Business models are frameworks businesses use to generate, deliver and capture value for their stakeholders while meeting financial and other goals, such as social and environmental sustainability.
Digital technologies can assist businesses in developing and implementing innovative business models, including digitalization, business model innovation (BMI) and process management.
Customer Relationship Management (CRM)
Customer relationship management (CRM) refers to a set of strategies and technologies designed to manage interactions between an organization and current and potential customers. CRM tools automate marketing, sales, service-related processes to enhance customer experiences while driving revenue growth.
CRM (Customer Relationship Management) systems collect customer data from various sources such as emails, phone calls, live chat sessions, social media and direct mail. They give customer-facing staff detailed data regarding individual customers’ personal details, purchasing histories, preferences and concerns.
Utilizing CRM systems, businesses can efficiently track and analyze customer data to gain insight into each segment’s individual needs, then utilize this knowledge to develop personalized products or services tailored specifically to each customer’s individual requirements. This ultimately builds loyalty while simultaneously increasing retention rates and Life Time Value (LTV).
Data Analytics
Data analytics translates raw numbers into informative and educational insights that facilitate informed decision-making and improve performance for businesses by streamlining processes and responding to market trends.
Data-driven decisions can also reduce operational costs, leading to improved profitability and competitiveness. When combined with human insight and industry expertise, these decisions may provide companies with opportunities for improvement and innovation.
Experienced data analysts understand how their work affects broader business and industry environments, making recommendations that consider internal and external factors, as well as any competing initiatives. Furthermore, these analysts can clearly communicate their findings so they are understood by non-data analysts allowing greater depth of intuition while supporting successful business model transformation efforts (Foss & Saebi 2017).
Artificial Intelligence (AI)
Technological innovation often raises both awe and alarm, especially when it appears overnight and spreads rapidly across society. AI, for instance, has generated both admiration and worry due to job loss concerns such as disinformation spread online or worker displacement.
Contrary to older automation technology, which focused on automating mundane or error-prone tasks, artificial intelligence (AI) is designed to learn and perform cognitive work; meaning it can automate more complex or creative processes such as predictive analytics, pattern recognition and customer segmentation.
AI can also be utilized for process automation, image/text/video analysis and recommendation engines. Furthermore, it can help solve complex problems and make strategic decisions previously beyond humans’ reach – for instance analyzing market data or forecasting weather or stock trends.
Internet of Things (IoT)
Smart devices connected to the internet are increasingly common. HP connected printers and Brita water pitchers, for instance, automatically reorder consumables when their inventory runs low to ensure you never run out.
Industrial settings benefit greatly from IoT sensors as well: IoT sensors enable monitoring machinery to optimize performance, decrease maintenance time and risk and facilitate predictive maintenance – an approach known as predictive maintenance.
IoT technology can also be leveraged to increase employee safety in hazardous workplaces, with wearable devices equipped with IoT applications being worn to track workers and their movements to ensure they remain safe at all times. Furthermore, this enabling technology can support circular business models that emphasize maintenance over disposal; one example being Amazon Dash buttons which use IoT to order replacement household products when their supply runs out.
Digital Twins
Digital twins are virtual representations of physical objects like cars, buildings, bridges and jet engines that collect data via sensors on them that is then mapped onto a digital model to display information about how well that object is performing. Anyone observing its digital twin can gain insights into its performance as they see how its performance matches up against that of its physical counterpart.
Companies use digital twins to test different key performance indicators and business models in order to increase efficiency, such as Chevron using one to reduce maintenance costs in its oil fields and refineries. Digital twins also help businesses predict when equipment will likely fail, thus cutting repair costs and downtime significantly.
Implementing a digital twin requires a robust IoT infrastructure and data engineering expertise, including computing power that will support complex objects like airplanes or engines.