Fundamentals

Imagine a small bakery, fragrant with yeast and sugar, diligently tracking each customer’s favorite pastry. This simple act, recording preferences, isn’t just about remembering faces; it hints at a deeper, often untapped resource within small and medium-sized businesses (SMBs) ● data. Data monetization, in its most basic form, involves turning this collected information into tangible value. For many SMB owners, the term might sound like corporate jargon, something reserved for tech giants.

However, the core concept is surprisingly accessible and increasingly vital for even the smallest enterprises. We are not talking about selling customer lists in a shady back alley deal. Instead, it’s about strategically leveraging the information your business already generates to improve operations, understand customers better, and ultimately, unlock new revenue streams. This journey, from raw data to realized value, begins with understanding the fundamental shifts data monetization Meaning ● Turning data into SMB value ethically, focusing on customer trust, operational gains, and sustainable growth, not just data sales. introduces into SMB implementation strategies.

Unpacking Data Monetization For Small Businesses

Data monetization for SMBs isn’t some futuristic concept; it’s grounded in everyday business realities. Consider the local coffee shop that notices a surge in iced latte orders during warmer months. This is data in action. Monetization occurs when they use this insight to optimize inventory, staff accordingly, and even tailor promotions to capitalize on this seasonal trend.

At its heart, data monetization means recognizing that the information your business collects ● from sales transactions to website clicks, customer interactions to operational metrics ● possesses intrinsic value. This value can be unlocked through various strategies, ranging from internal improvements to external offerings. For an SMB, this could translate into better customer service, more efficient marketing campaigns, streamlined operations, or even the creation of entirely new products or services based on data-driven insights.

Data monetization for SMBs is about recognizing and strategically leveraging the inherent value of business information to drive growth and efficiency.

The crucial shift in perspective is viewing data not merely as a byproduct of operations, but as an asset. This asset, when properly managed and analyzed, can inform decisions across all business functions. For an SMB owner juggling multiple roles, from marketing to customer service, data-driven decision-making can be a game-changer. It moves beyond gut feelings and anecdotal evidence, providing concrete insights to guide strategic choices.

Think of a clothing boutique analyzing sales data to identify slow-moving inventory. Instead of guessing what to discount, they can pinpoint specific items and optimize pricing strategies to clear stock and improve cash flow. This targeted approach, powered by data, is far more effective than broad, untargeted sales.

Why Should SMBs Care About Data Monetization?

In a competitive landscape, SMBs often operate with limited resources. Every decision, every investment, carries significant weight. Data monetization offers a pathway to amplify resources and gain a competitive edge. It allows SMBs to work smarter, not just harder.

Imagine a plumbing service tracking customer call patterns. Analyzing this data might reveal peak demand times, common service requests, or even geographic areas with higher service needs. This information can then be used to optimize scheduling, allocate resources efficiently, and target marketing efforts to areas with the greatest potential. The result is improved service delivery, reduced operational costs, and increased customer satisfaction.

Furthermore, data monetization can unlock new revenue streams for SMBs. This doesn’t necessarily mean selling raw customer data, which raises privacy concerns and might not be feasible or ethical for many SMBs. Instead, it could involve packaging anonymized and aggregated data to offer value-added services. For example, a consortium of local restaurants could pool their anonymized sales data to identify popular menu items and dining trends in their area.

This aggregated data, while protecting individual restaurant information, could be valuable for food suppliers, market research firms, or even other restaurants looking to refine their offerings. This collaborative approach to data monetization can create new revenue opportunities while fostering a stronger business ecosystem.

Getting Started ● Practical Steps for SMBs

The prospect of data monetization might seem daunting, especially for SMBs with limited technical expertise. However, the initial steps are surprisingly straightforward and often involve leveraging tools and systems already in place. The first step is simply recognizing the data you already collect. This includes sales data from point-of-sale systems, website analytics, social media engagement metrics, customer feedback Meaning ● Customer Feedback, within the landscape of SMBs, represents the vital information conduit channeling insights, opinions, and reactions from customers pertaining to products, services, or the overall brand experience; it is strategically used to inform and refine business decisions related to growth, automation initiatives, and operational implementations. surveys, and even operational data like inventory levels and employee schedules.

Often, this data resides in disparate systems, spreadsheets, or even notebooks. The initial effort involves consolidating this information and creating a centralized view.

Once data is consolidated, the next step is basic analysis. This doesn’t require advanced data science skills. Simple spreadsheet software can be used to identify trends, patterns, and anomalies. For example, sorting sales data by product category can reveal top-selling items and underperforming products.

Analyzing website traffic can identify popular pages and areas for improvement. Customer feedback can highlight areas of satisfaction and dissatisfaction. These initial analyses provide valuable insights that can inform immediate operational improvements. The key is to start small, focus on readily available data, and gradually build analytical capabilities as needed.

Consider a small retail store using a basic point-of-sale system. They can start by exporting their sales data into a spreadsheet and analyzing sales by product category, day of the week, or time of day. This simple analysis can reveal peak shopping hours, popular product categories, and even seasonal trends.

Based on these insights, they can optimize staffing schedules, adjust inventory levels, and tailor promotional offers to maximize sales during peak periods and clear out slow-moving inventory. This is data monetization in its simplest, most practical form ● using readily available data to make informed business decisions and improve operational efficiency.

To further illustrate practical steps, consider the following list of initial actions SMBs can take:

1. Identify Data Sources ● List all systems and processes that generate data within your business. This includes POS systems, website analytics, CRM software, social media platforms, and operational logs.
2. Consolidate Data ● Bring data from disparate sources into a central location. This might involve using spreadsheets, cloud-based data storage, or simple data integration tools.
3. Basic Analysis ● Use spreadsheet software or basic analytics tools to identify trends, patterns, and anomalies in your data. Focus on descriptive analytics ● understanding what happened and why.
4. Actionable Insights ● Translate data insights into concrete actions. This could involve optimizing operations, improving customer service, refining marketing strategies, or developing new products or services.
5. Iterate and Improve ● Data monetization is an ongoing process. Continuously monitor results, refine strategies, and explore more advanced analytical techniques as your business grows and your data maturity Meaning ● Data Maturity, in the context of SMB growth, automation, and implementation, signifies the degree to which an organization leverages data as a strategic asset to drive business value. increases.

Starting with these fundamental steps, SMBs can begin to unlock the potential of data monetization. It’s about shifting from data collection as a passive activity to data utilization as a proactive driver of business growth and efficiency. The journey begins with recognizing the value in the information you already possess and taking simple, practical steps to harness its power.

SMBs don’t need to be tech giants to benefit from data monetization; starting with simple steps and readily available data can yield significant improvements.

Data monetization, at its core, is about making smarter decisions. For SMBs, this translates to increased efficiency, improved customer experiences, and ultimately, a stronger bottom line. The fundamentals are accessible, practical, and within reach of any SMB willing to embrace a data-driven mindset. The next stage involves moving beyond the basics and exploring more sophisticated strategies to fully capitalize on data assets.

Intermediate

Moving beyond the rudimentary stages of data awareness, SMBs ready to delve deeper into monetization discover a landscape ripe with strategic opportunities. Consider a regional chain of fitness studios. They’ve diligently collected member data ● attendance patterns, class preferences, demographic information.

At an intermediate level, they transition from simply tracking this data to actively leveraging it for targeted marketing Meaning ● Targeted marketing for small and medium-sized businesses involves precisely identifying and reaching specific customer segments with tailored messaging to maximize marketing ROI. campaigns, personalized workout recommendations, and even dynamic pricing models for off-peak hours. This phase of data monetization implementation involves a more sophisticated understanding of data analytics, customer segmentation, and the integration of data insights into core business processes.

Strategic Data Asset Development

The intermediate stage of data monetization for SMBs Meaning ● Data Monetization for SMBs represents the strategic process of converting accumulated business information assets into measurable economic benefits for Small and Medium-sized Businesses. centers on transforming raw data into strategic data Meaning ● Strategic Data, for Small and Medium-sized Businesses (SMBs), refers to the carefully selected and managed data assets that directly inform key strategic decisions related to growth, automation, and efficient implementation of business initiatives. assets. This involves more than just collecting and analyzing data; it requires a deliberate effort to structure, organize, and refine data to maximize its value. Think of a local e-commerce retailer that has been gathering customer purchase history and browsing behavior.

At this stage, they might invest in a Customer Relationship Management (CRM) system to centralize customer data, segment customers based on purchasing patterns and preferences, and use this segmentation to personalize email marketing campaigns Meaning ● Marketing campaigns, in the context of SMB growth, represent structured sets of business activities designed to achieve specific marketing objectives, frequently leveraged to increase brand awareness, drive lead generation, or boost sales. and product recommendations. This proactive data management transforms scattered information into a cohesive and actionable asset.

Developing strategic data assets also involves data enrichment. This means supplementing internal data with external data sources to gain a more comprehensive understanding of customers and market dynamics. For instance, the e-commerce retailer could enrich their customer data Meaning ● Customer Data, in the sphere of SMB growth, automation, and implementation, represents the total collection of information pertaining to a business's customers; it is gathered, structured, and leveraged to gain deeper insights into customer behavior, preferences, and needs to inform strategic business decisions. with demographic data from third-party providers, geographic data to understand regional preferences, or even social media data to gauge customer sentiment and brand perception.

This enriched data provides a richer context for analysis and enables more targeted and effective monetization strategies. The focus shifts from basic descriptive analytics to more predictive and prescriptive analytics, anticipating customer needs and proactively optimizing business operations.

To illustrate the concept of strategic data asset Meaning ● Strategic Data Asset: Information SMBs leverage for competitive edge, informed decisions, and sustainable growth. development, consider the following table outlining different stages of data maturity and associated activities for SMBs:

As SMBs progress to the intermediate stage, they move beyond simply reacting to past data to proactively shaping future outcomes through strategic data asset development. This transition requires investment in technology, processes, and skills, but the potential returns in terms of enhanced customer engagement, operational efficiency, and new revenue opportunities are substantial.

Automation and Data Monetization Synergies

Automation plays a crucial role in amplifying the impact of data monetization for SMBs at the intermediate level. By automating data collection, analysis, and action execution, SMBs can scale their data monetization efforts and achieve greater efficiency. Consider a landscaping company that uses GPS tracking for its service vehicles and digital scheduling software.

At this stage, they can automate the process of analyzing route data to optimize service routes, reduce fuel consumption, and improve service efficiency. They can also automate customer communication based on service schedules and real-time updates, enhancing customer satisfaction Meaning ● Customer Satisfaction: Ensuring customer delight by consistently meeting and exceeding expectations, fostering loyalty and advocacy. and reducing administrative overhead.

Automation amplifies data monetization by enabling efficient data processing and action execution, scaling impact and improving operational efficiency.

Furthermore, automation facilitates personalized customer experiences Meaning ● Tailoring customer interactions to individual needs, fostering loyalty and growth for SMBs. driven by data insights. For example, the fitness studio chain can automate personalized workout recommendations based on member attendance patterns, class preferences, and fitness goals. They can also automate targeted marketing campaigns based on customer segmentation, sending personalized offers and promotions based on individual preferences and past behavior.

This level of personalization, powered by data and automation, significantly enhances customer engagement Meaning ● Customer Engagement is the ongoing, value-driven interaction between an SMB and its customers, fostering loyalty and driving sustainable growth. and loyalty, driving revenue growth and customer lifetime value. Automation transforms data insights from static reports into dynamic, real-time actions that directly impact business performance.

To further illustrate the synergy between automation and data monetization, consider the following examples of automated processes and their data monetization impact for SMBs:

• Automated Inventory Management ● Using sales data and predictive algorithms to automatically adjust inventory levels, minimizing stockouts and overstocking, optimizing working capital.
• Automated Customer Service Meaning ● Customer service, within the context of SMB growth, involves providing assistance and support to customers before, during, and after a purchase, a vital function for business survival. Chatbots ● Leveraging customer interaction data to train chatbots to handle common customer inquiries, freeing up human agents for complex issues, improving customer service efficiency and availability.
• Automated Dynamic Pricing ● Analyzing demand data and competitor pricing to automatically adjust pricing in real-time, maximizing revenue and optimizing pricing strategies based on market conditions.
• Automated Lead Scoring and Nurturing ● Using lead behavior data to automatically score and prioritize leads, triggering automated email sequences and personalized content to nurture leads through the sales funnel, improving sales conversion rates.

These examples demonstrate how automation can be strategically integrated with data monetization to create a powerful engine for SMB growth and efficiency. The intermediate stage is about leveraging technology to automate data-driven processes, scaling impact, and moving towards a more proactive and data-centric operational model.

Navigating Data Privacy and Ethical Considerations

As SMBs advance in their data monetization journey, navigating data privacy Meaning ● Data privacy for SMBs is the responsible handling of personal data to build trust and enable sustainable business growth. and ethical considerations becomes paramount. Collecting and utilizing customer data comes with responsibilities, and SMBs must ensure they are compliant with relevant data privacy regulations Meaning ● Data Privacy Regulations for SMBs are strategic imperatives, not just compliance, driving growth, trust, and competitive edge in the digital age. and adhere to ethical data handling Meaning ● Ethical Data Handling for SMBs: Respectful, responsible, and transparent data practices that build trust and drive sustainable growth. practices. This is not just about legal compliance; it’s about building trust with customers and maintaining a positive brand reputation. Consider a local healthcare clinic that collects patient data for appointment scheduling and medical records.

At this stage, they must implement robust data security Meaning ● Data Security, in the context of SMB growth, automation, and implementation, represents the policies, practices, and technologies deployed to safeguard digital assets from unauthorized access, use, disclosure, disruption, modification, or destruction. measures to protect patient data from unauthorized access and comply with HIPAA regulations. They also need to be transparent with patients about how their data is being used and obtain informed consent for data collection and utilization.

Ethical data handling goes beyond legal compliance and involves considering the broader societal impact of data monetization practices. SMBs should avoid using data in discriminatory or manipulative ways, ensure data transparency and control for customers, and prioritize data security and privacy. For example, the fitness studio chain should avoid using demographic data to price discriminate or target vulnerable customer segments with predatory marketing tactics.

They should provide customers with clear and accessible privacy policies, allow customers to access and control their data, and implement strong data security measures Meaning ● Data Security Measures, within the Small and Medium-sized Business (SMB) context, are the policies, procedures, and technologies implemented to protect sensitive business information from unauthorized access, use, disclosure, disruption, modification, or destruction. to prevent data breaches. Building trust through ethical data practices Meaning ● Ethical Data Practices: Responsible and respectful data handling for SMB growth and trust. is essential for long-term sustainability and customer loyalty in the data-driven economy.

To ensure responsible data monetization, SMBs should consider the following best practices:

• Data Privacy Compliance ● Understand and comply with relevant data privacy regulations such as GDPR, CCPA, and HIPAA. Implement necessary data protection measures and policies.
• Data Security Measures ● Invest in robust data security technologies and practices to protect data from unauthorized access, breaches, and cyber threats.
• Transparency and Consent ● Be transparent with customers about data collection and usage practices. Obtain informed consent for data collection and provide clear privacy policies.
• Ethical Data Usage ● Avoid using data in discriminatory, manipulative, or unethical ways. Prioritize fairness, transparency, and customer well-being in data monetization strategies.
• Data Governance Framework ● Establish a data governance Meaning ● Data Governance for SMBs strategically manages data to achieve business goals, foster innovation, and gain a competitive edge. framework that outlines data policies, procedures, and responsibilities. Ensure accountability and oversight for data monetization activities.

Navigating data privacy and ethical considerations is not an obstacle to data monetization but an integral part of responsible and sustainable data utilization. SMBs that prioritize ethical data Meaning ● Ethical Data, within the scope of SMB growth, automation, and implementation, centers on the responsible collection, storage, and utilization of data in alignment with legal and moral business principles. practices build trust, enhance brand reputation, and create a foundation for long-term success in the data-driven marketplace. The intermediate stage of data monetization implementation is about balancing strategic opportunities with ethical responsibilities, ensuring data is used not only effectively but also responsibly.

Ethical data practices are not just about compliance; they are about building trust, fostering customer loyalty, and ensuring long-term sustainability in data monetization.

Moving from fundamental awareness to intermediate strategic implementation, SMBs unlock significant value from their data assets. By developing strategic data assets, leveraging automation, and navigating data privacy ethically, SMBs position themselves for enhanced competitiveness and sustainable growth in the evolving business landscape. The advanced stage of data monetization takes this further, exploring innovative data products and external monetization opportunities.

Advanced

For SMBs that have mastered the fundamentals and intermediate strategies of data monetization, the advanced stage represents a paradigm shift. It’s no longer solely about internal optimization or enhanced marketing; it’s about transforming data into a standalone revenue stream, a distinct product offering, or a strategic asset Meaning ● A Dynamic Adaptability Engine, enabling SMBs to proactively evolve amidst change through agile operations, learning, and strategic automation. for external partnerships. Consider a sophisticated agricultural technology SMB providing precision farming solutions to local farmers. Having collected granular data on soil conditions, weather patterns, and crop yields across numerous farms, they move beyond simply advising individual farmers.

They aggregate and anonymize this data to create valuable datasets and insights that can be sold to agricultural research institutions, insurance companies, or even commodity trading firms. This advanced phase is characterized by data productization, external data monetization, and leveraging data for strategic ecosystem development.

Data Productization and New Revenue Streams

At the advanced level, data monetization transcends internal applications and evolves into the creation of data products. This involves packaging data into consumable formats that can be sold or licensed to external customers. Data products can range from raw datasets and aggregated statistics to sophisticated analytics reports and predictive models. Think of a regional logistics company that has amassed detailed data on transportation routes, delivery times, and fuel efficiency.

They can productize this data by creating a real-time transportation analytics dashboard that can be sold to other logistics companies, supply chain managers, or even urban planning agencies. This transforms operational data into a tangible, revenue-generating product.

Data productization requires a shift in mindset from viewing data as a byproduct to seeing it as a core product offering. It involves identifying valuable data assets, defining target customer segments, developing data products that meet customer needs, and establishing pricing and distribution strategies. For example, the agricultural technology SMB can productize their aggregated farming data by creating different data products tailored to specific customer segments.

They could offer raw datasets for research institutions, summarized reports for insurance companies assessing crop risk, and predictive yield forecasts for commodity traders. Each data product is priced and packaged to maximize its value for the target customer segment, creating diverse revenue streams from a single data asset.

The spectrum of potential data products for SMBs is broad and varies depending on the industry and data assets. Here are examples of data product categories and potential applications:

Data productization opens up entirely new revenue streams for SMBs, moving beyond traditional product and service offerings. It allows SMBs to capitalize on the inherent value of their data assets and participate in the growing data economy. The advanced stage of data monetization is about becoming a data provider, not just a data user, and leveraging data as a core business asset.

Data productization transforms data from an internal asset to an external revenue stream, allowing SMBs to participate in the data economy as data providers.

External Data Monetization Strategies and Partnerships

Beyond data productization, advanced data monetization involves exploring external monetization strategies and partnerships. This includes directly selling data, licensing data access, participating in data marketplaces, and forming strategic data partnerships with other organizations. Consider a local energy utility company that collects smart meter data from residential and commercial customers.

They can explore external monetization by selling anonymized and aggregated energy consumption data to energy research firms, grid management operators, or even appliance manufacturers for product development. This external data monetization extends the reach and impact of SMB data assets beyond their own operations.

Strategic data partnerships can create synergistic value and expand monetization opportunities. SMBs can partner with complementary businesses, data aggregators, or technology platforms to enhance their data offerings, reach new customer segments, or access new data sources. For example, the agricultural technology SMB could partner with a weather data provider to enrich their farming data with more granular weather information, creating a more comprehensive and valuable data product for farmers.

They could also partner with an agricultural marketplace platform to distribute their data products to a wider audience of farmers and agricultural businesses. These partnerships leverage external expertise and resources to amplify data monetization impact.

Various external data monetization strategies Meaning ● Leveraging data assets for revenue & value creation in SMBs, ethically & sustainably. are available to SMBs, each with its own advantages and considerations:

• Direct Data Sales ● Selling data directly to end-users or organizations. Requires establishing sales channels, pricing models, and data delivery mechanisms.
• Data Licensing ● Licensing data access for a specific period or use case. Provides recurring revenue streams and control over data usage.
• Data Marketplaces ● Listing data products on online data marketplaces to reach a wider audience of potential buyers. Leverages existing infrastructure and market reach.
• Data Partnerships ● Forming strategic partnerships with other organizations to co-create data products, share data resources, or access new markets. Creates synergistic value and expands monetization opportunities.
• Data-As-A-Service (DaaS) ● Providing data access and analytics capabilities as a subscription service. Offers recurring revenue and ongoing customer engagement.

External data monetization strategies require careful consideration of data privacy, security, and compliance. SMBs must ensure they are adhering to data privacy regulations when sharing data externally and implement robust data security measures to protect data assets. Transparency and ethical data handling are crucial for building trust and maintaining a positive reputation in the external data marketplace. The advanced stage of data monetization is about strategically navigating the external data ecosystem, forming partnerships, and creating sustainable revenue streams from data assets.

Data-Driven Ecosystem Development and Competitive Advantage

The ultimate evolution of advanced data monetization for SMBs is leveraging data to build and participate in data-driven ecosystems. This involves creating platforms, communities, or networks around data assets that foster collaboration, innovation, and shared value creation. Consider a regional network of independent auto repair shops. By pooling their anonymized repair data, diagnostic information, and customer feedback, they can create a shared data platform that benefits all participating shops.

This platform could provide insights into common vehicle issues, best repair practices, parts sourcing, and customer satisfaction benchmarks. This data-driven ecosystem enhances the competitiveness of individual shops and strengthens the collective network.

Data-driven ecosystems create network effects, where the value of the ecosystem increases as more participants join and contribute data. SMBs can leverage their data assets to build ecosystems within their industry, geographic region, or customer segment. These ecosystems can foster collaboration, knowledge sharing, and innovation, creating a competitive advantage Meaning ● SMB Competitive Advantage: Ecosystem-embedded, hyper-personalized value, sustained by strategic automation, ensuring resilience & impact. for participating SMBs.

For example, the agricultural technology SMB could expand their data platform to create a broader agricultural ecosystem that includes farmers, suppliers, processors, and researchers. This ecosystem could facilitate data sharing, knowledge exchange, and collaborative innovation across the agricultural value chain, creating shared value for all participants.

Key elements of building data-driven ecosystems Meaning ● Interconnected business network fueled by data for SMB growth & informed decisions. include:

• Data Platform Infrastructure ● Developing a secure and scalable data platform to collect, store, and share data within the ecosystem.
• Data Governance and Standards ● Establishing data governance policies, data quality standards, and data sharing protocols to ensure data integrity and interoperability within the ecosystem.
• Ecosystem Participation Incentives ● Creating incentives for SMBs to join and contribute data to the ecosystem, such as access to valuable insights, shared resources, or new business opportunities.
• Value-Added Services and Applications ● Developing value-added services and applications on top of the data platform that benefit ecosystem participants, such as analytics dashboards, predictive models, or collaborative tools.
• Ecosystem Governance and Management ● Establishing governance structures and management processes to oversee the ecosystem, ensure fair participation, and drive continuous innovation and value creation.

Data-driven ecosystem development represents the pinnacle of advanced data monetization for SMBs. It moves beyond individual data monetization strategies to collective value creation, fostering collaboration, innovation, and sustainable competitive advantage. SMBs that embrace this advanced stage can transform their data assets into catalysts for ecosystem growth and become leaders in the data-driven economy. The journey from fundamental data awareness to advanced ecosystem development is a progressive evolution, each stage building upon the previous one, unlocking increasing levels of value and impact from data monetization.

Data-driven ecosystems represent the highest level of data monetization, fostering collaboration, innovation, and collective competitive advantage for participating SMBs.

In conclusion, data monetization’s impact on SMB implementation is profound and transformative. From fundamental operational improvements to intermediate strategic asset development and advanced data productization and ecosystem building, data monetization offers a spectrum of opportunities for SMBs to enhance competitiveness, drive growth, and create new value in the data-driven economy. The key is to approach data monetization as a progressive journey, starting with the fundamentals, building strategic capabilities, and ultimately, leveraging data to its fullest potential as a core business asset.

References

• Brynjolfsson, Erik, and Andrew McAfee. The Second Machine Age ● Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company, 2014.
• Davenport, Thomas H., and Jill Dyche. “Big Data ‘101’.” Harvard Business Review, vol. 91, no. 5, 2013, pp. 68-76.
• Manyika, James, et al. “Big Data ● The Next Frontier for Innovation, Competition, and Productivity.” McKinsey Global Institute, 2011.
• O’Reilly, Tim. What Is Web 2.0 ● Design Patterns and Business Models for the Next Generation of Software. O’Reilly Media, 2005.
• Porter, Michael E., and James E. Heppelmann. “How Smart, Connected Products Are Transforming Competition.” Harvard Business Review, vol. 92, no. 11, 2014, pp. 64-88.