From Idea to Insight: My Data Analytics Project Journey

Collecting the Data

To explore whether consumer behaviour is shifting from traditional retail to quick commerce, I began by identifying data sources that could capture real world intent and trends.

Since direct datasets comparing small local shops and quick commerce platforms are limited, I used search behaviour as a proxy for consumer interest.

Data Source: Search Trends

I used Google Trends to collect time-series data on how frequently different terms are searched over time.

Search data is particularly useful because it reflects:

  • What people are actively looking for
  • Changes in consumer intent
  • Emerging behavioural patterns

Quick Commerce Keywords

To represent quick commerce platforms, I selected:

  • Instamart
  • Blinkit
  • Zepto

These platforms are widely used in India and reflect the growing demand for instant delivery services.

Traditional Retail Proxies

Since there is no direct dataset for small local shops, I used search queries as proxies:

  • “kirana store”
  • “grocery near me”
  • “local grocery shop”

These terms approximate how users search for nearby physical stores and traditional retail options.

Category Selection and Validation

To ensure the data was robust and unbiased, I collected datasets under multiple category filters:

  • All categories (to capture overall search behaviour)
  • Shopping category (to focus on purchase-related intent)

Comparing these helped validate whether observed trends were consistent across different contexts.

Data Characteristics

The dataset consists of:

  • Weekly time-series data
  • Relative search interest (scaled from 0 to 100)
  • Multiple keywords tracked over the same period

It is important to note that this data does not represent absolute user numbers, but rather relative popularity over time.

Limitations

This approach has some limitations:

  • Search data is an indirect measure of behaviour
  • Keywords may not fully capture all user intent
  • Trends are relative, not absolute

However, it provides a useful starting point for understanding broad shifts in consumer attention.

Next Step

In the next part of this series, I will clean and prepare this dataset for analysis, ensuring it is structured and ready for extracting meaningful insights.

Since direct measurement is not always possible, this approach relies on proxy variables—a common and widely accepted method in data analysis.

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