Evolution of SaaS in customer experience

What you are seeing in the image below is the marketing technology landscape for 2019(!) There are 7,040 companies, mostly SaaS, trying to help generate more money from customers.

2019 Marketing Technology Landscape (Martech 5000)
https://cdn.chiefmartec.com/wp-content/uploads/2019/03/marketing-technology-landscape-2019-slide.jpg

If we look into the industry’s solutions, we can see a big leap in three major areas:

Marketing

Marketing automation is a big thing, from social campaigns to lead generation and automation. Today’s tools let you track an anonymous visitor on a landing page and measure all her/his action on your website with a scoring mechanism to optimize the sale opportunity and even predict what is the right moment to engage a sell offer. With the growth of eCommerce over traditional physical stores the impact is clearly shown in the number of marketing services. The amount of solutions is so vast that it is hard to point here the best solution, but I will take a chance here to introduce you to Mautic which is an open-source platform with very high capabilities and great supporting community. You can explore more solutions at the Customer service automation guide that we will update from time to time.

CRM platforms and sales management tools

The big change in the industry accrued when Salesforce introduced a new SaaS CRM to the world in 1999. It was a huge change as companies no longer needed to buy very expensive software and manage their customers on expensive data centers (I remember the days when I managed a small center with Alpha computer running Unix). This change made life much more simple at the beginning and evolved much further to today’s solutions such as Hubspot, Pipedrive and many more. Actually, there are so many solutions these days, each one is very good at a specific task, it makes it harder to know what is the right solution for your company. So if you have good tips and experience from a specific tool, please share in the comments.

Customer support

The new companies’ approach in customer support and the big change in omnichannel communication, brought new and better solutions. For good several years, the CRM platforms were good enough to handle the customer relations and ticket any event within CRM. It was common when most communications were handled over the phone or mails. In 2011 Facebook released its messenger and changed the game again.  Solutions have evolved with great live chat solutions like Intercom to a full help-desk solution by Zendesk and Freshdesk. Communication moved with the millennials to chat channels and companies needed to adjust and talk with their customers on Facebook Messenger, Whatsapp, webchat client, “the super app” – Wechat, email, and SMS. It was very natural for companies in this segment to evolve as the need increased.

Conclusion

The landscape is evolving and changing rapidly. New solutions are being introduced to adapt to the customers’ eco-system. Try not to tie yourself to a solution you can’t replace or upgrade quickly. Make sure you can use a solution that you owe the data and migrate easily. Check how a service can connect and integrate with others (A great solution for connecting different services with automation is Integromat). DO NOT USE tools with poor analytics.

Chatbots – What Are They and How To Use Them?

A guest post by David Law from https://www.focusonthecustomer.net/

Chatbots are becoming more and more common these days but do you know what they are and what they do? This post will explain what a chatbot is and how businesses can utilize them. They may seem like live chat tools but they are actually quite different. Utilized correctly they can really boost your business so they are worth considering.

What are Chatbots?

Looking at an example chatbot provider like Chatfuel you can see what a chatbot is. Chatbots are a piece of artificial intelligence software that simulates how an agent would speak but are completely automated. A whole range of areas use them and they work independently from the agent. In the Cognility example, you can upload a user guide and convert it into a chatbot that can help your customers with their product related questions.

Companies like Disney, Marvel, Microsoft, Amazon, Facebook amongst others are utilizing chatbots for a whole host of reasons to engage with their customers. Plenty of other companies of differing sizes are coming round to the idea of them as well.

Chatbots vs Traditional Live Chat Tools

Another common question is how chatbots differ from live chat tools. Traditional live chat tools are completely agent led. If you have no agents online then your live chats will go unanswered. Customer replies are put together either by using shortcuts (see my blog on Live Chat Hints and Tips for more info) or by agents manually typing replies as if they are sending emails. When a new chat comes in its completely dependent on an available agent being able to pick it up. If all agents are at capacity the customer has to wait or send a message instead.

Automation and availability are the biggest differentials between the two. A chatbot can be online 24/7 and doesn’t have a capacity limit. It can take hundreds or thousands of chats at any one time. Whereas with live chat even though you can load up shortcuts, it’s up to your agents to actually use them. If you have a maverick agent who never uses shortcuts and writes everything from scratch it will dilute your messaging. With a chatbot, you load up the responses to the questions and you know one hundred percent that the replies are going out as you want.

How Can They Be Utilised by Your Business

Now that you know what a chatbot is and how it differs from traditional live chat tools the next question is how can they be utilized by your business. Depending on what services you provide and the sorts of contact volumes you deal with chatbots can make sense in reducing the workload of your agents.

One of the best examples of a chatbot is when a business used them to load up case studies from customers who left a competitor company. The chatbot would ask the customer if he used the service in the first question and then ask what provider he uses (assuming that he did use someone else) and then would show the customer the specific case study of a customer who left the same service provider for them. This was all done without agent involvement. Fantastic use of this technology.

In Cognility, for example, they enable customers to upload user guides and then create a chatbot to answer questions based on them. Just imagine the time savings and reduced workloads by using this.

Another more general way this can be used is to direct customers to the right agents. If you have two or three standard questions that you ask at the beginning of a chat you can automate it. If you ask what department, what product, what query a chatbot can easily handle this. This will save time and mean that when customers speak to an agent they have all the information that they need to deal with the inquiry.

Do you currently use chatbots in your business? What is your opinion on them? Add a comment, share and join the conversation.

Why voice assistants are another step in human language evolution

Part 3 in our bot series

We have explained earlier what are voice assistants and listed a few very popular chatbot platforms. But why even bother getting into this adventure?

Before we explore the customer service market trends, let’s take a step back and observe how people communicate, and how do they communicate with businesses. We will take a short detour from the main course — in order to return to it much stronger.

If you think about it, communication is driven by evolution – natural and man-made. We can start from hundreds of thousands – even millions – of years ago when language evolved in early humans. They could finally convey information across time and space, without the need for both parties to actually be watching the event at the same time.

But it’s still was very limited: both parties for the conversation have to be at the same location – or at least, at a shouting distance. The amount of information is limited by the person’s memory — and by the time the parties have to talk. The invention of writing took humans a step further: one could now store much more information, and have it persist through location and time: you could read the document long after it was written and in a different location altogether. The first document we know of, written in Cuneiform, is not a poem or a war story: it’s a list of tax amounts no human could possibly remember.

Next came paper, which has less weight, so people could transfer it to greater length. Printing was revolutionary in that it introduced a new factor into the communication game: information could now be spread to more people, in minimal cost of printing another copy. Totalitarian regimes have limited ownership on printing machines in the fear knowledge would be spread without their control. Telegraph has shortened the time to pass the information, and telephone – the ease at which anyone, and not only professional Morse clerks, could deliver it. And of course – how can we forget -the Internet, allowing anyone to talk, watch and play with anyone on the globe.

So every time a new communication technology was invented and adopted, several things happened:

  1. The amount of data that could be transferred increased.
  2. The time and cost to transfer a fixed amount of data was reduced
  3. The number of endpoints to which the information is delivered increased

One cannot underestimate how profound was the change to people’s lives, on every new communication improvement.

Now, with the increase of computational power and machine learning models, we are at the beginning of another leap – with one new characteristic never imagined before. On all of the leaps before, knowledge was exchanged between two humans. For the first time, we can omit one side of the line, and put a bot instead. These bots – in chat and voice – can now stand as one endpoint of the conversation, and transfer information for millions of correspondents fast and cheap, as we would expect from a person. Now you can still think of print, radio, and TV be a form of such knowledge transfer for the masses, but here, finally, this form of mass communication could be bi-directional.

For businesses, such an ability can have a huge impact. A chatbot can understand the user’s intent and provide exactly the information the user needs. No more a single human agent needs to talk with a single customer. A bot can converse simultaneously with millions, at almost zero cost per additional user.

The cost of a customer service center

Calculating your customer service center cost is fairly simple, but it varies between industries. According to the Bureau of Labor Statistics, the 2018 median pay for customer service representatives was $33,750 for people with only a high school diploma, with an hourly rate of $16.23. Assuming an agent can have at least 5 calls per hour, a service call would be around $3. When products are more complex they demand a much more skilled agent, and in some industries, a call cost can start at $30 per Hello. A customer service center also carries hidden costs. According to research conducted by The Quality Assurance & Training Connection (QATS), the annual turnover rate in USA call centers is 30-45% with a cost of over $6000 for replacing an agent making $12 per hour. It means that in a 100 agent call center, only 70 agents are qualified to attend customer service calls.

Chatbot benefits for customer care

chatbot report - potential benefits
https://www.drift.com/blog/chatbots-report/

According to Drift, the main benefit of chatbots is 24/7 service following by a quick response to users. Answering a simple question came “only” in third place. This is aligning directly with a survey from 2013 showed customers were waiting on hold on average for a total of 13 hours a year.

“Chatbots trends and stats for 2019” by the TIDIO blog, is showing an increase in Customer Service Chatbot adoption by buyers.

chatbot conversation flow

Customer service bot case studies

In this section, we will explore several chatbots and asses their impact. The first chatbot is called AVA that was created for Adobe and it was built on the IBM Watson chatbot platform. The main purpose of AVA was to be the front line customer service interaction solution for online communication. The main impact of AVA was response time reduction. From some interactions that took Adobe a day and a half to response were reduced into several minutes (by %99). Adobe stated that AVA is now:

  • Supports 30,000+ conversations per month
  • Recognizes 40 distinct use cases to quickly resolve easy requests
  • Cuts resolution from 38 hours to 5.4 minutes for most inquiries
  • Cuts cost per case from $15-$200 to $1

Such an improvement has a direct effect on increasing customer satisfaction and reducing customer retention. It is no secret Millenials are driving the industry and represent the major spending power these days. With modern-day standards, Millennials are much more sensitive to customer service quality and are willing to spend more on a product for better customer experience. In this post, we will not go deep down into the details, but a great blog post by Nextia has put together Customer Service Statistics and Trends for 2019.

The second use case we would like to present is TOBi – A chatbot by Vodafone. TOBi started as a simple Q&A chatbot and overtime got improved with new skills. In its second phase, TOBi got connected to Vodafone’s backend data center, in order to answer questions relating to the user’s account information. Today TOBi is taking care of 70% of customer’s queries in several areas – among which one could find technical questions, buying a new phone and more.

Jon Davies, head of the digital at Vodafone, shared what he has learned from building TOBi. In order to build a good chatbot you need to follow 4 steps:

  1. Gain trust of your customer – When TOBi starts talking, he presents himself as a chatbot. That sets expectations and trust – if he hadn’t, the user will eventually know it’s a chatbot in front of her. You do not want to get into this frustrating moment after the conversation has started.
  2. Build confidence – Improve your Chatbot skills gradually. Show users your chatbot is good at a narrow, small task, before adding more skills to it.
  3. Give your chatbot a personality – It is much more fun when you have a character and a story behind your chatbot. It is much more human-like. That’s what people want!
  4. Provide human agent support – in fact, these days chatbots are still very limited. They won’t be able to understand your customer as a real human would. Therefore, providing a way to have access to a human customer service agent is critical.

Plan the right customer service bot

With all that, as we wrote in previous blog posts, chatbots still suffer from many technological limitations. So before going into building a chatbot you need to carefully plan your path. Here’s a shortlist of what we usually recommend to our customers:

  • Choose one problem, with a well-defined flow (or a tree of flows) that your chatbot will be solving at first
  • Narrow the flow even further by using buttons and other pre-defined UI controls. This will introduce fewer cases for the developers to deal with.
  • Check who is the audience of this chatbot. What personality do they expect from it?
  • Once you have a plan, use numbers to compare: what is the cost of your customer service center, and what’s the cost of building a bot and operating it? In how much time will the organization receive its Return On Investment?

Summary

In summary, chatbots can be very cost-effective — if you choose the problem right, build them with the right platform and calculate the ROI correctly. And to add a nice touch to your efforts, you get to act as a player in a much bigger and dramatic human evolutional step.

Building Customer Service bots: Part 2 – What tools can we use?

In our previous blog post, we discussed how chatbot solutions are limited in what they provide. We also said that if you do build them correctly, they will perform, and perform they will. Before we go into how to build those good bots, let’s go over some different platforms in which we can do the building. We will explore several leading solutions, built by large tech giants and startups alike. One thing to keep in mind, though: this would be a limited list. In the past few years, a good number of solutions have emerged, and they vary in capabilities and cost structure.

The way we see it, the platforms should be evaluated according to several key areas:

  • Functional Scalability: how well the platform can scale for large bots with complex business specifications?
  • Development effort: for those real-life complex cases, what kind of development effort is needed?
  • Customer support fit: is the platform a good fit for building a customer facing bot?

Let’s start:

IBM Watson

IBM provides a very sophisticated chatbot solution. It has been used by some of the bigger players – from Harman Kardon to Vodafone. IBM is one of the pioneers in this field, and so, the Watson chatbot framework does have some nice feature in its bag. However, the editor it comes with uses an old paradigm, first-generation paradigm – table-based rule structured UI. If you are going to use this editor, then building a complex, multi-domain bot would be impossible. Thus, you’ll need to hire IBM-certified integrators (for example, LivePerson) to do the work. On the good side, it has a good NLU engine and even a limited context understanding. Context understanding is key to reduce the number of rules needed when designing a “smart chatbot” – more about it under the Servo section. When designing a chatbot with Watson, you will still need to invest a lot of work in training the NLU, building the chatbot logic flow and connect it to the correct database fields and custom code for more complex cases.

Google – Dialogflow

Dialogflow is built by API.AI, a startup company acquired by Google a few years ago. Its technology is now integrated and provided as part of Google’s services. From our perspective, Dialogflow is a very similar offering to IBM’s Watson, barring the context understanding: from our tests, it does not have the ability to easily jump between sub-conversations. Dialogeflow brings other benefits, though:

  • More compatibility with chat clients such as Facebook Messenger, Slack, Skype, and more
  • A great NLP/U. In fact, Dialogflow is primarily an NLU engine

Again, as Dialogflow is more than all an NLU engine, a customer experience manager in need for a chatbot, would need to get a development team building the bot’s logic and conversation flow.

Microsoft Bot Framework and LUIS

Microsoft chatbot framework appeals first of all for developers. A framework is a layer of code accelerating some of the development, allowing a less steep learning curve for the programmers and ultimately faster time to market. The framework is tightly coupled by Microsoft’s Language Understanding Intelligent System NLU engine (hey, I hope it’s ok to come up with a reasonable deciphering of LUIS). It also uses with their Azure cloud hosting, which actually is the whole point of Microsoft investing in this field: they want to lure developers and companies to use Azure hosting, which is one of their main revenue channels now. To companies already using Microsoft Azure, it’s great. LUIS core technology is very similar to Google’s and IBM, in which you will need to train the NLU, build the rules and connect to the right database fields and devices. However, their framework does provide a better scaffolding on which the developers can build the chatbot for you.

With all that being said, so far we have seen a set of tools that still rely significantly on developers. What about zero-coding platforms?

Manychat

manychat platform view

Manychat claim to fame, among other points, is by allowing anyone, not only developers, to create bots. “Building a bot is easy, fun, and proven to get results.” their slogan goes. Indeed, Manychat platform is very popular, especially for Facebook Messenger chatbots. It has a great graphical rule-engine flow editor, a dashboard with analytics and audience information, live chat interface and many great, intuitive-to-use features. Its main drawback stems from its dedication to Facebook Messenger. While being very suitable for marketing and social platform lead generation, this would not be a great choice for customer support. First, not every customer might have a Facebook account. Second, the interaction with the customer would need to be done under FB Messenger limitations and regulations: this means that if your customer has already logged into your site, the bot would know nothing about her, and could not make any decisions or recommendations based on her past conversations.

The other disadvantage is actually the zero-coding promise: for meaningful actions, the bot has to make decisions based on business logic. This means it has to look up in databases and documents, activate some API calls and see the user’s past actions. In Manychat the road for that is convoluted: you have to use external APIs and cannot build this as an integral part of the bot.

Still, Manychat presents a great tool, super useful for marketing purposes.

Chatfuel

Founded in 2015, Chatfuel is one of the pioneers in the chatbot field. Its offering is very similar to Manychat. It’s aiming to help to build chatbots with no-coding, has a nice rule sequence interface, analytics and more. It also suffers from the same problems: Facebook dedication, convoluted coding, and sales-and-marketing orientation.

Rasa

Rasa provides the leading open-source NLU solution in the market. As in Watson, Dialogflow and their kind, Rasa’s main strength is in its NLU. Rasa saw the future coming and collected together a bunch of NLU and NLP components, such as Spacy, NLTK, and others. Since then they have progressed to include more modern deep-learning-based components and in general, are keeping up respectably with their giant counterparts.

On the flow front, Rasa tried to add a new chatbot building paradigm, based on training: where the developer needs to supply the right answers to questions, without building a set of rules. This is called Rasa Core and it supposes to learn over time and ultimately give the expected answer. We’re still not sure if Rasa Core would live up to its promise, but even if it will, it’s not necessarily the best approach to build big chatbots. Chatbots are actually big applications, with a lot of state to handle. It is uncertain whether a new paradigm could replace years of well-tried engineering design patterns.

Servo

First things first, the author of this blog post is one of Servo’s founders. Servo was built to provide a low-code platform for creating industry-grade bots – bots that access data sources, has an elaborate set of business rules, and use modern engineering methodologies like source versioning, design patterns and more. It’s open-source, but has a best-of-breed flow editor, that implements Behaviour Trees, a state-machine paradigm that was originally built to create AI in games. That’s why its called ‘low code’ – you don’t have to code, but if you do, it’s not a lot.

It also boasts a flow debugger, a strong context engine, and hierarchical memory modeling. The developers can configure to use their favorite NLU engine – be it Rasa, Watson, Wit.ai or others. It can create multi-lingual bots for multi-platforms, such as Facebook Messenger and Alexa, all using the same behavior tree. It allows sub-processes, which is essential for building big, complex systems.

It’s not hard to see this is my favorite engine. With that, it also requires a dedicated developer(s) – less than the rest, perhaps, but still.

Summary

All in all, there are many solutions on the market that will provide a decent user experience, and you will need to choose the right platform based on your needs. Things to consider are:

  1. Cost – mainly for development and maintenance. Each platform brings its own pricing model (some free). Some can be very expensive when your chatbot usage grows and it is very unlikely that you’ll be able to move to a new platform and build it from scratch.
  2. Flexibility – check what connectivity you need and what platform brings good support for future integrations
  3. Functional Scalability – you will start from a simple chatbot but it will need to grow its skills in the future. Which platforms can handle complexity at best?
  4. Eco-system – If you are already using a platform such as Azure or Watson for your core business, staying at the same eco-system might save you cost.

Customer Service Chatbots: Part 1 – How they are built, and what are their limitations?

Or: when chatbots do more bad than good

For the customer service industry, chatbots bring a new opportunity in improving customer service and customer satisfaction using the latest technology based on machine and deep learning to create sophisticated chatbot representatives.

It is commonly thought that chatbots are the ultimate answer for customer service cost reduction. But, as many companies learned the hard way, chatbot technology is not there yet.

As a chatbot framework for developers, we have talked with many companies and explored many customer service solutions. In this set of posts, we would like to present some of our experience and knowledge. We will explore different solution examples for a better understanding of how chatbot technology could be used

Artificial intelligence in Chatbots

Before we start to explore solutions, we need to understand what is a chatbot and how it works. In essence, chatbot platforms still are a rule-based or flow-based logic engine that executes commands. From one side, an input sentence is entered by a user. Based on the sentence, the chatbot will provide a pre-programmed answer. The AI part of a chatbot, which people view as the most sophisticated, is called NLU, which stands for Natural Language Understanding. The NLU is a piece of a machine learning program that needs to understand a sentence written by the user and reduce it into simpler classification, in a way the rule-engine can use. For example: “I want to fly to Paris next week” will be converted into an intent – “Book-a-flight”, a first entity, also called slot – “Date” and a third entity – “Destination”. So the rule engine will receive the following data structure:

Intent: “Book-a-flight”
Entity 1:
name: date
value: “Aug 7 2019”
Entity 2:
name: destination
value: “Paris”

In such a case, the chatbot should ask for the missing information – which is the origin and a more specific date, in order to provide flight options. Only then could it – and should it – search in for available flights.


How does one go and build a chatbot? in the diagram below, you can see the general architecture of a chatbot system — actually, the structure of any AI system. A more elaborate discussion on AI system architecture could be found in our conversational AI framework article.

.

Chatbot limitations

So far, we talked about a fairly simple example. And it seems pretty easy, right? indeed. But — and that’s a huge, huge “but” – it would become much more complex if a user would say “I want to fly next week to Paris with my wife”. In such a case, a good chatbot is expected to understand that two tickets are needed. What about “I want to fly with my dog to Paris”? Or: “Can I fly with my insulin pump?”

These examples show how the number of scenarios that chatbots need to handle grows very very rapidly. And that happens because of three main reasons:

When do chatbots get dumb?

First and foremost, today’s chatbots – and AI in general — do not really understand the world. The technology is not yet there. How would a bot know that a pet is different from a partner? that medicine is not a passenger? Today’s AI is statistical. It does not really have any knowledge about physical sizes, social relationships and all of those things we know intuitively.

If you would tell a bot, for example, “My coat is too big for this suitcase, so I need to fold it” – it would never know (under today’s AI) which one should be folded – the suitcase or the shirt. Or “I want to buy two burgers and one coke. Actually, change it to a milkshake.” – A human knows a coke is a drink and most likely it should be changed to a milkshake, Chatbots do not know anything like that. Last example – there could be so many – “My laptop doesn’t charge when it is in sleep mode” – NLU doesn’t know a laptop can sleep.

Second, even if you train the NLU engine to recognize all those cases, a customer will always surprise you with a new question. After all, she came here for questions for which a simple web search was not enough!

Third, and maybe the biggest problem of them all, is that a chatbot does not really know when it doesn’t understand. Yes, there is a score for every intent and entity that the NLU returns – but where do we draw the line? How do we ask for clarifications?

Why all this is bad for business?

All of this is what makes the chatbot example above so hard to build and maintain. The developer of the customer service chatbot needs to train the NLU for every new option AND integrate it into the chatbot logic rules-engine. So the amount of work – and hence, the cost – does not worth the benefits.

To conclude, even though chatbots had a great promise to improve customer satisfaction, they can handle very simple and limited tasks. The technology is still in its infancy, and it would take years, if not decades or centuries until it becomes a good human replacement.

How do we create a successful chatbot, then?

Chatbots could be very, very helpful in customer service, and that would be the topic of the next chapters of this series. TLDR? goals and make-believe. Constrain your chatbot to a narrow topic and limit the dialogue to that goal. Then, make the user believe she’s having a conversation.

Tips for writing a great user manual

Writing a great user manual is not an easy task and gets harder for a complicated product. The following tips will guide you on building one:

Photo by Alvaro Reyes on Unsplash

  1. Know the product you are building the manual for
    • Use the product
    • Ask questions about it
    • Take part of the design process if possible
  2. Know your audience
    • Test the product on a focus group to see how they use it
    • Take note of misunderstandings and failures in product usage; These will give you important ideas on what’s needed to be emphasized in the manual
    • After having a user manual draft, have users read it and evaluate their understanding. Improve iteratively based on their feedback
  3. Make it simple
    • Write it as you speak to your users
    • Use short, clear sentences
    • Do not assume your users understand the parts’ names. Make sure you redirect them with visual indications and images when needed
  4. Use visual materials
    • People understand and remember visuals much better and faster than text
    • Always add explanations for images. Never use images with no text attached to it
    • Inside the image, use clear and large enough fonts
  5. Make a clear logic for the user manual steps
    • Build a user journey map before creating the user manual, to understand what the user is doing on each step. For example, should he or she make sure no hidden parts are lost in the package? Or, do some parts arrive in a second box? you have to know that.
    • Always indicate if some steps need tools or third party product
  6. Safety
    • Make sure you meet the safety regulations
    • Indicate hazards prior to a step and bold it for the user
  7. Regulations
    • Always follow the product regulations. In some cases, regulations will indicate what information should be added to your user manual. For example, it can be by CE or ASTM. Consult with the product manager and QA department, which usually hold this information.
  8. Document structure
    • Plan your user manual layout. It can be a spread, a booklet, an accordion or anything that makes it stand out and easy to understand
    • Make your layout simple. Use fewer columns as you to reduce confusion by the user
    • Put the supporting graphics near the relevant paragraph. Do not let your users search for the right image
  9. Compatibility
    • Plan where your user manual is going to be published and how. Most consumer goods products, for example, comes with a printed user manual. This user manual is also presented online on desktop and mobile. If you can plan your user manual to be easily read on different platforms, you make it more accessible to your users
    • Cognility automatic chatbot builder provides an automated tool based on deep learning technology, that turns any user manual into a chatbot. The chatbot focuses on understanding the user’s needs and provides the right information to the user quickly and efficiently.