In this first of two-part posts where I’m going to detail instead all the steps to build a complete Food Delivery chatbot. Thanks to Xenioo’s built-in AI, Database, and advanced Conversation features you’ll see how it is possible to realize such a powerful and smart chatbot without writing a single line of code.

The How To Build posts series aims to show how a complete chatbot can be built using all of the available Xenioo features with the highest possible detail. By the end of each post, you will gain insight into basic and advanced Xenioo functionalities and be ready to try more complex approaches. All of the additional material used for these posts and the complete chatbot are made available here for download or inside the Xenioo templates section.

The case for a Food Delivery Chatbot

During the last two years, we’ve witnessed tremendous growth in e-commerce chatbots. By either working as “on the side” consultants or being the first line of sales, these chatbots are for all intent and purpose driving thousands of product sales. The Food industry, and in particular the home delivery services part of it, has definitely embraced this trend by acquiring all the good aspects of this kind of revolution:

• Your chatbot can work with multiple customers simultaneously. You’re not missing any more phone orders because customers find a busy line and stop waiting;
  
• If your restaurant is also serving seated customers, no one needs to answer any call and get distracted from regular service;
  
• Orders are written clearly and checked multiple times by your customers. No more wrong orders because of over-the-line misunderstandings or badly written notes;
  
• Your customers can update orders anytime, even after placing them, instead of calling your restaurant multiple times;
  
• Doing some kind of promotion? Reaching out to your previous customers would be a lot easier than putting out paper fliers no one is reading.

If you feel like your business could benefit from one or more of these improvements keep reading as we’re going to build the definitive food delivery chatbot!

Building our chatbot

The first step for building a successful chatbot is to have a list of the main features we’ll need to implement so that we know how to set up our flow. A food delivery chatbot needs to be capable, at the very least, to:

1. Take orders from customers using a free dialogue approach;
2. Allow customers to review, edit and confirm their orders;
3. Take direct payment from our customers;
4. Get customer location to set up a delivery route;
5. Engage with our delivery personnel, dispatching orders and routes;
6. Let the customer check the order state;
7. Collect service feedback after the order has been delivered;
8. Signup our customers for a promotional campaign so that we can engage them with new offers;

Not a bad list of features right? Now let’s see how we can tackle all of these points with Xenioo: let’s create a brand new chatbot in our account and let’s start building!

Creating our Food Menu

In order to allow our chatbot to get orders we need to first create some kind of menu. Our menu will contain all of the information about each and every food served by our imaginary restaurant.

To create our menu we will use all the power of the Xenioo Database feature: we’ll store in our menu collection all of the information we need to display, search and categorize the food we sell.

Let’s head to the Database section of our brand new chatbot and create a new collection named “menu”. We will be adding to menu items data such as name, description, price (obviously!), image, and, since we’re at it, also possible intolerances that our customers may be worried about.

Using the group property of our collection fields we’ll make sure to split our data into different sections so that Xenioo will build a nice data-entry form for us, without writing a single line of code. Once our collection is created we can head to the add new button and select “Add new record with form” to start our data entry.

Xenioo has automatically built for us a complete data entry form with the right input control for each of our fields. Boolean fields are shown as checkboxes, numeric fields have already set decimals or whole numbers and file type fields can manage your attachments and automatically store them in your account storage. All we have to do now is add all of our menu items.

As we have seen also in our previous Let’s Build Post, records can be exported and imported directly using simple CSV files. This makes it incredibly easy for you to manage your data: if you happen to have already a menu in an excel format or, later on, if you wish to maintain your whole food offering using excel. Better yet, if you do not wish to create collection fields manually, you can just import your CSV file directly and Xenioo will import all columns as fields and do all the heavy lifting for you.

Using Xenioo granular users permissions activated by the Pro Team package, it is also possible to create a user that can access only the Database section of the chatbot. Just imagine building a chatbot for your restaurant customer and then giving them a full back office to let them maintain their own data. How much development time would software like this take usually?

Training our NLP Model

Once we’re happy with our menu data we can move on to training our NLP model. What we’re going to do next is basically teach Xenioo how to handle the different requests that may come from our customers. To do this, the first thing to do is to move to the AI section of our chatbot and create a brand new intent.

Within a chatbot, intent refers to the goal the customer has in mind when typing in a question. For our restaurant, the very first intent will be “Food Ordering”.

Let’s go ahead and create the intent by clicking on the add intent button. All we need to do now is just give it a name and click on Save. We’ll be back later to add more details.

Once our intent is created let’s double-click on it to move to Expressions editing. In an intent, expressions are the utterances we’re expecting our customers to use to express their goals. We can set up some typical examples now and fine-tune our training later, even while our chatbot is live.

For our restaurant, a Food Ordering intent may have expressions such as “I want a pizza” or “I would like two margherita and a blonde beer” and so on. Let’s add some typical expressions to our expressions list.

You don’t really need to think of all of the possible ways your customers may order food. Just write some of the most typical and the Xenioo engine will be capable of understanding also expressions that diverge from what you’ve entered.

Of course having our chatbot understand our customer’s expressions is just half of the job: what we really need is to understand what exactly they are asking for. Is it a pizza? If so, what pizza? Do they want a beverage? What exactly do they want to drink?

The information that can be found inside an expression is called Entity. An entity represents a value that can be extracted from the expression and later used by your chatbot. For example in the expression “I want a pizza margherita” what we really need to know is that the required pizza is Margherita and that our customer wants just one.

Usually, we would just go ahead and click on the parts of the sentence that may represent our entities but since we already built our menu we could make things even faster and use our database as an entity list. Let’s move to the Entities section and create a new entity called “food” where we say that the data is coming from our collection.

All that we need to do here is select our collection and pick which fields we would like to represent the possible entity name and the possible synonyms of our entity. Once we’re done with the creation we can just hit save to confirm our entity.

Now, all we need to do to make Xenioo apply our entities to our expression is head to the Train & Test section. After a very short while, Xenioo will finish modeling our expressions. If we go back to our Food Ordering intent and double-click on it we’ll see how our expressions list has changed.

As you can see now some of our words are highlighted. That is Xenioo showing that some of the words are now recognized as entities. It means that the values that will be found will be made available to our chatbot during the flow.

Let’s try our NLP training by clicking on the Train & Test section again. On the input box, to test our training, we type “I’m eating a margherita and a cold blonde”. Press enter or click on Evaluate Expression to see the results.

The sentence is way different from any other we’ve been training but still, Xenioo manages to get a 61% score best of all, as you can see, we get our food recognized automatically. If we were in a real conversational situation, $food and $food2, our entities, would be available to our chatbot as standard variables, ready to be used in any part of the flow.

Speaking of flow, it is time to move to the build section and start creating our chatbot!

The post HOW TO build a complete food delivery chatbot (Part 1) appeared first on Xenioo.

A lost conversation is when a user leaves before signing up for your service or completing the questions you’ve prepared.

How can we get back to the user sometimes later to try to reconnect? Let’s read on and learn how you can easily do it with Xenioo.

First interaction to improve Chatbot Conversion Rate: “Hello? Is There Anybody Out There?”

One of the first options we can use to get back to our users is a broadcast. A broadcast is basically a scheduled push that can be set to be sent to a specific time, date and time or even on demand.

Let’s consider a very simple chat flow like this one:

This flow, on Whatsapp (as an example) lead to a conversation like this:

After this, our user leaves the conversation. Since we want to detect these cases, the first thing we need is a way to check if the user has passed our question. We are using a tag action for this and changing the flow as follows:

Basically what we are doing is set a WAITING_ANSWER tag before the question and remove it right after. This way we know that if a conversation has a WAITING_ANSWER tag then the user is waiting at the input, otherwise the flow has completed and we’ve our email.

Setting Up Your First Broadcast

What we need to do now is create a broadcast that now and then checks for all users that still have the WAITING_ANSWER tag and contacts them to check back. Using broadcasts you are basically creating a “Scheduled Task” that your chatbot can execute every specific interval.

To reach our goal we can easily create a On Interval broadcast that loops every 1 hour and is directed to only a specific audience, like in the picture below.

Since we don’t want to check back to all users every hour we make sure to create and select a specific user that can be quickly generated using the Broadcast Audience Creator. The resulting filters will look similar to this:

Once the basics are down we can move to the Design section of the broadcast, where we can lay down the part of the flow that handles the reconnection. The broadcast design is identical to the general chatbot design and in fact, you can do here everything you can do in your normal chatbot.

After saving (and enabling!) the broadcast we can go ahead and test our reconnection with our previous conversation. When the time you’ve chosen for your broadcast is triggered, Xenioo will automatically send your message to the user.

At this point our user is back in the game and its again up to us to re-engage the conversation so that it is lead to our desired conclusion.

Wait! We can do Better to improve our chatbot conversion rate

While our previous example is simple and effective, it has a couple of flaws that will become very clear as more users are reached and more time is spent online by our chatbot.

1. The broadcast is executed at a specific time, regardless of when the user abandoned the conversation. A user that left the conversation 5 minutes ago would be contacted together with someone who left it an hour ago. It would be better to have a relative time for every single user.
   
2. After the broadcast, there’s no way for the user to go back to the flow that was executing. Even if we get back to the user we’re not moving back to the question that is important for us.

Let’s address point one immediately by simply changing our broadcast type on-flight: from a simple On Interval, we switch to an On Trigger one.

On Trigger broadcasts are one-to-one user broadcasts that are triggered by a specific user action. While audience rule are still applied to this broadcasts only one user will be receiving it based on its conversation triggers.

For example, for our bot, we can select a trigger that starts 20 minutes after Last Interaction. This configuration means that after 20 minutes from the last time a user wrote something the trigger will start. This is just perfect for us because it’s exactly what we need. If our user goes idling for more than 20 minutes we go and try reviving the conversation.

“On Trigger” broadcasts are always targeting a single user in a single conversation but you can still use audiences to filter for a very specific user variable or tag so that even if the broadcast is triggered it is not sent.

What Was The Question Again?

Our second bullet point is little trickier but still very doable.

What we need to do first is split our flow into two parts like below:

We have basically moved all but one action to another interaction and linked them with a Go To Action. All of this can be done very quickly by simply dragging and dropping the desired contents. It has only one purpose: to have a single, repeatable interaction to be invoked later by our broadcast. We will see very soon why.

In our broadcast, we are changing the flow so that the user can be asked if there’s still a chance to complete our flow and if the answer is yes we can now redirect to the Ask Email interaction.

The user will see again the question (which is fine as we’re giving some context to the request) and will be back from where the conversation was left.

This is how our final conversation may look like:

A much better result. User is reconnected with a brief interaction and the chatbot is capable of reconnecting back to where the user left. A good user experience overall and a good way of pushing to all your idling contacts.

If you look at the conversation it now becomes clear why we did the initial split into two interactions. We can now redirect the user to the initial step of the question. And repeating the brief explanation text that gives context to the question.

I Wish I Had Just One Question Like You!

Yes, of course! Chatbots are rarely a matter of just one question and if you have more than two, or three? Or twenty? Having so many questions may make building this kind of approach really difficult.

But once again, we have a neat solution.

For any case when you need to redirect to a dynamic point in the conversation that you really don’t know beforehand you can use a bookmark action.

A bookmark action will basically put a bookmark (how unexpected! ;)) on the current interaction. After the bookmark action is executed you can use any Go To Action to redirect there in any part of the flow.

Lets expand our initial example to a more complex one. With multiple questions, we could design our chatbot to be something like this:

As you can see, the flow isn’t actually much more complex. It is just a bunch of questions one after another but we have now added a Bookmark action on top of each interaction. Now we know exactly where the user is stuck.

The change in our broadcast design is even easier.

All we need to change is the tiny Go To option from “Absolute” to “Bookmark”. What will happen is that Xenioo will automatically redirect the user to the last dynamic bookmark position.

And that’s it! We have now our full reconnection flow with personalized followup and dynamic question redirection. Now we are ready to boost our lead generation and improve our chatbot conversion rate.

Time Is Of The Essence

Privacy settings and platform-specific limitations may be stopping your reconnection plans. Reconnecting with users after a long while may catch them by surprise in an unpleasant way.

Some platforms like Telegram or WhatsApp will not stop you from doing so. But others like Facebook may try to ban your page for attempting mass contact outside of Message Delivery time windows.

Improving chatbot conversion rate: Wrap up

We just did a deep dive into some of the most interesting features of Xenioo and we saw some tips to improve chatbot conversion rate.

In this post, we’ve been highlighting both its power and flexibility. You should now have the basics for adapting your chatbot flow and create an even better chat experience.

If you are not sure about the best way to check back to your users or how to create a chatbot like this or even how to comply with different platform’s rules, feel free to contact our team at team@xenioo.com or leave a message on our Facebook Group.

The post How To Increase Your Chatbot Conversion Rate appeared first on Xenioo.

After you finally publish your chatbot and it’s out there in the wild interacting with your users, that’s when the real work starts.

Measuring exactly how well your questions are handled and how good each interaction is performing is essential for tuning each flow so that your results are maximized.

But how can we do it? And How can we check each and every user and create a global map of the best routes? How can we quickly realize which are the most difficult questions? How to pinpoint the flows where users are lost or get stuck?

We have been asking ourselves the very same questions on a daily basis and with our latest update, we’ve created something that can finally give an answer!

Xenioo Flow Statistics for chatbot performance

Xenioo Flow Statistics are a brand new feature (enabled by default on every account) that will automatically display usage statistics directly on your chatbot flow. With Xenioo Flow Statistics you have all of your user’s usage numbers clearly shown directly on your chatbot design and updated nearly real-time.

To understand how Xenioo Flow Statistics work, let’s build a simple chatbot. Have a look at the one below.

Our chatbot is doing a couple of things:

1. Is asking our users to give a product model and later their email
2. Is trying to stay in touch with users refusing to answer the previous questions

After publishing our chatbot it doesn’t seem to perform very well. We get very few people registering their product and we can’t really figure out why. We start checking each user conversation but as they grow in number it becomes pretty much impossible to track them all.

How can we verify our chatbot performance? Where are we loosing our users? Why aren’t they following our flow the way we want?

Cracking the numbers

In less than an hour after our chatbot goes live data starts coming in. Xenioo starts consolidating all your conversations data and giving it back to your chatbot flow.

Our design area starts to display our performance numbers right on the screen.

What we see on our flow above is exactly how our users are interacting with our chatbot. Our flow starts at the Start Interaction and there we’ve (obviously) 100% of our users. From there, Xenioo is displaying the percentage of people that moved from there to the following interaction.

So, reading the flow, we can see that 77.6% of our users chosen the Question 1 path while 22.4% opted for the No Questions one. Our aim is probably to collect as much information as possible and right now, we’re just loosing nearly a third of our potential contacts (and customers!).

For our test, let’s try to change around our start interaction to give our users a hint about something good happening if they participate in our survey. We publish again our chatbot come back the next day.

Daily Updates

It’s getting better! Now just about 13% of our users are not choosing to participate in our little test survey.

But we can do more with this data: let’s click on the Show Statistics button on our top toolbar to display all of the Xenioo Flow Statistics settings.

Xenioo Flow Statistics are consolidated daily and each day of your chatbot life is available and will stay as long as your retention allows. On the Date Range of our settings, let’s ask to display only the latest day, the one right after our flow update and then click Refresh.

Awesome! Checking only the latest day, we see that with our changes we’re actually reaching nearly 95% of our potential customers. A lot better than before. We can now move on to the rest of our flow.

Input Statistics

Activating Xenioo Flow Statistics, you can now see a small badge beside our Text Input action saying 56.4%. This is telling us that more than half of users fail to correctly input the expected text in that question.

How can Xenioo help us improve the performance of this question? Let’s click on the action.

Looking at the action details you will immediately notice an additional space where the percentage is repeated with a little more information. Near the short explanatory text, you may find a “Show Samples” link. Click on that link and Xenioo will automatically display a list of the 10 most recent wrong answers given to the question!

Now we know which is the input that stops our users. We can use NLP to help them out with their questions or give better explanation of which information we’re requesting.

Remember all the data is consolidated so if you’re looking at multiple days you’ll see the most recent inputs of all the filtered days!

Go with the Xenioo Flow for chatbot performance

By default, Xenioo Flow Statistics will display relative interaction values. This means that the percentage displayed on an interaction will be relative to the number of users of the previous one.

If you look at the percentages above what Xenioo is actually telling you is that of 100% of users visiting the bot, only 86.2% reach the Question 1 interaction. Of all the Question 1 users, only 66% reach the Ask Email.
The same way, as 13.8% of users choose not to reply, only 25% of those choose the Be Friends path.

Xenioo Flow Statistics also support absolute mode. Selecting the option from the Statistics Settings menu will automatically switch your view to display each percentage as an absolute value related to your start interaction.

While using the absolute mode, each interaction percentage is calculated in relation to the number of users that reached the start interaction and not to the number of users coming from the calling one.

Take Control

Phew! We have just learned a lot of new things today! We are looking forward to seeing how this new tool will help you take control of your chatbot performance.

What do you think? Is this the tool you’ve been dreaming about? Let us know by joining our Facebook group or by contacting us directly: we’re looking forward to your feedback!

The post Chatbot Performance, why is important to measure to improve your results appeared first on Xenioo.

In this second part, we’re going to delve deeper in other parts of advanced skill creation approaching. We will see topics like streaming, state management, and live publishing.

Playing audio

Sending audio to an Alexa device can be done in two ways. You either send a small sample, like a sound effect, or you send a longer and more complex streaming source.

The two options are not mutually exclusive but work very differently.

To send a small sample you can just add an Audio Action to your chatbot specifying the source file.

Leave all fields empty (as there’s no need for any additional information) and check the “Embed in speech encoding if available”.
This way Xenioo will automatically create an SSML audio message with your mp3.

If your audio is long (like for example a song or a podcast) and you also want to give the user the ability to pause, skip ahead or stop you have to go for the second option: audio streaming.

Epicentric is a full streaming platform supporting complex playlists and effects and so playing simple audio wasn’t enough for us.

Streaming your audio

To enable streaming you have first to let Alexa know that your skill will use the Audio Player interface.

This is done by enabling the interface inside the Alexa Developer Console.

As soon as you enable this interface, you’ll notice that your intents are automatically expanded to support standard playlist navigation: play, previous, next, stop and pause.

All of these intents are managed directly using Xenioo AI: just match each intent with the built-in Alexa intent key like we’ve seen in our previous post and Xenioo will automatically build the correct model to manage your player.

If you are building a multi-language skill, make sure to clone all intents in each language you want.

Xenioo will then automatically build an Alexa model for each language.

Working with Alexa States

Another important aspect of working with Alexa audio is responding to state changes during streaming.

Alexa will not only let you know that the audio has started but also when it is nearly finished and when it’s over. When the user stops or pause Alexa will let you know at exactly what position it was stopped and if the playlist needs to be set, renewed or cleared.

All of these events are automatically managed by Xenioo and redirect to your flow so that your chat can act accordingly. The usual approach is still here: remove all the nuisances from your workflow while keeping you in control of everything.

The key actions here are the Control Device State action and the Device State Switch Action. Both these actions will receive a state change from an Alexa device. They can be used to manage your flow accordingly.

Account Linking

Account Linking describes a set of procedures that can be used to link an Alexa skill to a specific account. For example, your user may be using your Alexa skill only with an active subscription to your service.

Once you’ve set up the account linking procedure for your application, you can use the alexa_account_link_token variable anywhere in your flow to identify (or login using your own API) your user.

Please note that although mainly static, the Alexa user-id value may be subject to sudden changes. Do not rely on this value to uniquely identify a user.

Final Publish

After implementing a full streaming skill with all the features we wanted, we are now ready to go live.

Xenioo is currently passing 100% of the routine security checks done by Amazon services. It is perfectly suitable for hosting your most advanced skills.

Building streaming skill? Want to have your own Podcast skill on Alexa? We already built a complete production-ready template covering all we’ve seen in this post for free!

Just join Xenioo and add it to your chatbots collection!

The post How to build an Alexa Skill With Xenioo (Part 2) appeared first on Xenioo.

Still, sometimes, a user likes to talk to a human being, or your NLP does not yet manage a specific problem.

In a situation where a human being is required to engage the user directly, most tools fall short. You usually end up with something that can create an exceptional chatbot or something that can manage a user-to-operator conversation.

With Xenioo, you get the best of both worlds, and in this post, we’re going to see how.

Creating our Customer Service Chatbot Team. Enters the Ninja

To get the most out of Xenioo conversation and operator management, we are going to activate the “Pro Team” package.

This additional package will not only upgrade some actions but also enable team fine-tuning and operators inside conversations.

This package is also available for free if you activate a Professional or Custom plan.

Adding our “Pro Team” package, we can immediately head to our chatbot settings and see how it has dramatically changed.

In our example, we’re going to add three new team members to our support bot. These users will have different jobs and different purposes so that we can explore how Xenioo lets you fine-tune permissions and conversations.

The first one that we’re calling Andrew is part of the Technical Support group. He will only work on customer support on the conversations section.

We will add him to our customer service chatbot team enabling only access to the conversation section. Specifically only to conversations that are associated with his group.

He will not be able to check and engage in any conversation unless specifically assigned to him or his group.

Anna, our second team member, is part of the marketing group and will be engaged by marketing questions.

She also needs to check reports now and then so we’re going to give her access to the bot dashboard and bot statistical data.

Mary, our last member, is a Supervisor and she will see all conversations and statistics. Also, she hasn’t developed the chatbot, but by overseeing conversation, she can choose to better train the AI.

For this reason, we’re also giving her access to a read-only view of the chatbot and full editing capabilities of the NLP section.

You can see, Pro Team opens up all of the permissions that allow you to choose what team members can do. It gives them access to your customer service chatbot being sure that they are not going to access information that is not meant for them.

Once we’re done creating our full team and permissions, let’s continue by adding operator management to our chatbot.

Do you want to talk to a human?

Once your chatbot has done all it could as 1st level support automation, it may be time to move the conversation to a real human.

With Xenioo, you can manage this situation with a very specific action called Set Chat Operator State.

With the Pro Team package, this action becomes even more powerful.

As you can see in the flow above, we manage some chatbot struggles by giving the option to talk to an operator. If the user chooses to do so, we redirect to an explanation message and activate takeover.

This action will:

• stop the chatbot from replying. Whatever the user writes will not activate any chatbot action until the conversation is handed back over
  
• assign the conversation to the Technical Support group (the Andrew group we’ve just created!)
  
• automatically pick a member of the above group based on three possible rules:
  • Random: a random member of the group is assigned to the conversation
  • Less Busy: Xenioo will automatically pick the team member currently engaged in fewer conversations
  • Specific: you can choose a specific member of the above group

So in a single action, we’ve just managed to create a rule we can use anywhere in our chatbot. Using these options, you can easily create multiple paths that allow your users to talk to Andrew or Anne depending on the flow.

For example, your chatbot could be in the marketing inquiries section and redirect the conversation to the marketing team.

Anne, in our example the marketing operator, will see the conversation popup in her Xenioo dashboard and can then engage in a one-to-one conversation with the user on any chatbot channel.

Simultaneously, Andrew could be handling multiple conversations with users having tech problems. Once he solves all of the issues, he can then give back control to the chatbot and even redirect the conversation to a specific Behavior or Interaction.

The possibilities are actually endless.

Conversation with users

All of your team is going to access different views of the same chatbot.

Andrew, in our example, is going to see something like in the picture below.

As you see, only the conversation section of your support bot is available and a user, requesting help, is waiting for a takeover.

Andrew sees this conversation because we just configured our chatbot flow to redirect the to the technical group the help request. Andrew can take over the conversation and chat with our users in real-time.

The channel is not important: Xenioo will handle all of the multi-protocol communication intricacies for you, and everything will just work.

Once the issue has been solved, Andrew can give back control to Xenioo and, if required, redirect the user to a specific Behavior or Interaction.

For example, he could redirect the user to a section where the chatbot collects some feedback on how the support session went.

Changing Conversation Data

Andrew, as an operator also has a very interesting capability: he can alter the customer service chatbot data by updating run-time variables and tags.

By just clicking on the right panel where all the current conversation data is displayed he can pick specific values and change them as well as add new tags.

This is very useful whenever helping the user requires not just guiding but also updating previous data that the chatbot may have collected.

Did the user specify the wrong product? Or a wrong id number? Our operator can intervene and change it in real time. Every change made is immediately reflected by our chatbot live.

Having a Support Supervisor

Sometimes, larger customer care teams may have a supervisor taking care of actually assigning customers requesting help to specific operators.

Much like you would do with real help-desk tickets. With Xenioo, you can also support this kind of scenario.

Mary, our supervisor example, can access any conversation of our chatbot and has a specific power. She can assign and transfer a conversation to any operator of any group.

By supervising conversations and managing alerts, she can decide to redirect a troubled user to any other operator.

In our example below, she’s redirecting the conversation to Andrew again.

Takeover can happen anytime in the chatbot flow and operators can step in at any point. This is useful when a user is stuck on a question and you want to help.

As the user may not be expecting the switch to human interaction, Mary also can display a specific alert message.

A complete Customer Service chatbot for problem-solving

The Pro Team package is a life changer for everybody working with the Customer Service chatbot.

Real-time multi-channel conversation means that you can be always available on Facebook, Web, WhatsApp, Telegram, and Slack and have one single entry point for managing everything.

What do you think? Do you have any specific requirements for your team? Let us know by joining our Facebook group or by contacting us directly: we’re looking forward to your feedback!

The post Managing a Customer Service Chatbot Team to support customers appeared first on Xenioo.