AI solutions providers

We are Artificial Intelligence specialists, we create powerful and intelligent solutions which leverage the most up to date AI technology.

We provide solutions across a range of different services (Alexa, Facebook Messenger, Google Home, Slack, Website Integration).

We look at your business and advise where artificial intelligence can be of value, whether it be marketing, e-commerce, business support or other applications.

Chatbots For Business

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.

Chatbots for business - what is a chatbot

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:

 

CelebrityChatbots for business : celebrity

www.m.me/katyperry
Katy Perry’s official Facebook Messenger bot.

Customer Service
Chatbots for business : customer support

Vodafone TOBi
Vodafone’s customer service chatbot is based on IBM’s Watson & provides a fully integrated webchat for customers.

ProductivityChatbots for business : Productivity

AceBot
https://slack.com/apps/A0GRU84TF-ace
AceBot a productivity tool with expense tracking & intelligent task management, deployed in Slack.

Sports and Events
Chatbots for business : sports

www.m.me/fredwhittonchallenge
The Saddleback Fred Whitton Challenge sportive bot is a smart events assistant providing event info to participants.

E-CommerceChatbots for business : e-commerce

www.m.me/LEGO
The official Lego Facebook Messenger bot. Ready to help your next LEGO purchase.

Health
Chatbots for business : healthcare

www.m.me/superizzyai
Izzy is a period tracking and pill reminder chatbot.

Chatbots for business

The benefits of using chatbots for your business

Provide stellar customer service 24/7

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.

Chatbots for business- Drive Sales, Engagement, reach

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.

It’s also important to note that 2 out of 3 customers actually prefer to message a business to submit an enquiry rather than use other more traditional channels such as email or phone. Every day 1.4 billion people around the world send over 50 billion messages to communicate with each other. As messaging becomes even more central in people’s lives, demand for service in messaging has continued to rise.

The rise of voice assistants

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.

The latest from Google  Popular voice assistants currently include Apple’s Siri, Amazon’s Alexa, Google Now, Google Assistant and Microsoft’s Cortana. The big players are investing heavily in perfecting voice interfaces, read the latest from https://www.thebotforge.io/google-assistant-news-from-io2018/ and https://www.thebotforge.io/google-assistant-demo-duplex-makes-phonecall-io-2018/

The reach of this sort of technology cannot be underestimated. You can read some of the stats and predictions for voice technology here.

Marketing clout

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.

Businesses are finding chatbots to be a great tool to engage with their market: “Our target customers are early adopters of social innovation so a chatbot is the perfect vehicle for us to communicate with them”, Sarah Gower, Adidas.

Sales

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.

 

Chatbots for business - conclusion

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.

A Sports Software Chatbot Case Study: The Fred Whitton Challenge Sportive automated assistant, advanced

We report on our AI chatbot sports software project to aid the organisers of one of the UK’s most well-known cycling events.

Fred Whitton sports event chatbot interface

 

Leverageing ai powered sports software with our core Aktivebot chatbot the goal was to create an automated assistant available 24/7 to reduce time and effort needed by event organisers to respond to event enquiries whilst still providing an easy way to contact the events team if necessary.

The Saddleback Fred Whitton Challenge is a charity event in honour of the late Fred Whitton consisting of a 112-mile charity sportive around the Lake District and is arguably one of the UK’s most well known and hardest sportives with over 2000 riders and 5000 applications this year.

 

 

The chatbot sports software was used by organisers to cover the event

The Fred Whitton Challenge has been running since 1999 and as a result is extremely popular with over 4000 followers on their Facebook page where a large number of ride questions were being asked via the message me button there. We wanted the AI chatbot to assist the event organisers in answering ride and registration queries and reduce the amount of time spent answering routine questions. We also wanted to provide the ability for users to look up their time for this year and previous years.

The chatbot we created is integrated within the “Facebook Messenger app” of the Fred Whitton page and users can contact it through the private “Messages” feature of their page, or directly through the Messenger App.

The sports software project

The project brief was for The Bot Forge to create an AI powered chatbot capable of handling event enquiries 24/7 which could be deployed into the Facebook Messenger framework and utilise rich ui elements. Future deployments could be aimed at website integration.

For such a long-running event, Human Race and the Fred Whitton organisers wanted to provide the optimum user experience and still make it easy for participants to message organisers directly through the chatbot if they wanted to contact a real person by messaging them directly.

The chatbot understands human language, leveraging advanced Natural Language Processing and answers questions such as “what is the fred whitton?”, “ I’ve injured myself at the weekend I need to defer till next year”,“ when can I get my race pack?”, “ help I need the GPS files for the route”, “ Is there any way to buy a jersey post-event?”,”I want to contact an organiser”, and “when will the results be available?” The chatbot replies to a question based on it’s own programmed data or points to the specific information on the Fred Whitton Website so that it works in tandem with the website itself.

Press the play button to watch a real conversation with The Fred Whitton Chatbot

The technology

We used Google Dialogflow to provide the NLP engine and Google Firebase for the fulfilment hosting. The fulfilment or web-hook is where we were able to compute more complex answers for the AI chatbot to give to users and create the correct responses for. For example when looking up users past ride times, the web-hook was able to look up past results for users from a results database. Facebook ui elements added rich content, particularly useful when asked about merchandise details and availability; linking directly through to the official shop.

The conversations

The real challenge in creating the chatbot was leveraging natural language technology that can support the range of questions that event participants might ask: for example, all the different ways that people might ask about the route. We are helped in this process by our own Aktivebot pre-created sports events intents.

Small talk

The chatbot includes the ability to provide small talk, which is used to provide responses to casual conversation. This feature greatly improved user experience when talking to the agent.

Initial question data

Initially, we imported the pre-created sports events intents (an intent represents a mapping between what a user says and what action should be taken by the chatbot).

We then looked at FAQ data provided by the Fred Whitton steering committee and historical questions to their facebook page which gave us some invaluable insight. Using this information we were able to create the conversational scripts and then implement the conversation ability with each question matching an intent

This was an iterative process. Matching user intents to core functionality and features and training the natural language processor to understand users and handle conversation failure scenarios gracefully.

The conversational UI was then fine-tuned, with rich elements implemented where necessary.

What were the questions?

Most asked questions by participants match the questions that the event chatbot is able to answer, i.e.:

  • Questions about registration: deferring places, available places, waiting list enquiries.
  • Questions regarding merchandise: jerseys for sale on the day.
  • Questions about the ride: route details, information about closed roads, clothing enquiries.
  • Questions after the event: results, photos availability, the next ride date.
Sports Software Chatbot- Top Intents handled by conversational agent

Top Intents handled by the conversational agent

The training

The questions were often related to ride specific information. This meant that for an optimal intent matching rate, it was necessary to work closely with the event organisers to provide answers to specific questions. The capabilities of an ai sports software chatbot will improve over time, the more messaging transcript data the better so the more it’s used the better and more accurate it will get. Hence the training logs were checked multiple times a day and improvements made where necessary. By focusing on all questions answered it is possible to greatly improve the intent matching rate of the chatbot over time.

The training data was invaluable for perfecting the bot conversations. The process highlights any need for new responses as a continuous cycle of continuous learning.

The “training” of the chatbot can then be used from one year to the next. Any event detail changes can be carried out easily.

Results

The sports software chatbot was launched on 21st March with the scope constrained to Facebook Messenger with no advertising whilst the chatbot was evaluated.

Activity

The high number of participants using the chatbot can be explained by the fact that visitors still have questions that the website itself does not answer or does not answer quickly enough. The chatbot was, therefore, a great place to provide up to the minute event information, such as information about closed roads and the slight route change which resulted in one more hill showing.

The chatbot was not heavily advertised so we envisage activity levels will improve as participants get used to the chatbot as a resource they can use and other strategies to engage users are utilised.

The chatbot was answering questions on the run-up to the event and also during and after.

Success rate

The success rate of the chatbot to answer queries was overall around 60%. With more focused training over a longer period with another event in 2019 we expect this figure to rise until our aim of an 80% success rate is reached.

Sports Software Chatbot Success Rate Over the Past 30 days,

Chatbot Success Rate Over the Past 30 days

Feedback

The chatbot worked well in Facebook Messenger as its one of the preferred channels for chatbots in general. Deploying the chatbot in a chat widget as part of the website itself would undoubtedly result in more engagement and something to consider for the future.

Help intents and the handover protocol were also very successful. If a user did not get a correct response and/or wanted to get help or contact an organiser directly this worked really well. The overall feedback from users was positive. There were always some intents which the bot would struggle to match the first time which would be handled gracefully; however, due to the ability to train the chatbot, leveraging AI the correct response would be prepared for next time.

I’m impressed with the chatbot it seemed to work well. I think it is a good source of help and with it learning as it goes along it would answer lots of questions going forward. If it cannot help it still contacts the organisers where we can answer.

Carolyn Brown: Fred Whitton Challenge Steering Group — Saddleback Fred Whitton Challenge

The Fred Whitton Challenge chatbot still has many areas where it can be developed and improved, particularly by providing more integration with existing systems and utilising push notifications: this will be something carried out in the future.

Overall the success of the chatbot hightlights the benefits of deploying this type of ai sports software in sporting events and is definitely something to consider to give event organisers an advantage in a competitive market

INBUSINESS COVERS OUR AI CHATBOT IN SCIENCE AND TECHNOLOGY SPOTLIGHT

It was great to have our AI Chatbot featured in the inBusiness magazine issue spotlight this month. You can read the feature here 

inbusiness ai chatbot article

Image: https://chambermk.co.uk/profile/inbusiness

Inbusiness is a bi-monthly publication and digital magazine created by distributed to over 3,000 business contacts in and around Milton Keynes. The June/July 2018 issue spotlight was science and technology so it was great that the editors of the magazine wanted to cover our Fred Whitton Challenge ai chatbot, particularly when the ai chatbot was created to assist the organisers of a charity ride.

You can learn more about our chatbot agency here.

We also cover further technical details about the project here.

Conversational AI Terminology Cheatsheet

Conversational AI technology is not new, but the advanced in the technology has driven a major growth in the industry and what can be achieved in its role solving business problems for many types of industries.

We talk about Conversational AI a lot on our website and blog, after all this technology is at the core of what we do at The Bot Forge.
You may well have encountered some of the different terminology used. But what do developers and technologists really mean when they use these terms? Having a simple understanding of some of the more frequently used terms can be useful when thinking and talking about your chatbot or voice assistant strategy. This conversational AI terminology cheatsheet aims to help you understand; no technical knowledge required!

  1. Algorithm

    An algorithm is a formula for completing a task. Wikipedia states that an algorithm “is a step-by-step procedure for calculations. Algorithms are used for calculating, automated processing and data processing and provide the foundations for artificial intelligence technology.

  2. Artificial Neural Network

    Artificial Neural Networks or ANN are artificial replicas of the biological networks in our brain and are a type of machine learning.  Although nowhere near as powerful as our own brains they can still perform complex tasks such as playing chess, for example AlphaZero, the game playing AI created by Google.

  3. Artificial Intelligence

    AI research and development aims to enable computers to make decisions and solve problems. The term is actually a field of computer science and is used to describe any part of AI technology of which there are 3 main distinctions (1)

  4. Big Data

    Big data describes the large volume of data – both structured and unstructured – that floods through a business and its processes on a day-to-day basis. In the context of AI big data is the fuel which is processed to provide inputs for surfacing patterns and making predictions.

  5. Chatbots

    I think we have mentioned these once or twice! A chatbot is a conversational interface powered by AI and specifically NLP. They can be text-based, living in apps such as Facebook Messenger or their interface can use voice-enabled technology such as Amazon Alexa.

  6. Cognitive

    Cognitive computing mimics the way the human brain thinks by making use of machine learning techniques. As researchers move closer towards transformative artificial intelligence, cognitive will become increasingly relevant.

  7. Conversational Design/Conversational Designer

    Whilst not a technical term its a relatively new role which has grown to being a vital one with the rise in the popularity of conversational experiences. It’s important to understand what this new breed of skilled professional brings to a chatbot project and why they are so important. Conversation design is the art of teaching computers to communicate the way humans do. It’s an area that requires knowledge of UX design, psychology, audio design, linguistics, and copywriting. All of that put together helps chatbot designers create natural conversations that guarantee a good user experience.

  8. Deep Learning

    Also known as a deep neural network, deep learning uses algorithms to understand data and datasets. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep Learning techniques have become popular in solving traditional Natural Language Processing problems like Sentiment Analysis.

  9. Entity and Entity Extraction

    Entities are also sometimes referred to as slots. An entity is used for extracting parameter values from natural language inputs. Any important data you want to get from a user’s request will have a corresponding entity.  Entity extraction techniques are used to identify and extract different entities. This can be regex extraction, Dictionary extraction, complex pattern-based extraction or statistical extraction. For example, if asked for your favourite colour you would reply “my favourite colour is red”. Dictionary extraction would be used to extract the red for the colour entity. What that means in the real world is types of product, locations, model numbers, parts numbers, courses etc: basically anything related to your business which needs to be understood and extracted from the conversation.

  10. Intelligent Personal Assistants

    This term is often used to describe voice-activated assistants which perform tasks for us such as Amazon Alexa, Google Assistant, Siri etc instead of text-based chatbots.

  11. Intent

    An intent represents a mapping between what a user says and what action should be taken by your chatbot. A good rule of thumb is to have An intent is often named after the action completed for example FindProductInformation, ReportHardWareProblem  or FundraisingEnquiry.

  12. Machine Learning

    Machine Learning or ML for short is probably used by you every day in Google search for example or Facebooks image recognition. ML allows software packages to be more accurate in predicting an outcome without being explicitly programmed. Machine learning algorithms take input data and use statistical analysis to predict an outcome within a given range. Machine learning methods include pattern recognition, natural language processing and data mining.

  13. Natural Language Processing

    Natural language processing (NLP) is broadly defined as the automatic manipulation of natural language, like speech and text, by software. NLP is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics to fill the gap between human communication and computer understanding.

  14. Natural Language Understanding

    A subfield of NLP called natural language understanding (NLU) has begun to rise in popularity because of its potential in cognitive and AI applications. NLU goes beyond the structural understanding of language to interpret intent, resolve context and word ambiguity, and even generate well-formed human language on its own.

    NLU algorithms tackle the extremely complex problem of semantic interpretation. That is understanding the intended meaning of spoken or written language. Advances in NLU are enabling us create more natural conversations.

  15. Sentiment Analysis.

    Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. More advanced analysis would look at emotional states such as “angry”, “sad”, and “happy”.

  16. Utterance

    An utterance is anything the user says via text or speech. For example, if a user types “what is my favourite colour”, the entire sentence is the utterance.

  17. Conversational IVR

    Conversational IVR is a software system which uses voice commands from customers. This allows them to interact with IVR systems over telephony channels.

    Whereas traditional IVR systems had speech recognition technology to handle simple voice commands such as “yes” or “no”.  Conversational IVR allows people to communicate their inquiries in more complete phrases via a natural language understanding. Callers can describe questions or concerns in their own words which is then matched to an intent by natural language understanding.

We hope you have found this Conversational AI Terminology Cheat-sheet helpful.
Comment if you think I’ve missed any terms out which should be on the cheat sheet.

If you want to talk about your chatbot project why not book a free consultation with us.

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