aheadCRM - Blog #ChatbotsaheadCRM - Blog #Chatbotshttps://www.aheadcrm.co.nz/blogs/tag/ChatbotsTue, 22 Sep 2026 12:02:01 -0700http://zoho.com/sites/<![CDATA[It is this time of the year again - your favourite articles of 2022]]>https://www.aheadcrm.co.nz/blogs/post/it-is-this-time-of-the-year-again-your-favourite-articles-of-2022

Happy New Year to everyone!

Apart from all the political suff that I do not want to dive into, the year 2022 has seen a lot of exciting events and topics, from the rise and fall of the Metaverse, to lots of defaulting crypto companies. Towards the end of the year, chatGPt stirred up quite some waves.

Not to forget the long story of Elon Musk taking over and handling Twitter ...

In more conventional areas, we have seen more of a renewed interest in affordable CX suites. Earlier in the year, also CDPs have been a big topic. Tips for good engagement to achieve customers having a positive experience is also always a topic.

(Nearly) all of this is reflected in the 2022 top ten of my blog, which has seen a good number of visits. 

A big and heartfelt thank you to all my readers!

Here are your top ten articles of 2022.

#10 How to engage for customer experience and success

#9 How to create value with a CDP

#8 The impact of the supply chain on the customer experience

#7 A great human - bot conversation with lots to learn

#6 How (and why) a marine consultancy made Zoho core of their business

#5 The almighty Metaverse - its Rise and Fate

#4 How to make Zoho a business's operating system

#3 How to Zoho'matize a business

#2 Don't mess with Zoho - A Zohoday 2022 recap

#1  Metaverse - the return of the Undead

All of these are well worth revisiting.

Thank you again for your readership. I wish you all the best for 2023.

Exciting times are lying ahead of us.

]]>Tue, 03 Jan 2023 11:04:10 -0500<![CDATA[a great human - bot conversation with lots to learn]]>https://www.aheadcrm.co.nz/blogs/post/a-great-human-bot-conversation-with-lots-to-learn

Inspired by a recent panel discussion as part of theIn the Hot Seat podcastthat I am involved in, I opened a chat with chatGPT3. ChatGPT is a language model by OpenAI that interacts in a conversational way. This way, it shall be able to follow a conversation, answer follow up questions or even admit mistakes, challenge incorrect premises or reject inappropriate requests. Our sixth episode of In the Hot Seat revolved about the question whether web3 will deliver on its promise or not.

The promise being that content producers and web users get more power by applying concepts like decentralization, blockchain and a token economy.

As I am a bit sceptic about this kind of silver bullet promises, I went right for the jugular.

A conversation between a human and a bot

Thomas: Tell me with arguments why web3 based on blockchain will fail

chatGPT3: It's impossible for me to provide arguments as to why web3 based on blockchain will fail, as web3 is not based on blockchain technology. Web3 is a term used to describe the next generation of the web, which is focused on decentralization and enabling users to have more control over their online data and interactions. Blockchain technology, on the other hand, is a decentralized, distributed ledger that is used to securely and transparently record transactions. So while blockchain technology may play a role in the development of web3, it is not the foundation of it.

Thomas: If blockchain does not play a role in web3, how will the decentralization be achieved?

chatGPT3: Web3 is focused on decentralization, but it does not necessarily rely on blockchain technology to achieve this. Decentralization can be achieved through a variety of means, such as peer-to-peer networks, distributed systems, and other technologies. Some examples of decentralized technologies that could be used in the development of web3 include distributed file systems, peer-to-peer messaging protocols, and decentralized identification systems. Ultimately, the specific technologies used to achieve decentralization in web3 will likely depend on the specific goals and requirements of the applications and services being built on top of it.

Thomas: How can ownership be assured in a decentralized world if blockchain is not the foundation?

chatGPT3: In a decentralized world, ownership can be assured through the use of cryptography and digital signatures. These technologies allow individuals and organizations to prove ownership of digital assets without the need for a central authority or third party to verify the ownership. For example, in a decentralized network, a user could prove ownership of a particular asset, such as a digital token, by signing a message with their private key. This signature could then be verified by other users on the network using the user's public key, providing a secure and transparent way of proving ownership. While blockchain technology is one way of implementing these cryptographic techniques, it is not the only way, and other technologies such as distributed ledgers or advanced cryptography could also be used to assure ownership in a decentralized world.

Thomas: Why then the hype around web3 based on blockchain?

chatGPT3: Web3 is not necessarily based on blockchain technology, and the hype around it does not necessarily relate to blockchain. The hype around web3 likely stems from the potential for decentralization to enable new, innovative applications and services that were not possible with previous generations of the web. Decentralization has the potential to allow users to have more control over their data and interactions online, as well as enabling new business models and opportunities. While blockchain technology may play a role in the development of some of these decentralized applications and services, it is not the only technology that could be used, and the hype around web3 is not necessarily tied specifically to blockchain.

Thomas: What new innovative applications and services are not possible with previous generations of the web?

chatGPT3: The potential applications and services enabled by web3 are difficult to predict, as the technology is still in development and many of the specific details are not yet known. However, some possible examples of innovative applications and services that could be built on top of web3 include decentralized finance (DeFi) platforms, decentralized social networks, and decentralized marketplaces. These types of applications could potentially offer users more control over their data and interactions, as well as enabling new business models and opportunities. Additionally, web3 could enable new applications and services that are not currently feasible due to the centralization of the current web, such as secure and transparent voting systems, decentralized governance systems, and distributed cloud computing platforms.

Thomas: Decentralized social networks do already exist. How can defi work without a decentralized ledger, i.e. a blockchain?

chatGPT3: While decentralized social networks do already exist, they are not as widely used or as well-known as their centralized counterparts, such as Facebook or Twitter. Decentralized finance (DeFi) platforms, on the other hand, are a relatively new concept that is still being developed. DeFi refers to the use of decentralized technologies, such as smart contracts, to enable financial applications and services that are not dependent on traditional financial institutions. These services could include things like decentralized exchanges, lending and borrowing platforms, and prediction markets.

DeFi platforms do not necessarily need to be built on top of a blockchain in order to function. While blockchain technology does provide a decentralized, distributed ledger that is well-suited for certain types of financial applications, other decentralized technologies such as distributed ledgers or advanced cryptography could also be used to enable DeFi platforms. Ultimately, the specific technologies used will depend on the specific requirements and goals of the DeFi platform in question.

Thomas: name me some defi platforms that do not use blockchain?

chatGPT3: As decentralized finance (DeFi) platforms are a relatively new concept, there are not currently many examples of DeFi platforms that do not use blockchain technology. Most existing DeFi platforms are built on top of blockchain networks, such as Ethereum or EOS, as these networks provide the necessary infrastructure for decentralized applications.

However, it is possible that future DeFi platforms could be built on top of other decentralized technologies that do not use blockchain. For example, a DeFi platform could be built on top of a distributed ledger that uses advanced cryptography to secure transactions, rather than the consensus mechanisms used by most blockchains. Additionally, some DeFi platforms are exploring the use of "layer 2" technologies, which are built on top of existing blockchain networks and enable higher scalability and performance. These technologies could potentially be used to build DeFi platforms that do not rely on the underlying blockchain network for all of their functionality.

Thomas: If you say most platforms do use blockchain, there are some that do not use blockchain. Please name some examples.

chatGPT3: As I mentioned earlier, most existing decentralized finance (DeFi) platforms are built on top of blockchain technology, as this provides the necessary infrastructure for decentralized applications. Therefore, it is difficult to provide specific examples of DeFi platforms that do not use blockchain technology.

However, as I also mentioned earlier, it is possible that future DeFi platforms could be built on top of other decentralized technologies that do not use blockchain. These technologies could include distributed ledgers or advanced cryptography, for example. Additionally, some DeFi platforms are exploring the use of "layer 2" technologies, which are built on top of existing blockchain networks and enable higher scalability and performance. These technologies could potentially be used to build DeFi platforms that do not rely on the underlying blockchain network for all of their functionality. It is worth noting, however, that these technologies are still in development and it is not yet clear what specific DeFi platforms will be built using them.

Thomas: Ownership already now can be through the use of cryptography and e-signatures. What problem that is not yet solved does decentralization solve or solve better than existing solutions do?

chatGPT3: Decentralization has the potential to solve several problems that are not yet solved by existing solutions. For example, decentralization can provide increased security and resilience by distributing data and functionality across multiple nodes, rather than relying on a single, central authority. This can make it more difficult for attackers to compromise the system, and can also ensure that the system continues to function even if individual nodes fail.

Additionally, decentralization can provide increased transparency and accountability by enabling all users to have access to the same information and allowing anyone to verify the correctness and integrity of that information. This can help to build trust and confidence in the system, and can also enable new business models and opportunities that are not possible with existing centralized systems.

Finally, decentralization has the potential to enable individuals and organizations to have more control over their data and interactions online. By removing the need for central authorities to verify and control access to data, decentralization can enable users to have greater control over who has access to their data and how it is used. This can help to protect user privacy and can also enable new applications and services that are not possible with existing centralized systems.

Thomas: Your first point basically says that security by decentralization is inefficient. Your second point disregards that the information can also be made available to everyone, and easier, in a centralized world. Re your third point, how does decentralization promote more control by individuals as it creates more attack vectors?

chatGPT3: I apologize if my previous response was unclear. Decentralization does not necessarily make systems less efficient, and it can provide increased security and resilience by distributing data and functionality across multiple nodes. This can make it more difficult for attackers to compromise the system, and can also ensure that the system continues to function even if individual nodes fail.

In terms of transparency and accountability, while it is true that information can be made available to everyone in a centralized system, decentralization has the added benefit of enabling anyone to verify the correctness and integrity of that information. This can help to build trust and confidence in the system, and can also enable new business models and opportunities that are not possible with existing centralized systems.

Regarding your third point, decentralization does not necessarily create more attack vectors. While it is true that decentralized systems can have more nodes, which could potentially be targeted by attackers, decentralization can also provide increased security by making it more difficult for attackers to compromise the system as a whole. Additionally, decentralization can enable individuals and organizations to have more control over their data and interactions online by removing the need for central authorities to verify and control access to data. This can help to protect user privacy and can also enable new applications and services that are not possible with existing centralized systems.

My point of view

First of all: Wow!

And I mean W O W.

Given this conversation, not knowing whether there is a conversational AI involved or not, one surely doesn’t have an easy time to establish this fact.

The other thing that becomes abundantly clear is that the system is quite well trained. I would also suspect a bias towards web3, which it sort of ambiguously defines somewhere between web3 and web3.0. Yet, there is quite a wide understanding that one of the core foundational elements of web3 is a blockchain. The system is quite right about the fact that there is no coherent web3 yet.

So, it argumentation wise leaves the door open.

It also, “behaves” rather like someone who is semi-educated and defensive. Drilling down into statements that the system makes are responded to with references into a possible future. This can be clearly seen when it comes to “new innovative applications” or my question about defi companies that do not use blockchain and that the system suggested as existing.

All in all, this system clearly shows the art of the possible. This raises quite some expectations to the future, and some concern. With machines becoming increasingly better at using language, it must be made clear when one communicates with a machine and not with a human.

Last piece of advice: Do not try to use it as a PA. It doesn’t like it. Really not …

]]>Tue, 06 Dec 2022 19:46:12 -0500<![CDATA[How to overcome the process knowledge challenge with a little AI]]>https://www.aheadcrm.co.nz/blogs/post/how-to-overcome-the-process-knowledge-challenge-with-a-little-ai

Many a company has a severe challenge with how their processes actually work. Documentation may or may not be there. Tribal knowledge exists. Lots of repositories, too, including file systems, collaboration tools, chat tools, email, etc. In brief, there is the need for a solution. 

Startup Sevantiz has taken on this challenge with its Flockwise platform, which is reason enough for the CRMKonvos team to invite Sri Sabesan, Mani Manivannan and David Pickrell into our studio to discuss their approach and how it relates to CRM (spoiler alert: it does) and CX (it does, too). 

But what is Flockwise?

Flockwise brings knowledge from  the documentation, tribal knowledge and the transaction systems to answer the questions that employees in businesses have every day.  Although we utilize a chat interface, It is much more than an Chatbot.    Flockwise is designed to  discover knowledge and serving it to the user in a way that the user get the right answer to the question. 

Flockwise creates the opportunity for the Enterprise Flock and their trusted advisors / consultants and outsourcers to capture knowledge and provide answers to the questions in a way that no other solution does.   Creating an efficient way for something that we have always talked about. And that is capturing expert knowledge in a way that is truly reusable for the benefit of productivity of the employees and customers and suppliers of an organization.

Sounds good? Then dive deeper into this CRMKonvos episode!

https://youtu.be/ISjHf3LNliQ

]]>Fri, 04 Jun 2021 07:25:10 -0400<![CDATA[Intercom takes on Zendesk]]>https://www.aheadcrm.co.nz/blogs/post/intercom-takes-on-zendesk

The News

Intercom is a conversational relationship platform that helps businesses build better customer relationships through personalized, messenger based experiences. It’s the only platform that delivers conversational experiences across the customer journey, with solutions for conversational marketing, conversational customer engagement and conversational support. Intercom is bringing a messenger-first experience to all business-to-customer communication, powering 500 million conversations per month and connecting 4 billion unique end users worldwide across its more than 30,000 paying customers, including Atlassian, Sotheby’s and New Relic. On August 12, 2020 Intercom launched a new product release with more than twenty functional enhancement that are intended to ‘supercharge customer support’. The company says that it is its biggest ever launch and targeted at ensuring that its customers “can provide prompt, personal support without sacrificing power or efficiency”. The twenty plus new features in this release cover enhancements across Messenger, Inbox and reporting. As part of this, there are new bots that can be used in the messenger, new data attributes that conversations can be tagged with and new rules and assignment features that enable deeper automation of conversations. This gets augmented by additional view capabilities in the support inbox that help getting better overview and by offering more task bots to the messenger that help providing a high customer experience while automating jobs. Additionally, Intercom improved its reporting capabilities by adding three dashboards covering conversations, effectiveness, and team resolutions with twelve new metrics plus improved filtering capabilities. Lastly, Intercom changed the side-bar navigation by providing clearer icons, changing their sequence and renaming one of the core sections, Platform, to Contacts. This has the goal of improving usability. According to Jane Honey, Senior Product Director at Intercom, the company is especially strong at proactive and messenger based support, while the part of Intercom’s software that supported human customer service, was not so powerful. This has been addressed with the recent product launch with the goal of combining the best of both worlds, the Intercom strength of enabling great customer experience via conversational and bot based support with the strength on the back-end side of ticketing systems.

The Bigger Picture

In general, there are two varieties of customer service tools. Historically, there have been ticketing systems that support the ‘traditional’ support channels like phone or e-mail. These have evolved well over years and work well for companies that implemented them. They offer powerful routing mechanisms to find the right person to answer an inquiry and, of course, have strong reporting mechanisms as well, to track and improve the performance of a service centre. Then there are a newer breed of systems that base upon conversations and bots. Many of these started not as customer service systems but have their origins in marketing. Being newer, they often are not as sophisticated in routing incidents or in managing the performance of a service centre as the more traditional tools. On the other hand, a conversation is more natural to humans and, if combined with a bot, many simpler inquiries can be answered without being worked upon by a human service agent. Still, there can be any number of conversation requests at any given time. As a result of this, the former variety of support systems tend to be better for the service team while the latter tend to provide a better customer service experience for the customer while imposing additional stress on the service agents. Customers want reliable support, and they want it fast. They do not want to wait for prolonged times until their requests are answered. Service centres want efficiency and scalability, service agents want and need a good user experience, too. As a consequence of this, both types of help desk platforms are increasingly utilizing AI based mechanisms to identify the customers’ issues, so that they can be resolved via chatbots before the need for them to be addressed by a human arises. If they need to be escalated to a human then they need to be routed to the right agent, with the complete history of the current incident and enough of a history about earlier ones for the agent to hit the road running.

My Analysis and PoV

Intercom is essentially a messenger first platform in contrast to Zendesk and as such appears to be rather competing companies like helpshift or Kustomer. Still, according to G2, Intercom performs well in the upper right corner of the help desk software category. The company finds itself in a tight group with Zoho Desk, Freshdesk and, of course, Zendesk. As the helpdesk software vendors are embracing conversations, bots, and AI to help their customers to resolve incidents fast and efficiently, the competition has become fierce. Additionally, this category is a playground for lots of nimble and flexible specialists that can easily compete with powerhouses like Salesforce. Lastly, especially the bigger players are getting increasingly similar from a functional angle. Their capabilities can be offered via all relevant channels. The conversation is more and more becoming main the vehicle as opposed to tickets, and the internal efficiencies of the service centres are supported more and more – by all of them. The difference is that traditional ticket based vendors are moving from a ticket metaphor towards AI and conversations, while the newer breed of vendors that base customer service on conversations are moving towards internal efficiencies. With its “conversational support funnel”, Intercom has a clear strategy on how to enable customer service, starting from proactive support that offers immediate, AI assisted support using chatbots, via improved automation and self-service towards direct support via dedicated service staff. Coming out of the messenger area, Intercom has not been so strong in the latter areas, hence, according to Jane Honey, this was the focus of the current release, and the reason for Intercom pitching with the slogan of ‘taking on Zendesk’. What she also said is that businesses haven’t yet fully caught up to the shift towards a messenger first support model. This is an important release for Intercom, as efficiency and a great user experience are as important as customer experience, especially in times where service centres get more and more decentralized and support operations out of necessity get more online. Combining the conversational experience with the power of tickets and enabling service agents to be equipped with the customers conversation history and an AI that suggests good solutions to provide to the customer is the immediate way ahead. The ability to pro-actively provide support will help even more. The time of the search box that is not connected to a service bot is clearly over. Let’s see how these enhancements pan out for Intercom.
]]>Sun, 23 Aug 2020 06:25:06 -0400<![CDATA[Salesforce Customer Service Solution becomes Botty]]>https://www.aheadcrm.co.nz/blogs/post/salesforce-customer-service-solution-becomes-botty

The News

On June 17, 1019, Salesforce announced an enhancement of its customer service abilities by adding further channels for customer service and adding chatbot capabilities to these channels. This has the goal of offering the ability to create a more seamless service experience by offering engagements on the channels that consumers use. For your easier reference here comes the announcement.

Expanding our Digital Customer Service Capabilities with New Channels and Bot Innovations

Author: Meredith Flynn-Ripley, VP of Digital Engagement, Service Cloud Disconnected customer service experiences are still far too common. Almost everyone has had to repeat basic information during routine interactions with companies, or found themselves unable to get answers to fairly simple questions on the channel of their choice. In fact, only 16% of consumers say companies excel at delivering connected experiences. I am happy to report times are changing, for two reasons. First, companies are realizing service can be their main competitive differentiator, and second, today’s empowered and vocal consumers refuse to tolerate bad service. 57% of customers will stop buying from a company not because they don’t like their product, but because a competitor provides better service. Today’s customer demands service on their terms, uses an average of 10 different channels to connect with companies — including messaging, chat, social, email and phone — and expects a personalized and consistent experience across all of them, every single time. Salesforce empowers companies to deliver on these expectations, with a complete customer service platform that powers connected customer experiences across channels from one central console. And today I’m excited to announce new innovations in Service Cloud that make every digital customer service interaction seamless regardless of whether you are interacting via the web, messaging apps, chat or otherwise. Expanded reach across digital channelsSay goodbye to the static “Contact Us” page. Our new channel menu gives customers an easy way to start conversations with companies on their preferred messaging and chat channels like, SMS, Facebook Messenger, WhatsApp or web chat, as well as traditional channels like voice. It can be embedded anywhere in a website including high traffic areas like checkout or home pages. And the channel menu can be set up with as little as four clicks, letting companies avoid expensive and time-consuming development work so they can get up and running right away. And, with our newly added support for WhatsApp and WeChat as well as existing channels like Facebook Messenger and SMS, companies can reach more customers through messaging than ever before. Automate service with Einstein Bots in more placesEinstein Bots are now available on multiple new messaging channels including SMS, Facebook Messenger and WeChat. Einstein Bots empower companies to scale their digital customer service by responding to customers immediately, automating routine service requests, gathering basic information and seamlessly handing off the conversation to human agents. And because they are connected to CRM data and processes, and powered by machine learning and natural language processing, Einstein Bots learn your business and get smarter over time. With Einstein Bots, you don’t have to be a data scientist to take advantage of cutting edge AI capabilities. We’ve made the setup process easier than ever, with a new map view that gives admins a visual guide to help design conversations, ensuring customers won’t get stuck in a dead-end during a service interaction. Dynamic routing directs customers to a specific queue based on their conversation, ensuring customers are on the best path to quick resolution with the right agents. And a new exact match capability lets admins quickly create intent models that train Einstein Bots to quickly recognize what customers are asking for and help them instantly.These new capabilities are making the service experience completely seamless. For example, a customer looking to replace a credit card can go to their bank’s website and easily access the channel menu. From there, they select the messaging app they prefer, and once in that messaging app, an Einstein Bot can quickly walk the customer through the card replacement process, handing off the interaction to a human agent at any point if necessary.This is just the latest in a series of recent customer service AI innovations. In March we announced new AI-powered recommendation and routing capabilities that make the service agent console more intelligent and the agent’s job easier. And in April we announced an integration with Google’s DialogFlow that extends the power of Einstein Bots. Check this out at Salesforce Connections 2019These and other new innovations will be on display this week at Salesforce Connections 2019, the digital marketing, commerce and customer service event of the year. Mark your calendar for the Service Cloud keynote, taking place Wednesday, June 19, at 10 am Central.Availability:The Channel Menu will be in pilot by Winter 2019. Messaging support for WeChat and WhatsApp, and Einstein Bots for Facebook Messenger and WeChat are all now in pilot. All other features listed are generally available.

The bigger Picture

The customer service market is very contested. Apart from the tier one vendors there are a good number of specialists which have their roots in developing solutions to enable a seamless, cross channel customer service. There are the likes of Freshworks, Helpshift, Intercom, to name just very few. Most of these smaller vendors are rightly positioned as best-of-breed vendors. Nearly all of them claim their ability to easily integrate into an already existing CRM system. Many of them harmonize with more than one of the top-tier CRM platforms (aka suites). Whether the integration claims are right or wrong, and mostly they are right, kinda sorta, this ability creates a serious threat for the suite vendors, as there are enough companies that run multi-vendor strategies. And once a vendor has a foot in the door it has drastically improved its ability to increase the foot print, which usually goes on cost of the platform vendor’s potential foot print. Therefore it is paramount for the platform vendors to offer functionality and services that are good enough and come at a reasonable price point. And in contrast to other business areas like e.g. marketing it is very easy to show value in customer service.

MyPoV and Analysis

The basic assumption that underlies this announcement is spot on. Customer service is still adversely affected by lacking channel integration. This makes engagement difficult for both sides as an ongoing interaction in context is made more difficult than necessary. The result is a poor customer experience, as the sum of all individual experiences is poor. Today’s customer expects to be able to be engaged with a brand/business a seamless, channel agnostic way. As I have said and written before, operationalizing Paul Greenbergs very valid definition of customer engagement: Customer Engagement is the ongoing interaction between company and customer using contact points that are offered by the company and chosen by the customer. This basically means that companies are wise to offer those contact points (which is more than described by the term touch points that has been usurped by marketing) that are preferred by their customers. As they are businesses they also need to do this in an efficient way. A Salesforce research found that customers use ten different channels to communicate with companies and that consistency across these channels is paramount for them. Times and again there are studies finding that customers leave a brand for reasons of poor experiences. While I do not fully agree to these studies (most of them are commissioned or conducted by vendors) it can be safely assumed that extreme experiences – good or bad – create lasting memories and therefore impressions and that the most recent experience is the one that has the potential of overriding older impressions. This is where customer service that can be easily found and resolves customer issues with minimal friction for the customer comes into play. It either corrects a poor experience or confirms an already good one. This is partly addressed by this announcement and the underlying release. Partly, because Salesforce will make it easier to engage on an increasing number of communications channels, especially chat based ones that get increasingly important. Salesforce also demonstrates some scenarios that stretch across channels, like engaging via a web chat and then informing the customer about the upcoming appointment or allowing to change it via text messages. However, the caveat is that the second part of the conversation that uses the other channel – the information about the upcoming appointment – is initiated by the bot, and not by the customer. I haven’t seen a scenario yet that allows the customer to (arbitrarily) change the communications channel with the AI, not a human agent, keeping track of the context. While this might be a stretch goal, it is exactly where we need to get to. This announcement is not breaking news, but streamlining and an iterative improvement of Salesforces already strong platform. Having said this, there are some useful enhancements, including the upcoming channel menu, the additional channels, and the enhanced visual conversation builder to define conversation flows. This drastically reduces the need for data scientists when building AI capabilities inside the company. Here Salesforce clearly closes a gap to specialists, like Cognigy or the former Recast.ai (now a part of SAP as SAP Conversational AI), to name only two of them. A good next step for Salesforce could be the building of ontologies for specific topics and the ability to help customers and agents to index and search structured and unstructured documentation, like e.g. empolis does, which can lead to dramatic improvements of search results and speed, which results in better experiences. I’d also like to see a tighter integration between customer service and field service, maybe even including a crowdsourcing capability, like the one SAP acquired from Coresystems. This would fit well into Salesforce being a platform company and into the trend of building ecosystems and even temporary ecosystems to address customer issues. Einstein might even be a helper in lining up the right partners. Finally, let me formulate a wish, which maybe comes from my German roots: Some of the topics that were announced here are in pilot or will come later this year only. This attempt at freezing a market only shows that the company is not ahead of the curve here. I wish this kind of pre-announcement would not be applied that much. Just for the record: Not only Salesforce exhibits this not so customer centric strategy. But maybe Salesforce wants to be better than the rest of the pack.
]]>Tue, 25 Jun 2019 10:08:42 -0400<![CDATA[SAPPHIRE 2018 - The Return of the Suite]]>https://www.aheadcrm.co.nz/blogs/post/sapphire-2018-the-return-of-the-suite
SAPPHIRE 2018. In an Orlando convention center, far away from Walldorf SAP holds its annual conference, boldly going where no one has gone before. But enough of this poor allegation to Star Trek although it reasonably sets the tone. The first two days gave a deep view into the company strategy. Condensed into two press releases the company laid out its vision of the future of CRM and intelligent enterprises. And, doing so, shot a few broadsides at the competition, especially Salesforce.

The News

“The legacy CRM systems are all about sales; SAP C/4HANA is all about the consumer … when you connect all SAP applications together in an intelligent cloud suite, the demand chain directly fuels the behaviors of the supply chain” said SAP CEO Bill McDermott. SAP intends to achieve this link of front- and back office by fully integrating an augmented suite of solutions that base on the SAP Hybris Cloud solutions into the digital core, the transactional back end. This integration is done via the SAP Cloud Platform. Additionally, it is fusing the new high profile acquisitions Gigya and CallidusCloud into the solution. C/4HANA C/4HANA; source SAP The Customer Data Cloud is what has been Gigya. This model, as well as S/4HANA, will be supported by the SAP HANA Data Management Suite, which is essentially a beefed up Master Data Management Solution around the SAP Data Hub. The second press release is about AI, IaaS, and more AI. SAP starts to talk more about conversational AI and Leonardo, as well as blockchain get more to the forefront. Mostly on the back end.

The bigger Picture

The CRM suite is back! This is an inevitable consequence of the ongoing Clash of the Titans, aka platform war and the realization that customer experience is actually a platform play. Just that the suite now looks different from how it looked like say, 15 years ago, when we saw huge monolithic applications like the venerable SAP CRM, or Siebel CRM. What stays is where at least Salesforce struggles – a common data model underneath the suite. I am, frankly, not so sure about Oracle and Microsoft here, but think that they are following a route similar to SAP. And then there is Adobe, which takes the way from marketing into commerce in a pretty strong move. The acquisition of Magento might be game changing, also considering the strong relationship to Microsoft. Especially Salesforce is forging ahead establishing new abilities for their customers to build new business models by helping their customers and improving internal efficiencies. Being the undisputed number one in the CRM market, Salesforce is the natural target of the other tier one companies. And as I (e.g. here), as well as others, have written: Salesforce is vulnerable. AI and conversational user interfaces cannot be done without. Conversational user interfaces are what will change our way of using computers as helpers. They are what makes computing ambient computing. So there needs to be significant investment in this area. There are lots of specialists around, of which SAP acquired one. Blockchain needs to be covered, anyways. In my eyes, blockchain is still a topic for the back end. Ledgers, tracking and tracing, and so on. Identity seems to be on the verge, and then there are a number of marketing related ideas that I personally still do not regard as proven enough to be viable. Time will tell. Being a big vendor means that one needs to have a foot in that game.

My PoV and Advice

For the keen observer it was all in the coming. SAP always was a suite company. Even R/2 and R/3 already were suites right from the beginning. While some part of the announcement is achieved by a rebranding the overall picture is strong. It plays to SAP’s strengths of being able to deliver end-to-end. A caveat is that SAP is still suffering from a loss of mindshare when it comes to CRM. The company is addressing this for several years now, but it is still not where it needs to be. But the picture is improving as the solution suite is strong. Maybe not everywhere as strong as the competition, but the promise of integration and the sheer power behind a determined SAP is certainly convincing. I know of Marketing Cloud wins against Adobe although some, for marketers, important functionality is stronger in Adobe. Similarly are there (pre acquisition) wins of Cloud for Sales/Callidus against Salesforce Sales Cloud and their CPQ. A common denominator here is that customers did not know how easy to use and powerful the SAP software is. Lacking mindshare, wrong perception. But, what this says is that CallidusCloud, along with Gigya, have been some of the best acquisitions that could have been made (well, include Hybris here). Putting the front office applications under the Hybris brand back the day certainly helped, too. After creating momentum there is brand harmonization now, which is a good thing. Still, my recommendation to SAP is to look for low hanging fruits that are of benefit to the users. It is not all about the consumer. It is also not all about the best possible behind-the-scenes functionality, unless it is of clear benefit to a significant user group. Win the hearts of the users, too! They are the ones who are craving for having their job made simpler. Helping them might be less headline-worthy, but it will pay off in spades. This is where the intelligent enterprise, machine learning, and especially conversational user interfaces, come into picture. The announcement about this is relating to the back office but then there are use cases for the front office, too. Build them, talk about them. Some of them are even already there. But when it e.g. comes to AI based opportunity scoring even I think of Clari, not SAP – which is a shame. Conversational AIs and machine learning have become table stakes. It is good to see SAP productizing SAP Leonardo more and more. So far it looked more like a consulting solution, but this seems to change to the better now. Offering blockchain services seems to be a must these days. It is refreshing that SAP focuses on the back end side here. I am sure there will be some viable customer facing use cases but these still need to mature. Till then it is good to improve existing grounds instead of trying to disrupt. As a little side note, and also looking into Gartners 2018 Magic Quadrant of IaaS solutions, SAP is betting on the right horses. IBM shows up for the private cloud solutions only and is partnered for private cloud deployments. Google, AWS, and Azure are available for public cloud deployments. The next IaaS infrastructure to be partnered with for public cloud deployments should be Alibaba Cloud. All in all, SAP seems on a roll.  
]]>Thu, 07 Jun 2018 12:34:21 -0400<![CDATA[Customer Service in a World of Ambient Computing - The Service Center View]]>https://www.aheadcrm.co.nz/blogs/post/customer-service-world-ambient-computing-service-center-view
A few weeks ago I wrote an article about customer service in a world of ambient computing. This article looked at customer service from a customer’s point of view. In it I described how I see customer service getting humanized again by leveraging the advances in AI technologies like Natural Language Processing, speech-to-text- and text-to-speech generation along with intent determination. Leveraging these technologies customer service will turn into a conversation and it won’t matter anymore whether service is delivered by a bot or by a human. For the customer it will all appear to be the same. Instead of FAQs or web searches, bots will be the first line of support and escalate a problem to humans if they cannot solve it on their own. The obvious question is whether there will be an impact on the customer service center? And it probably does. Call centers, and with it the service agents as well as their managers, already now are under intense pressure to deliver, and to deliver more efficiently. With the increasing use of call deflection technologies like FAQs and communities there is a trend for the incidents facing the agents becoming more challenging. For example Helpshift states that already with its technology it is able to deflect about 90% of all incidents, which are solved via the native in-app FAQ that is delivered by the them. This statement basically says that the support staff is basically relieved of dealing with simple matters but has the chance to take up the more challenging ones. Still, in a world of ambient computing any given app can have hundreds of millions of users. Let’s say that any given day just one per cent of 100 million users have an issue. The well working FAQ deflects 99% of these. That leaves the service center with 100,000 calls. In one day. And they are the harder ones. Still, let’s be optimistic and say that an agent can solve 10 issues an hour, giving him 80 in an 8 hour shift. This would mean an overall call center size of 1,250 agents is needed to cope with this demand. Each of them under a tremendous stress level. With the systems behind bots becoming more and more intelligent the difficulty of raised issues will increase, even if FAQs and web searches are essentially hidden behind a bot interface that essentially makes the human agent the second point of contact again as opposed to the third, which likely means that the customer’s level of annoyance is slightly less elevated than in a third level scenario. At the same time it seems that call center agents are not prepared for handling this stress level. The employee turnover rate remains high and is probably even rising.

What Does This Mean For The Service Center?

Call centers are therefore facing a double challenge
  1. Contain cost. This is achieved by more automation, which in turn puts more strain on the employees
  2. Employ and retain a highly skilled set of service agents, which additionally have matching character traits, which drives cost. Skilled people tend to be more expensive than unskilled ones, and moving a call center into a low-salary country helps only so much – if at all. Training comes at an expense as well. This will be somewhat augmented by reduced hiring cost
The solution to it will be multi-faceted and increase a trend that is already visible. Implement intelligent systems that more than offset the higher salaries demanded – and deserved – by the fewer call center agents, through an increased solution rate and through them being of more help to the service agents. These systems will significantly rely on machine learning out of a variety of sources Employ communities by incentivizing to other users to help other users. These communities will be managed by community managers, with increasing support by AI-driven bots. Highly data driven prioritization and intelligent grouping and routing of incidents to the best matching agent, bot or human. This will involve sophisticated Natural Language Processing capabilities but will help in solving multiple calls regarding the same problem in one process Improved collaboration, bot – bot, bot – human, human – bot, human – human, to further increase the service center’s efficiency. Bot to bot collaboration and bot to human collaboration are for smooth handovers, as for the foreseeable future bots will stay focused on narrowly defined scopes. Human to human collaboration is again a smooth handover to the right expert, but is also about educating the colleague by helping out with own specialized experience. Finally, human to bot collaboration is about the human training the system on the go. Last, but not least, by hiring the right people. An early 2017 study by Harvard Business Review on Kick-Ass Customer Service revealed that call center managers are hiring the wrong people. In scenarios that increasingly deflect calls it needs more highly trained controllers and rocks with a mindset for collaboration, rather than empathizers. While empathy is important what matters most when dealing with a customer in an aggravated mood is a fast and efficient resolution. With this, the role of the manager will change, too, into the direction of being a servant to the team and taking care of roadblocks for the agents and fostering collaboration. This collaboration mandatorily extends into the product department. The best issue is the one that doesn’t even occur. Data from the call center, and from the app itself, gives unique insight into possible problem patterns. And the best problem to have is the one that doesn’t even occur. DevOps gives an idea on how this can get achieved. Ah yes, don’t script too much. Scripts are a good guidance for someone unknowledgeable, which the future call center agent is not. The future of the call center lies in a high degree of automation, powered by highly skilled and motivated agents. That brings the human back to customers and agents alike.
]]>Fri, 11 Aug 2017 09:51:14 -0400<![CDATA[Freshworks acquires Bot Startup Joe Hukum - A Snap Analysis from Down Under]]>https://www.aheadcrm.co.nz/blogs/post/freshworks-acquires-bot-startup-joe-hukum-snap-analysis
A few days ago Freshworks announced the acquisition of startup Joe Hukum, making it its eighth acquisition. Joe Hukum builds a chatbot platform that enables companies to quickly build their own chatbots for sales-, service-, or marketing purposes. In contrast to the technologies built by Frilp (acquired October, 2015) and Chatimity (acquired October, 2016) that rely on NLP (Natural Language Processing, as opposed to Neuro-Linguistic Programming) technologies, these bots are built using a Decision Tree technology. In order to be able to provide more advanced speech recognition they can connect to services of the Stanford Natural Language Processing Group, wit.ai, or api.ai. The created bots can be connected to websites, apps, or Facebook. The press release got published on July 20, 2017, but you can read it right here, before moving on to My Take.   Freshworks acquires chatbot platform startup, Joe HukumCompany enhances capabilities to help businesses build and deploy bots     San Bruno,  July 2017 — Freshworks, the leading provider of cloud-based business software, today announced the acquisition of Joe Hukum, a platform that enables businesses to build their own chatbots based on logical workflows. This acquisition marks Freshworks’ eighth in just under two years, as it further bolsters capabilities to strengthen its business software suite. Freshworks had earlier acquired Chatimity and Frilp, key acquisitions that are enhancing neuro-linguistic programming (NLP) based Artificial Intelligence capabilities, while Joe Hukum’s decision tree based frameworks complete key capabilities to launch chatbot-powered solutions. Joe Hukum was founded in July 2015 by Arihant Jain, Ajeet Kushwaha, and Rahul Agarwal, who were the founding team behind two of India’s most prominent healthcare startups, HealthKart and 1mg. Joe Hukum’s technology is powered by a robust decision tree framework that automates sales, service, and support workflows, across various channels and user interfaces. The Joe Hukum team will be responsible for building bots on top of existing Freshworks products, enabling workflow automations for demand generation, knowledge management, and dynamic in-app self service. “We are seeing strong interest from our customers on how they want to leverage chatbots as they are looking for new ways to engage with customers on their web and mobile channels,” said Girish Mathrubootham, Founder and CEO of Freshworks. “As customer preferences shift from traditional phone tree based call center support, chatbots offer a new support experience, while essentially solving the age old challenge of triaging customer inquiries and routing that to the right support agent. These are still early days for chatbots, but Joe Hukum’s innovative team and technology will help our clients better engage and support their customers.” Consumers today spend the most amount of their time messaging, even more than what they do on social media. According to a Gartner report, by 2019, 40% of enterprises will be actively using chatbots to facilitate business processes using natural-language interactions. “Consumer behaviour is drastically changing, perhaps faster than technology can keep up. Having helped build a successful e-commerce company in India, I realized customers are constantly looking for simpler ways to engage with businesses and chatbots offered a simple, yet powerful way to address this need,” said Arihant Jain, Co-founder of Joe Hukum. “As a founder, I could not have asked for a better outcome for Joe Hukum. Being a part of Freshworks is super exciting for all of us as it gives us a platform that provides us scale and reach and impact hundreds of thousands of customers." Recently dubbed one of Forbes’ next billion-dollar startups, Freshworks has experienced an incredible year of growth. Each acquisition it makes not only brings new capabilities to the Freshworks suite of products but also brings intelligent and creative talent to the ever growing team. To learn more about Freshworks, please visit http://www.freshworks.com.   About Freshworks Freshworks Inc. is the parent company behind the suite of products which includes Freshdesk, Freshservice, Freshsales and Freshcaller. The company’s suite of products is designed to work tightly together to increase collaboration and help teams better connect and communicate with their customers and co-workers. Founded in October 2010, Freshworks Inc. is backed by Accel, Tiger Global Management, CapitalG, and Sequoia Capital India. Freshworks has its HQ in San Bruno, California and global offices in India, UK, Australia, and Germany. The company's cloud-based suite of SaaS products is widely used by over 100,000 customers around the world including Honda, Bridgestone, Hugo Boss, University of Pennsylvania, Toshiba and Cisco.

My Take

Bots are here to stay (and improve). This acquisition is in my eyes an interesting move that focuses on the near term gain that bots can provide through automation of narrowly focusing on specific topics. The decision tree technology that Joe Hukum provides gives exactly that. The customer provides a workflow chart or decision matrix, which then gets converted into a bot. This makes for easy configuration and likely also configurability. Decision tree technology is also more mature than other, AI based, technologies, but it takes a lot of the machine learning out of the picture. This is not necessarily a bad thing for adoption, but also have a look at the concerns below. Acquiring Joe Hukum describes a natural evolution after having acquired chat- and collaboration platforms and technologies in the past. Now Freshworks has gained the ability to improve process automation on their backs – along with another chat platform to consolidate. However, it ties neatly into Freshdesk as well as Freshsales, more importantly also on the channels that humans use. I do see some immediate use cases in lead capturing to conversion processes due to engaging into conversations and in the support area by using bots as the first level of engagement by making them the front end to FAQs. On the concerns side this acquisition is focusing rather on automation and optimization than on customer experience since, judging by the presented flows, there is an IVR-like guidance of the customer, instead of following the user’s flow. On the other hand even companies focusing on speech recognition for long times like Nuance Technology have a hard time with intent detection and the proper reaction to this intent. But then Nuance is focusing on (dynamic) decision trees, too. Another concern could be the reliance on third party speech recognition that Joe Hukum has. While this is inevitable when starting a solution fast speech recognition, and increasingly –generation, will become a core technology that will permeate business- and other applications in a world that moves to ambient computing. But here the technologies acquired earlier should be of help in this area.
]]>Tue, 25 Jul 2017 10:20:52 -0400<![CDATA[Customer Service in a World of Ambient Computing]]>https://www.aheadcrm.co.nz/blogs/post/customer-service-world-ambient-computing
Soon there will be an additional in-app customer service channel. So far we have a bunch of service channels, most of them requiring the user to leave the app to
  • Pick up the phone for a call
  • Browse for self support
  • Open up an additional chat window
  • Take on the social media channels
  • Move on to messenger applications
  • How about getting into your car to get to a store?
  • ...
And then a customer may be moving back and forth between these channels with all the potential of losing track of the incident status and the friction that cross channel customer service still causes. There is no doubt that providing in-app support is the best possibility to offer fast issue resolution. It can provide telemetry information from within the app, identify the user and therefore provides a lot of relevant context that makes it easier for a service agent to help the customer without unnecessary delays. The customers’ shift to emphasize on the “Now” is also seen by Google research.

Not Every Device is a Smartphone

But what if the customer cannot pick up the phone to engage in a typed conversation? The customer might be engaged in a VR game, or driving a car, or in any number of situations without having a free hand. Maybe the customer simply doesn’t want to pick up a phone? What if the app doesn’t offer a user interface at all beyond a little light that indicates ‘I am available’? This would e.g. be the situation in an ambient environment that senses the presence of a person and acts accordingly. An environment like this would mainly be voice and gesture controlled via devices like Amazon’s Alexa, Google Home, Apple’s HomePod, or Microsoft’s upcoming Home Hub. Systems like these will offer a keyboard as a secondary means to access service and support at best. But there is no need to look that far out. Imagine a gaming situation. Neither an Xbox, nor a Playstation, nor any other major controller offers a keyboard. In case of these or a VR or AR game the user would hold the controller and doesn’t have the leeway to get to a keyboard. So why would they offer a keyboard to enable conversational (or other) in-app support? There is no reason. Instead users will interact with the service system and –agent via gesture-, view- and speech based interfaces.

The next In-App Support Channel is Voice

Voice recognition technologies are maturing rapidly and start to achieve human level accuracy in understanding at least English language. Human level of understanding lies at a word error rate (WER) of about 5 per cent, which means that a human on average gets five out of hundred words wrong due to misrecognition, missing a word, or falsely inserting one. Machines arrive at human level of language understanding; source 2017 Internet Trends Report Machines arrive at human level of language understanding; source 2017 Internet Trends Report While Amazon doesn’t give any numbers on Alexa’s capabilities, Microsoft announced it reached a word error rate (WER) of 5.9 per cent in October 2016; IBM beat them to the punch with a WER of 5.5 percent in March 2017. And Google announced in May 2017 that it reached a WER of 4.9 per cent. Using speech recognition techniques on a level like this, in combination with natural language processing (NLP) and perhaps natural language generation (NLG), in-app service conversations can get both, very personal, and very immersive. And very effective. Speaking is the form of communication that comes easiest to humans. Speaking and conversations are tightly linked. Last but not least it is also a very efficient way of providing customer service as the human ability to exchange information is highest when speaking.

Bringing Human Back To Customer Service

Right now customer service 2.0, the automation of customer service, seems to move from call deflection via self-service and then bots to customer service 3.0. Helped by increasing maturity of NLP- and intent detection technology this iteration will move the bots from the second row into the front row. They will become the primary customer support interface. Instead of using a search box on an FAQ, customers will ask a bot for help via a chat interface. The bot itself then will be able to answer the question using e.g. an FAQ or database, or escalate the question to a human operator. The distinction between self-service and aided customer service will first blur, then vanish. The machine takes care of the simpler problems, the human of the more difficult ones. With further refinement of Natural Language Processing, text to speech-, speech to text technologies and intent detection, typing will give way to speech again, then introducing customer service 4.0. Customer service will fully turn into a conversation and it will not matter anymore whether it is synchronous or asynchronous. It also will not matter anymore whether customer service is delivered by a bot or a human, but will appear human. And that might have an impact on the call center and its operation itself that we will look at in a separate post. The bottom line is: Customer service will be humanized again.  
]]>Tue, 11 Jul 2017 07:48:23 -0400<![CDATA[Zendesk - A Mobile CustServ Native?]]>https://www.aheadcrm.co.nz/blogs/post/zendesk-mobile-custserv-native
Mid of April I published an article about the mobile in-app support landscape that, amongst other players, touched on Zendesk. In this article I stated: “Zendesk is not a mobile native. Their chat widget integrates into web pages and the company does not offer in-app chat. Instead the company offers solutions that hook into existing messaging apps like Facebook Messenger or Whatsapp.” This statement was based upon research that I did in the first half of the month with Zendesk publishing their Fabric based in-app support kit on April 19 of the same month. So, maybe I should have posted this article a little later, but good on Zendesk for getting on with mobile in app support. They had, as well as many other bigger vendors in the customer service and call center arena still have, a wide open flank here that gets covered by specialist vendors like Helpshift, Intercom, or LivePerson, or suite vendors like Freshworks.

Zendesk, a Mobile Native or Not?

I say that, although I maybe did them wrong by stating that they don’t do in-app FAQs – although I do not believe so, as the help center content seems to be delivered from the server and needs an online connection. Still I maintain that they are not a native player. I will explain my reasoning a little later, after summarizing what I got out of talks with Douglas Hanna and, more recently, Greg Dreyfus from Zendesk. As per now Zendesk offers two different SDKs for mobile. The support SDK and the Chat SDK (both links go to the iOS version, there are Android versions, too). The support SDK enables mainly three things:
  • The searching and showing of help center content
  • The opening and management of requests (support tickets)
  • And the prompting for app reviews
It is what I would call the essentials of customer service, extended to the mobile world. The chat SDK is what gives the (at least to me) interesting features:
  • Chat
  • Messaging
  • Attachments to chats and messages
  • Chat ratings
  • Emailing of chat transcripts
From a customer base, Greg tells me that between 4,000 and 7,000+ customers (4,000 using the Android SDK and 3,000 the iOs SDK) are using the support SDK and a tenth of that the chat SDK. This makes sense as, without further technology, the support SDK helps deflect calls via self-service and keeps requests asynchronous and therefore helps in keeping the service center small. This is in line with Gartner Group’s observation in their 2017 Magic Quadrant on Customer Engagement Centers that most Zendesk deployments are for 20 agents or less, although there are implementations with 100+ agents, too. On the other hand, the chat SDK is what enables a company to become more customer friendly. It enables the customer to initiate a chat and to leave a message (request, ticket) if no operator is available. I didn’t get any numbers of active devices that are equipped with either of the SDKs.

What does Zendesk think about AI and Bots?

To Zendesk, AI is an important tool in the shed. However, as per Greg, for the time being it needs to be confined to the back end and predictive analytics, as the intelligence is not reliable enough (yet) to handle customer requests unsupervised. So there are no bots on the immediate horizon, as they would need to be limited to overly narrow areas of knowledge. Having said this, Zendesk is looking into this topic and observing the progress, as also evidenced by their release of their Guide product.

My Take

Zendesk is admittedly one of the big kids on the block of customer service centers. Their software is covering all major channels and it offers rich functionality. There are good reasons for it appearing prominently in the G2 Quadrant for Helpdesk Software and in the Leader’s quadrant of Gartner Group’s Magic Quadrant for Customer Engagement Centers. Zendesk has a broad and satisfied customer base mainly in the SMB market. The thriving Zendesk apps marketplace is helping to get even more coverage, or to improve the solution where marketplace partners see opportunities. So, they are clearly doing a lot of things right and are probably even a good alternative for Salesforce, especially as they have a mobile offering, which is a distinct weakness of Salesforce, which on the other hand has a strong position for customers that do not only look into customer service functionality. I, however, do think that both, the mobile support SDK as well as the chat SDK can benefit of some improvement. I have the impression that the support SDK pulls help center content from a server. If that impression is true this should be changed to offering the content directly on the device. If I am right here, this is only a minor one, though. After all, cell coverage can be assumed to improve. Still, the search experience is far better in case of the FAQ being delivered directly into the app. Still, Zendesk is working on adding offline search capabilities in order to offer a better experience. Where I think Zendesk got it wrong is their approach of in-app chat. The way Greg presented the functionality to me it appears that a chat request waits until getting picked up by an agent – which could be any agent, if the customer does not choose to pick one of the offered categories. In case no agent picks up, the customer can convert it into a ticket herself, turning the chat into a message. This seems to come from a strict definition of chat being synchronous and is in my eyes rather service center oriented than customer oriented. It also seems that there is no use of meta information available through the SDK or the text of the request itself to attribute and then route a request into the appropriate queue. This concept appears to me as the extension of web chat into the app and is probably due to the way the back end is designed to work. Here also comes the advantage of a working bot framework. A bot, basing on a good knowledge base and a machine-learning framework will be able to automate a lot of requests in its domain. It can also support the agents by already providing relevant articles ordered by confidence in an assisting role. There are vendors that are capable of both. I think that it would be beneficial for Zendesk to become a leader on this way, as these functionalities will become key requirements in the upcoming world of customer service in an ambient computing environment.
]]>Tue, 04 Jul 2017 09:56:30 -0400