Analysis Report Four - Trend Analysis and Value Creation

Author

Nora Miller

Executive Summary

Modern digital organizations often use platform, marketplace, and subscription business models to grow quickly and make bigger profits. But as Feng Zhu and Marco Iansiti point out in “Why Some Platforms Thrive… and Others Don’t”, it is usually much easier to grow fast online than it is to keep that growth going for a long time. In this report, I look at how different platform features, like network effects, clustering, the risk of being cut out by others, the chance that users use more than one service, and how networks connect, can help or hurt organizations trying to use Bernard Marr’s digital business models. I also include research on current regulations and practical time-trend analysis with the Northwind database to show how tracking changes over time can help find new ways to create value. Finally, I explain how modern platforms use better data structures than older, traditional databases.

Introduction

To understand how modern organizations create and capture value, I bring together Bernard Marr’s types of digital business models and the main network ideas from Feng Zhu and Iansiti (Zhu and Iansiti 2019; Marr 2023).

Marr points out seven main digital business models, such as Marketplace or Platform, Subscription, Freemium, and E-commerce. All of these depend on digital connections to make money. But Zhu and Iansiti say that how well these models do depends on five important platform features:

  1. Network Effects: When more users join a platform, it becomes more valuable to everyone. This is especially true for Marketplace and Platform models like Amazon or the Apple App Store. If these effects are weak, growth can slow down.

  2. Network Clustering: If a platform is broken up into lots of local groups (like regional ride-sharing), it can be hard to grow because of competition in each area. But having a big, global user base helps keep everything connected.

  3. Risk of Disintermediation: Marketplace models run into problems when buyers and sellers start working with each other directly to avoid paying platform fees.

  4. Multi-Homing: If it’s easy for users to switch between platforms, they might use more than one at the same time. This makes it harder for companies to make a profit from subscriptions or aggregation.

  5. Network Bridging: If a platform can connect different groups or networks, it can protect itself from competitors.

If you look at these ideas in the context of Christopher Mims’s analysis of Apple (“Tim Cook’s Biggest Success Is Now Apple’s Biggest Liability”) (Mims 2024), you can see a classic problem that platforms face. Apple has built a strong marketplace and subscription system, protecting its high profits (about 70%) by making it hard for users to leave. However, by keeping such tight control, Apple can upset outside developers and attract attention from regulators worldwide. This shows that for a platform to last, companies need to balance making money with building trust.

Outside Research

The weaknesses in digital platforms described by Zhu and Iansiti are now showing up in real time as regulators around the world take action. Rules like the European Union’s Digital Markets Act (DMA) and antitrust cases in the United States are putting a lot of pressure on big tech companies. These companies are being pushed to let in other app stores, allow sideloading, and accept different payment systems instead of keeping everything locked down.

Companies that can’t keep up are finding out that just locking in users and charging high fees does not work as a long-term strategy anymore. Now, platform builders need to keep creating value, make it easy for users to use different services, and build trust in their community instead of pushing back against new regulations.

Data Visualizations

Visualization One - Multiple Shippers

How does the number of yearly deliveries for Speedy Express and United Package compare, and how has the total number of shipments on the platform changed from one year to the next?

SELECT CompanyName, COUNT(*) as order_count, 
       strftime("%Y",ShippedDate) as year
FROM orders 
INNER JOIN shippers
ON orders.shipvia = shippers.ShipperID
WHERE CompanyName IN("Speedy Express","United Package") AND year != "NA"
GROUP BY year, CompanyName
ORDER BY order_count
ggplot(data = myquery1,
       aes(x = year, y = order_count, group = CompanyName, color = CompanyName)) +
  geom_line(linewidth = 1) +
  labs(
    title = "Annual Order Volume by Shipper",
    subtitle = "High-level annual trend analysis across shipping partners",
    x = "Year",
    y = "Order Count"
  ) +
  theme_minimal()

Visulization Two - 2017 Monthly Analysis

Looking at just the year 2017, how many shipments did Speedy Express handle each month, and which month was the most customer demand.

SELECT CompanyName, COUNT(*) as order_count, 
       strftime("%Y",ShippedDate) as year,
       strftime("%m", ShippedDate) as month
FROM orders 
INNER JOIN shippers
ON orders.shipvia = shippers.ShipperID
WHERE CompanyName IN("Speedy Express","United Package") AND year != "NA"
GROUP BY month
ORDER BY order_count
ggplot(data = myquery2,
       aes(x = month, y = order_count, group = 1)) +
  geom_line(color ="steelblue", linewidth = 1) +
  geom_point(color ="steelblue", size = 2) +
  labs(
    title = "Speedy Express 2017 Monthly Order Drill Down",
    subtitle = "Granular Monthly Trend Analysis revealing operational volume shifts",
    x = "month",
    y = "Order Count"
  ) +
  theme_minimal()

The time-trend charts above show that by looking at patterns over time, like how much is shipped each year or when there are big spikes in shipping, you can spot changes in customer demand. But as Zhu and Iansiti point out in How Platforms Thrive, older databases like Northwind only keep records after things have already happened, such as logs of shipments that have been delivered.

Modern digital platforms, on the other hand, collect much better data for finding value. They track what users do as it happens, how people interact with each other, where users are at any moment, and if users are active on more than one platform. This means platform operators can do more than just look at past data. They can use real-time tools to see trends right away, lower the risk of users leaving the platform, and quickly adjust prices to keep everything running smoothly.

Recommendations for Industry

Reduce the risk of users skipping the platform by offering extra services that create real value. Instead of depending on strict rules or high fees that push people away, organizations should add helpful features like escrow, secure payments, and easy ways to solve disputes. These services give users good reasons to stay on the platform rather than go around it.

Build for global connections. Zhu and Iansiti point out that platforms focused on small local groups often run into competition and price wars in each region. Platform builders should try to connect their networks on a wider scale and find global points of contact. This helps protect the platform in the long run.

Use time-based data analysis. Platform analysts should look at trends over time, like tracking changes by date or checking how things change from month to month, as shown in the Northwind example. By doing this, they can keep an eye on how often users make purchases and predict changes in how much activity is happening on the platform.

References

Marr, Bernard. 2023. “The 7 Most Successful Business Models of the Digital Era.” Forbes, March.
Mims, Christopher. 2024. “Tim Cook’s Biggest Success Is Now Apple’s Biggest Liability.” The Wall Street Journal, January.
Zhu, Feng, and Marco Iansiti. 2019. “Why Some Platforms Thrive... And Others Don’t.” Harvard Business Review 97 (1): 118–25.