The Opportunity for 3PL Providers to Transform Shipper and Carrier Partnerships with AI

Written by Puneet Mehta  on   Mar 23, 2022

As consumer behavior rapidly changed during the pandemic, accelerating the shift from in-person to online shopping and the expectation for speedy shipping, the Third-Party Logistics (3PL) industry has boomed. 63.5% of shippers now outsource their warehousing activities to 3PL providers1, 81% outsource domestic transportation2 (which is the most common services outsourced1), and 44% outsource freight forwarding3. 3PLs are an essential component of moving goods through the supply chain and ultimately, moving the economy forward. 

The 3PL market was valued at around USD 1trillion in 2020, and is expected to reach more than USD 1.75 trillion by 20264. In 2020, C. H. Robinson5 and  XPO Logistics generated over 16.2 billion U.S. dollars in revenue alone6, and this is on the rise. In February 2022, XPO Logistics reported its highest quarterly revenues ever7.

3PL is a large business, and with that, it has become an aggressive and highly competitive one. With 58% of shippers indicating that they are increasing their use of outsourced logistics services8, capturing this growing market and deepening partnerships with shippers and carriers are top-of-mind for both enterprise and small 3PLs. 

The evolving 3PL competitive landscape and the increasing role of customer experience

The domestic freight industry is complex. According to a description from one account, “Truck freight is also one of the least efficient industries: It’s chaotic, highly fragmented, regional, and, plainly, a logistical nightmare9.”

There are currently 1.6 million carriers, 95% of which operate 10 or fewer trucks, 3 million drivers, and an estimated 100K shippers9. Moving goods through the supply chain is only made more complex by factors such as an industry-wide driver shortage, global supply chain shortages, rising end-customer expectations for quick delivery, and different requirements for vehicles such as refrigeration or flatbed.

To power this industry, smaller, more specialized 3PLs are competing with the likes of FedEx Supply Chain Services, UPS Supply Chain Solutions, Amazon Marketplace and Walmart Marketplace. And while getting costs down is crucial, customer experience is now becoming an important competitive differentiator.

Changing CX expectations of shippers and carriers

Expectations for good customer experiences are high across every industry, and shippers and carriers are no different. In addition to efficiency and lower rates, customers in this industry expect effortless, personalized interactions, proactive care, and 24/7 support, just like customers of an airline, meal-kit, grocery delivery or eCommerce company.

Research has shown that 73% of shippers interact with their 3PL on a daily or hourly basis8. Given that there are at least 100K shippers as mentioned previously, the number of contacts from customers 3PLs are getting is mindblowing. Couple this with the rising expectations and the workforce shortage in the wake of the great recession, and it’s clear that 3PLs can no longer rely on a human-only workforce. The need to adopt tools like conversational AI to scale personal 1:1 interactions will be the key to competing on customer experience.

The new standard for customer experience and the impact of AI

To capture the loyalty of in-house logistics personnel at fleet operators and the growing number of enterprise and small shippers, 3PLs can leverage AI to automate interactions across the entire journey. Some of the most promising applications of AI for 3PLs include:

  • Quote Automation: Historically matching carriers with shippers was a very inefficient process often conducted over the  phone or email and populating data including truck type, origin and destination, and seasonality in excel sheets. The process of providing quotes, though, is evolving. In fact, in its earnings release, XPO Logistics reported that “70% of its brokerage orders were created or covered digitally10.” 3PLs can leverage AI-powered assistants to provide immediate quotes on email, in-app or via chat and match shippers with carriers in seconds with zero human effort. These virtual agents can also answer questions that arise during the process to increase conversion rates.
  • Shipping visibility: AI-powered assistants can pull real-time status and location of shipments, 24/7, and identify any threats to promised delivery and pick-up times.
  • Proactive care: By tapping into back-end systems, conversational AI agents can anticipate issues and proactively communicate with both shippers and carriers before they even know an issue exists. If a shipment is delayed, for instance, 3PLs can automatically alert businesses which items failed to ship the previous day and why, which helps foster a partnership based on trust.
  • Driver assistance: AI agents can communicate with drivers in real-time to provide updated route information. If the driver experiences an issue, they can chat with an agent for roadside assistance, troubleshooting, and more.
  • Business planning and operations: AI agents can forecast future orders, manage deliveries and schedule pickup and drop offs. These agents can also send invoices, respond to questions and provide information on billing.

AI Agents will drive the 3PL industry forward

The burgeoning eCommerce market is becoming more diverse. Each company has its own needs and requirements, offers personalization to their customers, and expects the same type of treatment from the businesses that it does business with.

Simply coordinating shipments over phone and email is no longer a viable option. It will be the 3PLs that have an advanced technology stack, including AI-powered virtual agents, that will build those all-important long-term, profitable relationships with both shippers and carriers. With AI-powered agents streamlining processes and automating mundane tasks, the human workforce will be able to manage more complex issues and tasks, providing the best possible customer experience.

Discover the return on investing in AI for customer service for your 3PL business! We’ve crunched the numbers for you – check out our ROI calculator to learn more.

References

  1. https://www.statista.com/statistics/660142/transportation-and-logistics-most-outsourced-services/
  2. https://www.3plworldwide.com/statistics-for-3rd-party-logistics-companies
  3. https://www.plslogistics.com/blog/8-fascinating-statistics-from-shippers-about-3pls
  4. https://www.mordorintelligence.com/industry-reports/global-3pl-market
  5. https://www.statista.com/statistics/690326/c-h-robinson-total-revenue/
  6. https://www.statista.com/statistics/693130/xpo-logistics-total-revenue/
  7. https://www.bizjournals.com/triad/news/2022/02/09/xpo-4q-2021.html
  8. https://www.plslogistics.com/blog/8-fascinating-statistics-from-shippers-about-3pls
  9. https://www.ycombinator.com/blog/convoy-the-future-of-truck-freight/ 
  10. https://investors.xpo.com/news-releases/news-release-details/xpo-logistics-announces-fourth-quarter-and-full-year-2021 

msg.ai has a new, exciting name: Netomi

Written by Puneet Mehta  on   Aug 21, 2019

We’ve grown. We’ve evolved. We’re ready for a new brand identity. We’re now Netomi

Our Mission, Time and Love 

The two best gifts you can give someone are time and love. That’s what we want to enable every company on the planet.  

We’re giving time back to consumers that they previously spent waiting for a resolution. We’re giving time back to employees, eliminating repetitive and monotonous tasks. Now, these employees can spend their time on much higher value work. From this time, it grows brand love from both customers and employees, unlike anything we have ever seen before. 

Welcome to the relationship economy 

Thankfully, the world looks a lot different than it did when we started four years ago. We now live in a relationship economy. Customer experience is now at least as important as product quality and price in determining where people spend their money and assign their loyalty. Customer service has now become a major point of differentiation for companies of all sizes. 

Today’s customers expect more: outcomes, not just ownership; personalization, not generalization; constant improvement, not the status quo. The battleground for superiority in business is no longer earned from ruthless category dominance, but rather customer experience that relishes in effortlessness and ease. 

While our AI has always been leveraged to facilitate instantaneous and personal customer interaction at scale, we’ve now graduated to helping companies ensure that customer support is a business driver.  Named a Gartner 2019 Cool Vendor in CRM Customer Service and Support, our secure AI customer service platform is used by companies to automatically resolve over 50% of their incoming customer service tickets instantaneously on email, chat and messaging apps. 

In one example, we’ve helped our customer WestJet, embrace these new expectations by resolving more than 50% of all inquiries on chat instantaneously, which has driven a 24% increase in consumer satisfaction. These incredible results and more were recently recognized in a case study by Facebook. With such an impact, AI has become the biggest workforce multiplier since the invention of the computer.  

Our deep learning neural network ensures a company’s AI is continuously improving, and our rich conversation engine has hundreds of thousands of hours of real-world training and experience to ensure a greater understanding of context and delight. Providing a world-class customer experience, that significantly boosts customer satisfaction, is core to what we do and something we take a lot of pride in. 

Our new identity                                                                                                

Netomi, a combination of three words (Net, Om and I), is truly representative of why we exist. We empower businesses to leverage the “Net” (sum) of our AI and their human capital for connecting with what is fundamental to them (“Om, the primordial sound”) to bring out their best self (“I”) for their customers. 

We’ve picked the caret as our main mark to showcase how we are a business and workforce exponent. [^n = raised to the power n]. Whatever our customers are doing best, we raise it to power. 

We’re ushering in a new era of customer interaction that is magical and effortlessly easy. We’re creating a world where work is delightful and meaningful. We couldn’t be more excited about this future that’s already happening. While stepping into the next stage of growth, we have a lot of people to thank for the amazing ride so far – our customers, investors, advisors, platform partners and, of course, everyone at Team Netomi. 

Come join us on this exhilarating journey. This rocket ship taking off in 3…2…1!

The Turing Test Holds No Value In Assessing Conversational AI

Written by Puneet Mehta  on   Sep 25, 2018

This originally published in VentureBeat

AI is becoming the new user interface. From self-driving cars and Amazon’s Alexa to Robo-advisors and facial recognition locks, consumers are interacting with AI like never before. And this is just the beginning.

For years, AI enthusiasts have used the Turing test as a guide for developing conversational bots. Developed in 1950, the Turing test focuses on believability, analyzing a machine’s ability to behave indistinguishable from a human; researchers have long considered passing the test as the holy grail of AI. This benchmark, though, was created in an era when AI wasn’t common, and teams created machines with the goal of creating a human clone.

Over the past few decades, Hollywood’s portrayal of AI in movies like Her and I, Robot also sought to replicate human characteristics. Tinseltown’s version goes way beyond what today’s commercial tech can achieve, but we still seem to measure modern applications of AI against these fictitious interpretations.
 

Solve problems, don’t just ape humans

Today, we’re somewhere between the Turing test and Hollywood’s in-your-face robots. AI is surpassing human capacity in subtle but powerful ways like diagnosing diseases. It’s the technology powering some of the most advanced applications in the consumer tech market, and we’re just on the cusp of implementation.

In the application of modern AI, the number one goal is to solve problems. Reproducing human characteristics is only one ingredient in a complex concoction of an effective AI, and many human characteristics are even counterproductive. Yet we still see engineers building things like time delays in conversational AI responses to make it appear as though a bot is “thinking” and similar tactics to contort technology into passing the Turing test.

When aeronautical engineers designed the 747, they tested whether it could cross the Atlantic — they didn’t try to build a mechanical pigeon. Similarly, self-driving cars learn in a unique way and behave much differently than cars with a human behind the wheel. Why should AI have to hew to the human model?

With conversational AI’s growing prominence, it is critical to have a universal, realistic understanding of what we consider to be a success and what we deem fails to meet today’s standards. AI will make a different set of mistakes than humans do and will also learn from these mistakes differently. This means we need to measure success for machines differently than we do for humans.


Discover The 16 Best AI Chatbot Vendors With Reviews and Features.


New success metrics for AI

So how do we update the Turing test for practical applications of conversational AI? We need to get away from how “advanced” it feels and focus on the primary goal: efficiency. We should regard AI as providing a significantly better alternative to how we solve problems today. As we move forward, we also need to widen the scope to encompass all intelligent behavior that could be useful to the end-user. Here are several KPIs researchers could use to more accurately measure the success of AI.

How AI applies context

AIs should not work in a vacuum, but be situationally aware. Conversational AIs have increasing access to various contextual triggers that should tailor the experience, and they are in a unique position to leverage this data in ways that would not necessarily work with human agents. For instance, as a consumer, if a human customer service rep knew exactly where I was, I might feel creeped out. With an AI, I might think it’s cool, especially if it’s giving me something with immediate relevance.

How AI learns over time

An AI should learn from every interaction. For example, a researcher might consider if a bot provided the right information based on a person’s response and tone. They also might want to look more closely at the user questions that the machine is unable to answer. The sign of a good AI is not top performance on day one, but an upward-trending curve.

How comprehensive and connected an AI is

Most “great” conversational AIs so far are really good at one single thing, which is not practical for the long term. AIs need to connect with various systems to span the entire customer journey, enabling the person to complete everything in a single place. A retailer AI, for instance, needs to personalize product recommendations, manage a CRM, conduct orders, provide status updates, and manage customer support.

How well an AI holds memory

A person should never have to reintroduce themselves. Conversational AIs need to have short-term and long-term memory, keeping and acting on what an individual has liked in the past. When you call customer service, send an email, or walk into a store today, you’re a stranger; your preferences, past purchases, and social comments on a brand all are unknown. A compelling AI will act on the data seeds a person has sprinkled over the entire conversation.

How AI predicts needs

AIs need to tap into predictive algorithms that can anticipate what a consumer might need based on their historical context and current situation. The AI should analyze aggregate data to identify the best course of action from what has resulted in the most positive sentiment in similar circumstances.

How flexible AI is

AIs need to be where the consumer is. Good AIs can’t be available only on chat, or websites, or voice calls. What will distinguish successful AIs from the rest will be cross-platform performance and the ability to hold the same knowledge base at every touchpoint.

AI is not human. And humans are not AI. There are always going to be things that a human will do better — having empathy and solving complex first-time issues are a couple of good examples. Only when AIs exhibit the ability to solve problems more quickly and intelligently than humans can we start flying over oceans, sidestepping the blueprint of the mechanical pigeon.

Learn more about AI for customer service here.

For more information on conversational AI, discover how to provide brilliant AI-powered salesforce chatbot solutions to every customer, every time.

Stranger than fiction: Reinventing Retail

Written by Puneet Mehta  on   Aug 27, 2018

Reinventing Retail CX with AI

Matt Gunn and Puneet Mehta of Netomi, a Silicon Valley startup focused on conversational AI, discuss how deep learning will shape the future of retail as consumers engage with brands (and their AI) to achieve more personalized, intimate shopping experiences.

Listen here.

To get more of our insights on our retail bot and customer service before you contact us to request a demo, check out:

Would you trust an AI with your wallet?

Written by Puneet Mehta  on   Aug 23, 2018

Trust is having a moment. And for good reason.

The backlash Facebook is facing boils down to breaking the trust of consumers in how they protect their data. To rebuild consumer confidence, Facebook is turning to AI to solve their problems.

As AI is used more by brands to improve operations and decision-making, and even more so to interact with consumers on a daily basis, it is now more critical than ever to build a strong foundation of trust. The potential for AI is huge; in the near future, brands and individuals will be represented by an AI on the internet, and the only way for brands to reach their consumers will be to go through their AI. As this new AI era approaches, the trust threshold will remain fragile.

In 2015, when Sony Pictures launched “Slappy,” a conversational chatbot in support of the Goosebumps Movie, they saw astronomical engagement on Facebook Messenger: an average of 10 minutes per conversation, with some conversations lasting up to 2 hours. But the conversational AI was so good that some people thought they’d made an actual new friend. One user even invited Slappy to her chemotherapy appointment the next day.

This raises some serious ethical questions about how brands present their AI to their customers. Should your bot be upfront about being a bot or try to pass off as a human agent? Is it the customer’s responsibility to approach chat, voice or email interactions with caution? [For the record, I would recommend owning the fact that an AI is an AI].

In addition to how an AI is presented, trust in an AI is built (and broken) in many ways. As AI permeates more parts of the customer journey across marketing, sales and service, consumers will need to be able to trust your AI. 

How to get consumers to trust your AI

Trust an AI with your refined intent

Does the AI actually understand what the person is asking or trying to accomplish? If the AI is constantly getting confused, the person will not trust the AI customer support to help accomplish the task at hand and stop engaging.  AIs need to be able to understand the majority of what a person is communicating, including today’s digital slang (emojis, stickers). AIs also need to be built on a deep learning platform so it is constantly expanding its knowledge base, increasing the frequency of classifying intent correctly.

Trust an AI with your identity (piecemeal or not)

A person should never have to reintroduce themselves to an AI and rather all of the information they have revealed to the AI over the course of the relationship should help to drive relevance in future conversations. The AI should also connect to a brand’s CRM in order to personalize the interactions based on cross-channel profiles. However, in order to retain trust, always enable a person to opt-in for data sharing and storing.

Trust an AI with your wallet

AI will soon be the single channel for all brand and consumer activity, including purchases. Being able to trust the AI to store payment information will help to create a completely seamless commerce experience, greatly enhancing the customer experience. If a person doesn’t trust the brand to keep payment information secure, though, the magic of a single channel touchpoint will be lost. Clearly communicate how payment information is secure or stored if held by the brand or connect to a third-party like PayPal or Apple Pay.

Trust an AI with your emotions

If a friend tells a joke when you’re discussing something serious, you would probably grow frustrated or upset. Similarly, an AI needs to be able to read and react to a person’s sentiment and tone.  Beyond the text or spoken word, how is the person feeling? An AI can tap into this by looking at the conversation in its entirety, assigning a sentiment grade to every interaction in order to say the proper thing or send appropriate content. If an AI is a repeat offender of not understanding a person’s emotions (particularly on the poles, really happy / excited or really sad / mad), they might get fed up with the relationship.

Trust an AI to transfer context across AIs

Brands may soon have various AIs at work simultaneously managing different parts of the customer experience. There might be customer support AIs answering questions, AIs supporting human agents to reply quicker and more accurately, AIs that focus on guided selling and personal shopping, etc. AIs will be specialists. If, for instance, someone needs to return something she has ordered (with the personal shopping AI), she might need to be “transferred” to the customer support AI, who can help to facilitate the return.  If this “hand-off” occurs, the necessary information (like items, loyalty status, etc.) needs to be passed along, so the conversation isn’t starting from square one.

Today’s consumers are more comfortable with, and sometimes prefer, interacting with AI. There is no better way for brands to reinforce that positivity than by building trust. If trust is broken, it will be very hard to convince a person to give it another chance.

For more information, check out: The 16 Best AI Chatbot Vendors With Reviews and Features.

Self-driving customer relationships: What marketers could learn from Tesla’s Autopilot AI

Written by Puneet Mehta  on   Aug 23, 2018

AI is becoming the new user interface.

Instead of website searches, there are personalized conversations. Rather than being placed frustratingly on hold for customer support, there is immediate help on a person’s preferred channel. Instead of mass marketing, there are individual, 1:1 relationships.

As conversational AI becomes more central to businesses’ success (Gartner predicts that by 2021, more than 50% of enterprises will spend more on bots than traditional mobile), brands need to start thinking about training their AI just like training their employees. Developments in machine learning–including deep reinforcement learning–promise to revolutionize the way businesses think about AI, making it more intelligent and human-like as compared to simpleton bots, but as the technology continues to advance, so will consumer expectations for AI-driven experiences.

The AI Building Blocks

Context, short-term and long-term “memory”

The AI should not require a person to reintroduce themselves, but rather provide an experience that gets more personal and relevant over time. The little tidbits a person reveals, whether it’s style preferences, needs/goals or even budget, should help to shape future interactions. A person should never have to tell the AI the same thing twice, unless, of course, the data or personal preference is subject to change over time.

Ongoing learning

A core part of an AI’s “DNA” is that it should continuously improve. AI learning falls info a few core categories: improved understanding of natural language and intent mapping; how to best engage with specific audience clusters in a given moment and context; and a user’s propensity to convert based on a confluence of factors. An AI’s learning is never done.

Communication with existing business systems

An AI experience can’t exist in a vacuum. By nature, it’s an incredibly personal interaction and needs to tap into a brand’s CRM, commerce and other systems in order to make the experience as personal and relevant as possible. The AI should also feed the unique insights gleaned from 1-on-1 conversations back into these systems in order to create the most personal cross-channel experience.   A travel chatbot AI should also incorporate as many parts of the customer journey as possible. For an airline, it shouldn’t just provide travel inspiration tools, but also end-to-end booking and day-of travel support, such as rebooking and check-in. Similarly, retailers should provide guided selling and personal shopping, checkout, order tracking and support all within a single AI touchpoint. AIs true benefit comes to life when it’s powering the complete customer journey.

Human escalation protocols

As advanced as AI and customer service gets, there will always be things that a human can do better, such as complex problem solving or showing empathy in unique situations. When a situation arises in which the AI doesn’t have the skillset or emotional maturity to manage, the user can quickly get frustrated and conversations need to be seamlessly escalated to a human to take over. This handoff should happen within the same channel to cause no disruption to the consumer.

Brand safety controls

Brands need to protect themselves and make sure that they are not at risk of falling victim to merciless trolls. Therefore, every AI needs to be trained to disengage from conversations that could put them at risk. Whether it’s ending a conversation immediately when an inappropriate or sensitive topic comes up, warning users and having a three-strikes-and-you’re-out policy, or elevating to a human agent, AIs need to know what topics are off-limits and how to respond appropriately.

Measurements for business impact and AI performance

What good is an AI experience, if you can’t measure its effectiveness? Brands need to set goals and track conversion, whether it’s purchases, engagement, support or loyalty initiatives. Also, understanding how the AI itself is learning and optimizing over time as well as how the brand is saving operating costs typically associated with human agent support are key metrics to measure.

We’re at a critical point in the AI life cycle. Companies like Tommy Hilfiger, Target and others have shown consumers what a great AI experience can look like, and now the brands that don’t meet these raised expectations that are actually helpful risk losing consumers engaging with their AI, and their business altogether.

For more information, check out: The 16 Best AI Chatbot Vendors With Reviews and Features.