So what is Conversational UI? Historically interfaces or UI has been made up of visual elements: buttons, dropdown lists, date pickers, carousels. Now we have the technology to provide conversational ones as well via voice or text-enabled interfaces such as Amazon Echo or Facebook Messenger. Some applications also leverage the best of both worlds, combining traditional UI elements with conversational capability: for example Facebook Messenger.
The most important advancement in Conversational UI has been Natural Language Processing (NLP). This is the field of computing that deals with deciphering the exact words that a user and parsing out of it their actual intent and in what context. You can read about some of the terminologies here. If the bot is the interface, NLP is the brain behind its conversational ability.
Natural Conversational Experiences
The Bot Forge creates natural conversational experiences for voice and text-based applications leveraging the latest AI technology.
Our team is made up of natural language processing (NLP) and Natural language understanding (NLU) experts, artificial intelligence specialists, conversational architects, project managers, and interaction designers. Focused on forging engaging voice and text-based Conversational UI powered by NLP.
Our conversational interfaces can be deployed on websites, mobile applications, messaging applications and voice-enabled devices.
We use the most advanced machine learning technology to power our solutions so that recognising user intent and context works reliably and seamlessly. Our goal is to create awesome conversational experiences.
https://i0.wp.com/www.thebotforge.io/wp-content/uploads/2021/01/YouTube-Adds-Voice-Search-and-Commands-to-Control-its-Website.png?fit=750%2C650&ssl=1650750ajwthompsonhttps://www.thebotforge.io/wp-content/uploads/2020/01/thebotforgelogo@154x100-b.pngajwthompson2021-01-20 11:09:342021-01-20 11:11:42YouTube Adds Voice Search and Commands to Control it’s Website
https://i2.wp.com/www.thebotforge.io/wp-content/uploads/2020/12/What-Can-We-Expect-From-Conversational-AI-In-2021.png?fit=750%2C650&ssl=1650750ajwthompsonhttps://www.thebotforge.io/wp-content/uploads/2020/01/thebotforgelogo@154x100-b.pngajwthompson2020-12-18 12:10:532020-12-21 16:40:52What Can We Expect From Conversational AI In 2021
We are often asked this question by clients looking to start their first chatbot project. Like most software projects the price really depends on the scale and complexity of the project. This will govern the effort involved in building a chatbot.
So, let’s take a look at the key drivers impacting the price for custom chatbot development.
Channel
The first one is the channel in which the bot should function. By this, I mean where the bot will be used. It could be as a website widget implemented as a popup interface or webpage (You can learn more about webpage chatbots here) or deployed into existing messaging platforms. Facebook Messenger, WhatsApp (read more about WhatsApp chatbots here), Microsoft Teams, Slack, Telegram, Viber are just a few examples with some providing more complex UI elements which can be utilised in your bot. Other obvious channels to deploy a chatbot are voice; Google Assistant, Alexa or even IVR systems can often use the same conversational engine as your text-based chatbots.
For our custom chatbot integrations, we normally provide one channel with the project and then charge per extra channels as required. We’ve found that our clients will often want a web-based chatbot first and role out the chatbot onto different channels. It really does depend on the type of project. Extra channel costs can be in the region of £1000-2500.
There are a number of really good chatbot building platforms for creating simple UI chatbot interfaces such as Botstar, Chatfuel and Manychat; these offer some free templates and the ability to create chatbots which can be deployed to specific channels, for example, Facebook Messenger (our Carly chatbot is a great example of a Chatfuel bot). These Bot building platforms offer some good features: Visual Flow Editors, APIs & Webhook Integrations, Templates, Broadcasting etc. However, in our experience, we have found that even with free templates there is always some costs due to setup and minor customisation to attain the maximum chatbot behaviour. Our minimum price is around £1500 with some monthly costs for hosting and chatbot management and training.
Features
Depending on which channel the bot will work in there is also scope to provide other functionality such as voice capability for web chatbots or enhanced chatbot interface features. Again costs depend on the amount of complexity and effort involved in building each feature. As an example adding voice interaction capability to a Web chatbot would be £1000. Human-agent handover or WhatsApp integration is also a popular feature.
Natural Language Ability
If a chatbot is required to support more complex natural language processing(NLP- you can read more about some of the tech terms here) features and not just UI elements such as buttons then this will mean that additional effort is needed to train the bot and design and implement a more complex conversational flow. These sort of bots start at around £2500. We utilise the best of breed cloud NLP solutions for these types of projects. In particular, we use Google Dialogflow as we are Google Tech partners and experts in Dialogflow.
Conversation skills
The complexity, scope and volume of the required conversational ability effects cost. This relates to specifics such as:
The number of branches in the conversation tree.
Quantity of questions that have to be handled by the chatbot which can often be in the 1000s.
The complexity of conversational ability ie. support for complex user enquires.
So chatbots which combine richer elements with complex conversational ability can be in the region of £2500 – £5000
Languages
Chatbots are capable of supporting different languages, as long as these are supported by the NLP engine it’s possible to add different language permutations fairly easily. However, each language will need its own testing so costs can be in the region of £1500-3500 for each language depending on the size and complexity of the conversational ability.
Integration
Connect to existing systems, APIs, RPA, and knowledge bases
The other area which will impact cost is dependent on the planned role of the chatbot: what the chatbot will need to do carry out its role? Will the chatbot need to integrate with current systems to provide its responses? Will it need to hand over to live agents? Connect with CRM and ticketing solutions? Some chatbots may need to carry out complex interactions to provide answers to customer queries. Chatbots can leverage other AI systems to provide relevant information to govern conversational flow. For example, sentiment analysis.
With so many possibilities for chat bot features It’s hard to estimate the price here, but as a rule, these types of chatbots cost from £4000. For each integration, we would suggest a cost of £1000-2500 it really depends on the amount of development effort required for each one.
Deployment
Security
Often security demands for a chatbot project need specific features, for example, Hippa compliance. In these cases SSO, RBAC, and on-prem or private cloud deployment can be used to ensure compliance with company security policies. These can have an impact on overall project costs and again, costs are based on the demands of a specific project.
Maintenance
We offer our chatbot solutions based on a SAAS model. Costs incurred tend to be based on a yearly subscription and again depend a lot on the scale and complexity of the chatbot. These monthly costs will cover some of the following:
Daily supervised learning and improvements.
Monitoring conversations and confirming qualified intents as well as checking for unmatched intents and fixing them as needed.
Post-development support.
Third-party and integration maintenance. Making sure your bot is performing well and healthy!
NLP costs(if applicable).
Hosting and data storage.
Chatbot reporting interface.
As a rule, maintenance subscription costs tend to be in the region of 10-15% of initial implementation costs per month.
Flexibility
As you can see, the cost of a chatbot project can vary widely depending on the features required. Each chatbot project is different.
It’s worth keeping in mind that the cost of a capable chatbot does not have to be prohibitive and it’s often a good idea to start small and add features as business needs require them.
At The Bot Forge, whatever the cost we propose for your chatbot project our aim is to create value for your business and hopefully form a long-lasting relationship.
The Bot Forge are always available to discuss further improvements and functionality to add to your chatbot or just to talk to us about your next great idea.
https://i0.wp.com/www.thebotforge.io/wp-content/uploads/2019/06/how_much_does_it_cost_to_build_a_chatbot.png?fit=750%2C650&ssl=1650750ajwthompsonhttps://www.thebotforge.io/wp-content/uploads/2020/01/thebotforgelogo@154x100-b.pngajwthompson2020-02-10 13:41:092020-09-09 08:21:06How Much Does It Cost To Build A Chatbot In 2020?
I’m going to look at the challenges in creating a chatbot which can answer questions about its specific domain effectively. In particular, I’m going to look at the challenges and possible solutions in creating a chatbot with a reasonable conversational ability at their initial implementation. Every chatbot project is different but often clients come to us with a large knowledge base which they want a chatbot to support from its release but with very little training data.
We are going to concentrate on a Dialogflow project to look at some examples however the challenges and solution are similar for all the most well know NLP engines, Watson, Rasa, Luis etc.
The Challenge
One of the key problems with modern chatbot generation is that they need large amounts of chatbot training data. If you want your chatbot to understand a specific intention, you need to provide it with a large number of phrases that convey that intention. In a Dialogflow agent, these training phrases are called utterances and Dialogflow stipulate at least 10 training phrases to each intent.
Depending on the field of application for the chatbot, thousands of inquiries in a specific subject area can be required to make it ready for use with each one of these lines of enquiry needing multiple training phrases.
The training process of an ai powered chatbot means that chatbots learn from each new inquiry. The more requests a chatbot has processed, the better trained it is. The NLU(Natural Language Understanding) is continually improved, and the bot’s detection patterns are refined. Unfortunately, a large number of additional queries are necessary to optimize the bot, working towards the goal of reaching a recognition rate approaching 90-100% often means a long bedding in process of several months.
Data Scarcity
One of the main issues in today’s chatbots generation is that large amounts of training information are required to match the challenges described previously. You have to give it a large number of phrases that convey your purpose if you want your chatbot to understand a specific intention.
To date, these large training corpus had to be manually generated. This can be a time-consuming job with an associated increase in the cost of the project. One of the main issues we have faced is that often clients want to see quick results in a chatbot implementation. These types of chatbot projects are often use cases which are providing information regarding a wide-ranging domain and may not necessarily have a lot of chat transcripts or emails to work with to create the initial training model. In these cases there is often not enough training data and so it takes time to get decent and accurate match rates.
The Solution
THE BOT FORGE PROVIDES CHATBOT TRAINING DATA CREATION SERVICES
The Bot Forge offers an artificial training data service to automate training phrase creation for your specific domain or chatbot use-case. Our process will automatically generate intent variation datasets that cover all of the different ways that users from different demographic groups might call the same intent which can be used as the base training for your chatbot.
Multi NLP platform support Multi-language support
Our training data is not restricted solely to Dialogflow agents, the output data can be formatted for the following agent types:
rasa: Rasa JSON format
luis: LUIS JSON format
witai: Wit.ai JSON format
watson: Watson JSON format
lex: Lex JSON format
dialogflow: Dialogflow JSON format
We provide training datasets in 100+ languages
We offer our synthetic training data creation services to our chatbot clients. However, if you already have your own chatbot project and just want to boost its conversational ability we can provide synthetic training data to meet your needs.
Testing the Solution
We wanted to test the effectiveness of using our synthetic training data in a Dialogflow chatbot agent by varying the number of utterances per intent using our own synthetic training data.
Dialogflow test agents
We carried out three different tests (A B and C) with 3 separate Dialogflow agents. Each agent had identical agent settings. The agents had 3 identical intents to provide information about the topic of angel investors:
what_is_an_angel_investor
what_percentage_do_angel_investors_want
do_angel_investors_seek_control
In the first test (A) the chatbot was trained with 2 hand-tagged training phrases (utterances) per intent. Test (B) had 10 training phrases from our own synthetic training data per intent and test (C) had between 25 and 60 training phrases per intent.
The Test
We tested each agent with 12 separate questions similar to but distinct from the ones in the training sets.
We didn’t carry out any training during testing once the chatbots were created.
We recorded the % of queries matched to the correct intent, the incorrect intent or no match and also the intent detection confidence 0.0 (completely uncertain) to 1.0 (completely certain) from the agent response.
Test A provided a 50% match rate. We observed a significant improvement in test B with the introduction of some of our synthetic training data to the agent. We were able to improve the match rate from 41% to 91% whilst TestC with 25-60 training phrases enabled a match rate of 100%. The average intent detection confidence also grew
In summary, chatbots need a decent amount of training data to provide accurate results. If there is not enough training data then a chatbots accuracy is affected and it can take some time to train it whilst being used to reach acceptable performance levels. At the same time, it can be costly and time-consuming to create training data for a chatbot needing to handle large numbers of intents.
Our synthetic training data creation service allows us to create big training sets with no effort thus reducing initial costs in chatbot creation and improving the usability of a chatbot from the initial release stages. If you only have a limited number of training phrases per intent and have large numbers of intents, our service is able to generate the rest of variants needed to go from really poor results to a chatbot with greater levels of accuracy in providing responses. We have carried out these tests with Dialogflow, but our conclusions are relevant for ML-based bot platforms in general. We can conclude that our Artificial Training Data service is able to drastically improve the results of chatbot platforms that are highly dependent on training data
Chatbot Training Never Ends!
I’ve looked at the benefits of using our training data at the early stages of a chatbot project. However, it’s important to note that the key to success, in the long run, is to constantly monitor your chatbot and continue training to get smarter. Either by doing constant training with human effort or by scheduling regular training cycles, incorporating new utterances and conversations from real users.
If you want to know more about our chatbot training data creation services get in touch
https://i2.wp.com/www.thebotforge.io/wp-content/uploads/2020/01/training_a_chatbot.png?fit=750%2C650&ssl=1650750ajwthompsonhttps://www.thebotforge.io/wp-content/uploads/2020/01/thebotforgelogo@154x100-b.pngajwthompson2020-01-31 22:20:202020-01-31 22:24:54Training a chatbot
We build a lot of different types of chatbots at The Bot Forge and deliver these to a variety of platforms such as Web, Facebook Messenger, Slack or WhatsApp. To create our chat bots we often use different AI platforms which offer more suitable features for a specific project. All the major cloud and open-source providers have adopted similar sets of features for their conversational AI platforms and provide good NLU (Natural Language Understanding). There are also some strong options for open source privately hosted systems.
Conversational AI Platform Features
We wanted to spend some time looking at some of the more popular AI platforms in a bit more depth in this series. To help look at each one we have focused on the following specific features:
API and UI
A conversational AI platform should provide User Interface(UI) tools to plan conversational flow and help train and update the system
Context
As well as intent and entities, a context object allows the system to keep track of context discussed within the conversation, other information about the user’s situation, and where the conversation is up to. This is often the NLP feature which is vital in creating a complex conversation beyond a simple FAQ bot.
Conversation flow
Looking at the current position of a conversation, the context and the user’s last utterance with intents and entities all come together as rules to manage the conversational flow. This can be challenging to create and manage so a platforms’ tools in the form of a flow engine, in code and complimented by a visual tool can provide advantages depending on the chatbot project itself. Other features such as slot-filling (ensuring that all the entities for an intent are present, and prompting the user for any that are missing) can be important.
Whilst most platforms fall into this category some systems use machine learning to learn from test conversational data and then create a probabilistic model to control flow. These systems rely on large datasets.
Pre-built channel integrations
Having a conversational platform that supports your target channel out-of-the-box can substantially speed up delivery of a chatbot solution and your flexibility in using the same conversational engine for a different integration. This is one of the reasons we really like Dialogflow’s tooling.
Chatbot Content Types
Whist the focus of a conversational AI platform is understanding pure text, messaging systems and web interfaces often involve other content, such as buttons, images, emojis, URLs and voice input/output. The ability of a platform to support these features is important to create a rich user experience and help to manage the conversational flow.
Integrations
Bot responses can be enhanced by integrating information from the user with information from internal or external web services. We use this type of ability a lot in creating our chatbots and in our opinion feel its one of the most powerful features of a chatbot solution. With this in mind, the ability to configure calls to external services from within a conversation and use responses to manage conversational flow is important in building chatbot conversations.
Pre Trained Intents and Entities
Instead of creating entity types such as dates, places or currencies for each project some systems provide these pre-trained to deal with complex variations. In the same way, common user intents and utterances such as small-talk is offered pre-trained from some platforms.
Analytics and Logs
The key to creating a successful chatbot is that they need to be constantly trained and monitored. To aid in continuously improving the system once initially launched, the conversational tools should provide a dashboard of the user conversations; showing stats for responses, user interactions and other metrics. Export of these logs is also useful to import into other systems. Other important AI features enable easily training missed intents, catching bad sentiments and monitoring flow.
Techstack
It can be important to take into account what libraries are provided by an AI platform and in what supported languages. In the end, this may favour your choice of solution if it fits with your current codebase or teams skillset. However, as a full-stack javascript software house, we find Node.JS to be our server stack of choice when building our bots and most AI platforms cater for this.
Costs
These are the costs for the cloud hosting and cloud NLU solutions. An important aspect to consider particularly for large scale enterprise chatbots handling large volumes of traffic where NLU costs can reach £1000s a month.
Many providers offer a free tier for their AI platform solutions. A paid for tier will then normally offer enhanced versions of the service with enterprise focused features and support for greater volume and performance. Costs tend to be charged in one of 3 ways, per API call, per conversation or daily active user and also per active monthly user (normally subscriptions are in tiers). We try and look at costs as publicly published for the paid-for plans suitable for enterprise use in a shared public cloud environment.
The Platforms
Keeping all these feature sets in mind we hope to look at the following AI platforms over the coming posts.
Botkit
Chatfuel
Amazon Lex
Microsoft Luis
Google Dialogflow
Rasa
IBM Watson
Please get in touch if you feel we should look at a platform which we have missed!
Our first AI platform blog post will be coming soon!
We are often asked this by clients looking to start their first chatbot project. Like most software projects the price really depends on the scale and complexity of the project. This will govern the effort involved to build the bot and define whether it needs to be a custom chatbot or off the shelf solution.
So, let’s take a look at the key drivers impacting the price for custom chatbot development.
Channel
The first one is the channel in which the bot should function. By this, I mean where the bot will be used. It could be as a website widget implemented as a popup interface or webpage (You can learn more about webpage chatbots here) or deployed into existing messaging platforms. Facebook Messenger, Slack, Telegram, Viber, Google Assitant are just a few examples with some providing more complex UI elements which can be utilised in your bot.
There are a number of really good bot building platforms for creating UI chatbot interfaces such as Botstar, Chatfuel and Manychat; these offer some free templates and the ability to create chatbots which can be deployed to websites and Facebook Messenger. These Bot building platforms offer some good features: Visual Flow Editors, APIs & Webhook Integrations, Templates, Broadcasting etc. In our experience, we have found that even with free templates there are some costs due to setup and minor customisation. So our minimum price is around £600 with some monthly costs for hosting and chatbot management and training.
Natural Language Ability
If a chatbot is required to support more complex natural language processing features and not just UI elements such as buttons then this will mean that additional effort is needed to train the bot and design and implement a more complex conversational flow. These sort of bots start at around £1500. We utilise Dialogflow Natural Language Processing to offer these types of solutions.
The complexity, scope and volume of the required conversational ability effects cost. This relates to specifics such as: Number of branches in the conversation tree. Quantity of questions that have to be handled by the chatbot which can often be in the 1000s. The complexity of conversational ability ie. support for complex user enquires. So chatbots which combine richer elements with complex conversational ability can be in the region of £1500 – £3000
Integration
The other area which will impact cost is dependent: what the chatbot will need to do carry out its role? Will the chatbot need to integrate with current systems to provide its responses? Will it need to hand over to live agents? Connect with CRM and ticketing solutions? Some chatbots may need to carry out complex interactions to provide answers to customer queries. Bots can even call systems utilising Artificial intelligence to provide relevant information based on historical chat data, for example using, https://www.tensorflow.org. With so many possibilities for bot features It’s hard to estimate the price here, but these types of chatbots cost from £4000. For each integration, we would suggest a cost of £1000-2000 it really depends on the amount of development effort required for each one.
Languages and Features
Chatbots are capable of supporting different languages, as long as these are supported by the NLP engine it’s possible to add different language permutations fairly easily. However, each language will need its own testing so costs can be in the region of £500-1000 for each language.
Finally, depending on which channel the bot will work in there is also scope to provide other functionality such as voice capability for web chatbots or enhanced chatbot interface features. Again costs depend on the amount of complexity and effort involved in building each feature. As an example adding voice interaction to a Web chatbot would be £500.
Flexibility
As you can see, the cost of a chatbot project can vary dependent on the features required. However, the cost of a capable chatbot does not have to be prohibitive and it’s often a good idea to start small and add features as business needs require them.
At The Bot Forge, whatever the cost we propose for your chatbot project our aim is to create value for your business and hopefully form a long-lasting relationship.
https://i0.wp.com/www.thebotforge.io/wp-content/uploads/2019/06/how_much_does_it_cost_to_build_a_chatbot.png?fit=750%2C650&ssl=1650750ajwthompsonhttps://www.thebotforge.io/wp-content/uploads/2020/01/thebotforgelogo@154x100-b.pngajwthompson2019-06-17 18:42:012020-01-27 19:39:07How Much Does It Cost To Build A Chatbot In 2019?
If you are looking to streamline certain operations of your business, implementing a chatbot is a great way to go about it. After all, you can use technologies such as artificial intelligence (AI) and natural language processing (NLP) so that it can follow different types of conversations with users and provide relevant responses where necessary.
Chatbots are no longer a gimmicky tool available on the internet, as they have gained popularity and sophistication many users are incorporating them into their digital strategy. For example, a large number of businesses now use bots as part of their customer service. As these chatbots never go offline, they are always available to assist users, at any given time of day.
It is a lot cheaper for a business to implement a bot as part of its customer service than hire employees. If you employ an individual, you have to train the person and provide a salary and vacation time. One example is one of our clients https://amicable.io who replaced call centre resource with a chatbot to book client meetings.
Chatbots have a lot of advantages, which explains why businesses want to make the most of them. However, while creating these bots, it is natural to make errors, which hampers the user experience. As this might be the first or nth time you are developing a chatbot, you want to make sure it functions as expected. Here are six common mistakes to avoid along with how you can overcome them:
Assuming every user wants to talk
You tend to believe that everyone who visits your page or installs the chatbot on Slack, or other popular messaging platforms wish to start talking to it immediately. However, most of the people on the internet don’t want to communicate with the bot, unless it is necessary.
One reason why chatbots are great marketing tools is that they can engage with prospects by answering important questions. As a result, it brings down the sales friction, making it simpler for the user to invest in what you have to offer.
If your bot starts to message the individual as soon as he/she opens it, there is a high chance the person will find it annoying. A better practice would be to wait for the user to respond or you can leave instructions in the description on how to start conversing with the chatbot. Our sports events Facebook Messenger chatbot Carly utilises this kind of functionality enabling users to set push notifications for their any new sports events based on their own criteria.
Failing to track its performance
Since the chatbot makes use of the latest technological advancements in the industry, you might assume that you shouldn’t keep an eye on its performance. After all, you spent a considerable portion of your time training it, so that it can have a continuous conversation with your customers.
However, you will never know the effectiveness of your bot, if you don’t track the key performance indicators (KPI). These metrics provide a deeper insight into how you can continue to improve your chatbot. For example, you can see where most of the users tend to leave the conversation. With this data, you can think of different ways to keep them engaged so that they continue to talk to your bot. We feel that the history and training tools provided by Dialogflow enable us to track chatbot performance effectively.
Forgetting to list in directories
Once the chatbot is up and running on various messaging platforms or your website, you think you completed your job. All your visitors have to do is start talking to the bot, and it will help them in their tasks.
However, not everyone will know about the existence of your chatbot. Several messaging platforms may not have powerful search, which makes it harder to discover your bot. The best practice is to find third-party websites and lists your chatbot in it. As a result, if people look for your bot on Google or other search engines, the chances of it popping up in the first page of results goes up significantly. The best place to market your own new bot is on your website, why not write a blog post about your chatbot journey, you can guarantee other companies will be interested in your journey.
Impersonal conversations
The reason why people don’t like talking to bots is that the conversation tends to be boring and bland. As a result, they prefer to converse with human beings, as the experience is better in every way.
Think about it, would you like talking to a chatbot which sounds like it is speaking in a monotone? Rather than putting your bot in the same position, you should think of different ways to spice up the conversation. For example, you can ask the user what the chatbot should call the individual while talking to one another.
One thing is key here and we have seen this in our experience: to gain better customer satisfaction it’s better to explain to your users that your bot is a chatbot and not try and masquerade as a human. If users are aware they are talking to a chatbot from the off it will gain confidence and improve the customer experience as the user becomes more forgiving.
Not paying attention to its tone
Since the entire conversation between the chatbot and its users is going to take place via text, you need to pay close attention to its tone. Using the right type of communication will determine whether your bot performs well among its intended target audience.
While this tends to be challenging, there are several ways you can overcome this obstacle. For example, you can ask a small number of people from your target audience, what tone they would find appropriate. At the same time, you can also have a beta group, which allows you to experiment and see which one works well. Matching tone to the industry and subject matter is important to build a satisfying experience for your chatbot users.
Help and Live Agents
Since the purpose of the chatbot is to reduce the workload of your employees, you tend to assume that you don’t need a live agent. The problem with testing is that it may not take into account all the variables present in real-world scenarios. As a result, when you deploy your chatbot, it might not know how to handle a specific question.
Due to this reason, it can go on an endless loop, and the only way out of the conversation is to quit or restart the chatbot. An excellent way to overcome this problem is to allow your chatbot to ask a live agent to join the conversation during this situation. Once the employee helps out the user, he/she can provide information to the developers on how to improve the communication skills of the bot.
It’s also important to provide easy help for users to access during the bot conversation. At The Bot Forge we always implement a help feature for our chatbots so users know what they can do and how they get back on track our Facebook Messenger chatbot for the Fred Whitton Challenge is a perfect example.
Chatbots are becoming a great tool for businesses. You can use them to make life easier for your clients, by assisting them in various functions. By knowing what the common mistakes are, you can avoid them entirely and design the best bots in the industry
https://i1.wp.com/www.thebotforge.io/wp-content/uploads/2019/05/developing_a_chatbot_6_common_mistakes_to_avoid.png?fit=750%2C650&ssl=1650750ajwthompsonhttps://www.thebotforge.io/wp-content/uploads/2020/01/thebotforgelogo@154x100-b.pngajwthompson2019-05-02 08:08:372020-01-23 12:26:09Developing a Chatbot? 6 Common Mistakes to Avoid
The Bot Forge have been following the progress of the latest V2 api since its official launch in April this year, it’s no surprise that the Dialogflow team have made this announcement as they concentrate their efforts on the new API. However it does have some serious implications for existing chatbots utilising the v1 API.
Migration
You can see some more details about upgrading from V1 to V2 in the official guide here. We also aim to provide some more detailed information about carrying out an upgrade on this blog so watch out for that.
Anyone who already has built out their website chatbots using v1 API, then they should start planning for the migration sooner rather than later. Any new features should be added after the upgrade. The migration is potentially a non-trivial task, considering some chatbots have some fairly complex code driving their fulfilment. If you have a live bot in production our advice is to set up an upgrade chatbot as a copy of your existing bot project and then work through the upgrade there. You can guarantee that changing to V2 will mean that fulfilment and API calls may stop working. Once the upgrade is complete re-testing all bot functionality is strongly advised before setting live.
Chatbot Web Interfaces
We would recommend everyone who is creating custom website chatbots to do so using the v2 API. All our new chatbots are built using the v2API.
The big change for v2 is that it uses Google’s OAuth2 for its authentication, with v1 you could simply use the client access token when calling the v1 API. Implementing the features required to authenticate against the new v2 API means some significant extra development effort.
If you need assistance or advice with your own chatbot v2 upgrade please get in touch, we are Dialogflow experts and would be happy to help!
At the Bot Forge, we specialise in building chatbots so you feel free to contact us if you want to discuss further.
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The business landscape is evolving faster and faster, we look at using chatbots for business to help you remain competitive.
There is so much coverage of artificial intelligence technology and chatbots these days There is no doubt that chatbots are big news for many different industries, from e-commerce and fashion to healthcare and banking.
Whilst many big brands have already jumped at the opportunity to leverage the technology for others it’s challenging to see where they can be a benefit to your company and whether the cost and effort involved is worthwhile. Some of you may remember many years ago when you were approached by someone selling a shiny website and then later on a new app? You probably asked yourself a similar set of questions..that’s nice and shiny but why do I want it? is it right for us? First things first let’s look at some of the basics.
The chatbot lowdown
What is a chatbot?
Briefly put a chatbot is a service, powered by natural language processing rules and artificial intelligence (AI), that you interact with via a voice or text-based chat interface. AI technology is used to enable the service to respond to specific user interaction. For example, a user could ask a chatbot a question or give it an instruction and the bot could respond or perform an action as appropriate.
This chat service can take on any number of roles, providing answers, collecting customer information, suggesting products and making sales. They can live in any major chat product (Facebook Messenger, Skype, SMS, Slack, Telegram, Viber, Twitter, Website). They can also be deployed into voice-enabled assistants such as Amazon Echo or Google home. Chatbots can also be developed to include multiple language capability.
Where can a chatbot be used?
Chatbots have been deployed in many different guises as they are extremely flexible and able to take on whatever business need arises.
You could say the possibilities are endless, here are some examples:
Celebrity
www.m.me/katyperry Katy Perry’s official Facebook Messenger bot.
Customer Service
Vodafone TOBi Vodafone’s customer service chatbot is based on IBM’s Watson & provides a fully integrated webchat for customers.
For many businesses, the biggest challenge to serving your customers in several communication channels is responding quickly all of the time.
Constantly available
One of the great benefits of a chatbot is the constant availability. Customer expectations are high expecting a quick response to enquiries. With a chatbot, you can offer your customers a service which is available 24 hours a day even when there are no employees in the office. You can rely on your bot no matter what time of the day or day of the week or timezone the enquiry is coming from.
One example from my own personal experience was with a SAAS which had charged me incorrectly for an amount of money which caused my bank account to go overdrawn. I contacted the customer support chatbot via a web interface at 1 AM and the problem was rectified and money returned promptly the next day. I went from disgruntled to a satisfied customer in a 5-minute chatbot interaction, incidentally, I’m still a customer!
It’s also worth noting that chatbots can be enabled to understand multiple languages. NLP technology will understand queries in different languages and respond appropriately. So if you support a global customer base needing to support multiple language enquiries this does not have to be a problem.
System integration
With the correct integration development, a chatbot is able to answer complex enquiries by integrating with existing CRM, ERP, CMS, and other business-critical applications. Connect your chatbot seamlessly with your entire business ecosystem.
Scalable
Chatbots are scalable and capable of handling multiple enquiries, ready to step up when enquiry demands are at their peak.
A well implemented and executed chatbot can give businesses the ability to have more conversations and help more people at once than other alternatives, for example, live chat applications on websites.
This ability to handle the frequent enquires where the responses are often similar facilitates businesses in freeing up staff to deal with the more complex issues.
Although a chatbot cannot handle all customer queries, it can be used to deal with a large number of the routine business enquiries which most companies deal with on a day to day basis.
They improve customer satisfaction
To avoid frustration, a chatbot can be developed to use a “sentiment” function to pass users onto a real advisor if the bot can’t help or if they are not satisfied. Other benefits can be seen in customer service gains. According to Jon Davies, head of digital at Vodafone, their customer service chatbot, TOBI provides “a far more engaging and personal” customer experience, as well as improving completion rates and reducing transaction times. These types of successes are highlighted in improved net promoter scores (NPS).
Overall chatbots for business can excel in supporting customer service teams in their communications with customers. Providing accessible information 24/7 saves businesses money and time. By 2022 chatbots are expected to save $8 billion7.
Drive sales, engagement, reach
These days customers are savvier and demand an intuitive and seamless customer experience. Businesses need to consider using technology to fit in with their communication habits.
Familiar messaging technology
Many users prefer social media and mobile platforms for communication and expect businesses to be online when they are. If users are having a conversation with a chatbot in Facebook Messenger, they are using a conversation channel they are familiar with and they are already using the technology and don’t need to install a new app. The numbers of messenger app users have been steadily rising. As of April 2017 Facebook Messenger had 1.2 billion monthly active users worldwide
Use these channels to reach new and existing customers.
Voice assistant technology and it’s adoption has gathered serious momentum over the past couple of years. User expectations are rising as they become educated in what it can do. As customers realise that its capabilities go beyond setting a timer, turning down the lights or playing some music; they will look to this channel to make purchases, contact customer support or use as a tool for business specific tasks.
As an effective marketing tool chatbots can give your company an edge as they can enter into personalized and automated communication with your customers.
Using platforms such as Facebook messenger, substituting emails with push notifications can obtain much higher click-through rates. Used wisely opt-in targeted messages or push notifications have 90% read rates and a 40% click-through rate. Chatbots can be used to send users personalised tips, greetings and information, generating leads, harvesting reviews and forging stronger customer relationships.
Utilising these techniques a chatbot is able to reach participants wherever they are, regardless of where the chat session was initiated, whether on a mobile app, a website and even from social platforms such as Facebook Messenger.
Chatbots are ideal to answer first customer questions. if the chatbot decides that it can not effectively serve the customer, it can pass those customers to human agents. High value, responsive leads will be called by live agents increasing sales effectiveness.
Chatbots can be used to answer customer’s questions and promote products. Engage with the right customer by analyzing their profile and historical data and user characteristics. A bot can provide a channel for purchasing easily and quickly if requested.
Conclusion
I’ve really only scratched the surface of chatbot and voice interface technology capabilities and what can be achieved and how it can help your business be more competitive. However, it’s important to consider them carefully. It’s up to the business to decide if a chatbot is a right move for them, for some the business case may not be there or something to consider in the future. Building a chatbot because you think you should or because its the latest thing can only result in wasted time, money and effort.
I hope you find this post helpful in considering how using chatbots for business can help you to achieve a competitive edge.
If you already have a chatbot idea and want to look into this further have a look at our post planning the best chatbot
At the Bot Forge, we specialise in building chatbots so you feel free to contact us if you want to discuss further.
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