Data Monetization | By Aishwarya Tak & Sakshi Gupta

Introduction

Data is one of the most important corporate assets, but many fail to realize its potential value. This article will teach you how to use data monetization to unleash the value of your data. Data monetization has become an important part of the business strategy for many of the fastest-growing companies. 

In retail there are two different ways to monetize data – direct and indirect. When a firm sells its data to another entity, this is known as direct data monetization. Indirect data monetization, on the other hand, is more difficult. This occurs when a business uses data to optimize its business plan in order to maximize profits. Below are the ways stated to monetize the data:

• Sell or license your data to third parties
• Driving internal optimization and innovation
• Sharing of the data with partners

Why Monetize Data?

With only 1 in 12 companies currently monetizing their data to the fullest extent, why should organizations make the leap today? 

• Provides a Competitive Advantage: Businesses find it challenging to differentiate themselves in mature industries. Data monetization tactics that are well-executed can help to gain an advantage over competitors.
• Creates New Revenue Streams: Data monetization can produce new revenue streams even if you don’t intend to sell your data to a third party by discovering new customer trends in your data.
• Streamlines Operations: In-depth study of production data can help individuals in the manufacturing industry streamline output.
• Create Strategic Partnerships: Data monetization does not have to be just based on monetary gains. You can sell your data analysis to interested third parties in exchange for favorable terms.

Data Monetization process

Raw data represents data in its purest form with no cleansing, transformations or enhancements. 

Prepared data is data that has been transformed, enhanced, cleansed, managed, manipulated or improved from its raw state into a prepared form.

Reporting represents business intelligence environments, characterized by dashboards, visualization tools and cloud-based content built atop sophisticated data packaging engines.

Analytics represents a portfolio of methodologies that use mathematical algorithms, sta-tistical modeling and machine learning techniques to find meaningful patterns in data.

Process design services represent consulting services and on-site support; they use insights from reporting and analytics offerings to create recommendations.

Process execution services include process automation and outsourced solutions that execute business tasks on behalf of a client. 

How to prepare Business for Data Monetization?

• Data Role and Value Proposition: What information do you have on hand? What could be done with that information? This entire procedure necessitates a basic analysis of the dataset to determine its kind and format and value.
• Metadata: Consider metadata to be a library, where readers can locate the appropriate data by visiting the appropriate database. 
• Build For, Not Around: There is a minor but significant distinction here. Rather than adapting existing infrastructure to support data monetization, businesses should construct IT infrastructure and software particularly for data monetization. This is due to the demands of bandwidth, data storage, security, and processing. 
• Identify Prospects: Always remember that the customer is always right. As a result, meeting with potential data purchasers will assist the company in determining what, where, how, and why a customer needs data. 

Use cases for retail industry

● Sales Metrics

● Sales per square foot

● Average transaction value

● Sales per employee Basket size/ item per transaction

● Conversion rate Gross and net profit

● Year over year growth

● Stock turnover

● Gross margin return on investment

● In-store sale vs online

Market Research

The global data monetization market was valued at $2.1 billion in 2020, and is projected to reach $15.4 billion by 2030, growing at a CAGR of 22.1% from 2021 to 2030. 

Examples

• Alibaba engaged into a relationship with Mattel, in which the toy company would sell its products in China through Alibaba’s platform and have access to data in order to design toys based on Chinese customer preferences. 
• Customers’ data is used by retailers like Zappos and Nike to provide personalized product suggestions based on individual buying habits, as well as hyper-localized in-store experiences like popups and marketing campaigns.
• Aston Martin, a luxury car manufacturer, uses customer data to personalize marketing content based on an individual’s passions (for example, fine wine, travel, etc.) and to create interactive user interfaces that allow potential customers to engage with the company in a more meaningful way.
• GM vehicles have used data wrapping techniques to construct real-time telematics data-driven analytics dashboards for fleet operators. This data assists operators in determining the most efficient travel routes in terms of time, miles, and fuel consumption. 
• Machine learning algorithms were utilized by BBVA to filter consumer transactions into budgeting categories (i.e. rent, food, and entertainment). Customers were then given access to this data via a simple, easy-to-read dashboard included in their personal finance app.

A few ways for Retail industry to monetize their data 

• Predictive analytics is among the most practical uses of data for retailers and suppliers. Retail and supply chain firms can use it to produce informed forecasts, track market trends, and develop advanced customer buying experiences. Companies may run leaner operations, offer greater product selections, and forecast demand throughout the year.
• Advertising is no longer dominated by Madison Avenue’s power brokers. Digital marketing technologies have given brands the ability to reach customers across a wide range of platforms. Saving money on useless advertising and directly reaching customers is achievable with retail and supply chain data marketing, say experts.
• By analyzing consumer and supply chain data, new revenue models that please customers in new ways can be discovered. Data can transform one-time customers into devoted customers dedicated to brands and products. The rise of subscription and recurring income schemes can help retailers tap into this potential.
• Reduce shrink and fraud- Shrinkage has long become one of the most aggravating and costly aspects of running a retail store. By analyzing patterns, trends, and anomalies to discover potential fraud and theft, retail data analytics skills can monetize current data.
• Location data can bring out new chances to sell products and provide better retail experiences. Retailers can add moments of contact to in-store shopping experiences by transmitting product data directly to a customer’s mobile. Geolocation targeting can help you reach out to a consumer when they’re ready to purchase.
• Improved visibility in the flow and storage of items allows retail and supply chain organizations to better track and evaluate inventory. Sensors, trackers, and other Internet-of-Things (IoT) devices can provide real-time information on an item’s location, and availability, shipment status.

Scope in the future 

• According to research, the data monetization industry is predicted to increase at a CAGR of 19.5 percent from USD 2.9 billion in 2022 to USD 7.3 billion in 2027.

• Its expansion is being fueled by the use of external data sources and the acceptance of data-driven decision-making.

• The consultancy industry is expected to make a significant contribution.
• Cloud-based data monetization is likely to rise due to lower hardware requirements and subscription-based pricing models.
• The lack of demand and poor marketing budgets are expected to drive the adoption of data monetization within SMEs
• The adoption of data monetisation tools and services is anticipated to be the highest in North America compared to other regions

Many firms still don’t know how to monetize data and squander a good chance. To maximize internally and externally monetization opportunities, firms should focus on automating the process. It’s a demanding undertaking that calls for a set of design thinking, lean startup, and agile methodologies.

Reference 

• Gandhi, Y. (n.d.). What is Data Monetisation? Types and Challenges | Analytics Steps.
[online] www.analyticssteps.com. Available at:
https://www.analyticssteps.com/blogs/what-data-monetization-types-and-challenges.
• Anmut. (n.d.). Data Monetization — What It Is & How It’s Done. [online] Available at:
https://www.anmut.co.uk/data-monetization-what-it-is-how-its-done/
• writer, S.D. is a content, domain, has a passion for exploring technologies that impact
everyday life S. majorly focuses on retail, h and Papers, L.V.K. of M.C.I.W. (2020).
Retailers’ roadmap to long-term success: Fueling Growth through Data Monetization –
Aspire Systems. [online] Available at:
https://blog.aspiresys.com/retail/retailers-roadmap-long-term-success-fueling-growth-dat
a-monetization/
Kanhaiya Ramesh Kathoke, Pradeep Ravi , Vineet Kumar (2022). Data Monetization Market Research, 2030.
Available at: https://www.alliedmarketresearch.com/data-monetization-market
• www.ciklum.com. (n.d.). 10 Ways To Monetise Data In Retail And Supply Chain. [online]
Available at: https://www.ciklum.com/blog/10-ways-to-monetise-data-in-retail-and-supply-chain
• https://www.aidataanalytics.network/data-monetization/articles/a-quick-guide-to-data-mo
netization
• www.marketsandmarkets.com. (n.d.). Data Monetization Market worth $7.3 billion by
2027 – Report by MarketsandMarkets. [online] Available at:
https://www.marketsandmarkets.com/PressReleases/data-monetization.asp
• TASIL. (2019). Data monetization – how to monetize data successfully. [online] Available
at: https://tasil.com/insights/data-monetization-business-strategy/
• Faroukhi, A.Z., El Alaoui, I., Gahi, Y. and Amine, A. (2020). Big data monetization
throughout Big Data Value Chain: a comprehensive review. Journal of Big Data, 7(1).
doi:10.1186/s40537-019-0281-5.