Using Lifestyle Data to Achieve Better Living: The Current Status of the Smart Home Project "HAUS UPDATA" (Part 1)

Nippon Steel Kowa Real Estate Co., Ltd. and Dentsu Inc., in collaboration with home appliance manufacturers, consumer goods manufacturers, sensing service providers, and others, conducted the first six-month pilot project of the smart home initiative "HAUS UPDATA" starting in May 2024. This project integrates diverse lifestyle behavior data to support updates to residents' lives.

For this pilot, IoT sensors and IoT appliances were installed in the homes of 10 monitor households. Consumer goods were also distributed to understand usage patterns alongside residents' lifestyle behaviors and environmental conditions. A proprietary data platform centrally managed this lifestyle behavior data, enabling the provision of personalized information and services tailored to each household's lifestyle.

The "Transformation SHOWCASE" features interviews with project stakeholders across two parts. Part 1 includes: - Mr. Nakajiro Hata, Future Style Research Office, Nippon Steel & Sumikin Real Estate, a partner company - Mr. Motoki Tanimura, Plasma Cluster & Healthcare Division, Smart Appliances & Solutions Business Unit, Sharp Corporation Ms. Emiko Hashimoto from Lion Corporation's Research & Development Division, and Ms. Erina Tanaka from Dentsu Inc.'s Data & Technology Center. They discuss the current state and future direction of data utilization from their respective perspectives, based on the results and findings from the first phase of the proof-of-concept project.

Building mechanisms to enhance consumer experience value and foster brand affinity

Tanaka: IoT adoption, including smart appliances, is advancing in daily life. Utilizing this data holds the potential to capture home living with unprecedented resolution. Because this data is so closely tied to daily life, it's more important than ever that consumers themselves find it convincing.

Against this backdrop, Dentsu Inc. and Nippon Steel & Sumikin Real Estate are advancing the smart home project "HAUS UPDATA." With the cooperation of various partner companies, we aim to jointly update consumers' lifestyles while utilizing behavioral data as a marketing foundation. Please share the background behind your participation in this pilot project.

Hatanaka: Nippon Steel Kowa Real Estate has long focused on smart homes. However, how customers actually utilize IoT appliances is left to their discretion. We believed that leveraging the lifestyle behavior data collected in this pilot could enhance the significance of smart homes and present new value to customers.

We also anticipated gaining a high-resolution understanding of customers' actual lifestyles. While we conduct nearly 100 interviews annually with condominium buyers, it remains challenging to grasp their real-life situations. We believed that digitizing lifestyle behaviors to understand actual living conditions and latent needs could inform future condominium development.

Tanimura: For this proof-of-concept project, Sharp provided not only IoT appliances but also a measurement device. This IoT kitchen scale, developed as a new venture in the healthcare field, measures seasoning usage. Sharp proposed it could "help collect data on the timing and consumption of household goods," highlighting its potential utility. We also felt promising about how data from IoT devices could be utilized.

Hashimoto: When we received the proposal for this project, we were told that "understanding and analyzing lifestyle behavior data would enhance customer experience value while also creating opportunities to build brand fans." We deeply resonated with this, as we also wanted to build fans for Lion products.

Furthermore, while surveys using video cameras to continuously observe usage patterns are possible for items like kitchen detergents, such surveys are not feasible in private spaces like toilets and bathrooms. This pilot project allows us to sense detergent usage in these areas too, raising expectations that we might uncover new lifestyle behavior data.

Visualizing daily activities to identify latent needs

Tanaka: Could you share the findings from this pilot project?

Hatanaka: When recruiting monitor households for the project, we received applications from about 100 out of 500 targeted households. Our first insight was that consumers don't necessarily resist providing data if you carefully explain who is collecting it, why, and for what purpose.

Another significant finding was the visualization of unrecognized, latent lifestyle behaviors. For instance, some households showed exceptionally long kitchen occupancy times in the data, yet residents didn't consciously perceive this—they didn't report notably extended cooking times in surveys. We also observed other cases where survey responses differed from sensing data, which was fascinating.

Tanimura: By installing various consumer goods in homes and using IoT sensors to track usage timing and consumption, we discovered that household chores are often performed multitasking. For example, while cleaning the toilet, someone might start a soak wash in the bathroom before finishing the cleaning and also prepare the washing machine. We saw the reality of multiple tasks running concurrently, intricately intertwined.

Tanaka: In this pilot project, we also delivered recommendations based on lifestyle data, suggesting product usage and living tips tailored to each person. How was the response to this?

Hashimoto: We collected bacteria samples from the toilets of monitor households and sent recommendations via SNS, such as "Bacteria levels are slightly higher than average" or "Why not try cleaning a bit more?" Additionally, when we found significant differences in bath cleaner usage among households, we delivered bathroom cleaning information aligned with each monitor's values. Following this, we sent a message stating, "It's better to apply cleaner evenly across the entire bathtub and clean without scrubbing." This resulted in a slight increase in usage. We learned that by providing appropriate recommendations based on each household's actual living conditions and values, consumers responded positively. By delivering information for more comfortable living, we believe we helped them feel they were updating their lifestyle.

Hatanaka: One user commented, "I was wondering if it was okay to clean the bathtub without scrubbing, and then the recommendation arrived, making me feel like my actions weren't wrong." That really highlighted the significance of sending recommendations tailored to each individual's lifestyle. Surprisingly, many also requested clear statements like "It's dirty," rather than vague, hesitant messages.

Tanimura: There's no single right way to do housework or spend time at home; it depends on each household's habits, personal preferences, and values. When we analyze lifestyle data and point out areas for improvement, people realize, "Oh, that's another way to do it," which seems to lead to better living.

Encouraging behavioral change through data-driven recommendations

Tanaka: Based on this pilot project, what are your future prospects for each company?

Hashimoto: While this pilot project allowed us to analyze users' daily actions via IoT sensors, it's not yet sufficient. We hope to connect to lifestyle upgrades by analyzing higher-resolution data.

Hatanaka: More detailed analysis of lifestyle data could even distinguish whether someone in the kitchen cooked from scratch or just heated a meal in the microwave. We could then tailor recommendations to each person's actual lifestyle—for example, suggesting a housekeeping service to someone spending excessive time cooking with a gentle prompt like, "Are you pushing yourself too hard?" This might also foster better communication within households, with partners realizing, "I had no idea my spouse spent so much time on chores." Moving forward, we aim to deepen collaboration with our partner companies and leverage data to contribute to better living.

Tanimura: I'm particularly interested in the strong affinity between smart homes and healthcare. Health status is closely tied to daily habits like diet and sleep duration. Effectively utilizing lifestyle behavior data could aid in detecting pre-disease conditions and enable approaches to encourage behavioral changes for those prone to forgetting medication or staying up late. For instance, automatically dimming lights at bedtime should naturally help regulate daily rhythms. I would be delighted if we could use lifestyle data to drive changes in daily living and behavior.

IoT appliances and IoT services won't gain traction unless users truly feel the benefits. Beyond just collecting and analyzing lifestyle behavior data, fostering positive changes in daily life and habits through data-driven communication is what will ultimately increase user satisfaction. In the second part, we'll introduce the outlook for the second phase of the demonstration project, building on the results from this phase.