Analysis Report Four - Trend Analysis and Value Creation

Executive Summary

This week we tied all of our previous research together by talking about trend analysis and value creation. Throughout the course we have discussed various aspects of data analysis and business strategy, including system integration, consumer privacy, and technological integration. With these fundamental understandings we determine that data collection and analysis is very important to a competitive business structure. This week we extended that understanding, by learning how companies can make data not only serve the organization but serve the customer directly.

Introduction

This week we took a look at a variety of reading, all surrounding the idea that platforming a business entity is one of the most effective ways a company con both increase data acquisition and profitability if done correctly (Zhu and Lansiti 2019). While there are numerous types of digital business models, each created to serve a different fiscal outcome, NorthWinds Database is most synchronized with a platform/marketplace concept. Looking at the “Why Some Platforms Thrive” article through a lense of business development, NorthWinds would likely successed as a distribution intermediary due to it’s large network effect. Not only does NorthWinds already have a large pool of distributors, shippers, and clients, they have the previous data that connects these unalike organizations. Using this data they could help consumers compare and contrast similar options from products to freight all in one place, leaving room for unbiased data and consumer feedback to better integrate the system down the line.

Of the challenges, NorthWinds biggest struggle starting off would likely be Risk of Disintermediation, or to put it simply customers buying direct from manufactures once a relationship is established. While this is a nateable and common issue, NorthWinds previous data sets them apart. Using previous data and allowing customers to mix and match components of their order all in one place allows NorthWinds a direct advantage over a direct B-to-C transaction, which does not provide the same transparency and feeling of choice. Additionally if the company chooses to have a section similar to that on Amazon, with individual reviews of things like shippers and products the company recieves even more data and customer generated content, which otherwise is highly unlikely.

Outside Research

Upon further research into how an organization becomes a platform I discovered it is largely thanks to IIS’s. As defined in ‘From Data to Value’ an “information-intensive service (IIS) is a type of service in which value is created primarily via information interactions” (Lim et al. 2018). This is incessant a virtual marketplace where value is derived from the convince and placement of products and services, rather than B-to-C distribution of purchased goods. Looking at this explanation as if it were a physical mall further proves the legitimacy of the Network Effect. Having more stores at your shopping center increase the customer attraction, and vise versa the more customers shopping at the mall the more potential clientele there is for each store. While there are numerous other ways platforms can profit, as described in ‘The 7 Most Successful Business Models Of The Digital Era’, for the sake of the NorthWinds Database this idea of a digital marketplace is likely most sustainable. NorthWinds could leverage their multiple shipping partners, suppliers, and territories to act as a globally clustered restaurant supply intermediary. This could also be considered a “sharing economy” which “emphasize the act of sharing and exchanging resources without operating a formal transfer in ownership” (Liang, Aroles, and Brandl 2022). This type of strategy is in alignment with our previous discussions about consumer privacy and trade offs, again reinforcing the idea that data collection can be plentiful, transparent, and mutually beneficial if set up to do so.

Data Visualizations

Visualization one shows how many units Chop-suey Chinese bought of their most ordered product (Chai) by year. The live edited version is also about orders of Chai but grouped by the month for the year of 2020. The second visualization shows which shipping companies were used to fufill CHOPS orders throughout the years. The third is also showing which shipping companies were used to fulfill Chop-suey Chinese’s orders in 2020.

SELECT Products.ProductName, COUNT(*) AS OrderCount,  strftime("%m",ShippedDate) as Month,  strftime("%Y",ShippedDate) as Year
FROM Orders
INNER JOIN "Order Details" AS od
ON Orders.OrderID = od.OrderID
INNER JOIN Products
ON Products.ProductID = od.ProductID
WHERE CustomerID =  "CHOPS" 
  AND Year != "NA"
  AND Year = "2020"
  AND Products.ProductName = "Chai"
GROUP BY Month, Year

ggplot(data = query1,
       aes(x = Month, y = OrderCount, group = 1))+
  geom_line() +
   labs(title = "Graph showing Chai Orders in 2020 by Month")

SELECT CompanyName, COUNT(*) AS OrderCount, strftime("%Y",ShippedDate) as Year
FROM Orders
INNER JOIN Shippers
ON orders.ShipVia = shippers.ShipperID
WHERE CustomerID =  "CHOPS"
  AND Year !="NA"
GROUP BY Year, ShipVia
ORDER BY OrderCount

ggplot(data = query3,
       aes(x = Year, y = OrderCount, group = CompanyName, color = CompanyName))+
  geom_line() +
   labs(title = "Graph showing Shipping Companies Used in throughout the years")

SELECT CompanyName, COUNT(*) AS OrderCount, strftime("%m",ShippedDate) as Month, strftime("%Y",ShippedDate) as Year
FROM Orders
INNER JOIN Shippers
ON orders.ShipVia = shippers.ShipperID
WHERE CustomerID =  "CHOPS"
  AND Year !="NA"
  AND Year = "2020"
GROUP BY Month, ShipVia
ORDER BY OrderCount

ggplot(data = query2,
       aes(x = Month, y = OrderCount, group = CompanyName, color = CompanyName))+
  geom_line() +
   labs(title = "Graph showing Shipping Companies Used in 2020 by Month")

Recommendations for Industry

This data is helpful as it can be used to make product recommendations for restock orders or similar products. If they company were to take my suggestion to platform the company into a restaurant supply intermediary, they would be able to use this data to compare options for customers worldwide based on real customer data. They could create a feedback system that would allow customers to rate the different aspects of each order to better serve other customers. Additionally they could use the data collected from the sales to help distributors find potential customers based on past orders. There are numerous ways this data could be used to help platform this organizations data it a real interconnected system of processes, without having as much responsibility and liability.

References

Liang, Yin, Jeremy Aroles, and Bernd Brandl. 2022. “Charting Platform Capitalism: Definitions, Concepts and Ideologies.” New Technology, Work and Employment 37 (2): 308–27.

Lim, Chiehyeon, Ki-Hun Kim, Min-Jun Kim, Jun-Yeon Heo, Kwang-Jae Kim, and Paul P Maglio. 2018. “From Data to Value: A Nine-Factor Framework for Data-Based Value Creation in Information-Intensive Services.” International Journal of Information Management 39: 121–35.

Zhu, Feng, and Marco Lansiti. 2019. “Why Some Platforms Thrive.” Harvard Business Review, 120–25.