Analysis Report Four - Trend Analysis and Value Creation

Author

Olivia Mitchell

Executive Summary

Digital platforms create value by connecting users, collecting data, and using that information to improve customer experiences. This report compares the platform characteristics discussed in How Platforms Thrive (Cusumano, Gawer, and Yoffie 2021) with the digital business models described in The 7 Most Successful Business Models of the Digital Era (Marr 2023) and explains how data analytics supports these strategies. Using the Northwind database, a time trend analysis of customer HANAR identifies changes in purchasing behavior over time and demonstrates how organizations can use these insights to improve decision-making, personalize services, and create greater value for customers.

Introduction

The assigned readings explain how digital platforms and modern business models have changed the way organizations create value for customers. How Platforms Thrive explains that successful platforms grow by connecting different groups of users, creating network effects, and using data to improve interactions and decision-making (Cusumano, Gawer, and Yoffie 2021). The reading also explains that platforms must manage challenges such as competition, customer loyalty, and maintaining valuable relationships between users.

The 7 Most Successful Business Models of the Digital Era describes several common business models, including advertising-supported, e-commerce, marketplace, subscription, freemium, aggregator, and crowdfunding models (Marr 2023). While each model generates revenue in different ways, they all depend on technology and customer data to better understand user behavior and improve the customer experience. Together, these readings show that organizations can create long-term value by combining platform strategies with data analytics to make better business decisions and build stronger relationships with customers.

Outside Research

The issues discussed in the readings are easy to see in the current business environment. Apple is one example of a company that uses several digital business models at the same time, including e-commerce, subscriptions, and a marketplace through its App Store. Apple’s devices and services work together as a connected platform, which creates high switching costs and makes customers less likely to leave for another company. However, Apple has become increasingly dependent on revenue from services, subscriptions, and App Store commissions, while its strict control over the platform has caused conflict with developers and regulators (Apple Inc. 2026).

Apple’s situation connects to the platform characteristics discussed in How Platforms Thrive. Its large network of customers and app developers creates strong cross-side network effects because more apps make Apple devices more useful, while more device users attract additional developers (Cusumano, Gawer, and Yoffie 2021). At the same time, Apple tries to reduce multi-homing and disintermediation by keeping app distribution and many digital purchases inside its own system. These controls help Apple keep customers and collect revenue, but they can also hinder competition and make developers feel too dependent on the platform.

Apple is currently facing pressure under the European Union’s Digital Markets Act (Apple Inc. 2026). Apple has been required to offer developers additional options for app distribution, payment processing, browser engines, interoperability, and access to analytics in the European Union. Apple argues that opening its system creates new privacy and security risks, while regulators argue that these changes give customers more choices and allow other businesses to compete. This struggle shows that the same platform characteristics that help an organization succeed can eventually become a weakness. A closed network can increase loyalty and support subscription and marketplace revenue, but too much control can reduce trust, limit innovation, and attract government attention. Organizations must therefore balance profitability with fairness, customer choice, security, and the needs of the businesses that depend on their platforms.

Data Visualizations

SELECT strftime("%Y", OrderDate) AS year,
       SUM("Order Details".UnitPrice * Quantity) AS revenue
FROM Orders
INNER JOIN "Order Details"
ON Orders.OrderID = "Order Details".OrderID
WHERE CustomerID = "HANAR"
GROUP BY year
ORDER BY year
ggplot(data = hanar_year,
       aes(x = year,
           y = revenue,
           group = 1)) +
  geom_line(color = "hotpink") +
  labs(
    title = "HANAR Revenue by Year",
    x = "Year",
    y = "Revenue"
  )

Visualization 1 shows HANAR’s total revenue by year from 2012 through 2025. Revenue changes from year to year, with the highest level occurring in 2022. Looking at revenue over time helps identify long-term customer trends and provides a starting point for determining when purchasing activity was strongest.

SELECT strftime("%m", OrderDate) AS month,
       SUM("Order Details".UnitPrice * Quantity) AS revenue
FROM Orders
INNER JOIN "Order Details"
ON Orders.OrderID = "Order Details".OrderID
WHERE CustomerID = "HANAR"
AND strftime("%Y", OrderDate) = "2022"
GROUP BY month
ORDER BY month
ggplot(data = hanar_month,
       aes(x = month,
           y = revenue,
           group = 1)) +
  geom_line(color = "deepskyblue") +
  labs(
    title = "HANAR Revenue by Month (2022)",
    x = "Month",
    y = "Revenue"
  )

After identifying 2022 as the highest revenue year, I drilled down to examine monthly purchasing activity. This view provides a more detailed look at HANAR’s buying behavior and helps identify months where spending increased or decreased. Time trend analysis like this allows organizations to recognize seasonal purchasing patterns and better plan inventory and marketing efforts.

SELECT CategoryName,
       strftime("%m", OrderDate) AS month,
       SUM("Order Details".UnitPrice * Quantity) AS revenue
FROM Orders
INNER JOIN "Order Details"
ON Orders.OrderID = "Order Details".OrderID
INNER JOIN Products
ON "Order Details".ProductID = Products.ProductID
INNER JOIN Categories
ON Products.CategoryID = Categories.CategoryID
WHERE CustomerID = "HANAR"
AND strftime("%Y", OrderDate) = "2022"
GROUP BY month, CategoryName
ORDER BY month
ggplot(data = hanar_category,
       aes(x = month,
           y = revenue,
           color = CategoryName,
           group = CategoryName)) +
  geom_line() +
  labs(
  title = "Monthly HANAR Revenue by Product Category (2022)",
  x = "Month",
  y = "Revenue",
  color = "Category" 
  )

The final visualization breaks the monthly revenue into individual product categories. Instead of only knowing when HANAR spent the most money, this graph shows which product categories contributed to those spending patterns. A digital platform could use this information to recommend related products, personalize promotions, forecast demand, and improve inventory decisions. Platforms often have access to even more customer data than the Northwind database, allowing them to create more accurate recommendations and improve customer value over time.

Important

Your visualizations this week should contain time trend analysis. In your live-edit during your presentation, perform a drill down from a high level year analysis to a lower level month or day analysis and rerun your geom_line() with the new data.

Recommendations for Industry

  1. Use time trend analysis to understand customer behavior. Monitor purchasing patterns over time to identify seasonal trends and changes in customer demand.
  2. Personalize recommendations using customer data. Use purchase history and platform analytics to recommend products and services that match customer interests and buying habits.
  3. Improve inventory planning. Analyze customer purchasing trends to ensure popular products are available during periods of high demand while reducing excess inventory.
  4. Strengthen customer relationships through platform data. Use insights from customer interactions to improve communication, create targeted promotions, and encourage long-term customer loyalty.
  5. Be transparent about data collection and privacy. Clearly explain how customer data is collected and used so organizations can build trust while still benefiting from data analytics.

References

Apple Inc. 2026. “Digital Markets Act.” https://www.apple.com/legal/dma/.
Cusumano, Michael A., Annabelle Gawer, and David B. Yoffie. 2021. “How Platforms Thrive.” MIT Sloan Management Review.
Marr, Bernard. 2023. “The 7 Most Successful Business Models of the Digital Era.” Forbes. https://www.forbes.com/sites/bernardmarr/2023/03/14/the-7-most-successful-business-models-of-the-digital-era/.