Applying Big Data for Mass Customization

Many companies have a strong interest in applying big data for marketing, but predominately at the promotional level. Few articles have effectively covered the application of big data for product modification and the user experience level. The streamlined acquisition of more data on customers, combined with the application of smaller batch production and cloud software means greater product flexibility.

It is much easier to change the dashboard of a SaaS program to red than it is of a downloadable software that lacks the functionality of user-interface edits. Smaller manufacturers servicing small business are streamlining their operations due to the entry of companies like Threadless and entrance of a plethora of startups pushing the latest technology on Kickstarter, Indiegogo, and the like. This article is designed to help companies bridge the gap between granular big data to a mass customized user experience that will lead to higher amounts of loyalty and greater market positioning.

First, we begin with a twostudies of how Amazon was able to increase their profitability by customizing the user shopping experience with the application of big data and Windows was able to build a highly flexible operating system that manages to intact its childlike simplicity. Amazon’s seemingly simple recommended products are modified based not only on the user’s shopping patter, but the aggregated information from millions of other users. This creates a more customized shopping experience that presents products for cross-selling opportunities and advertising them at the most appropriate time.

The latest version of Windows is also working on mass customization through the application of big data. It collects user information that is then used to build applications that users may download to build a fully customizable experience. As users engage with the windows operating system, the company becomes more aware of the application needs of its customers.

The recently launched Windows app store enables users to download the applications they deem relevant to customize their experience. Consumers that were once only able to change the desktop color of their operating system from green to brown, novel at the time, are now able to download a full suite of third-party applications to have full flexibility over their system interaction.

Assessing the benefits to your organization

It is evident applying big data for the purposes mass customization by a large software company is possible, but can you yield the same value in your business? We believe that it is not only possible, but quite simple and a matter of only shifting your approach to the solution. For instance, most companies are focused on cutting costs and reaching a greater level of efficiency. Analyzing data and shifting the production or delivery process for mass customization is contradictory to this traditional thinking.  Therefore, many companies must understand the value that mass customization will bring to their firm quantitatively and the trade-off that results from it in terms of operations efficiency and profit margins.

The following questions will help you to determine if the added value is worth the effort:

  • What is my current market positioning and how will greater customization influence it?
  • Will customers pay more or be more satisfied by providing mass customization?
  • What will be the additional direct costs associated with integrating these features?
  • Can we charge or generate more money directly from the mass customization?

Generating insights from your data

Most people reading this article will already be aware of how to set-up, manage, and analyze data sets in a large scale. However, the tangible value from the insights begins with asking the right questions about how to collect and analyze information for the purposes of mass customization. Your company must first address what exactly you are seeking to accomplish through the customization. If it is to acquire market positioning, then this may vary from the attempt to sell a greater amount of products like Amazon’s case. The insights all begin with first posting a question before the queries are formed, as exploratory data analysis may not work well when adding new features for the sake of speculation may in-fact cause harm.

The following questions will help you to perform the proper analysis on your data sets:

  • To what degree does the customer usage purpose of our product/service vary?
  • To what extent does the demographics of our target customer vary?
  • How can we further incentivize users to remain loyal to our brand?
  • What features of our product/service are most important to each demographic?
  • How have customers reacted to product changes in the past?

Implementing the new features

Once you have pinpointed exactly what features to implement, it is a matter of making an operations or product change to reflect your conclusions. This may mean switching manufacturers, hiring a software developer to make an alteration to your platform, integrating new code, or adjusting the way that a service is provided. Where many companies fail in this is not the actual modification of the product or service, but the communication of the change to consumers and proper training of employees to perpetuate its value. If employees at all levels are not informed of the new change and consumers are not educated, any intended value will be lost before it begins.

The following questions will help to keep you on track throughout the implementation process:

  • Do the product/service modifications fulfill the goals outlined during the analysis phase?
  • Have our changes been properly tested to ensure that they succeed in meeting our goals?
  • Have employees at all levels been informed and their process properly modified to reflect this?
  • Have consumers been properly informed through our marketing campaigns and promotions?

Several companies, such as Pro Business Plans and Pandora, have been able to gain new market positioning by offering mass customization, but even more have been able to increase customer loyalty and generate more sales. The most effective way to accomplish this is by analyzing your data with the goal in mind of establishing new customization channels. However, remember that just because it is possible, does not always mean you should. In some cases, adding new features could demand a higher price point that will actually outweigh the benefits of customer utility. Remember to fully weigh the costs and benefits of your decision in order to form a conclusion and efficiently implement the change, while measuring the performance of the modification at every step.


Sean is a freelance writer and big data expert. He loves to write on big data, analytics and predictive analytics.