Category: Chat

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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

Wie Maverick Buying mit Chatbots verhindert werden kann

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Der Beitrag Wie Maverick Buying mit Chatbots verhindert werden kann erschien zuerst auf BOTfriends.

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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).

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.

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.

We define the concept of chatbot orchestration platform and why this is a revolution in the way that chatbots are built

Orchestration is the automated configuration, coordination, and management of computer systems and software [1]. The term became popular with the mainstream adoption of web services and service oriented architectures (SOA) as there was the need to align and complement several independent services to respond to the business needs while satisfying its constraints (in terms of quality, reliability, price,…). Xatkit brings this concept to the chatbot world.

Xatkit is the first full-fledged chatbot orchestration platform.

You can use Xatkit as a tool to simplify your chatbot development projects thanks to our chatbot DSL and state machine semantics that provide good abstractions to facilitate the creation of complex bots. Kind of a low-code approach for chatbots. But Xatkit is much more than this. Xatkit is a framework that enables you to freely combine the best NLP technologies for your needs. Do you need a neural network to understand the user intentions? A sentiment analysis feature? A language model for translations?. Take whatever you need, from the best providers and combine them in a single chatbot. Let’s see how we achieve this.

First step: Avoiding the Not Invented Here syndrome in the chatbot world

Xatkit does not pretend to reinvent the wheel. We don’t suffer from the Not Invented Here syndrome. We develop our own solutions when we see a gap in the market but aim to reuse and enable as much as possible all the great NLP/NLU technologies that already exist out there. With so much research and development around natural language technologies, we believe the best way to serve our clients is to give them access to all the cutting-edge developments instead of getting them stuck and locked-in in ours.

You can use Xatkit as a tool to simplify your chatbot development projects thanks to our chatbot DSL and state machine semantics that provide good abstractions to facilitate the creation of complex bots. Kind of a low-code approach for chatbots. But Xatkit is much more than this. Xatkit is a framework that enables you to freely combine the best NLP technologies for your needs. Do you need a neural network to understand the user intentions? A sentiment analysis feature? A language model for translations?. Take whatever you need, from the best providers and combine them in a single chatbot. Let’s see how we achieve this.

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.

Instead, many other companies follow the opposite route and try to compete against each other by duplicating the same features again and again. How many intent recognition providers the world really needs?. According to many chatbot vendors, several dozens!, many of them using a very similar approach. Xatkit is designed to use the one you like the most. This way you can use your favorite provider while we focus our efforts on making your life easier. For instance, by using Xatkit as platform, you can in the future switch providers (e.g. going from DialogFlow to IBM Watson) without changing a single line of your chatbot definition. Xatkit comes with a few predefined connectors (regular expressions, DialogFlow or nlp.js) but thanks to our architectural design and extension capabilities we can easily add new ones.

Second step: Good chatbots need much more than just intent matching

The key NLP component of every chatbot is the intent recognition component that takes as input the user utterance and identifies the intent behind it (plus recognizing the possible parameters in it). But a quality chatbot needs more than this. You may want to have a troll detector to block those comments right away without having to fully process them. Or a sentiment analysis component that then you can use to decide how to better keep visitors engaged depending on their mood. Or maybe apply an internal translation to support a visitor that may be addressing the chatbot in an unexpected language. Some of these actions should take place before the intent recognition phase. Others after. Others before AND after.

Xatkit includes the concept of Language Processors to support these requirements. Processors can be preprocessors, postprocessors or both. And following the philosophy explained above, we either create our own processor (e.g. to remove stop words from any entities) or adopt any library/language model/… solution out there that we believe can do a better job. For instance, Xatkit integrates a processor based on Stanford CoreNLP for the sentiment analysis.

At Xatkit, we believe building a chatbot should be as fun (and as easy) as playing with Lego. You take the pieces you need and assemble them together to create your ideal NLP pipeline. It doesn’t matter where you bought that piece. And you can always buy a new extension to enhance your bot functionality.

Of course, we are working hard to provide the right abstractions to “defeat” the well-known saying that states that “Software reuse is more like an organ transplant than snapping together Lego blocks” (John D. Cook)

Final step: Xatkit puts it all together

All this sounds nice, but you may be thinking, who is gluing all these components together? Who makes sure they all execute in sync and share the data they need at the right time?. This is where our chatbot runtime engine comes into play. Xatkit is, at the same time, a chatbot definition and chatbot orchestration platform, giving you support for the full life-cycle of you chatbot. Via a simple configuration mechanism you can indicate how to deploy your bot (what components to use, what concrete providers / services connect to, etc) and Xatkit will be the genius conductor that will orchestrate the chatbot for you making sure each component follows its role in the chatbot choreography. Ready to give it a try?

The conductor in the featured image is Mirga Gražinytė-Tyla. Lego photo by Kelly Sikkema on Unsplash

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An open source chatbot orchestration platform was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Around 50% of all searches on the web are voice searches.

In 2020, Campaign.com shared this staggering stat, based on Comscore research. In 2019, Statista.com backed this data up by saying that around 42% of the worldwide population used voice search within the past month. Adobe talked about 48% of consumers using voice for “general web searches.”

However, due to the way the Internet is constructed and my curiosity, the 20th or 30th article about voice search I’ve read is debunking Comscore’s 50% prediction. According to it, Comscore projects “at least 50% of all searches are going to be either through images or speech in five years.”

Whatever the exact number is, the buzz on voice search was in the air in 2020 for a reason, so now is a great time to learn more about this trend to be prepared for the future.

Smart speaker market

Smart speakers with voice assistants, which are responsible for 39% of all voice searches, are rapidly expanding in North America.

Around 26% of Canadians and 24% of Americans owned smart speakers with personal assistants as of early 2020. These numbers are even higher for households.

Alexa is the leading brand in the US smart speaker market, owned by 74% of Americans. Сanadians are not so apparent in their choice — 53% of Canadians prefer Google Home while 48% of Canadians own Alexa.

Forecasts are even more promising. The worldwide smart speaker market revenue is going to increase by 44% in the next five years, reaching $35.5 billion in 2025.

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.

What should marketers do?

Quick answer — optimize their websites for voice search.

The problem is that voice search and regular search are different.

With voice search, you’re playing an all or nothing game — you have one chance to be picked up by a voice assistant.

However, it is most likely to happen if you’re first in search results, particularly in SERP features.

You can’t measure ROI from voice search optimization. For now, Bing or Google web analytics don’t provide tools to identify how much traffic comes in via voice search.

But what you can do is tailor your SEO strategies based on the knowledge of smart speakers’ behaviour.

Here are some key takeaways from the SEMrush Voice Search 2020 study that might help you understand voice searches’ main characteristics and how they differ across the most popular smart speakers.

Recommendations for voice search optimization

1. Put specific questions in your titles or subtitles, starting from the 5 Ws: “Who,” “What,” “When,” “Where,” “Why” + “How.” Consider adding the FAQ section to your website.
2. Use long-tail keywords and more conversational keywords. Use AnswerThePublic to discover how exactly people are searching for stuff.
3. Give short and sweet answers to particular questions within your content.
4. Optimize your website for local voice search as 22% of voice queries are for local-based content.
5. Make your website load fast on all devices.
6. Focus on ranking in featured snippets. Most importantly, ranking in featured snippets is our primary measurement to identify how well a website is optimized for voice search.

Lionbridge case study

Some companies have already realized the importance of the voice search trend and optimized their websites for voice search queries.

Lionbridge, an American company specializing in translation and game localization, attributes a 127% growth in page views to their voice search optimization strategy. In just a year, they improved their ranking in featured snippets from 0.7% to 27% and increased their SERP positions by 22.5%. Here is a detailed case study explaining how exactly they’ve achieved such results.

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Why Should All Marketers Learn More About Voice Search in 2021? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.