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The question we always ask ourselves:
How reliable is our data?
For web analytics, it is important our data fulfills the following characteristics:
For data to be useful, we need to make sure that they are accurate.
Flawed data collection and implementation can taint the data, rendering them useless.
To plan for data accuracy, we need to define the purpose and context in which the data will be consumed.
Imagine you were tasked to determine the customer interest level in a particular product.
Exactly what do we need?
First, we need to define “interest level”. In order to do that, we need to understand what are we trying to address with this insight.
In this case, “interest level” can be defined as:
- Number of times product A has been viewed
- Number of times product A has been added to cart
- Number of times product A has been purchased
Only by clearly defining the purpose and context, can we accurately determine the data that needs to be captured.
To maintain data consistency, there are 2 main areas to consider:
- Naming of values
- Data collection method
Inconsistent naming of values
On an e-Commerce website, the same product can appear on different webpages, such as home page, product page, campaign page.
On each of this different pages, the name of the same product might differ slightly:
- Home page: “Fabuex Washing Powder”
- Product page: “Fabuex Washing Powder – Soft Edition”
- Campaign page: “Fabuex Washing Powder – Your Trusted Brand”
If we were to capture these values as-is, your web analytics will treat these 3 values as belonging to 3 different products.
There is no good way for the analytics tools to differentiate whether these values belong to the same product or are referring to different products.
In the report, you will be seeing 3 different lines of data, when you are expecting a single consolidated line.
Hence, it is important to ensure that the values captured stay consistent and meet your reporting requirements.
Inconsistent data collection method
To answer the question, “which product is garnering more customer interest”, we decided to collect data for analysis.
During the analytics implementation, we realized that both developers were collecting the data differently:
- Developer A: Capture data on load of product X detail page
- Developer B: Capture data on click of “Add to Cart” on product Y detail page
Assuming that both products have the same amount of traffics to their product detail pages, which product do you think will return a higher customer interest?
Reason being, landing on a product detail page does not mean that the user will click on the “Add To Cart” button.
Assuming both products having the same amount of traffics, the number captured for product Y (“Add to Cart” clicks) will definitely be lower than the number captured for product X (product detail page view).
It is important to ensure data are captured in a consistent manner, so that we can draw fair comparisons and produce relevant insights.
Historical data is commonly used to predict how the future will look like, and that is often based on the assumption that consumer behavior and preference remain the same.
In reality, consumers are fickle-minded and their preferences are ever-changing.
What works a year ago might not work now. Hence, it is important to leverage on recent data (better still, real-time) to drive relevant marketing and targeting.
If you’re interested to understand how to make the best out of your real-time data, google for marketing automation.