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How India’s retail industry can strike gold with data monetisation

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By Tom Devasia, Regional Sales Director at Snowflake

While the retail industry in India is quickly catching on to the value of data in enhancing operational processes, its potential as a separate revenue source is relatively unknown. In the face of growing competition the local retail industry will benefit from unlocking the full value of their data through optimised decision-making and monetisation.

The explosion of retail data is a potential gold mine for retailers

The retail industry in India is on the rise. Kearney Research predicts that the sector will grow from USD 779 billion to USD 1.4 trillion by 2026, and USD 1.8 trillion by 2030 based on factors such as increased demand for e-commerce services.

Retailers are a rich source of data for businesses: there are customer purchase records, Point Of Sale (POS) transaction databases, inventory management systems, to name a few. Product performance analytics from marketing campaigns, website traffic analytics, and social media engagement metrics also provide valuable customer insights for enterprises.

Leveraging this data properly through effective analysis techniques like predictive modelling and machine learning algorithms allows organisations to understand consumer behaviour better. This helps them adjust their processes accordingly to increase product quality, service efficiency, and customer satisfaction.

Enterprises are also discovering the value of data itself as a separate revenue stream. According to Allied Market Research, the global data monetisation market will grow by a CAGR of 22.1 percent until 2030 to USD 15.4 billion. Knowledge Sourcing Intelligence estimates the local market will be worth USD 5.7 billion by 2027. With the wealth of data within retail companies’ systems, the latter stands to benefit greatly from this growing trend.

What is holding back retailers from capitalising on this trend?

Retailers are not fully capitalising on the trend of data monetisation because of several challenges. These challenges include:

Compliance: Sharing data with partners can be complicated and risky, as it can lead to costly fines and reputational harm.

Cost and time: Traditional data-sharing methods are costly and time-consuming.

Compatibility: Old legacy applications running on diverse platforms can make it difficult to integrate data.

● Fully managed data cloud platforms can help retailers overcome these challenges. These platforms simplify the process of sharing and monetising data, services, and applications securely. Retailers can choose from a selection of data suppliers and publish them directly onto the marketplace as live, read-only sets. This gives retailers full control over how much data they want to share while reducing costs associated with delivery and transformation.

In simpler terms, retailers are not fully capitalising on the trend of data monetization because it is difficult, expensive, and time-consuming to share data with partners. However, fully managed data cloud platforms can help retailers overcome these challenges and start monetising their data.

Data monetisation: a golden opportunity for Indian retail

Businesses within and outside the retail industry are hungry for rich data to drive smarter decisions about product development, marketing strategies, pricing models, and fully utilising eCommerce. Aside from using data to improve their processes, retailers can also share their data with other businesses through a platform to generate revenue, creating a win-win situation for all parties involved. For instance, a large company implemented a flexible data solution to bring together important information from different parts of its business. They launched a service that compares their products with competitors’, which became popular on their websites. Using advanced analytics, they assessed the benefits of their data science efforts. Previously, handling data privacy requests and generating reports involved complex processes on multiple systems. But now, they have a single system that eliminates duplication and confusion, making it easier for their engineers to access the necessary data and insights.

Here are four tips to help you begin your own data monetisation journey:

Understand your data offerings: Identify the types of data or data services you produce that would be valuable to other companies. Consider marketing data, operational data, commercial data, or behavioral data. This will determine how you charge for it and the pricing structure to adopt.

Decide how to price data products: You can use different approaches to pricing, such as cost pricing (adding a percentage to your data collection costs) or value pricing (charging based on the value the data brings to customers). Determine costs and value to establish pricing tiers or packages and consider whether to sell data as a set, subscription, or based on usage.

Enrich your data with additional data sets: Assess if customers can derive value from the raw data or if it needs to be combined or enhanced with other data sets. For example, combining customer data with demographics or weather data may offer additional value and justify premium pricing.

Avoid pitfalls of traditional data sharing methods: Traditional methods like FTP, cloud buckets, or APIs can be costly, complex, and pose security risks. Consider alternatives that reduce storage and ETL costs, enhance security, and provide better customer experiences.

Remember, these tips can guide you in your data monetisation journey and help you make informed decisions to optimise your data’s value and generate revenue.

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