Intelligent products boost customer experiences
But companies should be careful how data is used

Intelligent products and services are perpetually evolving, and there is increased demand to use data for real-time innovation to deliver the hyper-personalized experience customers expect.

Companies that strategically use data-driven technology such as predictive analytics will gain a competitive advantage in customer experience, but they must also prioritize the challenges inherent in collecting, using, and safeguarding client data.

Track how quickly your heart rate returns to resting after a brisk trail run. Pre-program your car to have the seats already heated for the drive back. Personalize your home-security settings with facial recognition to unlock your door and turn on the lights as you approach your house. The data generated and collected from such intelligent products can optimize our lives in dynamic ways, with advancements on the horizon.

“Intelligent products that can adapt their performance based on customer needs will make the competitive difference,” said Nicolas Veauville, Product Innovation Leader, Versuni kitchen appliances, formerly Senior Director at Philips Domestic Appliances, according to the 2022 report Intelligent products and services, produced by the Capgemini Research Institute. “I believe that bringing an intelligent product is not the end goal; the end goal is to create a better experience for our customers, so that it drives more revenue over the lifetimes of our brands.”

Using artificial intelligence (AI) and machine learning (ML) allows for continuous improvements from data usage, ensuring a product’s longevity, not obsolescence. And as products improve their behavior over time, they become more adaptive and responsive to specific user needs. All this power provides opportunities for companies to gain a competitive edge when delivering a hyper-personalized experience for customers, but it also presents three distinct challenges.

ENSURING PERSONALIZATION WHILE SAFEGUARDING CLIENT PRIVACY

Designing intelligent products that enhance users’ health and fitness journeys by providing personalized prompts and insights can build customer loyalty, such as a cutting-edge wearable device that cues users to pick up the pace for the last kilometer of a run, guiding them to a personal record. The Capgemini report, which compiled results from a survey of 1,000 global companies, found that 87 percent of organizations say intelligent products and services are crucial to their business strategy. Yet previous research by Capgemini highlighted that 62 percent of the organizations it surveyed cited cybersecurity and data-privacy threats as reasons they struggle to scale up IoT, or Internet of Things, applications.

Identifying a solution to these challenges means finding a balance to help companies achieve hyper-personalization by combining several techniques to preserve user privacy.

• Federated learning: AI models are trained across decentralized user data while keeping the information on users’ devices. This ensures sensitive information never leaves their control. Biometrics are used to link identity with an action, but the data stays on the local device.

• Differential privacy: Differential privacy mechanisms are integrated into data collection and analysis to anonymize and safeguard user data. That means personally identifiable information is never leaked.

• User consent management: Users are offered clear choices about data usage and personalized features, respecting their preferences to opt in – or out – and decide what types of data to share.

SELECTING OPTIMAL AI DEPLOYMENT LOCATIONS

As touched on above, consider the use case of a company specializing in intelligent home automation devices. It needs to decide where to deploy artificial intelligence within its products, whether to centralize AI processing or distribute it across devices. Other considerations include performance, security, cost, and sustainability.

The ideal outcome of choosing an optimal AI deployment location includes enhanced product performance, robust security, cost efficiency, and a reduced environmental footprint. However, the wrong choice could lead to poor user experiences and financial setbacks. So, what’s the solution? We recommend a hybrid approach that combines three elements. 

• On-device AI: To offer users more control, AI deployment is enabled on user devices, allowing for personalized experiences.

• Edge computing: This brings AI processing closer to the devices, reducing latency and improving real-time responses.

• Cloud integration: A cloud infrastructure is used for tasks that benefit from centralized processing, allowing scalability and data storage.

ADOPTING A DATA-FIRST APPROACH IN PRODUCT AND SERVICE DEVELOPMENT

Consider the case of a car maker that wants to leverage data as an asset for value creation rather than merely accumulating it for future extraction. The issue is when it amasses data without a clear strategy for its use. In fact, the 2022 report found that 53 per cent of companies stated they had “critical talent gaps” in the area of “data governance, management, and data science professionals.”

In comparison, a car maker that adopts a data-first approach will benefit in several ways, including real-time vehicle performance enhancements, dynamic safety features, and immediate personalization of the driving experience. However, the challenge lies in utilizing data effectively and promptly. Once again, Capgemini recommends combining several best practices rather than pursuing a single approach.

• Data as a strategic asset: Data is viewed as a strategic asset and a valuable resource, driving decision-making with regard to product features, design, and improvements that are made based on data analysis, supported by empirical evidence from data.

• User-centric design: Start with the customers’ preferences and needs, to ensure data-driven enhancements align with their expectations.

• Impermanent data: Move from “store all the data” to “store the right data,” rethinking practices for cost-efficiency, privacy, and sustainability by emphasizing the efficiency of collecting only essential data.

Intelligent products, whether used by avid trail runners or automotive manufacturers to manage fleet vehicles at scale, are making our day-to-day lives more seamless and helping to solve complex global issues. Companies that use data to improve the customer experience – prioritizing people and embedding privacy in data-driven product design – will benefit from long-term loyalty that drives growth and profitability.

“INTELLIGENT PRODUCTS THAT CAN ADAPT THEIR PERFORMANCE BASED ON CUSTOMER NEEDS WILL MAKE THE COMPETITIVE DIFFERENCE. I BELIEVE THAT BRINGING AN INTELLIGENT PRODUCT IS NOT THE END GOAL; THE END GOAL IS TO CREATE A BETTER EXPERIENCE FOR OUR CUSTOMERS, SO THAT IT DRIVES MORE REVENUE OVER THE LIFETIMES OF OUR BRANDS.” – NICOLAS VEAUVILLE, VERSUNI

INNOVATION TAKEAWAYS

BALANCE PERSONALIZATION AND PRIVACY

Enable hyper-personalized experiences but integrate privacy mechanisms into the data collection and allow users to retain control of the types of information they share.

CHOOSE AN OPTIMAL AI DEPLOYMENT LOCATION

Use a combination of edge computing, cloud integration, and on-device AI to deliver a solid user experience, robust security, cost efficiency, and a reduced environmental footprint.

ADOPT A DATA-FIRST APPROACH

Strategic planning and decision-making should start with data to develop and enhance intelligent products that put the customer experience and outcomes first.

Interesting read?

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 7 features 16 such fascinating articles, crafted by leading experts from Capgemini, and partners like Aible, the Green Software Foundation, and Fivetran. Discover groundbreaking advancements in data-powered innovation, explore the broader applications of AI beyond language models, and learn how data and AI can contribute to creating a more sustainable planet and society.  Find all previous Waves here.