TechBytes with Lucas Persona, Chief Digital Evangelist at CI&T

Tell us about your role and the team/technology you handle in your current company.

My primary role at CI&T is helping my team leverage advanced and emerging technologies in ways that will provide the most significant impact on our clients’ businesses.

Most of my team’s focus is on the role that AI and Machine Learning will play in the future. We also validate other emerging technologies like Blockchain, Internet of Things (IoT), and Augmented Reality/Virtual Reality.

How much have the Machine Learning-based Commerce standards evolved in the last two years?

Internally-Focused Optimization, such as forecasting price, assortment, fraud protection, and other factors is where Machine Learning has historically had the most significant impact on commerce.

More recently, particularly among Digital and Machine Learning-native organizations, there’s been a strong uptake in implementing Machine Learning to improve the Customer Experience.

Tell us about the recent and futuristic application of Machine Learning and Data Science across various online platforms?

A future with self-driving cars, general Artificial Intelligence, Medical Diagnosis, and Robotics is what we tend to read about in the media.

However, I believe that Machine Learning will have a profound (if primarily ‘silent’) impact on the continuous augmentation of existing platforms.

Teachable Digital platforms that continually refine and adapt according to customer needs is where the real future of Machine Learning in organizations lie. For example, take a music subscription platform that only plays the music you want to hear, because it understands your listening habits and tastes. It may not feel futuristic, but it fundamentally transforms the experience.

The most futuristic application of Machine Learning is providing a unique and personalized journey for every single customer. It is platforms like these that will increase customer loyalty, generate new revenue streams, and accelerate growth more than any other technology.

Which industries are most-responsive in delivering meaningful Customer Experience? How do these industries leverage Machine Learning differently in their operations?

Customer-centric focused cultures that integrate Machine Learning as a critical element across business segments (as opposed to operating in a silo) is the key driver in enabling responsiveness.

Digital-native organizations that span a wide variety of industries, including companies like Airbnb, Google, and Spotify, are really driving the use of Machine Learning for Customer Centricity and Data-Driven decision making.

Retail is adopting Machine Learning as a matter of survival, seeking to create meaningful Customer Experiences. And while the financial services industry has historically used machine learning and advanced Analytics, the industry is now trying to use Machine Learning as a means to provide better Customer Experiences, given the increased competition faced from FinTechs.

How does Machine Learning unlock opportunities in mobile growth?

It’s essential that organizations view every customer interaction as an opportunity. Machine Learning can help reduce cognitive load, resulting in better Customer Experiences.

Long-term customer loyalty is strongly correlated to fulfilling customers’ needs.

Tell us more about your recent report on Machine Learning models for delivering relevant Customer Experience?

The report ‘Machine Learning: The Next Generation of Customer Experience’ from Harvard Business Review Analytic Services, in association with CI&T, outlines how Machine Learning has become more accessible than ever and can be applied across all organizations today.

The report also recognizes that traditionally in the past, most organizations had implemented Machine Learning for internal optimization, but that leaders in the space such as Airbnb and Uber are now aggressively applying ML to optimize the customer journey and unlock potential throughout that journey.

Customer Experience is defined as the perceived combination of desires, expectations, emotions, decisions, and outcomes that every customer acquires throughout their journey with a particular organization. Machine Learning continually raises the bar by personalizing each customer’s experience in ways that are simply not achievable without it.

What are the key differences between Customer Experience and Personalization?

Personalization is part of successful Customer Experiences. Some organizations try to create personalization through customization, where the customer selects what and how they want to do things. Others use a ‘blanket approach’ by applying Personalization and clustering strategies to personas.

I believe both are decent directions but, fall short of true personalization for meaningful Customer Experiences, where a customer sees the organization, brand or service as a companion.

True personalization occurs when an organization or brand seeks to understand the customer for the customer’s benefit. This is only possible by embedding Machine Learning-based personalization throughout the entire customer journey.

Consider the case of the medical diagnostic presented in the report. It’s undoubtedly already a stressful time for the patient. A situation of unknown lab results is exacerbated by trying to decipher the names and meanings of complicated tests. But then a Digital Companion comes along and establishes empathy by showing the patient that they are not alone. The companion reduces stress and cognitive load by clearly spelling out the details of those complicated tests.

A successful Customer Experience delivers Personalization that positively affects the emotions, expectations, and perception of outcomes of that customer.

Are companies getting distracted with CX? Do you think customer service is getting replaced by Customer Experience Initiatives?

Customer Experience represents the entire set of interactions between a customer and service, brand or organization. There will always be a customer service component as part of those interactions. The key thing for organizations is a mindset shift toward a culture that focuses on the customer experience as a whole, rather than the typical siloed approach that we often find today.

The problem is that many companies deal with customer service issues reactively. Proactive Customer Experiences, on the other hand, may actually reduce these reactive customer service practices.

I recommend that companies focus on Customer Experience as part of their core business in reaching out and fully delivering products and services for customers.

Tell us about some outstanding Customer Experience campaigns that you have participated in the last two years? How do you measure the success of these CX campaigns?

I believe that Customer Experience goes way beyond campaigns. As mentioned in the report, Airbnb and Uber have been creating excellent Customer Experiences for some time. Similarly, Spotify and Google use the power of Machine Learning to improve every interaction customers have with their products.

Success is measurable with both business and customer results. From the business side of things, it means longer customer retention, additional revenue, and increased customer satisfaction, among other factors. For customers, it means efficiency, surprise and delight, and reduced cognitive load.f