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Botium Box offers two methods to test an SMS Chatbot. It can act as a user sending SMS messages, or can interact with the API behind the chatbot. Sending SMS messages looks like an optimal choice because it covers all the stack, but it has a major drawback, the increased costs.

For smaller systems sending SMS messages is usually a good decision. It is the all-purpose solution, and it is easier to setup.

More complex chatbots can have more sophisticated configuration, API-testing all conversation branches, and SMS based testing for checking performance and availability of the full stack for example.

Tip: If your API delivers NLP information like recognized intent and entity, then in case of API testing you can even use NLP Analytics in Botium Box (depending on license).

API Testing

There is no step-by-step solution for API testing. We need a Botium-connector which fits to your API. You can use the suitable generic connector of Botium Box (in most cases Generic HTTP(s)/JSON interface). Or you can write your own.

SMS testing with Botium Twilio SMS connector

Botium Box uses Twilio to communicate with any SMS chatbot.

You’ll need a Twilio account, register a telephone number in Twilio, configure Botium Box for Botium-to-Twilio messages, and Twilio for Twilio-to-Botium messages.

You can use trial account for Twilio, but there are limitations, and the budget is limited. We use an upgraded account here.

Please keep in mind that SMS communication has no session. If you have for example development, and production tests, then it is a good practice to register telephone numbers and configure connection in Botium Box for each. Otherwise you can get some random test fails if they are running in parallel.

Trending Bot Articles:

1. How Chatbots and Email Marketing Integration Can Help Your Business

2. Why Chatbots could be the next big thing for SMEs

3. My Journey into Conversation Design

4. Practical NLP for language learning

Twilio account

Follow the steps start trial and upgrade.

Register a telephone number

Navigate to Active Numbers, click on Buy a Number. Consider choosing the same country as the one, where your chatbot’s number is located. And choose ANY or SMS in capabilities, and buy a number.

Check SMS capability (optional).

Notice the telephone number, and the credentials (settings, general)

Configure Twilio in Botium Box

Now we have to setup Botium Box

Under the Chatbots list click on the Register new chatbot button.

Fill the form with the values from the Twilio Dashboard. (Sending SMS to field is the telephone number of your SMS chatbot.)

Once the form is complete, Botium Box will be able to receive messages. But we can’t test the connection yet. We have to configure Twilio to send messages to Botium Box.

After you choose an API key, you can see the endpoint for receiving messages on the registration form. Copy the endpoint url.

Configure the Twilio webhook

Open the settings for the registered number in Twilio, and paste the endpoint as webhook in the Messaging section (not in the Voice & Fax section!) and save it.

Test the connection and save it in Botium Box

Everything is set up. Now we can test it with pushing SAY HELLO (CHECK CONNECTIVITY) button. (We expect that the chatbot responds if we send hello. If it’s not the case, then skip the test. You can test it manually using Live chat feature of Botium Box)

Everything fine, we can save the form.

If is not working, then check the error message displayed on the connectivity test, or check Twilio logs on the phone number.

Disable parallel execution in Botium Box

If you have more Agents (workers) in Botium Box, and more test cases in a test set, then Botium Box executes the test set parallel. It is good for most of the connectors, but not for Twilio SMS connector as mentioned before.

We have to set the Parallel Jobs Count to 1 (or choose a dedicated Agent) in project settings to disable parallel execution.

Use Botium Box

The connection is configured, you can use all features of Botium Box!

See this article in spanish here! 🇪🇸

Don’t forget to give us your 👏 !

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Testing SMS Chatbots with Botium Box was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

An employee is one of the key assets of the enterprise.

The productivity of employees is directly proportional to enterprise success.

Employee productivity can craft or shatter the success of an enterprise.

Employee productivity is a little like breathing. It is very crucial in keeping your company alive & blooming.

Since the past decade, enhancing employee productivity has been one of the top challenges for enterprises, and most organizations are maximizing their investments to improve their employees’ output.

One of the critical bottlenecks of productivity is a regular distraction for the employee.

According to the research, on average, an employee will distract 54 times a day.

Among which, 80% of the interruptions are trivial & completely stoppable.

By providing the best employee experience with the support, get them back to work simply and hurriedly.

It saves around 2hr/day spent by an employee to come back from distraction.

Most of the organizations are opting for Conversational Automation to provide positive support service experiences for the employees to improve productivity.

The Conversational Automation platforms can resolve the issues before employees recognize them. AI Automation & cognitive Automation have enormous potential in increasing employee productivity and employee experience.

“By 2021, 70% of the organizations will assist their employee productivity by integrating artificial intelligence in the workplace.” [Source:Gartner.com ]

Conversational Automation is a boon for every firm. Companies can use it as an effective method to simplify internal processes, and employees can make the most out of their time at work.

In this blog, we will look after how Conversational Automation can help your employees become more productive.

It is always beneficial to know the basics for a better understanding of the concept.

So, let’s begin!

Trending Bot Articles:

1. How Chatbots and Email Marketing Integration Can Help Your Business

2. Why Chatbots could be the next big thing for SMEs

3. My Journey into Conversation Design

4. Practical NLP for language learning

Employee productivity?

The employee productivity calculated by using the below formula:

Productivity = Units of output/Number of working hours

Employee productivity is the amount of output produced by an individual employee in an hour they work.

The more productive the staff is, the more worth they generate for their employers.

Note: Employee productivity should not be bewildered by labor productivity.

If employees are productive, the business:

• Develop
• Remains competitive
• More profitable
• Meets consumer demand

Reminder:

One employee cannot be 100% productive.

Reasons for employees being less productive?

There are several reasons for the employee productivity go down; they are:

• Ineffective work environment
• Poor work climate
• Low employee engagement
• Poor communication and leadership
• Training

Now, you got to know the root causes of the decline in employee productivity.

Primary productivity stoppers at the workplace?

Without spending any more time, let us jump onto the core discussion.

Streamline & automate time-consuming day to day activities

Every day most of the employees need to perform various day-to-day activities for which they need to approach the contact center. Among them, countless tasks distract employees from their primary responsibilities, which are low in complexity but exceptionally time-consuming in amassing.

The tasks can be requesting for leaves, scheduling meetings, logging time-off, and so on will impact the employee’s overall productivity, mainly if the various systems are scattered in the different physical locations.

In today’s fast-changing digital world, streamlining and automating the business process has become a necessity for enterprises if they want to stay ahead of their competitors.

With the help of Conversational Process Automation, enterprises are competent to automate the repetitive & time-consuming process that leads to cost minimization and better productivity & efficiency.

Employees can use a digital agent as their digital assistant for better-performing tasks, and with this, they can decrease the time they spend away from meaningful work.

Employee Onboarding

Onboarding a new employee and speeding him up is a time-consuming process that requires a tedious amount of admin to complete the process.

Following the traditional methods can not be the most efficient way to onboard an employee, and because of which employee productivity will go down.

Conversational Automation (AI-powered Automation) agents come as the savior for the organizations. With the help of digital agents, the companies can minimize the amount of work done by the managers who need to register a new team member to the various services & systems within an enterprise.

The conversational Automation also connects various back-end systems via APIs and enables the entire onboarding process to be managed through a single and easy-to-use digital agent.

As we are doing everything via automated agents, there is no need for human agent intervention. The manager can focus on crucial or complicated strategies that need more attention.

Keep your employees updated

Currently, companies are continually advancing with the latest technological advancements. To keep employees updated about the latest happenings in products, services, and internal procedures will eat out a lot of time & effort.

That is affecting employee productivity indirectly.

With the help of conversational Automation, we can provide regular updates to the employees at their desks.

The companies will send information using digital pop-ups whenever new or updated information is needed to inform the employees.

As the conversational automation agents send the information to the employees whenever required, there is no need for the unnecessary congregation by which the employee productivity will increase significantly.

Personalized Attention

Giving personal attention to every employee in the organization is not practicable at times, particularly in large enterprises.

To accomplish such a proportion, the company needs to empty its pockets, which entails logistics on an immense scale.

It is where the need for Conversational Automation comes into the picture.

The digital agents use natural language capabilities, data analytics & embedded analytics to know various facets of an employee’s milieu in a move ahead that result in an active support conversation.

The virtual agents will incessantly be trained by building on preceding queries to give timely answers to an individual’s support queries.

This personalized attention & support enhances employee engagement, and that leads to higher employee productivity.

Training

Employee training plays a significant role in finding out how well they are prepared for jobs. The more they are trained, the more productivity they are likely to be.

Think about it!

I think most of you agree with my above statement.

Using Conversational Automation in enterprises will help businesses to migrate all their employee training sessions online.

Once you onboard a new employee, you need to train them to catch up on speed with the existing employees.

It is where intelligent automation Chatbots presume importance.

The bots use machine learning and robotic process automation to provide better training for employees.

Providing advanced training via virtual agents will facilitate a smooth transition for the employee to the new workplace.

Conclusion

With the above-provided information, it’s evident to everyone that conversational Automation will help your employees become productive.

It’s proven that the implementation of AI workflow automation in companies has seen a boost of 39% of their revenue.

Proper utilization of any technology, whether it is AI-based Automation/conversational service automation, will eventually translate into higher productivity.

If you haven’t deployed, Conversational Automation in your workspace, we believe it is the apt time to do so.

For more updates on conversational AI, stay tuned with us.

Don’t forget to give us your 👏 !

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How can Conversational Automation help your Employee become Productive? 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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Disclaimer: this post also serves as my notes over the years building various chat bots with various vendors, so this will get updated from time to time.

Disclaimer 2 : to make this post neutral, I won’t be mentioning any vendor names here.

My professional career requires me to dabble a lot in support suites, and soon enough chat bot became a part of the equation. I’ve experimented with a lot of approaches in my quest to build a quote unquote magical bot —from building them inhouse complete with our own NLP library, exploring local and international vendors, to doing a hybrid approach — all in order to build one that we all dream will be smart enough (one day) to tackle the virtually all of incoming inquiries from customers; and smart enough to sound like a human — you know the dream. Many dreams shattered later, feet grounded to earth; these are my notes of the lessons I’ve learned the hard way from building a handful bad bots, to bots that are decent enough to earn a stable stream of good CSAT ratings even in their bad days (e.g. production issues).

First thing first — It’s important to understand that it would be extremely hard if your aim is to build your chatbot with the goal of your bot being able to answer ALL of your customer’s inquiries — at least with the current technology. At best, they can answer general questions or handle fixed-flow request (e.g. checking a payment status, simple onboardings). Hence, it’s always a good idea to build a chatbot with a fallback mechanism in place (e.g. live agent fallback), and give clear expectations from the very beginning to your users of what the chatbot can and cannot do. Think of it as a system of ‘chat support service’ — bot is just one half of the system, but what completes is the presence of measures to flexibly respond to unprecedented situations; only then the system is close to complete.

First and foremost — verify what are the capabilities that your vendor can support.

Obviously, unless your business has invested a lot in NLP technologies, your best bet will be to look for a vendor to build the base bot for you, before you fine-tune it on your own. Generally, a few rules of thumbs that I use when scouting for a vendor, aside from standard service quality checks (e.g. how fast they respond to your inquiries etc) :

• The flexibility of in designing the conversation flow and UI/UX elements, plus training your bot with new topics, questions, and utterances. If they say that you can customize on your own via the dashboard, drill it down during the demo / trial time. Otherwise, if they say that they will provide a dedicated account manager / product team for you, lock down their commitment on how fast they can deliver your request. This is incredibly important because there’s no prevalent formula in building a good bot; it all depends on the nature of your users and your business — which means you will be spending A LOT of time A/B testing UI/UX elements, scripts, conversation flows, etc. Minor details especially matter when it comes to building a chatbot. For example; we had issues for months in trying to boost the number of CSAT responses; we’ve tried everything — an input based CSAT where user needs to type the numbers, button-based CSAT, changing the number of rating options, etc. Accidentally, we found out during our nth test, that changing the CSAT format into clickable star ratings actually boosted our CSAT feedback to over 500%. This formula might not work for your chat bot, hence why you need to verify your vendor’s flexibility in supporting your A/B tests. Imagine if just to ask for a UI change in CSAT format, it’ll take you two months to get it implemented (we’ve been there!).
• The bot vendor’s NLP proficiency in your target language. This might not be too relevant if you’re only supporting English language bot, but you might have realized during your search for a great NLP library, that outside of the English community, it’s usually few and far between; depending on how active and prolific the NLP community for your particular target language. You can also opt for a strictly input/button based bot, but I found out that new user / repeating user are less willing to engage with this type of bot except for very simple queries because these users already have the perception that the bot will not be able to help with their questions. Hence supporting a free form, NLP based input from your customers actually will help with your bot’s engagement rate, and to make your bot sounds natural at least during the happy paths. My recommendation is to actually test your vendor’s live bot in their customer’s web/app, or at the very least, ask them to provide you with an interactive demo. This is incredibly important because improving NLP is a long term investment; this can’t be done overnight (or even within a few weeks/months!). Once you’ve locked in a vendor, switching cost is high especially because of the number of resources you will have to pour to train or improve the bot, so make sure you know what you’ll get before you sign the deal.
• Explore the vendor dashboard’s analytical capabilities — a few things that I usually look for when analyzing a potential vendor’s dashboard includes the bot performance statistics (e.g. handover rate, CSAT rating, unidentified utterance, and the capability to see individual messages / download raw data). Either they provide this by default, or the vendor has the capability to build custom analytics. This is monumental because a good analytical capability makes a world’s difference in helping you train the bot post live. Why? With most products, it’s easy to get product feedbacks, be it bug reports, or feature requests via app reviews, or support channels, etc. With chat bots, the only way to get direct feedback is by observing your analytics. You’ll be surprised how many vendors actually do not provide CSAT analytics by default.

Trending Bot Articles:

1. How Chatbots and Email Marketing Integration Can Help Your Business

2. Why Chatbots could be the next big thing for SMEs

3. My Journey into Conversation Design

4. Practical NLP for language learning

Your work doesn’t end when your bot finally goes live. In fact, it has only just begun.

Expect to put a lot of resources to finetune your bot after it goes live. In fact, be prepared to pour even more time and work to finetune your bot. Your bot vendor most of the time does not have your domain/industry’s specific knowledge, hence the task to fine-tune your bot to cater for your customer’s needs fall upon you. Your vendor might be able to help you implement your plan, but the analysis, research, scripting, conversation flow design, training, fallback design and many other inputs will need to come from you.

Design your conversation flow with the intention to help your customer

If there’s a frustrated user who needs to talk to a human agent, don’t make it difficult for them to do so. Understand that these user segments are not intended to be handled by your bot; forcing them to talk to the bot does not help either them or your business. While this doesn’t necessarily mean that you should put the option to connect to Agent directly from the get-go, provide an easy to access fallback option on every scenario when bot fails to answer.

For example ; let’s say that your bot is unable to identify your customer utterance. In cases like this, offer your frustrated customers a few options — a. Bot can’t understand your question, is this what you mean? b. If not, do you want to talk to our agent?

You might think that your users by default will choose to talk to agent, but think of it this way :

• Your bot being unable to answer, frustrating your customer, AND your customer getting frustrated due to not being able to talk to agent are actually two different problem statements. Solve them separately instead of looking for a single solution that solves both. Your customer might want to talk to an agent because your bot is unable to answer their questions — hence the solution is not preventing them to talk to your agent because the root cause is your bot capability itself. On the other hand, a frustrated customer because he/she’s facing a technical issue or fraud are not your bot’s target user anyway. Don’t frustrate them even more by making it difficult for them to get the help they need. Remember the reason you’re building the bot in the first place; it’s to cater to your customer needs better.
• The percentage might varies, but typically 40% of inquiries that come to businesses are actually frequently asked questions that do not need a human touch. If you are optimizing your bot to handle these commonly asked questions, there is no reason for them to talk to the agent anyway.
• Sometimes we underestimate the number of users who actually prefer not to talk to human unless they really have to. They actually comprise a larger percentage of your user than you might have imagined.
• If the majority of your users ask to talk to the agent, you might want to analyze further what went wrong. It could be that they might be repeating customers who have built a distrust over the capability of your bot to solve their problem, perhaps due to a bad first experience with your bot; or your bot has gained notoriety of being incapable to solve the customer’s issues, thus is being perceived as a hindrance instead of a helping hand.
• Making it easier for users to talk to human actually helps you to buy some time to improve your bot. Your bot will not be immediately smart from Day 1. Undoubtedly, you will need to pour a lot of effort, resources and time to tweak your bot; with the presence of a human as a fallback option, this will actually buy you time to be able to think, research, and experiment what will actually work to improve your bot’s accuracy and coverage.

Opt for interactive, visual-based call-to-action (CTA) inputs for flows like CSAT to improve rate of CSAT survey

I mentioned it above, but this is something that I learned from my past experience building a few bots — the principle is that your users actually have zero incentive (unless they’re a disgruntled customer) to provide you with feedbacks; hence make it as easy and painless as possible for them to provide their feedback. Opt for a way that requires almost no effort from user to provide their feedback — I found that visual-based CTA inputs that’s commonly used / easy to understand intuitively actually works much better than type-based CTA (which requires actual effort from users to a) comprehend how the rating scale works, is 1 the best, or is 5 the best? b) how should I provide the input? Numbers only? numbers with description? c)and finally, type the numbers).

The nuances in your copy are important. A/B test them.

I’ve handled multiple products in my professional career years, be it an onboarding tool or a fraud detection system, but I found that chat bot is actually that one product that heavily relies on frequent A/B testing for the little details and nuances, much much more than any other products. You’ll be surprised by how little changes to your script (e.g making it in bullet point format instead of paragraphs, tweaking word choices, etc) can make a major impact on your user’s engagement metrics.

I usually do it several ways; going through the users’ list of unidentified utterances or negative feedback analytics, but I found that context is also incredibly important to understand the nuances of the user’s feedback; hence I often go through our user’s actual conversation with the bot in order to understand the conversation nuances and context. For example, a particular script that we use to prompt users to provide input — either click the button to choose to connect to agent, or type your questions if you want to access the FAQ; often leads to a bad CSAT rating. Going through the conversations, we found out that users often misinterpret the instruction; thinking that if they typed the questions they’ll be connected to the agent. A simple formatting change in that particular question actually reduced the occurrence of bad CSAT ratings for that particular flow.

Finally, design a fallback mechanism for every possible turn of events that your bot can’t handle

This does not just refer to human fallback, but includes various types of possible scenario that your bot might not be able to handle. Some example scenarios :

• Your user wants to connect to human, but your agent is only available during business hour. This is a tough scenario to crack; hence you’ll need to experiment with what will work for your business; either by managing your users expectation upfront that your agent is only available during business hours, provide a contact form they can use during non-business hours so that they can still ask their questions, etc etc.
• Your bot can’t exactly identify your user’s utterance. You might want to explore a few flows; such as prompting users to the closest topic that bot think might be what your customer is looking for; ask them whether they want to connect to an agent, have your bot ask your user to paraphrase their questions, etc.
• Your bot got unexpected input for that particular conversation junction. Some kind of input validations might be useful to implement in this kind of scenario.

Closing

Finally, one lesson of great importance that I’ve had as a take away from all these learnings is that to QC often by going through the actual messages. Some issues or nuances are hard to capture using analytics alone. Make it a habit to go through your user’s end-to-end conversations, you’ll be surprised how much more issues you can capture (that might not be apparent in your analytics), or how straightforward it is to solve a particular problem that you’re trying to solve.

Don’t forget to give us your 👏 !

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Building a chat bot that actually works for your business (part 1) was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

When I started writing about chatbots they were largely academic projects or intense in-house creations for firms with huge customer bases. In the fast-moving five-plus years since, they have become a staple part of business offerings, spawned a startup armada with fast-evolving services, and that’s just the beginning.

Call them chatbots, conversational AIs, virtual agents or avatars. Whatever the name, digital chatterboxes are fast becoming a familiar sight on the front of websites, within apps and business services. Soon we won’t be able to move for AI-based advice and information services.

But it was so different only a couple of years back. When Siri (10 years old in October) and Alexa arrived, they heralded the start of a personal assistant revolution. Yet, despite the Apple and Amazon hype, they were not all that useful and it has taken years of constant improvement for them to become the household buddies we use today.

Early chatbots were even starker. “I don’t understand that,” “can you rephrase that” or “please call our helpline” were constant blockages in our attempts to get things done. In the few short years of commercial bots, the situation has improved. But there’s a way to go yet.

How businesses adopted bots

The early bot-as-a-service firms were keen to take advantage of the hype around improved customer service. The proposition was a compelling one, chatbots handle the high-volume, simpler, customer queries; freeing up real agents to deal with more complex, high-value queries.

Many would offer to build the bot for the company, using scripts or simple natural language processing and machine learning concepts. Others would let firms build their bots and run them in the cloud, across apps and websites.

However they were built, bots started springing up everywhere — to the usual mix of responses. Many consumers took to Twitter or other platforms to complain about limited functionality and dead-ends. Quite a few early-generation chatbots were rapidly pulled from service, never to be seen again.

Trending Bot Articles:

1. How Chatbots and Email Marketing Integration Can Help Your Business

2. Why Chatbots could be the next big thing for SMEs

3. My Journey into Conversation Design

4. Practical NLP for language learning

Chatbot evolution at speed

Lessons learned, bot developers and end-users have taken a more measured approach. Bots are being deployed where people want to use them, notably on Facebook Messenger, eBay and LINE through APIs and plug-ins. That enables many brands to continue a conversation across social media and bring people to their products.

Bots have also got smarter, showing off images instead of text, using emojis or multi-choice entry, anything to speed up the conversation, which is where they show the most value. Reducing the text content also limits the likelihood of an unsuccessful outcome.

New-generation bots can also use text-to-speech and speech-to-text analysis to deliver a more personal-sounding service, while with the rise of deeper machine learning and AI tools, bots can produce conversations that increase engagement and help deliver better quality results, while adding a wow-factor that can help create a great impression.

The new chatbot landscape

COVID saw a huge increase in bot adoption, driven by necessity. With stores and hotels closing, medical services overloaded and service provision across all industries changing at a frenetic pace, bots could deliver the latest news, advice and information, easily updated and save care lines for vital cases.

As well as for external customer service and sales, firms are using bots for internal use, like HSBC’s latest creation ORRA. Operational Resilience and Risk Application (ORRA), uses AI and ML to reduce the time employees are spending on manually intensive queries, improve the consistency of policy response, and understand what kinds of questions were being asked.

And soon bots will talk to each other on our behalf, driving machine-to-machine conversations that agree everything from delivery times to contracts, based on times or terms that the bot knows are acceptable to their user.

Redeveloping the bot market

Now the return to normal is on, as Gartner puts it, “When the world returns to a degree of normality, adoption of digital channels, including conversational AI technologies, is likely to continue at a much higher rate than before the pandemic. Companies already on the way to adopting these technologies will therefore have an advantage.”

Welcoming new adopters are the original bot companies like SnatchBot, which have learned a lot over the years and evolved their products to meet current and emerging market needs. There are also the likes of Microsoft, Salesforce and other big vendors who have acquired or bolted on bot technology into their services. SnatchBot provides a templated approach, allowing anyone to build a bot fast. Others will still deliver a bespoke design service if your company lacks the technical skills to develop your own.

Finally, there’s a new generation of providers, built around business automation services. Take BRYTER, they started off providing automation for lawyers, but are fast expanding into all areas of professional services. Their build-it-yourself automation approach enables any professional, knowledge worker, team or department to rapidly build, test and deploy a chatbot, modify it live and monitor outcomes.

The ability to do all that without extensive IT resources and the inevitable delay will be digital gold dust to many firms. Whatever type of bot or service your company uses, the need for smart interactive services is growing fast. A market prediction is that by 2025 almost 95% of interactions will be handled by an AI, with many customers unable to tell if they are “talking” to a person or bot.

Whatever the line of business, there are bots that can solve immediate and strategic needs. And any business not using them will be hugely inefficient compared to those that do.

Don’t forget to give us your 👏 !

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The changing face of chatbots beyond 2021 was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.