#62 | DCX - Perspectives and insights on digital customer experience
Are You Ready for Machine Customers? 20 Companies Building the Autonomous Future; Links to Industry news and the DCX Thought Leader Profile of the Week
Thank you to all 562 CX professionals from 37 US states and 68 countries for being loyal weekly readers of DCX. I am incredibly grateful for your continuous support and engagement.
Are You Ready for Machine Customers?
The year is 2030. You awake in your smart home and announce, “Alexa, reorder coffee pods.” Your autonomous car has already scheduled its own maintenance appointment. At work, the AI on your team has identified emerging supply chain issues and ordered new parts.
The machines around you complete tasks without human involvement. Intelligent assistants, autonomous platforms, and robot collaborators. In this increasingly automated world, machines take on new roles and responsibilities - Machines as customers, partners, and colleagues.
This is the imminent future. The rise of sophisticated AI and robotics will transform how businesses operate. Machine customers will be commonplace. Smart devices will interact seamlessly with companies to procure goods and services.
The advancement in technology will bring about a paradigm shift where machines are no longer limited to mere tools. They will become your customers, partners, and colleagues, working alongside humans to enhance efficiency and productivity in different aspects of life.
The question is:
Will your organization adapt to meet the needs of these machine customers? IMHO, the companies that embrace this machine-driven world will thrive. Those that deny this change are destined to fall behind.
Machines are coming.
They will augment our lives. But to benefit as businesses, we must enlarge our perspective. Today, let’s reimagine your business through the lens of serving both humans and machines. The machine age dawns.
The emergence of machines as customers is closer than we think. As of 2023, Amazon Alexa already has hundreds of thousands of skills that enable automation. Myriad autonomous AI models, such as Auto-GPT and GodMode, are beginning to enable complex automated tasks. And, per Gartner Research, executives believe 25% of consumer purchases will be delegated to machines by 2030.
These machine agents will book travel via voice assistants, schedule vehicle service through connected cars, or order retail products through automated purchasing bots. As machine capabilities advance, the use cases will expand.
These machine customers will interact with our business in new ways. Rather than targeting only human needs, we must now learn to serve AI systems and smart devices designed for efficiency and convenience.
Understanding Your Future Machine Customers
Who exactly will our machine customers be? While we often think of machines in the abstract, it helps to get specific. Here are some likely personas we may serve:
The Smart Home - Devices like Alexa and Google Home will become shoppers for our products. As virtual assistants grow more sophisticated, they can automatically track usage and reorder items. Anticipate the Smart Home wanting bundle discounts, subscription models, and personalized promotions based on past purchases. Voice-first interfaces and seamless IoT integration will be key.
The Autonomous Vehicle - Cars like Tesla will schedule their own maintenance appointments. They'll provide telemetry data to optimize service and predict necessary repairs. Expect them to demand rapid scheduling, exceptional uptime, and real-time updates. Digital channels, predictive analytics, and automation will be critical to delivering convenience.
The AI Doctor - Medical devices will track patient health and order their own supplies. A smart insulin pump, for example, could monitor insulin levels and order refills as needed. These B2B customers will require impeccable data security, regulatory compliance, and transparency. We must build trust and earn permission to play a role in sensitive healthcare workflows.
The Smart Factory - Advanced manufacturing facilities will have interconnected devices and machinery capable of optimizing production processes and ordering their own parts for maintenance. These machine customers will require supply chain efficiency, reliable delivery schedules, and quick response times for unexpected issues. Integration with enterprise resource planning (ERP) systems, real-time data analytics, and machine learning will be essential to meet their needs.
The Robotic Retailer - Future retail stores may have automated checkout systems, robotic stockers, and drones for delivery. These machine customers will require seamless inventory management, efficient payment processing, and real-time customer support. Integration with e-commerce platforms, computer vision technology, and mobile apps will be key to providing a smooth shopping experience. Expect them to seek bulk discounts, efficient replenishment, and dynamic pricing based on market demand.
As these examples show, the future of machine customers will be diverse and widespread across industries. We need to stay ahead of the curve by understanding and anticipating the needs of these machine customers. Adapting to new technologies and providing personalized solutions will be critical to thriving in this emerging landscape.
Transforming Your Business for the Age of Machines
To get ahead of this trend, you need to take three key steps
Identify Automation Opportunities
What can you automate? To identify and vet automation opportunities, focus on high-frequency, low-complexity tasks such as checking balances or ordering recurring purchases. These tasks are ideal candidates for automation as they are repetitive and do not require extensive human intervention. By automating these tasks, you can save time, reduce errors, and improve efficiency.
Identifying automation opportunities involves analyzing existing workflows and processes to pinpoint areas that can benefit from automation. This can be done through close collaboration with relevant teams or departments, gathering employee feedback, and examining task frequency and complexity data.
Once potential opportunities have been identified, it is crucial to vet them thoroughly. This involves evaluating the feasibility and potential benefits of implementing automation in each case. Factors to consider during the vetting process may include the cost of implementation, the availability of suitable automation tools or technologies, potential risks, and the expected return on investment.
Invest in A Scalable Conversational AI Platform
Investing in a scalable conversational AI platform is a crucial step to enhance the capabilities of chatbots and enable them to engage in natural and seamless conversations with machine customers. By implementing such a platform, your chatbots will be able to understand and respond to customer queries, provide personalized assistance, and offer efficient solutions in a more human-like manner.
This can greatly improve the overall customer experience and increase customer satisfaction. Additionally, a scalable platform ensures that your chatbot can handle a growing number of interactions and adapt to evolving customer and machine needs without compromising performance.
Build Extensive Measurement and Analytics Capabilities
Adopting a proactive approach to building extensive measurement and analytics capabilities is important. Firstly, implement a robust tracking system to monitor various metrics related to bot performance. This system should include the ability to measure conversation completion rates, which allows for an analysis of how effectively the bot is able to engage and assist users.
Additionally, it is crucial to identify specific areas where the bot may be encountering difficulties. By analyzing these pain points, you can pinpoint areas for improvement and refine the bot's capabilities. This could involve examining conversation logs, user feedback or conducting user surveys to gather valuable insights into areas of struggle for the machine.
Furthermore, a comprehensive analytics framework should be established to monitor and interpret the collected data. This framework should incorporate advanced techniques such as natural language processing and sentiment analysis to understand user interactions and satisfaction levels better. By leveraging these tools, you can gain valuable insights into user behavior, preferences, and patterns.
Regularly reviewing and analyzing the gathered data enables you to make data-driven decisions and continuously enhance the bot's performance. This iterative process ensures that improvements are made over time, leading to more effective and efficient bot-to-bot interaction.
To support machine customers, we must fundamentally upgrade our business operations.
Areas that will need enhancement include:
APIs and Integrations - Seamless connectivity will be critical. We must expose inventory, pricing, and order management via real-time APIs. Basic web interfaces won't cut it. Intuitive programmatic access must become core to our platforms.
Scalability - Peak demand could exponentially increase as machines transact 24/7. We'll need infrastructure that auto-scales to maintain uptime and performance.
Security - Security is paramount with sensitive data like healthcare records and financials. We must implement rigorous access controls and encryption.
Agility - We'll need rapid iteration to keep pace with upgrades. Taking months to release features won't fly. Cloud, microservices, and DevOps will enable continuous deployment.
These changes won't be easy. But the payoff will be future-proofing our business. We can turn infrastructure built for machines into a competitive advantage.
Next Steps to Lead the Future
To prepare for the coming machine customer revolution, it is crucial to take tangible next steps. These steps will help in effectively getting ready for the changes ahead.
Form a Cross-Functional Planning Team
Bring together leaders from IT, product, marketing, operations, finance, legal, customer service, and other key groups.
This team will coordinate your machine customer strategy across departments.
Appoint a senior executive to spearhead the effort and align groups.
Schedule recurring meetings and working sessions to maintain momentum.
Leverage the diverse skills and perspectives of the team.
Conduct In-Depth Market Research
Commission quantitative research on machine customer needs and preferences. Surveys, conjoint analysis, and data modeling.
Conduct qualitative ethnographic research. Observe machines in real-world environments to uncover unarticulated needs.
Analyze research collectively to validate assumptions and identify gaps.
Update our segmentation models and value propositions accordingly.
Partner with Machine Customer Leaders
Reach out to companies pioneering machine customer engagement like Uber, Domino’s, and Disney.
Explore partnership opportunities for advice, technology, and capabilities.
Learn from their failures and successes. Why did certain approaches work or falter?
Collaborate on pilot projects to collectively push boundaries.
Map out a 3-Year Machine Customer Roadmap
Detail key milestones across technology, operations, marketing, service, and finance.
Outline resource requirements and budget needed to execute.
Gain alignment on interdependencies across groups required for success.
Set ambitious but achievable goals for expanding machine customer capabilities.
Launch Pilot Projects
Start with bite-sized pilots versus huge bets to de-risk and learn quickly.
Focus on easily automatable tasks with clear ROI.
Measure results rigorously and feed insights back to the roadmap.
Celebrate wins, no matter how small, to build momentum.
The rise of machine customers can seem daunting. Some will ask: What happens to the human touch? It's natural to have concerns amidst disruption.
However, machines present a multi-billion dollar opportunity to grow revenue and customers. Serving them allows us to expand our mission. And bots will augment humans, not replace them. Staff focused on complex sales and support will become more valuable.
Change requires adaptation. We've transformed from the PC revolution to the mobile era and cloud computing. Progress is in our DNA. We have the creativity to adapt again.
This transition may feel uncomfortable. But avoiding discomfort means stagnation. We must challenge ourselves to lead in a new age. If we approach this expansion with our values intact, the best is yet to come.
This won't happen overnight. But by taking the first few steps, you will gain momentum. Your future leadership position depends on proactive preparation starting today. The machine customer revolution has arrived. It's time to mobilize your organization and boldly lead the way.
Not a sponsor, just a fan:
My new favorite AI tool for Mac only - Finito.ai.
With Finito, you can use AI in any app. Break out of the browser with Finito.
20 Companies Building the Autonomous Future
Companies building the infrastructure for autonomous machine engagement
John Deere - Machine Learning & Automation enables autonomous operation of machinery with AI.
Siemens - MindSphere is an IoT operating system that enables autonomous machine coordination through AI.
Bosch - Phantom Edge uses edge AI to enable on-device machine learning for autonomy.
GE - Predix is an industrial IoT platform that connects machines and optimizes autonomously via ML.
Rockwell - FactoryTalk Analytics generates manufacturing insights for autonomous optimization.
ABB Ability connects assets and uses AI for autonomous functions.
Schneider Electric - EcoStruxure uses IoT and analytics for autonomous machine coordination.
Fanuc - FIELD system enables autonomous collaboration between robots via AI.
Micron - Autonomous Machines develops firmware for autonomous vehicle management via AI.
Analog Devices - Embedded AI enables edge AI for industrial autonomy without the cloud.
Consumer-facing companies using AI for autonomous machine interactions
Tesla - Tesla vehicles
use AI and computer vision for features like autonomous driving, self-parking, and advanced driver-assistance systems enabling autonomous interactions between cars.
Waymo develops self-driving technology to allow cars to operate autonomously, including ride-hailing services, delivery, and long-haul trucking. Enables autonomous coordination between vehicles.
Uber is developing self-driving car technology to enable autonomous vehicles to transport riders without human drivers. Includes coordination between multiple autonomous vehicles.
Nuro has developed delivery robots and autonomous vehicles to transport goods locally using machine learning and robotics. Enables autonomous delivery fleet management.
Starship Technologies - Using AI and robotics, Starship produces autonomous robots that deliver packages, groceries, and food. Allows robotic delivery fleet coordination.
Savioke builds autonomous robots deployed in the services industry for delivery and facility automation enabling robot-to-robot coordination.
Simbe Robotics Tally robot uses computer vision and AI to autonomously scan shelves in retail stores providing inventory data to optimize operations.
LG home appliances use AI and automation for features like smart diagnosis using sensors and connectivity between washers, dryers, and refrigerators.
iRobot's Roomba robotic vacuums use AI and mapping to autonomously clean efficiently by coordinating with other Roombas.
Maytronics' Dolphin robotic pool cleaners autonomously scan pools and optimize cleaning routes by communicating with other units.
Show your support for the DCX newsletter by signing up:
Tackle Workplace Stress and Prevent Burnout
Early intervention is the key to successfully managing workplace stress, and Ambr provides precisely that. Ambr empowers managers to diagnose and address the organizational root causes of stress and burnout, boosting well-being and reducing business costs linked to attrition, sick days, and lost productivity.
For a limited time, take advantage of our offer - a 25% discount on your subscription to a healthier, more productive workplace.
Ready to make the change? Start your journey to a stress-free workplace with Ambr.
This week I asked our colleagues in the Customer Experience Professionals Group on Linkedin about their preference for Quant vs. Qual in evaluating CX. More below.
In this week's survey, we discovered something interesting about how people evaluate customer experience. Most respondents (52%) believe that quantitative and qualitative metrics are equally important. This means that a well-balanced approach, combining numbers like CSAT scores with valuable customer feedback, is the preferred method for gaining a complete understanding.
Even more intriguing is that a larger percentage of people (28%) prioritize qualitative metrics, such as feedback, over quantitative metrics like CSAT (12%) after considering the balanced approach. This highlights the significance of gathering comprehensive insights from customers through surveys and interviews to complement the raw data.
Moreover, a small yet significant portion of respondents (8%) track other metrics not mentioned in the survey. These metrics could include things like customer lifetime value, repeat purchase rate, and even social media sentiment. It's crucial for companies to take these unique metrics into account, as they align with specific business goals and go beyond the usual customer experience measurements.
Overall, these findings suggest that there isn't a clear winner regarding the preferred type of metric. Instead, companies should adopt a customized approach that suits their distinct business needs and the characteristics of their customer base. Successful customer experience programs should incorporate multiple types of metrics to achieve a well-rounded evaluation.
Accountability, Anyone?
Dedicated to supporting you on your journey to success.
Tools for a better life. Learn More.
Free Download: The Accountability Exercise
Using AI to Build Stronger Connections with Customers (hbr.org)
Generative AI applications are spurring innovation. However, many people are concerned about data privacy and customer experience. Companies must prioritize model risk, data privacy, and ethical use of data when utilizing AI technology to ensure customers have a good experience.
AI-enabled customer engagement has the potential to create more value for customers, employees, and shareholders and can be used to personalize customer experiences. Companies should start with no-regret cases to get comfortable with the technology and eventually use it to enhance employee and customer interactions.
What Is Conversational Commerce in 2023? | Vonage
Everyone is staring down at a mobile device. If there was ever a captive audience — this is it. So it’s no surprise that savvy businesses use the intersection of their e-commerce and social channels to engage consumers and complete sales, as consumers initiate and complete transactions from their smartphones. There’s even a name for this innovative transaction: conversational commerce.
What is a CDP?
Each week, I share a profile of a person from LinkedIn whom I believe can positively impact your life and career.
This week, meet Gerda Swinkels-Legierse, the Customer Centricity Program CRM manager at DAF Trucks NV.
Gerda Swinkels-Legierse is a seasoned, certified professional specializing in Customer Experience (CX) management strategies. She bases her passion on understanding humans and fostering a solid connection between people to create improved customer and employee experiences.
Throughout her career, Gerda has contributed to several renowned companies, including Carglass, Allianz, and Xerox, focusing on creating customer-centric business environments while leading functional and behavioral change. Her expertise spans various domains, such as Customer Experience Strategy, organizational change & design, program & project management, sales & service retention, and personal development.
Additionally, Gerda served as the Global Head of Customer Experience at Grodan, where she led a transformation from a product-oriented to a customer-focused approach achieving a significant improvement in Global NPS. Her commitment to customer experience extended further during her tenure at Allianz Direct as a Customer Retention Manager. Gerda was instrumental in improving retention rates at various churn moments by developing and managing customer retention strategies.
On top of her professional accomplishments, Gerda is also academically impressive, holding a Bachelor's degree in Business Administration from Hotelschool The Hague and a Master of Science from Nyenrode Business University.
Gerda has a reputation for being an inspirational leader and is recognized for her creative and enthusiastic approach to problem-solving. Each role she has held has been strategically guided by her belief in collaboration and creating positive energy that drives teams forward.
Thank you for reading this week.
Please share with others you think would benefit.