Why RevTwo AI is Not a ChatBot

Chatbots have been around for some time.  Some say that the first one was developed by an MIT professor in 1994. We use them every day as consumers, often without even knowing it, with applications like Facebook messenger, WhatsApp, Alexa, Google Assistant, and others.  We enter text into an app, or we speak into an instrument, asking questions, and the chatbot responds with answers.

Many people think chatbots are AI. The fact is that chatbots do use AI, but they do so in order to understand the text or the language that they’re trying to consume. There is a whole field of computer science called Natural Language Processing (NLP) involving linguistics, information engineering and AI that is designed to improve computers’ understanding of human language.

But chatbots are not AI. Chatbots use NLP to understand the text or the language and then they choose a fixed step or steps to perform. This can come from programmed content (“What are your store hours?”) or it can come from interfacing with backend systems (“When will my shipment arrive?”). The answer chosen by the chatbot is determined solely based on NLP.

Continue reading “Why RevTwo AI is Not a ChatBot”

AI – Why Does Everyone Think It’s Hard?

RevTwo participated in a field service conference last week to show off our latest version of RevTwo AI. We moderated a couple of sessions and also participated in the event’s technology showcase.

At one of the breakout sessions on AI, I asked the 40 or so people in the room this question: “On a scale of 1-10, with 10 being hardest, how many of you think implementing an AI program for service and support will be a 10?”

More than half of them raised their hands. Some of the reasons given were:

-“We’d need at least 1-2 years to organize our data…”

-“We have enough trouble just getting our devices connected. AI is important, but it’s way out there for us…”

-“Data Scientists are expensive…”

-“Our data is %$#@$%&…”

It seems to be the same old story.  Everyone is looking at AI as a multi-year exercise.  Organize your historical data.  Put it into a data lake.  Hire data scientist(s) to pore through it and identify patterns…

That is the hard way. And the old way. And it’ll probably cost you $2-3M in time and effort.

Our latest version of RevTwo AI uses data and observations to provide solutions and/or guidance towards a solution using data and observations collected at the time of the support event. You don’t need to go back and review reams of data. You don’t need a data lake. You don’t need a data scientist(s). You can start training the RevTwo AI to begin resolving issues TODAY. And it’s not a 10. If you want to learn more, please contact us.

Is Your Call Center Turnover Rate High?

We talk to a lot of call center agents, and their job is stressful. Statistics bear this out, as Call Center attrition rates are among the highest of virtually all professions. Attrition rates are averaging anywhere from 30-45% annually, depending on what study you read.

RevTwo AI can help mitigate against high attrition rates in the call center. How? Some examples:

Problem #1: Agents spend an inordinate amount of time just trying to initially understand the customer’s problem. When a call comes in, how much time do your agents spend trying to figure out the status of the machine, what its software version is, when was the last PM, etc? In the meantime, the customer is down and frustration is building.

Solution: RevTwo AI gets data from the product and/or the operator, and can automatically assess this information in seconds and communicate it to the agent. This frees up the agent to spend more of their time on tasks that have an impact on the customer. Making customers happy motivates agents to do better.

Continue reading “Is Your Call Center Turnover Rate High?”

Why not AI instead of an IoT Platform?

Many OEM’s are struggling with IoT. From my perspective, if you have more than 25% of your devices connected to the Internet and under management, you are doing quite well compared to everyone else.

In addition, OEM’s have come to an IoT crossroads today. Many are faced with the challenge of migrating from their current IoT platform, which they’ve been using for years, to another one.  Others are earlier in their journey and are trying to make sense of the number and disparate quality of other IoT platforms that are out there.

Continue reading “Why not AI instead of an IoT Platform?”

There are many different ways to utilize RevTwo’s AI. Even if your device isn’t connected. Even if you can’t access a smartphone.

We just finished our first experience with exhibiting at Field Service USA. It was a great experience and we met a lot of companies interested in our technology.

We showed a demo at our booth of one of the ways to use our AI. For an unconnected device, we install a special script which creates a dynamic QR code showing the status of the device at any point in time. We call this an “Issue Fingerprint”. When there’s a service event, the user selects a function on the device that displays the QR code, then scans the code with a mobile device and submits it to the AI in the cloud, which returns a root cause and solution to the mobile device. You can see a simple demo of how this works here.

Continue reading “There are many different ways to utilize RevTwo’s AI. Even if your device isn’t connected. Even if you can’t access a smartphone.”

Are You Still Having to Look at Log Files?

I was talking to a customer last week about the workflows they use when a customer contacts the support organization for service.

The most common workflow I see is something like this: customer calls the help desk, the agent asks a bunch of questions, and if they can’t resolve the issue then the agent gets a specialist or an engineer involved. They may open a remote access session to connect to the device (IF it’s connected…) If the problem still can’t be resolved, then they dispatch a field service engineer to the customer. While onsite, the engineer either triages and resolves the issue on their own or they talk to their own help desk to help get things sorted out.

This particular customer said “we don’t necessarily do that. If the help desk can’t resolve the issue, then we ask the customer to send us a set of log files for us to analyze.” Then if they can identify an issue through the examination of these files, they dispatch the engineer.

Going through log files is time-consuming and complicated. You have to look at the files sent to you by the customer when there’s an issue and then you have to compare those files with other log files that represent a healthy machine.

Continue reading “Are You Still Having to Look at Log Files?”