Chatbots for Enterprise Customer Service

Chatbots for Enterprise Customer Service

Chatbots for Enterprise Customer Service

access_time Aug/04/2017

Chatbots have proven to be an extremely versatile tool. They're currently being used by a huge number of companies that operate within different industries. Not only this, these automated assistants are also used for an array of purposes, some of which include collecting information, providing assistance with various issues, being used as marketing tools, assisting sales teams, and much more.

Organizations of all sizes have implemented both internal- and external-facing enterprise chatbots that help their team members; in other words, they create chatbots that specialize in either helping their customers or their employees. External-facing assistants, also called customer service chatbots, are the most common types of enterprise bots. They help customer care teams handle the large volume of inquiries they receive every day.

Related: Top 10 Best Chatbot Platform Tools to Build Chatbots for Your Business

Not only do enterprises handle a large quantity of customer service inquiries, they also provide help for a huge variety of different issues. These are often spread throughout different departments, and not all customers end up speaking to the right representatives. Enterprise chatbots work extremely well as filters that can either solve problems that don't require human assistance or allow a human to jump in and take control when necessary.

One of the main disadvantages enterprises have is making their customers feel like they're receiving their undivided attention. Even if this is possible through live support, it’s neither cost-effective nor productive to dedicate so many team members to helping clients. Artificial intelligence-powered enterprise bots offer a great option that can hold conversations with current and future customers, making them feel more appreciated.

While chatbots are a great option for enterprises, finding the right combination requires hard work and time investment. With feedback from customers of ChattyPeople, I've put together a list of characteristics that make an effective chatbot for enterprise customer service.

Related: Enterprise Chatbots Platforms and the Future of Work

Creating a Customer Service Chatbot for Your Enterprise

Before selecting any features, you have to find a reliable chatbot-building platform that is dependable.

Now you can start thinking about the features you want your bot to have. This is one of the most critical parts in the success of your chatbot, so think about your target audience and decide on what they deem as important. You need to consider details about your target audience, such as:

  • Age range
  • Location
  • Social status
  • Gender
  • Any details that will help set tone of voice and personality

Remember, the first edition of your chatbot will probably not be your final version. You should integrate your chatbot with tracking tools so you can measure the performance of your virtual assistant. You can then make changes based on the information gathered to improve user experience and increase efficiency.

Related: Enterprise Chatbots and the Conversational Commerce Revolutionizing Business

Characteristics of an Efficient Customer Service Chatbot

Customer care is one of the most important aspects of any enterprise, regardless of the industry in which it operates. Supporting current and future customers by addressing their issues will help earn your company a reputation for quality and reliability.

There are certain features that most effective customer care chatbots have in common. Keep in mind that enterprises have slightly different needs than smaller organizations. The sheer volume of requests can be overwhelming, so keep these features in mind when creating your enterprise chatbot.

Related: How to Create a Facebook Messenger Chatbot For Free Without Coding

The Ability to Understand Natural Language

Chatbots have come a long way since they were first developed. In the early stages, chatbots could only respond to specific commands and weren't able to understand different variations. Now, AI-powered bots can understand natural language and can fulfill requests without depending on a specific command line.

Proactivity

Keep in mind that AI assistants are fairly new, and most users have never interacted with them. Encourage your target audience to use your chatbot by making it proactive. When users open a text window to talk to your bot, it should automatically introduce itself and provide a list of options from which they can choose.

Related: Make Chats With Chatbots Work

Integration with E-commerce Platforms

Chatbots are very versatile, to the point where they can take orders through Facebook Messenger and comments. Integrating your bot to e-commerce platforms can help you monetize your social media pages easily and efficiently.

A Defined Set of Features

Chatbots can have a huge number of features, but your best bet is to start simple, with a well-defined set of options. This will allow you to tweak your bot to perfection while giving it time to learn and adjust to its responsibilities. Once it has mastered easy tasks, you can give it the capacity of fulfilling more complex operations.

Optimum Functionality

From a user's point of view, there's nothing more frustrating than a chatbot that says it can do something when it really can't. Make sure your bot is able to carry out all tasks you advertise; that way, your audience will never be disappointed with your virtual helper.

Related: The How-To: Using Chatbots As A Tool For Customer Service

Logical Structure

It's important to think about how users will navigate through your bot and figure out a logical design. Presenting all features at once will overwhelm your users, so you have to present just a few at a time. Your bot should be able to ask questions and follow a logical sequence in order to present the adequate options to address the issue at hand.

User Experience Facilitators

Remember that bots are there to help your customers, so implement anything you think will help users solve their issues. Buttons, links, and the ability to be transferred to a human are good examples of features that facilitate user experience.

Predictive Capabilities

AI-powered chatbots have the ability to learn from each interaction. Besides gathering information for advertising purposes, these bots also learn to become predictive and offer a solution before the user brings up any issues.

A Touch of Personality

It's always tricky to give bots personality, especially enterprise ones. Giving a chatbot a little likeability can encourage your team to interact with it and even become a healthy office mood-setter. Remember that your chatbot's personality should always represent your brand's overall tone.

Regular Updates and Optimization

I mentioned before that chatbots should be connected to tracking tools so you can review their statistics. You should use these to make adjustments and fine tune your bot to improve user experience and productivity over time.

Finally...

Although enterprises have different requirements than other organizations, chatbots still provide a great solution to their customer service needs. Gone are the days where your current and future customers have to call in during office hours for some assistance. Enterprise chatbots can now answer questions, serve as a self-serving tool, and even provide specific information about your products and services on demand. Use the tips I outlined above to create an enterprise bot that helps you build a lasting relationship with your audience.

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Panasonic Uses AI to Keep Drowsy Drivers Awake

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According to Panasonic, there's actually five levels of drowsiness: not drowsy at all, slightly drowsy, drowsy, very drowsy and seriously drowsy. The Japanese company developed an in-car system that monitors and detects driver drowsiness before it happens and reacts to it.

The system works through a combination of a camera and sensors which constantly monitor the driver. It can accurately measure blinking features, facial expressions, heat loss from the body and illuminance. This is combined with information gathered about the in-vehicle environment. The sensor and environment data is then processed using artificial intelligence and a judgement made on how drowsy the driver is.

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The key to keeping a driver awake is thermal sensation. Typically, people get drowsy when they are too warm, and that is made worse when the environment is dim. So by predicting the drowsiness state, Panasonic can adjust the thermal sensation of the driver using airflow within the vehicle. Changing the air flow and general temperature combined with adjusting the brightness of the environment can counteract the oncoming drowsiness.

Of course, Panasonic's system can only do so much. If a person is driving in a very tired state then no environment changes will maintain a wakeful state. Panasonic has this covered too, by detecting the higher levels of drowsiness. If such a detection is made, an alarm is sounded and a command to rest issued.

What sets Panasonic's system apart from existing detection systems is its silent operation and ability to predict the driver's state. Most of the time the driver will have no idea they are being monitored and the environment around them is adjusting. They will most likely just feel more awake for the entire journey.

Panasonic expects to have the system available to test by vehicle manufacturers in October. If they like what they see then we could see new vehicles incorporating it next year.


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Elon Musk and Mark Zuckerberg Are Arguing About AI -- But They're Both Missing the Point

In Silicon Valley this week, a debate about the potential dangers (or lack thereof) when it comes to artificial intelligence has flared up between two tech billionaires.

Facebook CEO Mark Zuckerberg thinks that AI is going to “make our lives better in the future,” while SpaceX CEO Elon Musk believes that AI a “fundamental risk to the existence of human civilization.”

Who’s right?

Related: Elon Musk Says Mark Zuckerberg's Understanding of AI Is 'Limited' After the Facebook CEO Called His Warnings 'Irresponsible'

They’re both right, but they’re also both missing the point. The dangerous aspect of AI will always come from people and their use of it, not from the technology itself. Similar to advances in nuclear fusion, almost any kind of technological developments can be weaponized and used to cause damage if in the wrong hands. The regulation of machine intelligence advancements will play a central role in whether Musk’s doomsday prediction becomes a reality.

It would be wrong to say that Musk is hesitant to embrace the technology since all of this companies are direct beneficiaries of the advances in machine learning. Take Tesla for example, where self-driving capability is one of the biggest value adds for its cars. Musk himself even believes that one day it will be safer to populate roads with AI drivers rather than human ones, though publicly he hopes that society will not ban human drivers in the future in an effort to save us from human error.

What Musk is really pushing for here by being wary of AI technology is a more advanced hypothetical framework that we as a society should use to have more awareness regarding the threats that AI brings. Artificial General Intelligence (AGI), the kind that will make decisions on its own without any interference or guidance from humans, is still very far away from how things work today. The AGI that we see in the movies where robots take over the planet and destroy humanity is very different from the narrow AI that we use and iterate on within the industry now. In Zuckerberg’s view, the doomsday conversation that Musk has sparked is a very exaggerated way of projecting how the future of our technology advancements would look like.

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While there is not much discussion in our government about apocalypse scenarios, there is definitely a conversation happening about preventing the potentially harmful impacts on society from artificial intelligence. White House recently released a couple of reports on the future of artificial intelligence and on the economic effects it causes. The focus of these reports is on the future of work, job markets and research on increasing inequality that machine intelligence may bring.

There is also an attempt to tackle a very important issue of “explainability” when it comes to understanding actions that machine intelligence does and decisions it presents to us. For example, DARPA (Defense Advanced Research Projects Agency), an agency within the U.S. Department of Defense, is funneling billions of dollars into projects that would pilot vehicles and aircraft, identify targets and even eliminate them on autopilot. If you thought the use of drone warfare was controversial, AI warfare will be even more so. That’s why here it’s even more important, maybe even more than in any other field, to be mindful of the results AI presents.

Explainable AI (XAI), the initiative funded by DARPA, aims to create a suite of machine learning techniques that produce more explainable results to human operators and still maintain a high level of learning performance. The other goal of XAI is to enable human users to understand, appropriately trust and effectively manage the emerging generation of artificially intelligent partners.

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The XAI initiative can also help the government tackle the problem of ethics with more transparency. Sometimes developers of software have conscious or unconscious biases that eventually are built into an algorithm -- the way Nikon camera became internet famous for detecting “someone blinking” when pointed at the face of an Asian person or HP computers were proclaimed racist for not detecting black faces on the camera. Even developers with the best intentions can inadvertently produce systems with biased results, which is why, as the White House report states, “AI needs good data. If the data is incomplete or biased, AI can exacerbate problems of bias.”

Even with the positive use cases, the data bias can cause a lot of serious harm to society. Take China’s recent initiative to use machine intelligence to predict and prevent crime. Of course, it makes sense to deploy complex algorithms that can spot a terrorist and prevent crime, but a lot of bad scenarios can happen if there is an existing bias in the training data for those algorithms.

It important to note that most of these risks already exist in our lives in some form or another, like when patients are misdiagnosed with cancer and not treated accordingly by doctors or when police officers make intuitive decisions under chaotic conditions. The scale and lack of explainability of machine intelligence will magnify our exposure to these risks and raise a lot of uncomfortable ethical questions like, who is responsible for a wrong prescription by an automated diagnosing AI? A doctor? A developer? Training data provider? This is why complex regulation will be needed to help navigate these issues and provide a framework for resolving the uncomfortable scenarios that AI will inevitably bring into society.

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Why This Old School Activity Is Beneficial to Your Brain

In a constantly connected world, it may seem like there is no need to handwrite anything anymore. But despite living in a time where a legitimate summer movie offering is about the inner lives of emoji, it would appear that cursive is thriving after an extended hiatus.

In early July, a law mandating the teaching of cursive in Louisiana in public schools went into effect, following a similar law passed in Arkansas in 2016. California, Florida, Virginia and Texas are also among several other states who have comparable laws on the books.

Virginia Berninger, a professor of educational psychology at the University of Washington who focuses on human development, told The Washington Post that in her research, “what we found was that children until about grade six were writing more words, writing faster and expressing more ideas if they could use handwriting -- printing or cursive -- than if they used the keyboard.”

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And young kids just learning how to write isn’t the only group that the activity benefits. Studies have shown that in a college setting, those students that took notes by hand were able to absorb and remember more than those who used their computers.

The act of physically writing things down doesn’t only improve your memory. Dave Kohl, professor emeritus at Virginia Tech who taught business and economics, found in his research that people who make it a regular practice to write down their goals earn nine times more over their careers than people who don’t.

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Trouble Scaling? Examine How You Approach Digital Transformation.

Innovation pays off -- and if you’re not regularly investing in it, your company’s growth may be suffering. Seamless collaboration and automation yield bigger savings, greater operational efficiency, more strategically aligned decisions and, ultimately, better business outcomes. In today’s digital world, the only way to achieve this is through innovative technology. 

While effective technology is the clear way of the future (and indeed, the future is now), many organizations still aren’t up to speed. In fact, new research from Harvard Business Review Analytics Services (HBR), in partnership with Scout RFP, found that a large number of companies are lagging in the use of advanced digital technologies for non-customer-facing operating activities. One of the most prominent areas where digital transformation lacks is sourcing and procurement -- which is especially problematic due to increased pressure on businesses to increase efficiency, strategic thinking and collaboration.

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Organizations that have yet to prioritize digital transformation are hobbling their success and hindering their scaling efforts. A sourcing executive interviewed for HBR’s research paper drove home the crucial contributions of technology: “At a company growing as quickly as ours, technology has been essential to our ability to scale. We needed to scale up without asking for a huge investment, which meant we needed to automate.” Greg Tennyson, CPO of VSP Global, echoed this sentiment: “Automation allows all parties to get things done as quickly as possible without a lot of fanfare or oversight.”

So, this begs the question: Why aren’t businesses adopting technology to automate critical business functions as quickly as they should?

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Top reasons, according to HBR research, include the inability to experiment quickly, legacy systems, the inability to work across silos, a risk-averse culture and a lack of corporate vision for digital. Take a 1-2-3 approach to mitigate these roadblocks:

  1. Appreciate: Understand how the right technology can transform your business -- and all the benefits it can unleash.
  2. Educate: Shift the mindset of your company -- all the way up to the top executive level -- to one that embraces and paves the way for a digital transformation.
  3. Automate: Research your options and select the technology that makes most sense for you. Ensure high adoption rates through a comprehensive adoption process.

Owens Corning, a global manufacturer of insulation, roofing and fiberglass composites, took this approach. As Gregg Focht, global indirect sourcing operations leader, noted in HBR’s research paper, “It’s our annual ritual to establish goals for value creation. They’re generally stretch goals, and we’re constantly looking for new and creative ways to meet them.” For the sourcing team, this entailed finding a technology that could meet their needs. “Because our team is global, we needed an easier way to collaborate, a way the older legacy systems could not provide. To address this disconnect, we adopted an automated and cloud-based solution so that the team, the business and suppliers could collaborate in real time.”

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For Owens Corning, these efforts paid off handsomely. In 2016, the sourcing team contributed millions of dollars of hard savings, which helped helped the company achieve record levels of adjusted EBIT and free cash flow.

This is just one example of how the digital transformation can impact companies of all sizes and, ultimately, lead to better outcomes and a bigger business impact. Ready to embrace the potential of effective technology in your own organization? It’s as simple as 1-2-3.

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Why Fintech Startups Need Smart Analytics

Financial technology has been all the rage these days, and there’s no shortage of fintech startups popping up across the globe. However, investments have been slowing down, indicating that the industry is reaching saturation for certain verticals. As such, startups should expect stiff competition.

In addition to increasingly fierce competition and possible consolidation in the horizon, many fintech first-timers fail to take data seriously, especially when it comes to the many question marks surrounding the booming industry. One of the elements fintech actors should consider is of course data and its plethora of applications.

Writing in a blog post about the importance of analytics, Hagit Ben Shoshan, VP of customer success at digital intelligence platform CoolaData, encourages startups to embrace analytics early on. “Don’t wait until your startup is big to start implementing analytics. Understand your user behavior as early as possible to be better prepared for your next high stage of growth.”

Businesses must ensure that they are making smart and guided decisions in order to be competitive. Today, such a level of decision-making is made possible through big data.

"Today’s data-driven professional needs the ability to navigate a wide variety of disparate data sources in a self-service environment, and derive insights before making a decision," notes Sisense CEO Amir Orad in a recent blog post. "Enterprise data tools should empower business units to be data-driven in this sense, rather than retroactively justifying decisions with canned reports," he added.

Indeed, analytics isn’t some buzzword or novelty anymore, and data advocates believe that tech startups must be making data part of their organization’s foundation. Data has proven capable of revealing potential areas of both risk and opportunity that aren’t overtly noticeable.

Data’s importance is even more amplified in industries that revolve around numbers such as finance. The large volumes of data that can be tracked and analyzed in fintech should prove a very rich resource that fintech companies would surely benefit from. This need for analytics cuts across verticals.

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Here are five fintech verticals where smart analytics are crucial to success.

1. Trading

The emergence of data and machine learning has given rise to robo-advisors where artificial intelligence is used to provide customized investment advice to individual users. Fintech ventures such as Betterment and Wealthfront both leverage analytics in order to track user behavior and improve their customer experiences. Betterment cites how analytics helped it introduce a tax impact preview feature that allows users to see their potentially incurred taxes in advance.

Other trading platforms are integrating analytics as well. Recently, CoolaData introduced an integration with the MetaTrader platform that allows brokers to track performance and generate reports quickly. This allows brokers to readily monitor their performance and provide interventions when necessary.

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2. Insurance

Insurance has always been a numbers game. Insurers base the price of insurance premiums on actuarial tables. With the explosion of data, insurance companies can now factor in more information in generating their tables. For insurers, implementing smart analytics allows them to be on top of trends.

The increased connectivity of smartphones, internet of things devices and cars allows for more data about customers to be gathered and used for risk analyses. For instance, geolocation and telemetry data can be used by auto insurers to identify higher risk motorists. A Deloitte paper sees this use of big data and analytics as good for consumers since pricing would better reflect risk.

3. Payments

Payments is possibly the most competitive fintech segment today. More markets are aspiring towards going cashless, and companies from traditional institutions such as banks, tech giants like Google and Apple, incumbents like PayPal and up-and-coming startups are now competing for relevance. There is much demand for real-time transactions in B2C, B2B and even peer-to-peer segments. Forty-three percent of small- and medium-sized businesses around the world claim that receiving real-time payments is crucial to their organizations.

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But, beyond speedy transaction, merchants are actually looking for other potential sources of competitive advantage. Payment providers can offer value-added services such as transaction data reports for use in monitoring market trends and creating spending profiles. Merchants and marketers can use these for marketing campaigns and personalization efforts.

4. Real estate

Real estate appears to be one of the less talked about verticals in fintech but it is a major vertical nonetheless. Real estate is a $217-trillion industry worldwide, and 75 percent of that is from residential property. New ventures could very well explore this as a blue ocean market for fintech.

Like many other industries, analytics has steadily changed the real estate game. It isn’t enough to go by the age-old real estate adages of “location, location, location.” Analytics now allows smarter ways for homebuyers to locate their perfect investments. Services such as Zillow and Trulia have been using information such as census data, property listings, crime statistics and geographic information systems data (GIS) to generate accurate information about properties.

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5. Security

Fintech is a prime target for cybercriminals due to the nature of the information and assets that it handles. As such, fintech companies must put security at the core of their operations. Use of stolen credit card information still plagues many payment processors and merchants. This year, there has been a 200 percent rise in testing or using card information to purchase cheap items to confirm that they work. There’s even a rise in the use of stolen bank account information to purchase goods and pay bills online.

Behavioral analytics play a very large role in fraud detection. Through the combination of analytics, algorithms and artificial intelligence, fraud prevention systems can identify fraudulent behavior with a fair degree of accuracy. These systems can even use historical data from existing consumers to flag unusual activities that are usually attributable to fraud.

Moving ahead

While fintech is still some time away from global breakthrough and dotcom bubbles are a constant threat, the industry enjoys the benefit of data and smart analytics. With that said, fintech can avoid the many pitfalls of other industries thanks to advancements in data collection and the consequent ability to understand consumer behavior.

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