can build your own.

Speech Recognition vs. NLP

Most people will be familiar with Amazon Echo, Cortana, Siri or Google Home, all of which have an interface that is primarily conversational. They are also all using NLP.




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Aside from these intelligent assistants, most Conversational UIs have nothing to do with voice at all. They are text driven. These are the bots we chat with in Slack, Facebook Messenger or over SMS. They deliver high quality gifs in our chats, watch our build processes and even manage our pull requests.




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Conversational UIs built on text are nice because there is no speech recognition component. The text is already parsed.

When it comes to a verbal interaction, the fundamental problem is not recognizing the speech. We’ve mostly got that one down.

OK, so maybe it’s not perfect. I still get voicemails every day like a game of Mad Libs that I never asked to play. iOS just sticks a blank line in whenever they don’t know what exactly was said.




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Google, on the other hand, just tries to guess. Like this one from my father. I have absolutely no idea what this message is actually trying to say other than “Be Safe” which honestly sounds like my mom, and not my dad. I have a hard time believing he ever said that. I don’t trust the computer.




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I’m picking on voice mail transcriptions here, which might be the hardest speech recognition to do given how degraded the audio quality is.

Nevertheless, speech recognition is largely a solved problem. It’s even built right into Chrome and it works remarkably well.




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After we solved the problem of speech recognition, we started to use it everywhere. That was unfortunate because speech recognition on it’s own doesn’t do us a whole lot of good. Interfaces that rely soley on speech recognition require the user to state things a precise way and they can only state the limited number of exact words or phrases that the interface knows about. This is not natural. This is not how a conversation works.

Without NLP, Conversational UI can be true nightmare.

Conversational UI Without NLP

We’re probably all familiar with automated phone menus. These are known as Interactive Voice Response systems — or IVRs for short. They are designed to take the place of the traditional operator and automatically transfer callers to the right place without having to talk to a human. On the surface, this seems like a good idea. In practice, it’s mostly just you waiting while a recorded voice reads out a list of menu items that “may have changed.”




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A 2011 study from New York University found that 83% of people feel IVR systems “provide either no benefit at all, or only a cost savings benefit to the company.” They also noted that IVR systems “score lower than any other service option.” People would literally rather do anything else than use an automated phone menu.

NLP has changed the IVR market rather significantly in the past few years. NLP can pick a user’s intent out of anything they say, so it’s better to just let them say it and then determine if you support the action.

Check out how AT&T does it.

AT&T has a truly intelligent Conversational UI. It uses NLP to let me just state my intent. Also, notice that I don’t have to know what to say. I can fumble all around and it still picks out my intent.

AT&T also uses information that it already has (my phone number) and then leverages text messaging to send me a link to a traditional visual UI, which is probably a much better UX for making a payment. NLP drives the whole experience here. Without it, the rest of the interaction would not be nearly as smooth.

NLP is powerful, but more importantly, it is also accessible to developers everywhere. You don’t have to know a thing about Machine Learning (ML) or Artificial Intelligence (AI) to use it. All you need to how to do is make an AJAX call. Even I can do that!

Building An NLP Interface

So much of Machine Learning still remains inaccessible to developers. Even the best YouTube videos on the subject quickly become hard to follow with subjects like Neural Networks and Gradient Descents. We have, however, made significant progress in the field of Language Processing, to the point that it’s accessible to developers of nearly any skill level.

Natural Language Processing differs based on the service, but the overall idea is that the user has an intent, and that intent contains entities. That means exactly nothing to you at the moment, so let’s work up a hypothetical Home Automation bot and see how this works.

The Home Automation Example

In the field of Natural Language Processing, the canonical “Hello World” is usually a Home Automation demo. This is because it helps to clearly demonstrate the fundamental concepts of NLP without overloading your brain.

A Home Automation Bot is a service that can control hypothetical lights in a hypothetical house. For instance, we might want to say “Turn on the kitchen lights”. That is our intent. If we said “Hello”, we are clearly expressing a different intent. Inside of that intent, there are two pieces of information that we need to complete the action:

  1. The ‘Location’ of the light (kitchen)
  2. The desired state of the lights ‘Power’ (on/off)

These (Location, Power) are known as entities.

When we are finished designing our NLP interface, we are going to be able to call an HTTP endpoint and pass it our intent: “Turn on the kitchen lights.” That endpoint will return to us the intent (Control Lights) and two objects representing our entities: Location and Power. We can then pass those into a function which actually controls our lights…

function controlLights(location, power) 
  console.log(`Turning $power the $location lights`);
  
  // TODO: Call an imaginary endpoint which controls lights   
}

There are a lot of NLP services out there that are available today for developers. For this example, I’m going to show the LUIS project from Microsoft because it is free to use.

LUIS is a completely visual tool, so we won’t actually be writing any code at all. We’ve already talked about Intents and Entities, so you already know most of the terminology that you need to know to build this interface.

The first step is to create a “Control Lights” intent in LUIS.




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Before I do anything with that intent, I need to define my Location and Power entities. Entities can be different types — kind of like types in a programming language. You can have dates, lists and even entities that are related to other entities. In this case, Power is a list of values (on, off) and Location is a simple entity, which can be any value.

It will be up to LUIS to be smart enough to figure out exactly what the Location is.




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Now we can begin to train this model to understand all of the different ways that we might ask it to control the lights in a different location. Let’s think of all the different ways that we could do that:

  • Turn off the kitchen lights;
  • Turn off the lights in the office;
  • The lights in the living room, turn them on;
  • Lights, kitchen, off;
  • Turn off the lights (no location).

As I feed these into the Control Lights intent as utterances, LUIS tries to determine where in the intent the entities are. You can see that because Power is a discreet list of values, it gets that right every time.




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But it has no idea what a Location even is. LUIS wants us to go through this list and tell it where the Location is. That’s done by clicking on a word or group of words and assigning to the right entity. As we are doing this, we are really creating a machine learning model that LUIS is going to use to statistically estimate what qualifies as a Location.




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When I’m done telling LUIS where in these utterances all the locations are, my dashboard looks like this…




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Now we train the model by clicking on the “Train” button at the top. Do you feel like a data scientist yet?

Now I can test it using the test panel. You can see that LUIS is already pretty smart. The Power is easy to pick out, but it can actually pick out Locations it has never seen before. It’s doing what your brain does — using the information that it has to make an educated guess. Machine Learning is equal parts impressive and scary.




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If we try hard enough, we can fool the AI. The more utterances we give it and label, the smarter it will get. I added 35 utterances to mine before I was done and it is close to bullet proof.

So now we get to the important part, which is how we actually use this NLP in an app. LUIS has a “Publish” menu option which allows us to publish our model to the internet where it’s exposed via a single HTTP endpoint. It will look something like this…

https://westus.api.cognitive.microsoft.com/luis/v2.0/apps/c4396135-ee3f-40a9-8b83-4704cddabf7a?subscription-key=19d29a12d3fc4d9084146b466638e62a&verbose=true&timezoneOffset=0&q=

The very last part of that query string is a q= variable. This is where we would pass our intent.

https://westus.api.cognitive.microsoft.com/luis/v2.0/apps/c4396135-ee3f-40a9-8b83-4704cddabf7a?subscription-key=19d29a12d3fc4d9084146b466638e62a&verbose=true&timezoneOffset=0&q=turn on the kitchen lights

The response that we get back looks is just a JSON object.


  "query": "turn on the kitchen lights",
  "topScoringIntent": 
    "intent": "Control Lights",
    "score": 0.999999046
  ,
  "intents": [
    
      "intent": "Control Lights",
      "score": 0.999999046
    ,
    
      "intent": "None",
      "score": 0.0532306843
    
  ],
  "entities": [
    
      "entity": "kitchen",
      "type": "Location",
      "startIndex": 12,
      "endIndex": 18,
      "score": 0.9516622
    ,
    
      "entity": "on",
      "type": "Power",
      "startIndex": 5,
      "endIndex": 6,
      "resolution": 
        "values": [
          "on"
        ]
      
    }
  ]
}

Now this is something that we can work with as developers! This is how you add NLP to any project — with a single REST endpoint. Now you’re free to create a bot with some real brains!

Brian Holt used the browser speech API and a LUIS model to create a voice powered calculator that is running right inside of CodePen. Chrome is required for the speech API.

See the Pen Voice Calculator by Brian Holt (@btholt) on CodePen.

Bot Design Is Still Hard

Having a smart bot is only half the battle. We still need to account for any of the actions that our system might expose, and that can lead to a lot of different logical paths which makes for messy code.

Conversations also happen in stages, so the bot needs to be able to intelligently direct users down the right path without frustrating them or being unable to recover when something goes wrong. It needs to be able to recover when the conversation dies midstream and then starts again. That’s a whole other article and I’ve included some resources below to help.

When it comes to language understanding, the AI platforms are mature and ready to use today. While that won’t help you perfectly design your bot, it will be a key component to building a bot that people don’t hate.

Great UI Is Just Great UI

A final note: As we saw from the AT&T example, a truly smart interface combines great speech recognition, Natural Language Processing, different types of conversational UI (speech and text) and even a visual UI. In short, great UI is just that — great UI — and it is not a zero sum game. Great UIs will leverage all of the technology available to provide the best possible user experience.

Special thanks to Mat Velloso for his input on this article.

Further Resources:

Smashing Editorial
(rb, ra, yk, il)


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The Rise Of Intelligent Conversational UI

Get Access to All the Recordings From Marketing Optimization Week

Just last week you may have joined us, along with 8,000 other marketers online for our first ever Marketing Optimization Week. Held over four days (February 20-23), experts from Hanapin, Emma, Zapier, Drift, Microsoft and more shared their tactics for refreshing your marketing and getting past a results slump.

Running 14 sessions total with 13 amazing partners, we were pretty excited to see marketers get so much out of the event:

I presented on the topic of “How to Improve Your Adwords Conversion Rates” as part of the PPC track (we had four tracks in all, including PPC, AI, Marketing Strategy and Automation). Today I’ll share some of the PPC-related takeaways, both from my session and others.

Making the Most of Your PPC Spend

To start, here’s my pop quiz:

If you’ve optimized your AdWords campaigns to no end, but are still seeing smaller and smaller efficiency gains, do you:

  1. Throw more money at it (cost per acquisition be damned!)
  2. Keep on truckin’. (Refine your keyword strategy further and test new ads), or
  3. Start looking at where your ads are pointing to

Call me crazy, but option 3 seems like a no-brainer, right?

It’s like my pal Joe Martinez, Director of Paid Media at Granular Marketing says:

“Ads get traffic. Landing pages get conversions.”

In other words, no matter how good your keyword and bidding strategies are, your ads can’t do the work alone.

The savviest PPC marketers are optimizing as much of the funnel as they can get their hands on, because AdWords CPC’s have nearly tripled since 2012. To ensure you’re not blindly spending, you need to look at where your ads are pointing to.

The question I have is: with landing pages being such low-hanging fruit in terms of paid ad success, why haven’t all marketers figured this out yet?

I tackled this in my presentation covering:

  • What landing page changes you can make now to lift conversion rates
  • How to make these changes without talking to your developer
  • How to set up an A/B test in less than 30 seconds
If you haven’t already, you can sign up to get all the recordings here

Other PPC-Specific Sessions You Can Check out

Throughout the week it was pretty satisfying to see a big focus on post-click optimization as a major area to consider for improving results and getting the most out of your PPC ad spend.

My personal favourite talks within the PPC track were:

  • PPC Woes And What To Do About Them by Beth Thouin and Richard Beck of Acquisio
  • Beef Up Your Quality Score With Landing Page Updates by Jeff Baum and Diane Anselmo at Hanapin, and
  • Unicorn Marketing: Getting Unusually Great Results Across Every Marketing Channel by Larry Kim of Mobile Monkey

Finally, here are some of my top takeaways from the talks above:

1. Optimize your landing pages to get ahead

Acquisio structured their session around addressing the biggest woes PPC marketers face everyday and they provided actionable tips for prolonging the effectiveness of your campaigns past three to four months.

According to Beth and Richard, one of the best ways to get ahead of the competition (and keep your campaigns fresh and high-converting) is to work on your landing pages. Make sure your images are high-quality, pages load fast, and there’s clear message match between your ads and resulting landing pages.

It’s like Richard said during the session: “[forget] the bucket with holes in it! Not having a good landing page is like having a bucket with no bottom in it when it comes to PPC campaigns.”

2. Focus on navigation to increase your Quality Score

So often we get caught up with page load time, copy, and SEO that we forget to focus on intent and how people expect or want to navigate through our landing page information (i.e.: easily). Hanapin’s session went over just how important Quality Score is for PPC campaign performance and how one factor in improving your score via the landing page experience is navigation.

Jeff and Diane use the analogy of a shoe store: the experience after clicking through on a search ad should be akin to walking through a neatly organized shop where everything is labelled, certain types of shoes are grouped together, and you can easily find what you’re looking for in a matter of minutes. When in doubt: the simpler you make your landing page navigation/information hierarchy, the better.

3. Stop trying to optimize donkeys. They will always be donkeys.

During his session at Marketing Optimization Week, Larry Kim outlined the difference between a unicorn and donkey. What’s a marketing unicorn? Typically, these are the pieces of content or campaigns that outperform the rest. They usually make up only a small percentage of everything you run. One of the main points in this talk that resonated with me was that we should stop trying to optimize donkeys and focus exclusively on the unicorns.

Unicorns are unicorns across channels, so when you find one, take it and apply it across your other channels, including PPC. To find unicorns we need to audition lots of content ideas, identify which ones have unusually high engagement rates, and optimize those few for engagement even further.

These takeaways just scratch the surface from Marketing Optimization Week (there are more tracks and engaging speakers). Be sure to grab the recordings and share them with your team!

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Get Access to All the Recordings From Marketing Optimization Week

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Automated Browser Testing With The WebDriver API

Manually clicking through different browsers as they run your development code, either locally or remotely, is a quick way to validate that code. It allows you to visually inspect that things are as you intended them to be from a layout and functionality point of view. However, it’s not a solution for testing the full breadth of your site’s code base on the assortment of browsers and device types available to your customers.

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Automated Browser Testing With The WebDriver API

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How to Use Email Automation to Boost eCommerce Conversions

email automation

It’s not every day that marketers use the words “email” and “CRO” in the same sentence. After all, most email marketing strategies for eCommerce are mainly focused on sending newsletters, promotional emails, transactional emails, and maybe even cart abandonment messages. If you’re really savvy, you might even be sending post-purchase emails to leverage the traffic you already converted in the hopes that those shoppers will come back to buy more. But here’s the thing: When you focus your email marketing efforts solely on the end of your sales funnel, you’re actually neglecting the majority of your site traffic. That’s traffic…

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How to Use Email Automation to Boost eCommerce Conversions

Internationalizing React Apps

First of all, let’s define some vocabulary. “Internationalization” is a long word, and there are at least three widely used abbreviations: “intl,” “i18n” and “l10n.” All of them mean the same thing.

Internationalizing React Apps

Internationalization can be generally broken down into three main challenges: Detecting the user’s locale, translating UI elements, titles as well as hints, and last but not least, serving locale-specific content such as dates, currencies and numbers. In this article, I am going to focus only on front-end part. We’ll develop a simple universal React application with full internationalization support.

The post Internationalizing React Apps appeared first on Smashing Magazine.

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Internationalizing React Apps

Redux · An Introduction

Redux is one of the hottest libraries in front-end development these days. However, many people are confused about what it is and what its benefits are.
As the documentation states, Redux is a predictable state container for JavaScript apps. To rephrase that, it’s an application data-flow architecture, rather than a traditional library or a framework like Underscore.js and AngularJS.
Further Reading on SmashingMag Why You Should Consider React Native For Your Mobile App Test Automation For Apps, Games And The Mobile Web Server-Side Rendering With React, Node And Express Notes On Client-Rendered Accessibility Redux was created by Dan Abramov around June 2015.

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Redux · An Introduction

A Good User Interface Means Something Completely Different On Mobile Devices

Responsive design is the hottest topic in front-end Web development right now. It’s going to transform the Web into an all-singing, all-dancing, all-devices party, where we can access any information located anywhere in the world. But does responsive design translate well from the text-heavy Web design blogosphere to the cold hard reality of commercial systems?
Rumors came through our office grapevine that management was looking to revamp our mobile presence.

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A Good User Interface Means Something Completely Different On Mobile Devices