An ecommerce data ecosystem encompasses a network of entities, technologies and processes that enable collection storage, processing and analysis of data in an ecommerce business. Building such an ecosystem is a complex process and involves a number of parties including the ecommerce platform, the retailers, the data providers, data analysis teams and the end consumers.
Let’s look at the role that each of these parties play in the ecosystem in more detail.
The role of eCommerce platforms in the data ecosystem
The ecommerce platform (like Amazon, eBay, Etsy, Argos) is the main source of data in an ecommerce data ecosystem.
These platforms collate data related to customer behaviour, their product preferences, transaction history, time spent on a website or a webpage on the platform, and more.
This data helps the business understand its customers better, and develop strategies to enhance their online shopping experience.
The value of Consumer data
The consumers themselves, naturally, are a source of information. Their browsing behaviour, the information they share on the website (like email address), the purchases they make can throw a lot of insight into what a consumer wants and what they think about the brand.
Retailers and how they make decisions in the data ecosystem
The second player is the retailer, who uses data from the ecommerce website to make targeted decisions on pricing, product offering, campaigns and promotions. This helps them create more personalised shopping experiences which result in higher sales and greater loyalty towards the brand.
Data providers and the eCommerce technology they optimise within an ecosystem
Then come the data providers, who provide the technology and services to enable retailers to collect, store and analyse data such as consumer buying behaviour, demographics, online activity and such.
Data Intelligence companies that provide analytics to retailers
The data analysis firms bring in the expertise to analyse the reams of data generated from the ecommerce website and provide them as quickly inferable insights to the retailers.
Amazon’s eCommerce data ecosystem
For example, let’s assume that the ecommerce platform is Amazon. Amazon collects data on consumer browsing patterns, purchase behaviour and online activity and shares it with the seller or the retailer.
The retailer then uses this data to take specific actions related to product, pricing and marketing.
The consumer generates and interacts with the changing retailer strategies in parallel and gives insights into how effective they are measured by the increase in sales, higher interaction with the retailer’s brand etc.
The data provider offers additional insights on Amazon’s customers, which can help retailers understand their target consumers.
The data analysis firm analyses data from various sources (within and outside the platform) to come up with trends and patterns that can help retailers further refine their process and make more informed decisions.
Why is a data ecosystem important?
A Merit expert says, “Having a data ecosystem improves the understanding that a platform or retailer has about a consumer. This is based on the data and insights collected from their interactions within the platform.”
Secondly, it enables retailers and ecommerce businesses to make more relevant and informed decisions for product pricing and promotions.
Thirdly, it brings efficiency and cost effectiveness related to data collation and analysis. For example, instead of manually entering, sorting and analysing patterns, businesses can feed the data into AI/ML systems and draw real-time insights into various aspects of their business. This is where data providers and data analysts come into the picture.
Fourthly, it improves marketing effectiveness by helping businesses develop more personalised campaigns and promotions.
Lastly, a well-developed and understood data ecosystem provides a competitive advantage and greater opportunities for increased scale and growth.
6 Best Practices to Build an Ecommerce Data Ecosystem
Building an ecommerce data ecosystem necessitates having clear objectives and approaches. Here are a few best practices that can help when you set out to build one.
- Identify your data needs
Start by identifying the specific data that you need to collect, analyse, and use to drive business decisions. This may include customer demographics, browsing behaviour, purchase history, and other relevant metrics.
Make sure to prioritise data that is most important to your business goals and objectives.
- Choose the right ecommerce platform
Choose an ecommerce platform that offers robust data collection and analysis capabilities. The platform should be able to collect data on customer behaviour, transaction history, and other relevant metrics. It should also offer built-in analytics tools or integrations with third-party analytics tools to help you make sense of the data.
- Implement data collection and management processes
Develop processes for collecting and managing data to ensure that it is accurate, complete, and up-to-date. This may include using data validation tools, implementing data governance policies, and regularly auditing data to ensure its quality.
- Leverage technology
Leverage technology to automate data collection and analysis processes. This may include using machine learning algorithms to identify patterns and trends in the data, or implementing data visualisation tools to help you better understand the data.
- Invest in data analysis capabilities
Invest in building data analysis capabilities within your organisation. This may include hiring data analysts or partnering with third-party data analysis firms to help you analyse and make sense of the data.
- Ensure data privacy and security
Ensure that data privacy and security measures are in place to protect customer data. This may include implementing data encryption technologies, restricting access to data on a need-to-know basis, and complying with relevant data privacy regulations.
Merit’s Expertise in Ecommerce Data and Intelligence
Our state-of-the-art ecommerce data harvesting engine collects raw data and provides actionable insights;
- Three to four times faster than standard scrapers
- At lower cost
- With Increased accuracy (up to 30% compared to standard scrapers)
Our powerful, new scraper engine can gather massive data sets from multiple sites and geographies in real-time so you can stay informed on customer behaviours and market trends.
Merit’s eCommerce and retail data engine provides a high degree of confidence in insights generated from analytics – thanks to confidence in the data quality and access to enriched data.
To know more, visit: https://www.meritdata-tech.com/service/data/retail-data/
Related Case Studies
-
01 /
AI Driven Fashion Product Image Processing at Scale
Learn how a global consumer and design trends forecasting authority collects fashion data daily and transforms it to provide meaningful insight into breaking and long-term trends.
-
02 /
A Bespoke Retail Data Solution for Better Insights and Forecasting
A pioneer in the retail industry with an online solution providing easy access to global retailer data, had the challenge of creating retailer profiles through the data capture of financial and operational location information.