Category: Chat

How to build Alexa Skills without
coding knowledge (2021)

When talking about Alexa Skills building, the common things that come to someone’s mind is AI, programming language, complicated coding, and so on.

You may also think that projecting and executing the creation process is very complicated too.

For most of the process, all of the difficulties mentioned above remain true. We state “for most of the process” since nowadays, it has become easy to build your own Alexa Skill, where all is needed is the idea of what you want it to be for.

Now, it is much easier to create them since the No Code trend has influenced how Amazon manages the Skill creation process.

How Amazon Alexa and Skill building started

It started in November 2014, when Amazon launched its series of Amazon Echo devices, which were Alexa-enabled smart speakers.

They weren’t Alexa themselves, but they were the best channel of receiving the voice request, sending it to Amazon Servers where the hard processing work is made, and obtaining the answer or action requested.

It started with nearly 100 abilities Alexa was capable of doing, even though it now has more than 120.000 Skills available.

This all was made possible in mid-2015 when Amazon released a dedicated platform that would allow every interested developer to create Alexa Skills: the Alexa Skills Kit.

How Amazon made it possible for everyone to build Alexa Skills

Since the developers weren’t the only ones with the desire to build Alexa Skills, use and publish them, Amazon came with a bright solution in 2018.

That solution was Alexa Skill Blueprints. A way for non-developers to build simple Alexa Skills and use them. They even prepared an entire series of tutorials on how to use them.

In their effort to gather more developers and work for creating more Alexa Skills, Amazon made it easier through the Amazon Web Services (AWS) console and Alexa Developer Portal.

This would take the Skill-building process to arise, especially with Amazon supporting the developers and those interested in improving the voice technology. It was the Alexa Fund: dedicated to those with the intention of working in new voice technologies.
There are also the Alexa Developer Rewards and Alexa developer promotions to encourage the developers committed to adding and improving the Skills available in the Alexa Skill Store. Great support would be AWS promotional credits, which would reduce the cost of AWS resources used by the developers while building Alexa Skills.

Trending Bot Articles:

1. Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3. Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

How to build your Alexa Skill without coding or technical knowledge

With everyone, be it an individual or a business, looking to create an Alexa Skill, new opportunities are arising. Every developer or developing platform is trying to simplify every step of the Skill Building process.

The number of people aiming to create an Alexa Skill is increasing. Not only to become a professional Alexa Skill developer but also for personal use. Be it as a hobby or for their businesses.

While for individual users the process of Skill building is simple, for the business ones is more complicated.

This is where a third party Developing Platform comes in to help.

With a simple interface, you can implement all the parts needed to complete the Skill and make it ready for publishing.

Not only that but also modify the content of the Skill if needed and also, test it, publish it and check how your Skill performs when made available in the Alexa Skill Store.

All of this is available and free to access on Ipervox.

Why is Ipervox the right choice to build your Alexa Skill?

In case you’re a complete beginner and need more information about what are Alexa Skills and how to create one, you can start by using the Ipervox platform.

Viewing the tutorials made available from our team or checking our FAQ site will make everything understandable.

This development platform has a simple and user-friendly interface, which allows every user to effortlessly build the Skill they want and how they want it to be.

With a well guided interface and a structure oriented in detailed Skill Building, Ipervox allows you to manage and easily control every step.

It helps reach the desired product at the end, giving shape to the initial idea that inspired you to create the Alexa Skill.

Above we mentioned Alexa as a tool for businesses. Ipervox has created several tools and guides to assist every business and entrepreneur in building their own Alexa Skill.

This will help you reach your audience and customers with the most straightforward tool there is: the Voice.

Voice technologies are emerging as the best and promising tool of the future. Not only to reach but also to engage with all of your clients.

Embracing it now means you will have a safe spot in the future. A future where Voice Apps become a common thing for businesses to use.

Create your Alexa Skill using the Ipervox online platform with the set of instruments made available from us. It will help you improve the interaction with your audience, gain their attention and their hearts.

If you want to create your Alexa Skill right now, all you need to do is click “Start for Free”, and you can start building a new channel. A channel that will connect you with your new audience.

Don’t forget to give us your 👏 !

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How to build Alexa Skills without
coding knowledge was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Getting started with the Relay SDK

Hey folks! Let’s get started with a quick example showing how you can use the Relay node.js SDK to create a simple number game. For this example, we’ll use Heroku to host a Websocket that will maintain a connection with the Relay Server.

First, create a new app using Heroku. We’ll be naming ours ‘relay-wf’:

Next, let’s setup our environment with git and Heroku CLI (instructions for windows/linux):

$ brew tap heroku/brew && brew install heroku
$ heroku login

Next, let’s initialize our environment:

$ cd relay-wf
$ git init
$ npm init
$ npm install relay-js

Next, we’ll setup our example interaction & deploy it to heroku. Here we are creating a basic app that let’s users guess two numbers and returns the one who guest closest

Trending Bot Articles:

1. Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3. Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

# workflow.js

import relay from 'relay-js'

const app = relay()

app.workflow(`numbers`, workflow => {
  relay.on(`start`, async () => {
    const user = await relay.getDeviceName()
    const random = Math.floor(Math.random() * 10) + 1
    await relay.say(`Player One, pick a number between 1 and 10`)
    const numberOne = await relay.listen(["$DIGIT_SEQUENCE"])
    await relay.say(`Player Two, pick a number between 1 and 10`)
    const numberTwo = await relay.listen(["$DIGIT_SEQUENCE"])
    if (Math.abs(numberOne - random) < Math.abs(numberTwo - random)) { 
      await relay.say(`Player One wins! ${numberOne} was closest to ${random}!`)
    } else { 
      await relay.say(`Player Two wins! ${numberTwo} was closest to ${random}!`)
    }
    await relay.terminate()
  })
})

Next, we’ll add our workflow configuration to Relay servers. First, fire up Dash by going to api-dash.relaygo.com (for production, this is dash.relaygo.com) and navigate to the Workflows section and select the Create button for ‘Custom Workflow’ :

From there, you can enter your workflow configuration. This includes the name of your workflow (here we’ve named ours ‘numbers’), the devices you’d like it on (we’ll push to just one device here, ‘Ibraheem’) and URI hosting the workflow (relay-wf.herokuapp.com). We’re also using the spoken phrase ‘pick a number’ to initiate our workflow from the device.

Save your workflow and then let’s deploy our workflow node.js app to heroku:

$ git commit -am 'Initial deploy'
$ export HEROKU_APP=relay-wf
$ git push master heroku
$ heroku logs --tail

And that’s it! If everything worked, you should now be able to speak ‘pick a number’ into the Relay assistant and trigger your number game!

Ready to start developing with Relay? Click here to signup for our Relay SDK beta.

Don’t forget to give us your 👏 !

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Getting started with the Relay SDK — node.js edition was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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No matter whether you are surfing online as a business user or a consumer, you’ll most likely run across customer-friendly AI-driven bots that support smooth communication. Here, in this article we’ll have a closer look at some intriguing points you may find useful. Although some concepts need more profound consideration, the article will be a good piece to start with.
This said, the key notions that open up the Chatbots topic are the following:

What are the chatbots?

Are chatbots still popular in 2021?

How can chatbots help business, namely real estate?

With these ideas in mind, let’s get started.

What technologies underlie chatbots: AI, NLG, ML

Chatbots can be defined as software programs built to ensure meaningful interaction between human users and computers via messaging, texts, live voice or video conversation. Interestingly, GoodFirms Chatbot Usage survey revealed that nearly 95% of respondents from the US, the UK, Canada, Germany, and other countries have used chatbots at least once in the past 12 month.

Chatbots, being one of the tech megatrends for 2021, are powered by the strengths of Artificial Intelligence. AI technology allows bots to understand human-aided communication and provide users with relevant responses based on vast knowledge databases.

When some human-initiated speech or textual data is input, it further involves the processing of data driven by Machine Learning and Deep Learning. ML and DL algorithms help analyze the input information.
The next big tech that helps chatbots be effective communication tools is Natural Language Generation or NLG. This one is for making computer-processed responses sound more humanlike to users while they are interacting with chatbots.
Finally, it is Predictive Analytics that provides the in-depth analysis of massive historical data assets to gain insights for building proactive communication models. Based on customer behavioral patterns generated with PA, chatbots can provide a much wider range of options for interaction.

Trending Bot Articles:

1. Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3.Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

Chatbots in 2021: What’s in their popularity?

Does investing in chatbots make any sense in 2021? Surely, does. The explanation is pure and simple, as the current and predicted numbers speak for themselves.

Market experts are sure that chatbots will handle up to 90% of user queries by 2022. The widespread chatbot adoption is projected to help businesses reduce their operational costs by over $8 billion annually.
At the same time, findings from a new report by Juniper Research show that consumer retail spend via chatbots will be around $142 billion by 2024. Compared to 2019, this shows great 400% YoY revenue growth.
There is no doubt, these facts prove the promising horizon for chatbot adoption across industries.

What industries can benefit from chatbots?

Used mainly for service industries, now chatbots can streamline communication with customers for nearly every B2C or B2B sector. Providing multiple website visitors or app users with quick and reasonable answers, chatbots is one of Gartner’s top tech trends aimed to add much value to the improvement of customer experience (CX).

The potential of use cases for utilizing chatbots are truly immense. From Finance to Healthcare to Education and Real Estate, companies leverage chatbots to attract and engage customers with their brand. Here’s a top 5 list of industries that supercharge the customer activities with smart AI-drive bots.

Chatbot Implementation: Are there any sticking points?

The proliferation of virtual assistants for business, however, can be restricted due to the lack of practical knowledge of how to implement chatbot technology. According to Accenture, this is the main reason that stops businesses from introducing chatbots across their customer touchpoints.
With surefire benefits for business, the idea behind chatbot implementation is quite clear — some professional assistance is badly needed to build and integrate conversational bots for your organization. Here’s what we at Adimen usually do for our clients.

What are the benefits of chatbots for real estate?

The pandemic has drastically changed the global consumer market. These changes drive customers to go digital and choose other channels while looking for the products and services they need.

It makes companies rethink and reshape their business strategies to meet and exceed the ever-growing customer needs. This is where chatbots jump into action. Here’s which benefits customers expect while using the chatbots on your website or mobile app, according to the chatbot usage report takeaways.

What’s there for enterprises you may wonder? Truth be told, real estate is a great example of how to harness the power of chatbots in business.
While going to buy or rent a decent house, lots of people just don’t have enough time to look through all the variants available in the property market. What’s more, real estate agents and managers usually have way too many questions from customers to answer, and concerns to alloy. On top of this, increasing the support service staff means additional expenses for your business that seem quite unwelcome in the pandemic times.

Our experience in building chatbots for US real estate agencies let us break down five key benefits that they get after bot integration. The chatbots developed are mostly for client support purposes. As recent facts show, however, the following advantages can be also achieved for marketing and sales departments, payments, server offerings, and suchlike.

Automation of open-source customer data.

Data collection with chatbots is a great asset for any real estate business. The bots gather customer non-personal data for further analytics of user buying intents. It also helps get well-trained ML models for returning more accurate search results for website visitors or smartphone users.

Automation of open-source customer data

Data collection with chatbots is a great asset for any real estate business. The bots gather customer non-personal data for further analytics of user buying intents. It also helps get well-trained ML models for returning more accurate search results for website visitors or smartphone users.

Round-the-clock customer assistance

To err is human, especially when it comes to 24/7 client support services. Today, customer demands are evolving. So, they won’t wait for years for their inquiry any longer. Here chatbots ensure greater services than any human ever can.

Lower operational costs

Trimming business costs by minimizing the number of your real estate agents and chat operators is surely what every business is looking for in 2021. Your chatbots require neither annual bonuses nor sick leaves. Cost-efficiency is what gives a competitive edge for your business.

Self-service tools provision

Next-gen customers are tech-savvy and no more look for time-consuming support service driven by human operators. With chatbots on board, your website or app enables users to personally search for and get any information on property objects they want to buy or rent.

Customer engagement and retention.

Great customer experience is all about personalization. Personal virtual assistants help make the customer buying journey be much more effective. Once lots of customer search and behavioral data is analyzed, you can win the loyalty of your customers via more targeted offerings and tailored brand promotions.

All in all, chatbots in 2021 are a highly effective tool for businesses in terms of cost and resource optimization, lead generation, customer engagement, and a greater omnichannel strategy.

Here at Adimen, we are downright ready for robust collaboration to help you make time- and cost-effective chatbots for your real estate business. Have you got another business, say, in fintech? No problem, we’ll have you covered on that as well!

Just drop us a line, and we’ll do the rest.

Don’t forget to give us your 👏 !

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5 Reasons to Leverage Chatbots for Real Estate in 2021 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

I’m learning all the time — conversation-driven development in a chatbot for Erasmus students with Rasa framework

I am sure that some of you are familiar with Test Driven Development or Behaviour Driven Development. I remember both of them from my Software Engineering at FEUP. When I have started doing chatbots, I have discovered something else, conversation-driven development.

According to the article entitled Conversation Driven Development found of Rasa blog:

Conversation-driven development (CDD) is the process of listening to your users and using those insights to improve your AI assistant.

The article states that the biggest problem for chatbot developer is anticipating user’s input. Instead of assuming what they are going to ask the chatbot we give them the opportunity to say what exactly do they want.

The process of Conversation Driven Development is the following:

1. Share
2. Review
3. Annotate
4. Test
5. Track
6. Fix

Based on the project I have done for my master degree dissertation and some other projects, I have prepared a case study of conversation-driven development for a chatbot for Erasmus exchange students coming to the foreign university.

Framework: Rasa + Rasa X

Purpose: The objective of the project is to assist Erasmus students coming to the university from many different countries and trying to navigate their student life in Poland. They require information about the procedures, documents, professors and campus life.

Trending Bot Articles:

1. Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3.Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

1. Share — according to the process it is good to share your chatbots with test users early on. I usually don’t spend much time deliberating on dialogue flows. I design a simple diagram with some paths to follow, I train the chatbot in Rasa and call my friends asking them to help. Usually, I make my chatbot available through ngrok tunnel but I found that this solution can be unreliable. Now I deploy my chatbot on a public server. http://35.205.219.197/guest/conversations/production/36a32a4440a84b7095ef45d55feb418c
2. Review — once some people talked to your bot, it is time to read the conversations they had. A lot of developers are focused on metrics like how many people have used that intent but it is better to just sit and read through the conversations because people may have interacted with the chatbot, not in the way that you have expected but it could be that their path should be included in the next version. Rasa X has the option of looking at the previous chats.

3. Annotate — after going through conversations it is time see the intents that would be great candidates to improve your chatbot’s NLU. You can see them in NLU inbox and mark them as correct. Otherwise, you can change the desired intent or create a new one.

4. Test — You can use the previous conversations as test stories that will allow to further verify your chatbot. You can write them in the test directory of your Rasa project and run them often to check how well your bot is performing or you could automatically convert the successful dialogues with the chatbot you had into test stories once the bot is on the server and it is connected to the version control.

rasa test

Testing chatbots in Rasa is not just about the stories. You can evaluate the nlu understanding, checking intent classification and so on. I will probably devote a second article just to the art of extensive chatbot testing.

5. Track — you have to track how well your chatbot is doing. For instance, is the chatbot successful in convincing people to use your online store or how often a particular user is taking to the bot and is she satisfied? You can track the sentiment of the conversation with BERT or just simple logistic regression or Bayes rules.

6. Fix — analyse the conversations based on the performance. If they went smoothly they can become part of the testing set. If not, find out what you need to improve. You could need more training data or fix your custom actions.

Conversation driven approach is user-centric. It is certain that it needs time and attention. I have to often jump between many different stages. Nevertheless, I am working on creating a great chatbot which adapts to the user and not another way around. After all, this is what good conversation is all about.

Don’t forget to give us your 👏 !

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I’m learning all the time — conversation-driven development in a chatbot for Erasmus students with… was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Voice is changing the way we interact with brands.

As 2020 finally came to an end, it is impossible not to mention the Covid-19 pandemic and how much it has shaped the way we live and work. We have all had to adapt rapidly to the new reality. Social interactions were disrupted, so we had to think of creative solutions to help us manage the transition from office job to work-at-home. To stay sane, stay connected, and to continue engaging in conversations, many turned their attention to digital assistants and smart speakers (1). Globally, smart speaker sales continued to increase in Q2 2020, defying the pandemic and growing 6% to reach 30 million units (2). The UK and the US market are at the forefront in terms of growth, with one in three people having access to a smart speaker in these markets (3). In fact, smart speakers were one of the most popular Christmas gifts of 2019, according to the conversation website (3).

In terms of the number of devices per household, it is estimated 22% of UK homes have a smart speaker now, up significantly from just 9% in 2017 (4); and these are much more than just music players. Smart speakers rely on a mix of technologies, including natural language processing and artificial intelligence, to answer questions, read the news and complete tasks accordingly, all hands-free. As the fourth generation of Amazon’s Alexa speakers becomes available in the UK, sales will continue to go up and a wider audience will incorporate smart speakers in their day to day lives. Experts predict smart speaker’s presence will increase exponentially year on year, and they are revolutionising the conversation between brands and consumers (5). It is important that businesses find ways to engage with this audience to remain relevant and connected with their customers. The potential is huge and there are four major brands who are already leading the way in the new year: Johnnie Walker, Nestlé, Domino’s pizza and Patrón Tequila have integrated voice in their business model (5). Others should think about doing it too.

Trending Bot Articles:

1. Case Study: Building Appointment Booking Chatbot

2. IBM Watson Assistant provides better intent classification than other commercial products according to published study

3.Testing Conversational AI

4. How intelligent and automated conversational systems are driving B2C revenue and growth.

It will soon be essential to have a marketing channel through voice. Brands who do it this year will position themselves at the forefront of an innovative experience for their smart speaker users, and this will make many people happy. Marketeers and brands need to continue thinking about creative voice experiences that facilitate the communication with their audience. It is important businesses focus their efforts to boost marketing activities, and meet the surge in demand of voice users. This is what consumers want. Think with Google reports 52% of those who own a voice-activated speaker would like to receive information about deals, sales, and promotions from brands (6), and that 11.5% of current consumers make a purchase via voice at least once a month (7). Voice is positioning itself as a fast rising e-commerce tool, and it has the potential to widely transform the experiences of users in a very near future. Juniper Research states that voice commerce will grow to reach over $80 billion per year by 2023 (8). This is a game changer for e-commerce. To ignore the demand for this technology will mean to miss out on opportunities to increase customer satisfaction. Businesses who want to engage personally and effectively with their customers need to join the race in 2021. Voice is the future and the future is now.

Voice assistant and smart speaker usage will continue to rise in upcoming years (4). In fact, Ovum predicted there would be more digital assistants than the world population back in 2017 (9). The number predicted was there would be 7.5 billion active devices in 2021 (9). We can’t stop but wondering if the rise in the number of smart speakers in households has not been in part, as a result of the human need to try and experience some meaningful interactions. As the pandemic hopefully comes to an end at some point this year, there is time to plan ahead and respond accordingly. Consumers want to stay vocal and want to interact in this way. Voice is a critical player of the connected e-commerce experience. Consumers and voice assistants go hand-in-hand and businesses can leverage this surge in interest to improve their customers’ experience and increase engagement.

As we all had to adapt to an ever changing world, and think of solutions to problems we had only seen in disaster films, it is important to stop and reflect while welcoming 2021. People’s needs and interests have evolved in 2020. Perhaps voice assistants make our lives easier, or perhaps they are fun to interact with. No matter the reason, they are here to stay. A significant number of us have adopted an Alexa in 2020 and many more will in 2021. Business strategists may want to consider the impact this has on them. There is an immense number of digital trend reports out there that highlight the importance of this growing market. The demand for smart speakers, in the UK and in the world, will continue to increase, and so should the popularity of the digital platforms that are able to integrate with these devices. Brands and businesses who join the voice revolution will certainly leave their customers in awe. Our advice for 2021? Meet your customers half way. Maximise your brand’s exposure and be part of the 2021 voice revolution.

Every great relationship starts with a simple ‘hello’ so please drop us a line on hola@vozlab.co.uk and we’ll get back to you.

Don’t forget to give us your 👏 !

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Are you ready for the voice revolution? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

In einem Unternehmen sollte die Material- und Dienstleistungsbeschaffung beim Einkauf liegen. Doch immer wieder bestellen Abteilungen in Eigenregie Waren und erzeugen damit unbewusst einen großen finanziellen Schaden.

Aber warum bestellen Mitarbeiter am Einkauf vorbei und wie kann dies verhindert werden?

 In diesem Blog Artikel erfährst du

• warum Mitarbeiter in Eigenregie Waren bestellen
• welche Folgen dies für die Unternehmen hat
• und wie automatisierte Bestellprozesse und Chatbots helfen Maverick Buying zu verhindern

Warum bestellen Mitarbeiter am Einkauf vorbei?

Für dieses Verhalten gibt es verschiedene Gründe. Sie lassen sich aber in der Regel auf zwei Ursachen zurückführen: Unkenntnis oder Unzufriedenheit.

Oft wollen Mitarbeiter Sparangebote ausnutzen und damit dem Unternehmen sogar helfen. Allerdings sind sie im Gegensatz zum Einkauf nur bedingt in der Lage Preisvergleiche zu ziehen und die besten Konditionen auszuhandeln.

Zudem wissen sie oft nicht, dass es bereits bestehende Rahmenverträge für bestimmte Waren gibt. Bei diesen Verträgen hat der Einkauf oft Sonderkonditionen oder auch zusätzliche Serviceleistungen ausgehandelt. Diese können dann nicht mehr ausgeschöpft werden.

Oder, sie schätzen die Bestellung als nicht so wichtig ein (“Ist ja nur ein Laptop”) und sind sich der Tragweite ihres Verhaltens nicht bewusst. 

Was aber führt dazu, dass sie aus Unzufriedenheit den Einkauf übergehen oder zu spät involvieren?

Kein fester Ansprechpartner in der Einkaufsabteilung

Wenn die Abteilungen nicht wissen, an wen sie sich wenden sollen, kann es schnell passieren, dass sie sich lieber selber um die Bestellung kümmern. Das Gleiche kann auch passieren, wenn ihr Ansprechpartner schlecht zu erreichen ist oder sehr lange braucht um zu antworten. 

Der Bestellprozess ist zu kompliziert und wenig alltagstauglich

Wenn ein Bestellprozess aufwendig gestaltet ist, ist die Versuchung groß diesen zu umgehen und auf eigene Faust eine Bestellung durchzuführen. Zum Beispiel, wenn  Formulare ausgefüllt werden müssen, diese aber nicht zentral zugänglich sind. Die Suche nach diesen Formularen kostet die Mitarbeiter wertvolle Zeit. Oder sie müssen Informationen, wie beispielsweise die Lieferantennummern, eingeben, die an unterschiedlichen Orten hinterlegt sind.

Der Bestellprozess ist zu langsam

Abteilungen können aber auch die Notwendigkeit sehen, den Einkauf zu übergehen, wenn der Bestellprozess zu langwierig und langsam gestaltet ist. Gerade in Notsituationen, wie beispielsweise einem Maschinenschaden in der Produktion, möchten die Betroffenen verständlicherweise so schnell wie möglich ihre Bestellung bearbeitet bekommen.

 

Welche Folgen hat Maverick Buying für den Einkauf?

Wenn Abteilung ohne den Einkauf Bestellungen tätigen, hat dies negative Konsequenzen für das Unternehmen:

Erhöhte Kosten

Dem Unternehmen entstehen höhere Kosten da Bestellungen zu schlechteren Konditionen abgeschlossen werden und keine umfassenden Preisvergleiche durchgeführt werden. Zudem können Preisersparnisse durch das Bündeln von Bestellungen nicht in Anspruch genommen werden.

Mangelnder Rechtsschutz

Wenn Mitarbeiter in Eigenregie Bestellungen durchführen, wird mitunter kein Vertrag mit dem Lieferanten abgeschlossen. Dadurch ist das Unternehmen schlechter geschützt, wenn die Lieferung Mängel aufweist, sich verspätet oder der Lieferant bankrott geht.

Mangelnde Transparenz bei den Einkaufskosten

Im schlimmsten Falle weiß beim Maverick Buying niemand welche Abteilung  was, wann, zu welchen Konditionen und welchem Preis gekauft hat. Dies erschwert es dem Einkauf ungemein einen Überblick über die Kosten zu behalten.

Angespannte Lieferantenbeziehung

Rahmenverträge können an Mindestbestellmengen gebunden sein. Wenn die Abteilungen aber nun am Einkauf vorbei bei der Konkurrenz einkaufen, können diese nicht erfüllt werden. Dies führt dann zu einem belasteten Verhältnis mit den Lieferanten.

 

Wie kann Maverick Buying verhindert werden?

Zunächst einmal muss bei den Abteilungen und Mitarbeitern ein Bewusstsein dafür geschaffen werden, welche Folgen Maverick Buying für das Unternehmen hat. 

Dann sollte der Bestellprozess so leicht und effizient wie möglich gestaltet werden. Es sollte für die Nutzer möglich sein, sich auch ohne viel Vorwissen an die Vorgaben halten zu können. Zudem sollte es einen festen Ansprechpartner geben, der gut zu erreichen ist und schnell reagieren kann.

Des weiteren sollte es für die Mitarbeiter leicht sein, auf die notwendige Informationen oder Formulare zuzugreifen. Hierdurch wird es den Mitarbeitern und Abteilungen erleichtert den vom Einkauf vorgegeben Bestellprozess einzuhalten.

 

Wie Chatbots Mitarbeitern helfen Bestellprozesse regelkonform abzuwickeln

Ein Weg dies sicherzustellen, sind Chatbots. Chatbots sind Technologien, die mit Hilfe von Machine Learning Anfragen automatisiert beantworten können. Sie sind des weiteren auch in der Lage Informationen von den Nutzern anzunehmen und mit Hilfe von RPA  in Datenbanken zu speichern. Sie sind für Bestellvorgänge aus folgenden Gründen geeignet:

• Sie können im Dialog die Nutzer durch den Bestellprozess leiten und so sicherstellen, dass alle notwendigen Angaben gemacht und keine Schritte übersprungen  werden.

 

• Die Nutzer müssen nicht mehr Formulare heraussuchen und dann ausfüllen. Stattdessen fragt der Chatbot die Informationen im Dialog ab und hinterlegt diese direkt im System.

 

• Wenn den Nutzern Informationen fehlen, können sie den Chatbot danach fragen und er sucht diese in Echtzeit heraus.

 

• Der Bestellvorgang erfolgt über einen Dialog. Die Nutzer müssen daher nur sehr wenig Vorwissen mitbringen. Der Chatbot leitet sie Schritt für Schritt durch den Prozess. Sollte ein Schritt nicht klar sein, kann der Chatbot ihn erklären und den Mitarbeitern helfen.

 

• Der Chatbot ist rund um die Uhr erreichbar und antwortet in Echtzeit. Die Mitarbeiter haben dadurch einen festen Ansprechpartner, der jederzeit erreichbar ist. Sollte der Chatbot einmal nicht weiterwissen, kann über ein Human Handover der Chatbot den Nutzer an einen Mitarbeiter weiterleiten. 

 

• Wenn Chatbots mit RPA kombiniert werden, können Bestellprozesse automatisiert abgewickelt werden. Dadurch werden diese effizienter und schneller .

Mit einem Chatbot als zentralen Ansprechpartner werden nicht nur die Mitarbeiter anderer Abteilungen unterstützt. Die Mitarbeiter des Einkaufs werden ebenso entlastet, da Standardanfragen von dem Chatbot bearbeitet werden. So können sie sich auf komplexeren Aufgaben konzentrieren.

Zudem werden alle Informationen direkt ins System eingespeist und automatisch aktualisiert. Dadurch können die Mitarbeiter des Einkaufs auf bessere Datensätze zugreifen und darauf basierend Entscheidungen treffen.

 

Wenn du mehr über den Einsatz von Chatbots im Einkauf erfahren möchtest, dann hol dir unser White Paper “How to use Chatbots in Procurement”:

Download Whitepaper

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With more than 2 million downloads, Rasa is an increasingly relevant open-source framework for the creation of conversation assistants. A Rasa conversation assistant can be provided to users as a chatbot via Facebook Messenger or Slack. It is also possible to publish the assistant as Alexa Skill. Rasa offers both the possibility to implement an assistant without any programming knowledge and to adapt the functional range of the framework to the respective needs via implementing extensions in Python. This article gives an overview of how Rasa makes it possible to easily create assistants. For this purpose, we create a chatbot, which can answer simple questions about its creators. The project is available at https://github.com/Steadforce/rasa-basic-tutorial. An exemplary dialogue that we can conduct is shown here:

Building a chatbot — Installation and set-up

Before we can start creating the assistant, we need to install Rasa. This requires Python 3.6 or 3.7. After the installation we can directly initialize a new project with the Rasa-CLI. The parameter — no-prompt during initialization sets up the project in the current directory and trains an initial model as a test. If this is not desired, the parameter can be removed.

pip install rasa 
rasa init --no-prompt

After successful initialization, a data structure is created as shown in the figure. In the following, we will discuss which data has to be created to create an assistant in Rasa.

Creating dialogs

First of all, we start by creating so-called stories. Stories define how the assistant reacts to user questions. This is done by Dialog Management and the created stories serve as training data for this.

Stories are created in data/stories.md. The start of a dialog is indicated by their names in the form of a markdown heading of type H2. User intentions are marked by an asterisk and actions of the assistant by an indented hyphen.

The exemplary dialogue, as shown above, can be presented as a story as follows:

## Bot-Challenge and ask for creator 
* is_bot 
- utter_i_am_a_bot 
* how_created 
- utter_created_by_steadforce

The story is called “Bot challenge and ask for creator”. The two # signs in front of it mark that this is the name of a story and therefore the beginning of such.

In the next line the dialog between the assistant and the user starts. It begins with a so-called intent, which is indicated by the * character followed by its identifier. An intent is an intention of the user, for example, the question whether the user’s conversation partner is a bot. This intent also has a name, in this example is_bot.

We will go into the various forms of this question in the description of Natural Language Understanding (NLU).

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The assistant reacts by replying with the action utter_i_am_a_bot.

This is also just the name of this action. We see the definition of the response text right away in creating the domain.

Next follows another intent of the user, to which we answer with utter_created_by_steadforce. In this pattern it is possible to create different stories, which serve as a basis for the Dialog Management of the assistant. Rasa also offers the option of creating stories in an interactive mode. These can also be saved in Markdown format and used for training.

Creating Natural Language Understanding (NLU) data

After creating sample dialogs based on the stories, we generate possible versions of the intents. When the user interacts with the assistant, the intent is recognized based on the input using so-called Natural Language Understanding. Again, we provide sample data, i.e. possible versions of the intent. We do this in data/nlu.md.

The individual intents must be specified in the format ## intent:<intent-name>. The training data need to be provided as a list. For a meaningful training we need at least four example data per intent. The following example shows this using the intent is_bot.</intent-name>

## intent:is_bot 
- Are you a bot? 
- Are you a human or a bot? 
- Am I talking to a bot? 
- You're not a bot, are you?

For the intent how_created we generate training data according to the same pattern as well.

Creating the domain

Now that we have set up sample dialogs and training data for the NLU, all we have to do is define the domain. The domain is the environment in which the assistant operates. Among other things, intents and responses are defined there. The configuration of the domain is done in domain.yml. In this example, we configure the domain as follows:

intents: 
- is_bot 
- how_created 
responses: 
utter_i_am_a_bot: 
- text: "Yes I am a bot and developed with Rasa" utter_created_by_steadforce: 
- text: "My developers were staff of Steadforce" 
session_config: session_expiration_time: 60 carry_over_slots_to_new_session: true

In this file the individual intents are listed and the texts for the responses, the answers of the assistant, are defined. Furthermore, the so-called conversation session is configured here. The conversation is a dialog between the user and the assistant. In this configuration, the session is terminated after 60 minutes without interaction by the user and slots are taken over into new sessions. Slots are key-value stores that the user can fill during the conversation. In this article we do not want to go into slots any further.

Setting the language

Before we can start training the bot, we have to do some configuration. Besides creating the training data for the NLU, we must specify the language of the assistant to enable a correct recognition of the users’ intentions. This setting can be made in the config.yml file.

In our example we want to set the language to German. By default, English is preconfigured as language. So, we have to replace the value en with de.

language: de

In this file you not only specify the language but also the NLU pipeline and configure the policies that implement the Dialog Management for the assistant. We do not adapt these configurations in our example and use the default values given by Rasa.

Create a chatbot — Training and a first attempt

Once we have made all the necessary configurations, we can run the training and test the assistant via the console. Both is possible using the Rasa CLI.

First of all, the training of the wizard has to be done.

rasa train

After a successful training the assistant can be used directly via the console.

rasa shell

Here we can test the conversation defined at the beginning and see if our assistant gives the right answers.

Prospects

In this example we have shown how to create a simple assistant that answers simple questions with fixed answers. Rasa also offers the possibility to execute Python code with so-called actions, for example to create dynamic answers. In addition, you can create assistants that fill out forms in the dialog. For this purpose,forms are used in Rasa. An application scenario for this are for example assistants that simplify reservation systems. If training data already exists in a certain data format, it is possible to create your own importers to avoid having to convert this data into the format described in the article.

Rasa offers an easy way to create conversation assistants. If required, it also offers a high degree of flexibility to make extensions and configurations to meet the needs of individual target groups.

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Create a Chatbot using Rasa was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.