GitHub’s scandalized ex-CEO returns with Chatterbug

Translation earbuds might eliminate some utilitarian reasons to know a language, but if you want to understand jokes, read poetry, or fall in love in a foreign tongue, you’ll have to actually learn it. Unfortunately, products like Rosetta Stone leave people feeling burned after claiming the process should be easy while never helping you practice talking with a real native speaker. You know, the skill you actually want. Just memorizing vocabulary doesn’t make you fluent.

So after teaching millions of people to code better, a team of former GitHub co-founders and executives this week launched Chatterbug to combine the best of online and face-to-face foreign language learning. Starting with German, Chatterbug uses a homegrown video chat alternative to Skype that lets you simultaneously talk, type, read, and screenshare your way to becoming conversational.

But one of the co-founders’ past may cast a shadow over Chatterbug. Tom Preston-Werner resigned from his role as CEO and co-founder of GitHub following an investigation into allegations of harassment and intimidation of a female employee by he and his wife Theresa Preston-Werner.

GitHub employee Julie Horvath told TechCrunch that Theresa had bullied her about not writing negatively about the company, said she could read employees’ private chats and had spies at the startup, and verbally bullied her.

While an independent investigation claimed to have found no evidence of illegal behavior or gender-based harassment on Tom’s part, it did conclude that the former CEO showed “mistakes and errors of judgment” and “insensitivity to the impact of his spouse’s presence in the workplace and failure to enforce an agreement that his spouse should not work in the office.”

Ex-GitHub CEO and Chatterbug co-founder Tom Preston-Werner

We asked Tom how he’s building Chatterbug differently this time around. “With some hindsight, the organic management structures at GitHub were a double edged sword. It unleashed a lot of creativity, but was fragile in handling conflict” says Preston-Werner. “From the very beginning of Chatterbug I’ve had serious conversations with the other founders on how to use those experiences to create a more robust channel of communications.”

Former GitHub head of comms and Chatterbug co-founder Liz Clinkenbeard tells TechCrunch “In retrospect, I think one of the major challenges at GitHub back then was that the company’s fairly flat structure sometimes made it difficult to know who to talk to about problems, and how to resolve them before they escalated.” With Chatterbug, she says the team has “been very open and deliberate about wanting to foster a safe and supportive work environment.”

It’s possible that Tom’s inclusion on the team could make it tougher for Chatterbug to hire talent, especially women. Though at least it seems the company is taking office demeanor and harassment issues seriously as it grows.

“I’ve always tried my best to empower my teammates and create a work environment that every employee will love. I haven’t been perfect at that endeavor in the past” admits Preston-Werner. “But I’ve learned much from those experiences and intend to use that knowledge to ensure that Chatterbug is a safe, welcoming, and productive place to work for women and other folks traditionally underrepresented in the tech industry.”

Cutting Skype Out Of Language Learning

Scott Chacon discovered what was broken about the current crop of language learning tools when he tried to pick up French via Duolingo and Japanese through Skype chats before spending time in the two countries. “I realized there was a gap between the digital apps that are super flexible but aren’t very effective at teaching conversation with real people, and the tutoring systems or in-person schools that were inflexible and super difficult to do” Chacon tells TechCrunch.

So he started building his own tools that would blossom into Chatterbug. The former GitHub co-founder and CIO recruited GitHub’s Clinkenbeard, director of engineering Russell Belfer, and Preston-Werner over late 2015 and early 2016. They raised a $1.8 million seed round from SV Angel and Berlin’s Fly Ventures to have early-stage allies on both sides of the pond.

Setting goals in Chatterbug

Now after some private trials starting in March, Chatterbug just launched the public beta of its German learning program, with Spanish and French coming next. And right out of the gate, it’s trying to set reasonable expectations for how fast people can pick up a new tongue. “The most difficult part of being in the business is that Rosetta Stone and other companies try to sell the idea that language learning can be easy” Chacon says. “Learning a language is not easy. It’s like a marathon.”

That’s why one of the first things you do in Chatterbug is adjust a slider for when you want to be fluent by, and it tells you how frequently you’ll have to study and be tutored. The app then gives you a foundation of vocabulary using “spaced repetition”, a study method employed by medical students where questions you get wrong get shown more often while you’re displayed fewer questions like those you got right.

Chatterbug understands when you almost get an answer right

Then Chatterbug schedules you for one-on-one tutoring over its video chat system designed specifically for language learning. Rather than having to commit to a weekly session time, only learn when your particular tutor is available, or fall behind if you miss a group class, you just punch in when you want to practice. Chatterbug pairs you with whatever appropriate tutor is available, gets them up to speed on your progress, and provides a personalized curriculum of exercises to do together based on what you’ve been screwing up.

The heavy engineering background of the Chatterbug team allowed it to create a WebRTC-based video chat that lets you view files together with your tutor and see each other’s cursors as well as talk and type. That’s a huge improvement over trying to pass PDFs back and forth or figure out what exercise the teacher is discussing.

Chatterbug’s video chat lets you talk, type, view files, and see each other’s cursors

The pricing model flexes to accommodate your pace. You can get all the self-study features plus one live lesson a month for €15 or eight for €80 with extra sessions costing €12 each if you want to take a vacation next year. Or for €195 you get unlimited sessions and can learn a language in just a few months. Chatterbug is also going B2B, appealing to businesses trying to educate employees by offering discounts and easy expensing.

Turning Anyone Into A Teacher

Chatterbug co-founder Liz Clinkenbeard

The startup’s data-driven approach could make it quick to expand to more languages and identify what’s toughest to learn. Chatterbug gives you the option to have it store recordings of your video sessions, and even give it permission to use them for research. Clinkenbeard studied linguistics at Harvard, and is using her expertise to help the company determine what are the most common vocab and grammar mistakes to help you avoid them.

Long-term, turning native speakers into tutors could offer new employment options to those lacking other quantifiable skills. “After leaving GitHub, I wanted my next project to be something that would positively impact a lot of people. As a filter, I’d ask myself ‘could this idea lead to the creation of a million jobs?’” says Preston-Werner.

Chatterbug faces a wide range of competitors like Rosetta Stone, Duolingo, Busoo, Babbel, and HelloTalk — some with deep pockets and a penchant for downplaying the difficulty of reaching fluency. Being real with people doesn’t always make for great marketing, and people who failed with other products exhibit a “healthy amount of skepticism” says Clinkenbeard. Then there’s the looming threat of advancing translation technology, like the new Google auto-translating Pixel Buds headphones.

Still, “I don’t think it will destroy the need for language learning” says Chacon. “At some point, in-person translators will be obsolete. Not sure if that’s in 5 years or 45 years.” But even if we solve information translation, culture translation will still be in demand. “You don’t want to wear an ear bud while you’re getting married” he laughs. At a time when the world is increasingly polarized and xenophobic, understanding your fellow humans without a technological intermediary could generate some much-needed empathy.



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iPhone X: What Is Apple’s New A11 Bionic Neural Engine

Apple A11 Bionic Neural Engine

On Tuesday September 13th, Apple unveiled latest A11 Bionic Neural Engine along with its new iPhone 8 and iPhone X. As AI is becoming a new trend in the smartphone market because of virtual assistants, Automation and processing Apple Inc. developed the new Neural Engine.

According to Apple, The new A11 bionic neural engine is the most powerful and smartest chip ever in a smartphone which is capable of doing 600 billion machine learning operations per second. The Company packed 4.2 Billion transistors in A11 Bionic’s six cores which make it 70% faster than old A10 Fusion chip.

iPhone X A11 bionic

The main task of A11 bionic to unlock the phone using Face ID and image processing in real time. Unlike other phones using machine learning A11 bionic doesn’t send data to servers to process data. However, the A11 bionic neural engine is capable of doing complex machine learning tasks on phone in real time.

Plus, the neural engine will be playing a big role in battery efficiency, it’ll increase battery life by two hours than previous iPhone 7.

This isn’t just a smartphone trend. Google also made an AI chip called Tensor Processing Unit which will be based on server side work for businesses. Not just Google and Apple, Microsoft is also developing similar cloud technology, And already has a chip for its HoloLens.

For those who don’t know, Apple isn’t new to machine learning and AI, in fact, apple was the first company to bring the first voice assistant to smartphone Siri. In June WWDC developer conference Apple introduced Core ML, a machine learning framework to build AI algorithms into apps.

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Google Adds Kotlin as an Official Programming Language For Android Development

On 17 May 2017, at the Google I/O keynote, Google’s Android Team announced first-class support for Kotlin. It is a new programming language built by JetBrains, Which also develop IDEs for languages like Java, Python etc.

However, JetBrains also develops the Android Studio — Google’s official developer tool. It’s a language that runs on the Java Virtual Machine, in the nutshell Android don’t use Java Virtual Machine but Java has a strong root. Its compatibility with Java makes it a popular choice for Android developers.

According to reports, Kotlin tools will ba available by default in upcoming Android Studio 3.0. Google plans to give language a long-term support for Android. It includes support for a number of features that Java itself doesn’t currently support.

Google says that It is “a brilliantly designed, mature language that we believe will make Android development faster and more fun.”

Readers who want try feature like “Convert Java File to Kotlin File” can go ahead and try Android Studio 3.0 preview using this link.  Read More Details on kotlinlang’s blog.

Do you think Kotlin can beat Java in Android Development? Share your views in comments.

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Stage d’été | internship program

XeConcepts is an IT company that offers her services to clients around the world. We work in the field of IOT, Web, AI, e-marketing, mobile, social networks, and growth hacking for startups and business.

This summer, we are looking for interns for a 3-month program:

2 in web development: #MeteorJs #NodeJs #Angular #php

3 in mobile development: #Android #Swift #Ionic

1 in community management



* have basics in web and mobile development.

* who are teachable and willing to learn and to apply their technical skills.

*communication skills to well express him/her self.


XeConcepts will be a perfect professional area where they can learn and get involved with the newest technologies. Each intern will be set to a technical adviser.

Recruitment Process:

Once we receive your CV via we will be in touch with you within 72hours. An Interview is required. After it, all candidates will be notified with the result of selection in a week.

To enhance your chance of getting selected, we encourage you to cite all your technical knowledge, experiences, and your previous internships.

Deadline for application:


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Top 5 Programming Languages For Artificial Intelligence (AI)

Artificial intelligence

AI programs have been written in just about every language ever created. The most common seem to be Lisp, Prolog, C/C++, recently Java, and even more recently, Python.

Programming Languages For (AI):

1. LISP:

In the 1970s and 1980s, Lisp was the best developed and most widely used language that offered the following set of features:

  1. Easy dynamic creation of new objects, with automatic garbage collection,
  2. A library of collection types, including dynamically-sized lists and hashtables,
  3. A development cycle that allows interactive evaluation of expressions and re-compilation of functions or files while the program is running,
  4. Well-developed compilers that could generate efficient code,
  5. A macro system that let developers create a domain-specific level of abstraction on which to build the next level.

These five features are valuable for programming in general, but especially for exploratory problems where the solution is not clear at the onset; thus Lisp was a great choice for AI research.  Over the years, these features started migrating into other languages, and Lisp no longer had a unique position; today, (5) is the only remaining feature in which Lisp excels compared to other languages.


Prolog is a high-level programming language based on formal logic. Unlike traditional programming languages that are based on performing sequences of commands, Prolog is based on defining and then solving logical formulas. Prolog is sometimes called a declarative language or a rule-based language because its programs consist of a list of facts and rules. Prolog is used widely for artificial intelligence applications, particularly expert systems.

3. Java:

Java uses several ideas from Lisp, most notably garbage collection. Its portability makes it desirable for just about any application, and it has a decent set of built-in types. Java is still not as high-level as Lisp or Prolog, and not as fast as C.

4. Python:

Python is the preferred choice of many to start with artificial intelligence because Python is one of the easiest and the fastest programming language out there, Mostly AI developers suggest Python for Artificial Intelligence development.

5. Haskell AI:

Most of the major algorithms are already available via cabal. Additionally, Haskell has CUDA bindings and is compiled to bytecode, and because it’s stateless and functional, programs can easily be executed on multiple CPUs in the cloud. So overall it’s an excellent language for AI development.

Did you find this article helpful? Don’t forget to give your feedback in comments!

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Google’s AutoDraw

AutoDraw: Google’s new Artificial intelligence experiment which pairs machine learning to help draw anything fast. Basically, a suggestion tool which reckons perfection for whatever shapes, figures, or anything ugly drawn by you.

May Google’s AutoDraw, be just a Drawing tool for us but it can help aesthetically impaired individuals who couldn’t doodle out of themselves.

AutoDraw is an example of machine learning, compares with the doodles we draw which in turn helps in teaching the AI’s neural network in predicting what different shapes and figures it represents in return suggesting the perfect drawing to our rough and lousy sketches.

Perfection is what you shouldn’t expect Afterall it’s AI it learns itself and suggests the best it can.

AutoDraw being a web-based program which supports working on all platforms may it be a laptop or a smartphone. It offers a nice and clean canvas to be played with.

With primary features To Draw, Type, Fill color, etc. Also, has a menu which contains features to draw with a new page, with different ratios of canvases. Also has Downloads and share options in it.

Google made this tool free and accessible to use for the general public. and it’s surely taking off, which is why they made it free.

Now check this link to start doodling 🙂 — Auto draw

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Google DeepMind Open-Sources Sonnet Library Now You Can Build Complex Neural Networks

Google DeepMind releases “Sonnet framework Library” by Google DeepMind. Sonnet Library contains 97.0% Python, 2.9% C++ and 0.1% Shell code in framework. Main motive to this release is to help other developers and researchers to build complex neural networks quickly and easily

DeepMind Open Source

Ater launching Google’s own open-source website, Now Google DeepMind open-sourcing its Sonnet framework library. Sonnet framework was developed by Google DeepMind developers using TensorFlow for quickly building neural network modules.

For Those who don’t know about Google DeepMind, Google DeepMind is the most advanced and powerful machine learning based Artificial Intelligence computer. Beating Chinese game Go’s world champion is one of the biggest achievement of DeepMind. Research of Go game known as AlphaGo.

By open-sourcing Sonnet DeepMind wants to expand the community of DeepMind, the main motive behind this release is that DeepMind developers want to help other to make complex neural networks for their projects/researches.

Sonnet uses an object-oriented approach, similar to Torch/NN, allowing modules to be created which define the forward pass of some computation, said DeepMind in their blog post.

Modules are ‘called’ with some input Tensors, which adds ops to the Graph and returns output Tensors”, Bringing transparently to variable sharing is achieved by automatically reusing variables on subsequent calls to the same module.

DeepMind have made changes to core TF to consider models as hierarchies, This will make easier for users to switching between modules while doing experiments.

Sonnet is available on Github Repository, DeepMind has also published a paper in which it’s describing the initial version of Sonnet.

This is not a one-time release, There are many more releases to come, and they’ll all be shared on our new Open Source page said DeepMind.

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Are you using Referral Marketing tactics in your product?

The theory + 7 super effective Referral Marketing tactics you can use in your product to drive your virality rates through the roof!

So let’s say you’re running a startup company developing a product, or you’ve just launched your own brand. You need traffic to your website or online property. *Desperately*

Traditionally, you would allocate your Marketing budget and spend heavily on TV and Online ads (Facebook, etc.).

Times have changed, though, and through the fields of Biology and Computer Science, a new trend has emerged: Viral or Referral Marketing.

So, the only question now is: Do you wanna go viral!?

We can teach you all the tricks and referral marketing tactics you need to know if you want to succeed. Just. Keep. Reading.

What is Referral Marketing?

Referral marketing – also known as Viral marketing – refers to marketing techniques that use pre-existing social networking services and other technologies to try to produce increases in brand awareness or to achieve other marketing objectives (such as product sales) through self-replicating viral processes, analogous to the spread of viruses or computer viruses.

But you have to admire the virus. It has a way of living in secrecy until it is so numerous that it wins by sheer weight of numbers. It piggybacks on other hosts and uses their resources to increase its tribe. And in the right environment, it grows exponentially.

Admire the virus, trust in the virus, become the virus! #viralMarketing101

A virus doesn’t even have to mate. It just replicates, again and again with geometrically increasing power, doubling with each iteration. Here’s an example of how BIG you can get if you build virality into your product the right way:


In a few short generations, a virus population can explode.

Don’t worry, though, we’re not going to hack your computer. Our only goal is to make your Referral Programs go Viral!

And now to the juicy part…

6+1 Referral Marketing tactics to use in your product

In order to achieve virality, there are specific steps you need to take. And while they might seem insignificant if you look at them one by one, all of them together can make your virality rate explode!

1) Leave happy customers behind

leave happy customers - referral marketing tactics

At the core of every and all referrals are great products, exceptional customer service, and a great overall buying experience.

Better yet, make your customers fall in love with your product! Nobody will invite a friend to a product or service they’re not fond of themselves. This is fundamental to any referral campaign. And yes, word of mouth still plays a huge role.

Walt Disney said it best:

Do what you do so well that they will want to see it again and bring their friends.

From a smooth onboarding process, pricing plans that don’t suck – yes, throw in that free plan you were thinking about – all the way to awesome customer support!

Make your customers go…

…then at the tight moment, trigger them to share your product with their friends!

2) Boost your Referral marketing campaigns with Paid Advertising

referral marketing tactic - Paid advertising

Despite referral marketing being a primarily organic channel, there’s no reason why you can’t use PPC campaigns on Social Media to boost your referral.

What this will do is start – and keep – the ball rolling in order for you to create the first k-factor. Once this threshold is reached, your referral program will start gaining size on its own.

facebook ads for referral marketing tactics boost

When using Paid advertising to boost referral marketing campaigns, your CPC (Cost-per-click) and CTR (Click-through-rate) are the KPI’s that benefit the most, as one paid click will bring more than one users into your website (through the referral program).

This makes Referral Marketing campaigns one of the best ways you can spend your money on Paid advertising.

paid advertising - referral marketing

Hammam Baths know their game well and often use Facebook Ads to boost their Referral Marketing campaigns. Through referral marketing, they were able to grow their email list to over 10.000 engaged users.

3) Optimize your Open Graph elements

Referral Marketing tactics - Optimize Open Graph

The main referral page for your viral marketing campaign is meant to be shared.

That’s why it’s of crucial importance that you take special care of it and actually spend time optimizing the Open Graph image that comes with it.

So what is an Open Graph element?

Open Graph is the content you get if you paste a link on Facebook or Twitter. It consists of 3 main parts:

  1. Title: The landing page/referral campaign title + your brand name. (Could be something like: “Join our new contest | XeConcepts”)
  2. Description: What is your landing page about? Here you can add more information and give context to your audience. E.g.: “Win a ticket to the next XeConcepts training”
  3. Image: Probably the most important element on a referral marketing landing page, because as they say an image is worth 1000 words.

Here’s an example of the Open Graph elements you get if you paste this URL on Facebook:

opengraph on referral marketing pages

⚡️ Growth tips: Add your logo & referral program name to the OG image. Don’t forget to update the image + page description if you change the details of your referral marketing program. It can totally ruin everything…

If you’re on WordPress, it’s really easy to optimize your Open Graph tags, just by using Yoast SEO. It will help you to add and optimize Open Graph tags on any web page.

If you are creating your own landing pages, fear NOT!

You might have to get your hands dirty with some code, but it’s nothing terrible. Here’s an exampleof Facebook Open Graph tags implementation:

  1. <meta property=“og:url” content=“” />
  2. <meta property=“og:type” content=“article” />
  3. <meta property=“og:title” content=“When Great Minds Don’t Think Alike” />
  4. <meta property=“og:description” content=“How much does culture influence creative thinking?” />
  5. <meta property=“og:image” content=“” />

The og:title, og:description and og:image elements refer to the Open Graph parts we talked about above.

How do I verify my Open Graph tags?

After you have properly implemented OG tags, install the iframely – URL Meta Debugger Chrome plugin to test your referral page.

Along with the Open Graph tags, this little cutie offers you the Twitter Cards preview, as well as the Original data, so you can make sure everything is going alright.

iframely preview - referral makreting

What if the Facebook preview is still wrong?

For the -not so very- rare cases that you update your Open Graph elements, but Facebook is still displaying the old website preview, you might want to use the OG Debugger.

It’s a tool Facebook invented for all of us, so we can test our Open Graph elements in real-time, and update the Facebook preview at will.

Facebook Open Graph Debugger- referral marketing

For more advanced information about Open Graph, check out the official website.

Now that you know all about how to make your Facebook previews look good, it’s time to move on to the next item on the list.

4) Include multiple sharing options

referral marketing tactics - sharing options

People don’t like to be limited.

They also don’t like to feel restrained.

Basic human nature.

referral tactics


If your audience is on Facebook, you should have Facebook as a sharing option. If they are on Twitter, they should be able to use it as well.

As for those *cough* hipsters out there who just discovered the hot new social media platform™, a link sharing option will do the trick for them. 😉

viral loops - referral marketing tactics

Here’s what the guys over at Viral Loops have implemented at the core of their product.

5) Use the right marketing widgets

Marketing widgets - referral marketing tactics

Along with having a dedicated referral program area on your website, you need to make the program discoverable to any customer.

And this is – clearly – a job for a widget to do!

Here are some ideas…

a) Top/Bottom bar: use it on your website when you want to drive people from multiple pages to your Referral marketing program. It’s ideal because it can stay at the top/bottom of your page for as many page views as you want – like for example until the user clicks on it – without being too distractive.

bottom bar for referral marketing tactics

b) Exit intent box: exit intent widgets are your last chance at communicating with your users before they leave your website. Use it wisely, and try to offer something of value in order to get them back to the website and make them give you their email.

E.g.: “Register now and get a 5€ coupon for every friend you invite.”

Widget referral marketing tactics

c) Chat box: users respond well when they think they are interacting with another living person. However, this is not always the case when you’re visiting a marketing-equipped website. Automated messages from chat boxes can pop up anytime, prompting you to take action, lead you to a referral page, or initiate a conversation.

– Chatbox: Hey there! Looks like you’re among the 5% top engaged users with our brand. Check out our ambassador program:

– Me: Gee, thanks! I’ll go have a look!

olark chat - referral marketing techniques

It’s not my intention to point you to a specific tool. There are many out there, including Intercom, Olark, Chatlio, Chatra and others. Pick the one that suits you the most and implement your referral marketing techniques.


⚡️ Growth tips: try to figure out what the best time is for a user to join your referral marketing program. Hint: It’s probably the time when they’re the most engaged with your brand/product. Could be when (or if) they make a good review or when they hit a number of pageviews that indicates high engagement.

6) Do Referral Program A/B testing

A/B testing referral marketing tactics

So you created a landing page for your Viral marketing program, setup your mechanism and automations and now wait for the results…

Is that all? The answer is clearly ΝΟ. At every stage of your campaign, you should be testing and optimizing everything in order to achieve the highest conversion/virality rate possible.

Maybe “Become an XeConcepts ambassador” works better than “The XeConcepts fans program”. Maybe not! Unless you put it to the test, you’ll never know.

A/B testing referral marketing tactics

If you’re looking for a tool to do that, Optimizely and VWO are two of the best A/B testing and personalization platforms out there.

Here are some ideas on elements you can optimize:

a) Referral Program Title: Pick something your users want to be part of, or will want to contribute to. The Hustle calls them Ambassadors, we call them GrowthStars. Use elements close to your brand.

b) Call to Action: In the online world, if you want people to take action, you better give them a good enough reason to do it. One way of improving the attractiveness of an offer is to answer the “what’s in it for me?” question right off the bat.

A/B testing referral marketing tactics

c) Email copy: Do you make it intriguing enough for them to click on your CTA’s and refer more friends? Are the friends your customers referring getting a proper email, explaining the Referral Program as well as your brand the right way (clear & easy to understand)?

Things to check for on your referral emails: Subject line – it plays the most important role on the email open rate -, CTA button, clarity of cause on your email copy.

referral marketing tactics email

Your friend {name} invited you to join {brand}, which is {what-it-is}. Join now!

7) Select the appropriate incentive

Referral marketing incentive reward

A referral program’s incentive has to “fit” your customers and your businesses. Do they like phones? Offer an iPhone to the highest ranking user. Wellness fans? Offer a spa treatment or a magazine subscription.

A referral program’s incentive has to “fit” your customers and your businesses.

Uber, for example, allows you to gift & receive what matters to you most: free transportation with them!

uber referral marketing tactics
Dropbox is using the exact same type of reward (free storage MB) as their referral incentive. AirBnb as well! 🏰

Here are some incentive ideas to get you going:

a) Support a cause: It doesn’t always have to be about earning things. People like to give away, especially to a cause they believe in. Supporting a cause will make your customers more loyal to your brand, and at the same time will allow you to support your fellow beings. Only good karma can come out of this!

b) Offer free Swag: Businesses with a loyal following tend to give away free swag and other merch. This is great because it works as an advertisement for your brand but also functions as a gift for your customers.

hotjar referral programAdmit it, you want a Hotjar hoodie now!

c) Offer a Discount: This is the most common type of Referral incentive, as it’s usually hassle-free to implement. It is also beneficial for your company in another way: it leads to a direct increase in spending. So, keep inviting your friends! 😉

d) Earn Credit: Amazon and Evernote, for example, allow you to earn credit for every referral that you make. Gives you exactly what you want/need: More of your favorite product for free! Pretty awesome if you ask us!

Growth Hacking Marketing course - referral marketign

What next?

what next on referral marketing tactics

Now that you know all the Referral Marketing tactics you need to be using in order for your product to go viral, it’s time to build your own Referral program!

No matter if you’re inspired by Dropbox, Uber or even AirBnb, it’s not hard to create your own Referral Program if you know the right tools and techniques! 😉

Tell us what do you think!


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Why Your Small Business Needs a Mobile App

If you are a small business owner and your business does not have a mobile app, you need to get one. Having a web presence alone is no longer sufficient, as online activity continues to shift to mobile. Simply put, smartphone apps have become too important a marketing tool for small business owners to do without.

Smartphone usage has been on a tear as of late. Nearly three-quarters of Americans check their mobile phones at least once per hour, according to a Gallup survey.  Almost all – 90 percent – of that mobile-phone time is now devoted to using apps, analysis reveals. Americans now spend more time looking at their mobile phones than they do watching television.

This rise in mobile-phone usage means that smartphone apps have become a key marketing tool for companies of all sizes, including small businesses. Mobile apps increase engagement with customers. They boost repeat visits, and permit a wide variety of online transactions, including the deployment of loyalty cards, push promotions, and ecommerce transactions.  Apps deliver coupons and send announcements that build your sales with customers. Apps also accelerate contact with your company, which enhances relationships with customers in a world where speedy responses are prized by buyers. Smartphone icons even help build brands by providing a visual design that customers recognize.

Many small-business owners still think that getting an app is expensive and difficult. They are worried about the need to build different apps for the various platforms customers are on – iOS, Android, Windows and Blackberry. They are unsure about building dynamic or static apps, and the difficulty of coming up with an app design. Moreover, most small-business owners don’t have the expertise to build apps themselves.

In XeConcepts, we are simplifying the process of creating and testing mobile-phone apps, putting the tools easily in small-business owners’ price range.

For more information, please contact us via email, and we will be happy to help you having your first app  and grow your business!


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Success stories applying data mining

What can data mining do?

Data mining is primarily used today by companies with a strong consumer focus – retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among “internal” factors such as price, product positioning, or staff skills, and “external” factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to “drill down” into summary information to view detail transactional data.

With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual’s purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.

For example, Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on analysis of their monthly expenditures.

WalMart is pioneering massive data mining to transform its supplier relationships. WalMart captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte Teradata data warehouse. WalMart allows more than 3,500 suppliers, to access data on their products and perform data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, WalMart computers processed over 1 million complex data queries.

The National Basketball Association (NBA) is exploring a data mining application that can be used in conjunction with image recordings of basketball games. The Advanced Scout software analyzes the movements of players to help coaches orchestrate plays and strategies. For example, an analysis of the play-by-play sheet of the game played between the New York Knicks and the Cleveland Cavaliers on January 6, 1995 reveals that when Mark Price played the Guard position, John Williams attempted four jump shots and made each one! Advanced Scout not only finds this pattern, but explains that it is interesting because it differs considerably from the average shooting percentage of 49.30% for the Cavaliers during that game.

By using the NBA universal clock, a coach can automatically bring up the video clips showing each of the jump shots attempted by Williams with Price on the floor, without needing to comb through hours of video footage. Those clips show a very successful pick-and-roll play in which Price draws the Knick’s defense and then finds Williams for an open jump shot.

How does data mining work?

While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought:

  • Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.
  • Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.
  • Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.
  • Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer’s purchase of sleeping bags and hiking shoes.

Data mining consists of five major elements:

  • Extract, transform, and load transaction data onto the data warehouse system.
  • Store and manage the data in a multidimensional database system.
  • Provide data access to business analysts and information technology professionals.
  • Analyze the data by application software.
  • Present the data in a useful format, such as a graph or table.

Different levels of analysis are available:

  • Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
  • Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.
  • Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID) . CART and CHAID are decision tree techniques used for classification of a dataset. They provide a set of rules that you can apply to a new (unclassified) dataset to predict which records will have a given outcome. CART segments a dataset by creating 2-way splits while CHAID segments using chi square tests to create multi-way splits. CART typically requires less data preparation than CHAID.
  • Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k 1). Sometimes called the k-nearest neighbor technique.
  • Rule induction: The extraction of useful if-then rules from data based on statistical significance.
  • Data visualization: The visual interpretation of complex relationships in multidimensional data. Graphics tools are used to illustrate data relationships.


What technological infrastructure is required?

Today, data mining applications are available on all size systems for mainframe, client/server, and PC platforms. System prices range from several thousand dollars for the smallest applications up to $1 million a terabyte for the largest. Enterprise-wide applications generally range in size from 10 gigabytes to over 11 terabytes. NCR has the capacity to deliver applications exceeding 100 terabytes. There are two critical technological drivers:

  • Size of the database: the more data being processed and maintained, the more powerful the system required.
  • Query complexity: the more complex the queries and the greater the number of queries being processed, the more powerful the system required.

Relational database storage and management technology is adequate for many data mining applications less than 50 gigabytes. However, this infrastructure needs to be significantly enhanced to support larger applications. Some vendors have added extensive indexing capabilities to improve query performance. Others use new hardware architectures such as Massively Parallel Processors (MPP) to achieve order-of-magnitude improvements in query time. For example, MPP systems from NCR link hundreds of high-speed Pentium processors to achieve performance levels exceeding those of the largest supercomputers.

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