by twieberneit | Jul 25, 2017 | Analysis, Blog |
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 Hukum Company 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...
by twieberneit | Jul 22, 2017 | Analysis, Blog |
MS Inspire, the annual Microsoft partner event, has just ended, wrapping up a flurry of news and announcements from Microsoft and its channel partners. Most announcements were interesting; some more than others, especially when considering these items together. I’ve been following Nimble CRM and its founder Jon Ferrara for a while now, so I was particularly interested in hearing about the launch of its global reseller program and its social relationships insights add-in for Microsoft Office 365 and Outlook Desktop/Mobile. Nimble made its announcements within the context of Microsoft’s increasing its emphasis on partner success, as evinced by its One Commercial Partner initiative to bring together partner-focused teams from across the company and its new ISV Cloud Embed services offerings for partners. Why is this interesting? Microsoft’s lifeblood is its partner ecosystem. In all likelihood, the company has the biggest, most robust partner channel around. Microsoft basically sets, and resets, the gold standard with their constantly evolving partner strategies. A case in point is Microsoft’s announcement to incentivize its partner-focused teams to sell 3rd party partner solutions with Microsoft first party solutions, Microsoft is making it even easier for MSP/CSV and ISV partners to leverage synergies and add value to customers, profits to partners and stickiness to Microsoft products. We also see Microsoft’s Azure co-sell program taking further steps with ISV Cloud Embed. This new program allows partners to embed Dynamics 365, Power BI, Power Apps, and Microsoft Flow into their front- and back-office solutions. (I wouldn’t be surprised if LinkedIn were also integrated into this mix in the near future). If you think about it, this co-sell program...
by twieberneit | Jul 13, 2017 | Analysis, Blog |
On July 12, 2017 Salesforce announced its new, Einstein-enhanced version of Field Service. This release brings mainly three innovations to the already strong Service Cloud, which is the leading Customer Service solution according to Gartner Group. Here the complete wording of the press release, in case you did not want to follow the link but still are interested in it: Salesforce Delivers Einstein AI and Analytics For Field Service Lightning Built on the Service Cloud Platform, new innovations for Field Service Lightning arm the mobile workforce with image recognition technology, smart equipment management and deep analytics to bolster productivity and efficiency Companies including Atlantic Energy are harnessing the power of Field Service Lightning to deliver insight, onsite SAN FRANCISCO—July 12, 2017—Salesforce [NYSE: CRM], the global leader in CRM, today introduced Einstein AI and Analytics for Field Service Lightning, empowering companies to deliver a smarter onsite customer experience that is built on the world’s #1 customer service platform. Field Service Lightning now brings together the insights and intelligence mobile workers need to increase productivity, boost onsite efficiency and drive revenue. The Salesforce Service Cloud has redefined customer service across every major technological shift—cloud, mobile, social, messaging and more. And last year with the introduction of Field Service Lightning, Salesforce extended the power of Service Cloud to create a full service platform for managers, dispatchers and mobile workers. However, as the multi-billion dollar field service market expands into new industries—including finance, healthcare, manufacturing and retail—there is even more demand to deliver onsite service. Field service technicians have to deal with complicated equipment, don’t always have the right parts and often lack...
by twieberneit | Jul 11, 2017 | Blog |
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...
by twieberneit | Jul 4, 2017 | Analysis, Blog |
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)....
by twieberneit | Jun 29, 2017 | Analysis, Blog |
Earlier in June I had the opportunity to talk to Barry Coleman, CTO of Agent.ai, an about 2-year-old company at the time of writing this. The company spun off of manage.com, a very different business that enable the delivery of in-app advertisements. In order to support this mission more and more, first internal, then external support capabilities were needed. At first they built chat functionality for internal and for support purposes. Then there was the question of how to efficiently provide 24/7 support. This resulted in giving birth to a bot structure that can help customer service agents in an assisting mode, called co-pilot mode, and an autonomous mode, called autopilot. And it gave birth to Agent.ai. Agent.ai’s mission is to enable “exceptional customer service for all”. While this mission is not particularly unique, their approach is. First, Agent.ai has built its customer service software around a machine-learning platform. Second, the company provides their solution without asking their clients for a huge upfront investment or the need to have of AI-proficient developers in house. Third, they wanted to avoid the pitfall of inflated expectations. With AI and machine learning being very hyped topics at the moment, this is a very valid concern. Going backwards through the objectives, Agent.ai opted for offering very specialized bots first. As there is no general AI yet, this is pretty straightforward. Specific, tightly framed topics are far easier to support with AI and exposed by bots than broader bodies of knowledge. For example, specializations include the handling of order inquiries or of support call closure surveys. The second objective was achieved by doing all...
