Now Live: Netomi’s Visual Response Builder and other exciting enhancements

Written by Mani Makkar  on   Mar 18, 2021

We’re handing over the keys. Brilliant customer journeys await and now you’re in the driver’s seat. 

Now live on Netomi’s AI Studio, our Visual Response Builder is a highly-intuitive new feature that allows customer service teams to create user workflows in a simple drag-and-drop interface. There’s no coding required. This follows the ability to create simple responses with buttons, quick replies or Web cards, or triggering a human handoff. 

Within the Visual Response Builder, customer service teams can easily configure responses, ask a question, use conditional branching to unlock unique flows based on a customer’s response, and easily escalate chats to a live agent. 

Other features include the ability to:  

  • Personalized customer service based on customer data or gather information from your customers and store it in your CRM
  • Detect, act on and store your business or industry-specific jargon 
  • Send custom tags back to Zendesk which helps easily classify tickets
With Netomi’s Workflow Builder, you simply drag and drop UX elements to create an engaging user journey.

That’s not all. We’ve also rolled out a few other very exciting new features: 

 

Netomi Conversational API

Integrate the power of Netomi’s AI into any third-party or bespoke agent desk and scale across channels with the Netomi Conversational API.  It’s never been easier to launch Netomi’s sophisticated platform on the platforms you’re already using and on every channel your customer reaches out. To learn more about how we integrate with your current systems, reach out to info@netomi.com.  

Human + AI Whatsapp support with our Sprinklr Integration

Offer support on WhatsApp? Let a Netomi AI chatbot act as the first line of defense to gather preliminary information or fully resolve everyday tickets. With our new out-of-the-box Sprinklr integration, you can hand conversations off between bots and live agents. Meet your customers where they are and never compromise the CX.

Interested in seeing a demo of these features and the entire Netomi AI platform? Get in touch today. 

Why Customer Effort Score is the most critical support KPI and how you can improve yours

Written by Emily Cummins  on   Mar 11, 2021

You’re stuck in traffic on the way to the airport. You message your airline asking if your flight is on time, saying you might miss it. The airline responds immediately with a one-touch option to rebook on the next available flight. You don’t have to call to speak with an agent or wait in a line at the counter when you arrive. You’re rebooked on the next flight in an easy tap while you sit bumper-to-bumper. 

Or, imagine your printer sends you a message when you’re about to run low on ink. The company messages you with the ability to reorder the exact cartridge that you need in one click, saving you from the frustration of actually running out or the painstaking search to find the right cartridge.

In both of these scenarios, the companies anticipate the next contact point and remove friction from completing a task. Increasingly, this is what your customers expect. Effort is becoming a crucial differentiator and the core way to compete on CX. This shift in customer expectations has made Customer Effort Score (CES) one of the most important KPIs for customer service and experience teams. Companies looking to turn customer support into a competitive edge need to be tracking CES. 

Here’s everything you need to know about CES.

Customer effort is the biggest indicator of loyalty

The most important thing that support organizations must focus on is making it easy to resolve issues. Effort is fast becoming the catalyst to loyalty: 96% of customers with a high-effort service interaction become more disloyal compared to just 9% who have a low-effort experience1.

An interaction is “high-effort” if a customer has to switch channels, repeat themselves, follow up at a different time or transfer to other departments. Interactions are “low-effort” if a customer can resolve issues in a single interaction and a company removes obstacles and anticipates issues in advance. In a Harvard Business Review study of 75,000 people, researchers found that “delighting customers doesn’t build loyalty; reducing their effort—the work they must do to get their problem solved—does.” The amount of effort a person has to put in impacts future behavior, the research revealed2

  • 81% who report high effort will talk negatively about a company
  • 94% would repurchase if an interaction was low effort 
  • 88% would spend more following a low-effort experience 

Customer effort has knocked CSAT off of the top of the podium in terms of anticipating loyalty. According to Gartner, “customer effort is 40% more accurate at predicting customer loyalty as opposed to customer satisfaction,” the long-standing holy grail of support KPIs1

Anticipate the next contact: Preventing downstream issues is a core way to reduce effort

Resolving issues in a single session and preventing downstream issues are the keys to reducing customer effort. 

Support teams usually focus on decreasing resolution time for a single interaction. This shouldn’t necessarily be the top priority. A longer interaction that provides a more thorough and anticipatory resolution and eliminates future contact not only improves the customer experience but reduces cost. In general, low-effort interactions cost 37% less than high-effort interactions. Low-effort experiences reduce costs by decreasing up to 40% of repeat calls, 50% of escalations and 54% of channel switching1

To anticipate the next contact, smart support AI agents can analyze historical data to identify when customers follow up and with what specific questions as soon as the customer makes contact. The AI agent can then alert a human agent on specific topics to bring up during a conversation, actions to recommend or questions to ask. This allows agents to preemptively educate customers or advise on how to avoid a call back before the conversation ends. Or the AI agent can create this more advanced flow without needing any human agent help, resolving not only the initial issue but anticipated downstream issues.

In a very basic example, if a customer is canceling their cable service because they are moving across town, an agent can offer appointment times to install cable in their next residence. In a more frequent example, retailers have been making great progress into making eCommerce returns effortless. Many companies include pre-paid return shipping labels within the original package, so if an item does not work out, a customer can simply put the label on the same box and drop it at a shipping store. 

When to use Customer Effort Score

Trigger a CES survey immediately following an interaction, such as: 

  • At the end of a customer service experience – including an email resolution, or live chat or messaging conversation 
  • After a customer signed on or subscribed following a free trial period 
  • Following a person experiencing a new service (such as virtual try-ons, contactless delivery, etc.)

You can also send a CES survey after a single customer has interacted with your support team a certain number of times (i.e. 3 or 5) to get an aggregate understanding of their perception of ease of service. This helps to reduce bias from a single experience, balance out human emotion that a customer could be feeling on a particular day, etc. 

How to measure your Customer Effort Score

Like customer satisfaction surveys, CES is measured by a simple question that is sent immediately following an event or interaction. Typically, companies ask something along the lines of “How easy was it for you to get the help you wanted today?”. The customer ranks the effort on a 5 or 7-point scale, from Extremely Difficult to Extremely Easy. 

How Customer Effort Score compares to CSAT and NPS

The other key customer metrics that companies use to get a pulse of customer sentiment are Customer Satisfaction Score (CSAT) and Net Performer Score (NPS). 

CSAT is sent immediately following an interaction (like CES), but measures if the person was satisfied with the experience, not how easy it was. While effort is a core aspect of the overall experience, CSAT focuses broadly on if a person’s expectations were met. One common way to measure CSAT is to ask: “How satisfied are you with your recent purchase/support interaction/service?’ Extremely Satisfied → Extremely Dissatisfied.”

NPS is another popular KPI for support teams. Rather than looking at short-term satisfaction like with CSAT, NPS is an indicator of long-term loyalty. With NPS, companies ask how likely a person is to recommend a product or service. Customers are categorized as “Promoters”, “Passive” or “Detractors.” 

CES, CSAT and NPS are all very valuable metrics. As ease of an experience becomes increasingly critical, however, incorporating CES measurement will be critical to indicate customer happiness with your customer support. 

How to make sure chatbots don’t negatively impact Customer Effort Score

By design, chatbot tools aim to provide immediate answers to customers across channels. This eliminates friction and extra time typically associated with talking to an agent. However, not every chatbot delivers on this promise. Some poorly designed bots actually add more friction, forcing more customer effort…. and frustration. 

To ensure your chatbot makes a positive impact on your CES, move away from rules-based chatbots to AI-powered bots that leverage natural language understanding (NLU) to understand what your customers are saying. Rule-based bots can only answer straightforward questions with a limited set of replies. Unfortunately, humans tend to ask even the same simple questions in a wide variety of ways. Cable company Comcast found that customers were asking even basic questions in more than 1,700 ways.

Above all, a customer talking to a chatbot should always be able to escalate to a conversation to a human agent – at any time. If a chatbot gets stuck, as is common with rules-based bots, a person is more likely to grow frustrated. Sometimes specific needs are better handled by an agent. In those cases, a bot should pass off a conversation within the same channel the customer is already on. 

In Closing: Effort needs to be a core focus to turn CX into a differentiator and drive future revenue

The customer experience is becoming even more critical. Companies are punished for poor experiences through poor online reviews, grievances posted on social media, or customers deciding to never purchase from a brand again. How easy it is for a person to get the support they need is what sets apart good and bad experiences. Brands that want to win their customers hearts and wallets must start measuring customer effort score and work towards consistent improvement on this metric.

Want to learn about the other top customer service KPIs? Check out our blog now.

References

  1. https://www.gartner.com/smarterwithgartner/unveiling-the-new-and-improved-customer-effort-score/
  2. https://hbr.org/2010/07/stop-trying-to-delight-your-customers

With AI Gender and Chatbot Personalities Skewing Female, Be Careful about Bias

Written by Emily Cummins  on   Mar 8, 2021

Today, March 8, is National Women’s Day. It’s a time when we reflect on incredible women who have made lasting impacts on the world. We celebrate the women in our lives  – the friends, family and coworkers – who influence and inspire us.

I’m reminded of stories about American suffragists who won voting rights for women and brave veterans who fight for our country. I’m once again entranced by people like Eleanor Roosevelt who “was a key figure in several of the most important social reform movements of the twentieth century1” and environmentalist campaigner Greta Thunberg who shows such courage at the tender age of 17. At Netomi, I look around at all of the amazing women that I work with across departments – Nadya Pierre in HR, Anmol Bansal in Customer Success, Ridhima Gugnani in Design, Sheena Noori, Maria Springer and Disha Nagra in Sales – among many others – and am inspired daily by their drive, creativity and work ethic.

As all of these incredible women get recognized, I couldn’t help but think about a new age of “females” that we’re interacting with daily: AI. Increasingly, we’re receiving resolutions to issues from companies via chatbots. We’re setting reminders and listening to music via virtual assistants on our countertops. We’re following driving directions and finding the fastest route with the help of in-dash virtual agents.


Discover the key questions to ask when scheduling a chatbot demo.


More often than not, these virtual assistants have female names and voices: Amazon’s Alexa, Bank of America’s Erica, WestJet’s Juliet, Microsoft’s Cortana, HSBC’s Amy. As these virtual agents become more ingrained in our everyday lives, it’s important that companies have diverse teams to weed out any underlying gender bias in the development and ideation process of these AIs.

AI systems tend to be female? Why? 

I’d like to believe AI systems skew female because women convey strength, comfort and gratitude.  These assistants are helping with tasks that have traditionally been completed by women – whether it’s scheduling an appointment or setting a reminder. While some argue this could lead to gender bias, I think we need to focus on the positive aspects of modern-day women. Today’s women are uplifting, hard-workers, always-on and always-available. Whether they are CEO’s, engineers, board members or stay-at-home moms, we can count on them to get the job done in a respectful, polite manner.


“It’s a well-established phenomenon that the human brain is developed to like female voices.”Clifford Nass


Women and the Trust Factor 

Companies introducing AI agents to interact directly with their customers and provide customer service resolutions need to appease the broadest audience.

As a society, we’re generally more trusting of females. We tend to believe that female voices are warmer and less threatening. In a 2012 study, people who used an automated phone system found female voices more “trustworthy.2

Co-author of Wired for Speech Clifford Nass believes “that people tend to perceive female voices as helping us solve our problems by ourselves, while they view male voices as authority figures who tell us the answers to our problems. We want our technology to help us, but we want to be the bosses of it, so we are more likely to opt for a female interface3.”

In customer support, for instance, companies are adopting AI to enable customers to get resolutions via self-service on email, chat, social and voice platforms. Trust is a huge part of customer service: customers believing that they are being heard, their issue or concern is being taken seriously, and the company is resolving their issue to the best of its ability. By using a female chatbot personality, this research would indicate that trust would be more inherent within automated customer service interactions. 

Research shows female voices drive a higher CSAT 

In addition to trust, using a female personality can drive higher customer satisfaction, according to numerous studies.

Business Insider cites a Stanford University study that argued that the “preference for female voices stems from the fact that the voice automatically triggers certain stereotypical expectations in our minds.” So, we “perceive computers as ‘helpful’ and ‘caring’ when they’re programmed with the voice of a woman.4

Amazon conducted extensive research when developing Alexa and found that a “woman’s voice is more ‘sympathetic’ and better received. By making Alexa a female, she seems more like a friendly older sister or girlfriend we — apparently — prefer interacting with, shopping with, and asking for help, rather than a computer, making it more likely we’ll make purchases.4

AI is becoming the new interface for consumer and brand interaction. To get customers comfortable with this new norm, making the experience as non-threatening and natural as possible is key. [Of course, in addition to highly accurate AI that understands customers and provides a meaningful resolution].

A call for diverse team to ward against programming stereotypical behavior

With all this being said, as a society, as technologists and as business people, we need to ensure women play key roles in the development of AI.

To address any underlying bias, albeit usually unconscious, your teams that are dreaming up, creating and bringing to market your virtual agents need to be diverse. In an article that brings together an impressive lineup of women technologists, Bethany Bongiorno, CEO of Humane, Inc. sums it up perfectly: “When a team lacks diversity they are at risk of assuming that their collective experience represents that of all humans. If that team does not include women, by default they are at risk of not building for the human experience of 50% of the world’s population — and, by extension, potentially 50% of their customer base.”

Can we help you nail the AI personality of your brand – Female, Male, Genderless? Let’s chat. We have in-house conversational AI design experts who can bring to life an AI that your customers will trust. 

References

  1. https://edsitement.neh.gov/lesson-plans/lesson-5-eleanor-roosevelt-and-rise-social-reform-1930s
  2. https://www.theatlantic.com/technology/archive/2016/03/why-do-so-many-digital-assistants-have-feminine-names/475884/
  3. https://www.wired.com/2015/10/why-siri-cortana-voice-interfaces-sound-female-sexism/
  4. https://www.businessinsider.com/theres-psychological-reason-why-amazon-gave-alexa-a-female-voice-2018-9?IR=T
  5. https://www.refinery29.com/en-us/2020/02/9461140/digital-voice-assistants-siri-alexa-female-bias

eCommerce KPIs: A Quick Start Guide

Written by Dylan Max  on   Mar 5, 2021

What Are KPIs for eCommerce?

Key performance indicators (KPIs) are metrics that can help you track and assess certain aspects of your business. KPIs are ideally highly specific, providing insights into the productivity of relevant business processes.

An eCommerce KPI can help you optimize the entire purchasing journey, and provide insights into aspects like customer engagement, shopping cart abandonment, order returns, bounce rates and more. Because all aspects of eCommerce are measurable, metrics and KPIs are critical to optimizing an eCommerce operation, overcoming competition and driving profitability.

In this article, you will learn:

  • Top eCommerce Metrics to Track Your Performance
    • Conversion Rate
    • Cart Abandonment Rate
    • Average Order Value
    • Cost per Acquisition
    • Site Speed
    • Customer Engagement
    • Return Rate
  • How to Choose the Right KPIs for Your Business
    • Focus on a Few Key Metrics
    • Identify Both Lagging and Leading Performance Indicators

Top eCommerce Metrics to Track Your Performance

When setting up KPIs, it is important to create a system that makes sense to all collaborators. The metrics below serve as industry standards, used by the majority of leading eCommerce and marketing businesses.

Conversion Rate

In eCommerce, a conversion occurs when a user takes a desired action, like making a purchase. A conversion rate measures the percentage of users who completed these actions. To calculate this percentage, take the total number of “converted” users, divided by the total number of users, and turn the total number into a percentage.

Shopping cart abandonment measures how many users started a purchase, by adding products to their shopping cart, but eventually left the website and did not buy the items they selected. If the abandonment rate is high, there may be a problem with the check out or payment process. The process may be too complex, or there may be other concerns, such as users concerned about security or the trustworthiness of the brand or website.

Average Order Value

Average Order Value (AOV) is the average amount spent by users of an eCommerce site in one purchase. A related metric is Average Abandoned Order Value (AAOV), which is the average value of orders canceled at the checkout or shopping cart stage of a purchase.

To derive insights, track your AOV and break it down by dimensions like:

  • Device type and browser/operating system
  • Traffic source
  • Paid campaign source

Knowing the sources that drive customers with the highest AOV can help you focus your marketing efforts to bring in higher-value customers, and increase return on investment.

Cost per Acquisition

Acquisition cost is the amount of money your organization spends to get a new paying customer. This includes:

  • Paid advertising
  • Promotions or discounts on list price
  • Email nurture campaigns
  • Any other investment made to bring in new customers

Analyzing cost per acquisition shows how much effort and monetary investment is needed to acquire paying customers. This should be compared with AOV and long-term metrics like customer lifetime value (CLV), to understand return on investment.

Site Speed

Website speed is a significant factor impacting user experience. Websites and checkout carts that are slow to load are often abandoned. Customers do not want to wait long, and expect good performance and efficiency from eCommerce sites. To ensure a positive experience, you can monitor the speed of your website using tools like Google’s PageSpeed Insights, optimize images on your website, and use a content delivery network (CDN).

Customer Engagement

Customer engagement KPIs measure the level of engagement between customers and eCommerce businesses. High engagement rates often indicate a deeper connection with brands, which often promote customer loyalty and retention. Typical indicators include social media shares and reactions, newsletter subscribers, and time spent viewing site pages.

Return Rate

E-commerce can only be successful if the customer is satisfied with the method of ordering, the goods and the speed of delivery, and has the right to cancel their purchase. At the same time, a high return rate carries a risk of loss, so online store managers need to pay special attention to  the percentage of returned orders.

However, in the online fashion industry and some other eCommerce segments, returns may actually be encouraged, in order to solve customer concerns about fit of clothing and other personal items. In these cases, the returns metric should be treated differently.

How to Choose the Right KPIs for Your Business

Focus on a Few Key Metrics

There are many KPIs to choose from, and a lot of data to analyze. In theory, it is possible to keep track of many KPIs, but this might not be a good idea. Each KPI provides different insights, and not all might be relevant to your current situation and objectives. Instead, you can choose certain metrics that are currently relevant to your site and leverage the insights quickly.

Identify Both Lagging and Leading Performance Indicators

The majority of businesses focus mainly on identifying lagging performance indicators, which analyze events that happened in the past. Cart abandonment rate and website speed, both measure past performance.

Leading performance indicators, on the other hand, try to predict future events and provide insights into trends that have not happened yet. You can try, for example, to predict sales opportunities and then create marketing funnels accordingly, or predict future website traffic.

KPIs eCommerce with Netomi

Netomi works with eCommerce companies to improve critical KPIs related to eCommerce customer experience. The support a person receives is having impacts well beyond the customer service team. Consider these eCommerce stats:

  • 90% of Americans use customer service as a deciding factor when choosing to do business with a company
  • 78% of customers say the quality of service is fundamental to earning their loyalty and repeat business
  • Seven in 10 U.S. consumers say they’ve spent more money to do business with a company that delivers great service
  • 61% have switched brands due to poor customer service with nearly half having done so in the past 12 months

Consumers expect immediate resolutions to their issues across an increasing number of channels. Our AI-powered chatbot tools automate resolutions to over 70% of everyday tickets including order status, refund requests and order cancellations. Key features of the Netomi AI chatbot platform for eCommerce companies include:

  • Natural Language Understanding (NLU): The Netomi AI can understand what your customers are saying no matter how they ask. So, whether they say “Where’s my order?” or “Is my sweater arriving today?” Netomi AI chatbots can provide the relevant response.
  • Integrations: Netomi can integrate with backend business systems to provide personalized resolutions, not generic information.
  • Deep Learning: Netomi’s platform leverages a combination of supervised and unsupervised learning to boost intent classification capabilities, increase confidence scores, and uncover new training opportunities.
  • Omnichannel: eCommerce companies can meet their customers where they are. Netomi’s AI chatbots scale across email, social, messaging, chat and voice.
  • Pre-trained AI with eCommerce skills: Netomi’s AI comes with pre-trained, thoroughly-tested eCommerce skills. Common eCommerce-specific topics like refunds, order modifications, order status and order cancellations have real-world experience making the bot able to understand a wider variety of ways a person might as a question from day one.

Would you like to learn about how Netomi helps improve eCommerce support KPIs? Check out the most powerful eCommerce chatbot on planet Earth.

Ebook: How Conversational AI Is Rewriting the Rules for Fintech Customer Experience

Written by Emily Cummins  on   Mar 1, 2021

Fintech companies are only getting half of the Customer Experience right. Fintechs have deftly unbundled traditional financial services into a growing variety of laser-focused and streamlined mobile-only and digital-first experiences. By focusing on the AI Customer Experience, fintechs have set new standards for ease of use and customer centricity.

Unfortunately, this innovation has not extended to support. While fintechs tend to excel at the core competency of onboarding customers and helping them use the app or service, they often fail to incorporate modern AI-powered support systems into their technology stacks to provide immediate resolutions to customer issues. As fintechs scale, they risk running into support problems.

To evolve into global brands capable of handling tens of millions of customers, tech-savvy fintechs need to embrace better customer support technologies and conversational AI in order to deliver effortless experiences for customers and rapid resolutions to support queries. 

Why The “Support Light” Option Is No Longer An Option

Just as fintechs are on the cutting edge of creating more efficient ways to use financial management tools, they need to offer a complete solution for customers. This means upping their support game and leveling up their holistic customer experience approach to make products more hospitable for all users – not just for digital natives and early adopters.

While most of them are doing a very good job on Customer Experience for onboarding and using the application, few have spent time studying the true pain points in support. Just as streamlining and automating processes has given fintechs a leg up on traditional banks, the upstarts can gain unfair advantage if they can apply their smarts to delivering support experiences as seamless as their apps.

The Conversational AI Arms Race In Financial Services

A growing number of large financial services institutions are investing big bucks in building Natural Language Processing banking chatbots like Capital One with Eno and Bank of America with Erica.

Erica, Eno and other pricey efforts have sparked an AI-driven arms race in customer experience and its close cousin – customer support. Financial services customers now expect and demand to interact in real-time with an intelligent conversational agent – human or bot – that provides good answers to complicated problems. This expectation is making more advanced customer service chatbots and proactive customer support table stakes and a linchpin in the war to attract and keep customers. In particular for fintech companies and direct or digital-first banks and payment platforms, users expect cutting edge CX and interactive chatbots as well as other intelligent support right off the bat. 

The six pillars of stellar financial digital customer support 

From digital millennials to Baby Boomers, adoption of digital channels for financial services applications are booming. In our interactions with financial services customers, we have identified the following key pillars of building a great digital CX. 

  1. Prioritize development of comprehensive mobile in-app support
  2. Elevate omnichannel with seamless integration
  3. Identify key customer processes and make them frictionless
  4. Be always-on and always-available
  5. Empower chatbot tools and conversational AI to handle quick tasks instantly
  6. Provide proactive service wherever possible

Conclusion: Fintechs Must Provide Holistic Customer Experiences Including Robust Support 

The way that people engage with financial services is rapidly shifting to mobile and digital and fintechs are leading the charge. By building their businesses as digital first and often mobile-only, modern fintechs are creating engaging, streamlined and intuitive user experiences in tune with the raised expectations of modern customers. That said, fintech upstarts have often not developed out their Customer Support technology or delved into advanced forms of automation and AI to improve support.

Want to learn more? Download your copy of How Conversational AI Is Rewriting the Rules for Fintech Customer Experience today! 

Check out the links below to get more of our insights on Fintech customer service solutions!