Product-Service Systems (PSS) are business models that go beyond delivering physical products by also offering complementary intangible services. With the rise of smart technologies and connected devices, these models have evolved into Smart Product-Service Systems (Smart PSS), enabling service providers to deliver personalized and data-driven offerings. By leveraging user-generated data from smart products, providers can tailor services to meet individual customer needs more effectively.

To harness the potential of smart devices in enhancing existing services or developing new ones, a structured Smart PSS design methodology is essential. Such a method enables enterprises and service providers to build personalized Smart PSS solutions using user-generated data, or to enhance current offerings through the application of deep learning techniques.

As a first step, service providers should adopt the customer's perspective to identify the root causes of any inconvenience experienced during service use. To support this, the use of a customer journey map is recommended as a tool to analyze and understand the customer’s mental and emotional experience throughout their interaction with a product or service.

A customer journey map is a visual method that outlines the steps a customer takes, combined with their subjective feelings at each stage. By illustrating the entire service experience through graphics and process flows, it helps organizations see their services through the eyes of the customer. This perspective enables companies to pinpoint weaknesses in the service process and use these insights as a foundation for improvement.

Furthermore, as many customer interactions today are closely tied to data collection, traditional customer journey maps may need to be adapted to incorporate data-driven insights that better align with the evolving needs of modern service providers. A modified customer journey map is needed to assist service providers in analyzing the customer experience, as illustrated in the following figure.

Two key enhancements include: the inclusion of “needed data” at each stage of the journey, and the addition of emotional states alongside their root causes, forming the basis of an emotional journey.

This modified approach is explained step by step in the following sections:

1. Service providers should place themselves in the role of a customer and break down the service process into distinct stages, reflecting how the customer experiences the journey.
2. Identify the key activities and touchpoints customers engage with at each stage. These are critical interactions that shape the overall experience and must be included in the journey map.
3. Consider the emotions customers might experience during each activity. Providers should empathize with the user to understand these feelings. Based on this, an emotional journey can be drawn to represent the fluctuations in customer sentiment. In this version, both the emotional state and its underlying causes are mapped, providing deeper insight into customer behavior.
4. Recognize the data customers rely on to complete key activities. This “needed data” should be clearly identified to help service providers understand what information supports each step of the journey.
5. Finally, service providers should reflect on potential problems or pain points within the process and explore corresponding solutions to enhance the customer experience.

The modified customer journey map is also applied to uncover user requirements across various industries, recognizing that different sectors may face unique service-related pain points. However, certain factors—such as system usability, user interface (UI) design, and pricing—remain consistently influential in shaping customer satisfaction across industries.

Therefore, when designing new services, providers must consider both industry-specific challenges and universal customer expectations.

In summary, the modified customer journey map allows service providers to gain a comprehensive understanding of the user experience, including the emotions associated with each step of the service process. By capturing these emotional insights, providers can more easily identify potential issues and opportunities for improvement, enabling them to make targeted adjustments that better align with customer needs and expectations.

After identifying user requirements, service providers must determine which pain points they aim to address or how to adapt existing services to better fulfill those needs. Once the specific challenges are defined, the next step is to gather the necessary data to support service redesign or improvement.

Data can be sourced from a variety of channels, including social media posts and replies, instant customer feedback, and open datasets provided by governments or academic institutions. Thanks to the widespread availability of online data, much of this information is easily accessible, well-organized, and often available at low cost.

However, publicly available data may not always provide the depth or specificity required for targeted service enhancements. To overcome this limitation, service providers are encouraged to collect their own data tailored to their objectives. For instance, one might scrape restaurant reviews from platforms like Google Maps to gain insights into customer sentiment and expectations.

The benefit of collecting data independently is that it can be customized to meet specific demands. However, this approach can also be time-consuming and resource-intensive, requiring careful consideration of the trade-offs between data quality and operational cost.

To effectively meet diverse customer needs, service providers must develop appropriate models that align with the data they’ve collected. Before selecting a suitable model, it is essential to clearly define the target problem and identify the type of data to be used. However, traditional machine learning models often struggle with unstructured data—such as text, images, and audio—due to their lack of inherent structure.

Focusing on text data one could demonstrate the advantages of integrating smart Product-Service Systems (Smart PSS) with deep learning techniques. Specifically, we utilize the Doc2Vec (Document-to-Vector) model, a deep learning method that extends the Word2Vec approach, and implement it using the Gensim® library.

Doc2Vec is an unsupervised learning algorithm designed to convert entire texts into fixed-length vector representations. It learns these representations by predicting words based on their surrounding context, leveraging the idea that the contextual relationships in a sentence can reveal its semantic structure. During training, sections of sentences are masked (hollowed out), and the model learns to predict these missing words using the surrounding context—without requiring labeled data.

Additionally, Doc2Vec incorporates a paragraph matrix that serves as a unique identifier for each document, capturing global information and maintaining coherence across different parts of the same text. This enables the model to retain not just statistical, but also semantic and contextual meaning, even across documents of varying lengths.

Compared to traditional techniques like TF-IDF, which rely heavily on frequency counts and disregard word order or meaning, Doc2Vec provides a richer representation of text. Unlike Word2Vec, which computes document vectors as averages of individual word vectors, Doc2Vec takes into account the structure and flow of entire paragraphs, making it more effective in capturing the topic or theme of a document.

Once trained, the resulting document vectors can be used for various tasks such as document classification, clustering, and similarity comparison. A commonly used technique for comparing these vectors is Cosine Similarity, which measures the angle between two non-zero vectors, offering a metric for semantic similarity between texts.

The final step focuses on encouraging service providers to develop a complete and practical solution tailored to customer needs. These solutions can take various forms—web platforms, mobile applications (such as Android apps), and other digital interfaces are commonly used to deliver services effectively.

For instance, the figure below illustrates the workflow of a smart Product-Service System (Smart PSS) designed for tourist recommendations, implemented by a network of independent taxi operators.

The system employs an Android application as the front-end interface for the proposed Smart PSS, while the back-end is powered by a Python server integrating a Doc2Vec model. The interaction process unfolds as follows:

1. In the first step (arrow 1), users input three types of information via the app: their current location, preferred travel distance, and a text description of personal preferences related to attractions and restaurants.
2. This information is then transmitted to a PHP and Python server (arrows 2, 3, and 4), where it is used as parameters to compute similarities with surrounding attraction data (arrow 5).
3. The top 5 most relevant results, based on similarity calculations, are sent back to the app (via arrows 6 and 7), and finally, the results are displayed to the user (arrow 8).

To enhance user experience, the recommendation system is also integrated with the Google Directions API to offer navigation services. It generates a personalized full-day itinerary, including suggestions for morning and afternoon attractions, as well as lunch and dinner spots.

The detailed workflow is as follows:

• Step 1: Users define a preferred travel radius and select types of attractions they are interested in.
• Step 2: They enter a brief text describing their preferences (e.g., “quiet places with scenic views”).
• Step 3: If users choose to skip planning for any part of the day (morning, lunch, afternoon, or dinner), they can do so.
• Step 4: The application uses the user’s current location to set the search radius.
• Step 5: For each attraction within that radius, the system retrieves the top five user reviews from Google Maps, then converts both the user’s input and the review texts into 50-dimensional vectors using the Doc2Vec model.
• Step 6: Using cosine similarity, the system compares the user's preferences to each attraction's reviews and ranks the nearby options accordingly.

Each recommended attraction includes details such as the name, user rating, estimated travel time, and distance.

From the ranked list, the system presents the top 10 recommended attractions, allowing users to make selections for each time period. Once selections are made, a custom day plan is generated and route-planned using the Google Maps API, which defaults to the shortest driving route. To support diverse travel preferences, the app also includes a transportation mode selector, enabling users to switch between walking, public transport, biking, or driving.

Source: https://doi.org/10.1016/j.compind.2021.103421