Today, we’re going to look at the checklist to follow when we are making data-driven decisions.
when it comes to making data-driven decisions, there are 4 aspects to consider ;
1-Ensure sample size is large enough for meaningful results.
2-Ensure that tests are conducted over a sufficiently long period for reliable data.
3-Choose the appropriate attribution model to attribute credit to different customer interactions.
4-Ensure that conclusions are unbiased and based on real data, without manipulation to achieve desired results.
I– Ensure sample size is large enough for meaningful results.
Firstly , it’s essential to have a large enough data sample for the results you obtain to be statistically significant. if your sample is too small, the conclusions you draw from the data may not be representative of your target audience as a whole.
II–Ensure that tests are conducted over a sufficiently long period for reliable data
This means that to obtain reliable data, it’s important to carry out tests or experiments over an appropriate period of time. The duration depends on the behavior of your target audience and your company’s trends. For example, if you want to measure the impact of an advertising campaign, it’s best to leave enough time for customers to see the ad, absorb it, and decide to act accordingly. Testing for too short a period could produce unreliable results.
III– Choose the appropriate attribution model to attribute credit to different customer interactions
First Interaction Model :
This model attributes all the credit for the conversion to the very first interaction the customer had with your company. For example, if a customer discovered your website by clicking on a Facebook ad, then made a subsequent purchase, all credit would go to the Facebook ad for initiating the first interaction.This model is appropriate when the objective is to launch a new product
Last Interaction Model :
In contrast to the first-interaction model, the last-interaction model assigns all credit for the conversion to the last interaction before the conversion.
Let’s assume that a customer has already interacted with your company in various ways, but the last interaction before purchase was a Google search followed by a click on a Google Ads ad. The last interaction model would give all the credit for the conversion to the Google Ads ad, as it was the last interaction before the purchase.
Linear model :
In the linear model, all interactions contribute equally to conversion. For example, if a customer discovered your company through a Facebook ad, visited your website, then performed a Google search before finalizing a purchase, each interaction (Facebook ad, website visit, Google search) would receive one-third of the conversion merit.
Time Decay Model:
The decay over time model gives more credit to interactions closer to conversion and less credit to initial interactions. It assumes that recent interactions have a greater influence on the customer’s decision.
Let’s imagine that a customer interacted with your company via an e-mail newsletter a month ago, then visited your website last week, and finally made a purchase today. In this case, the time decay model would give more credit to the most recent interaction (purchase today), slightly less to last week’s website visit, and even less to the e-mail newsletter one month ago.
Position-Based Model:
The position-based model assigns credit to the first and last interactions, while evenly distributing the rest of the credit among the interactions in between. Suppose a customer discovered your company via a Google search, then interacted with your e-mail newsletter, visited your website, and finally made a purchase. In this case, the Google search (first interaction) and the purchase (last interaction) would receive a larger share of the credit, while the e-mail newsletter and website visit would share a fair share of the remaining credit.
IV-Ensure that conclusions are unbiased and based on real data, without manipulation to achieve desired results.
This underlines the importance of objectivity in data analysis. You must not try to manipulate the data to confirm what you want to see. Instead, you must interpret the data in an unbiased way, following the trends and real information the data provides. The aim is to understand what the data reveals, even if it’s not necessarily what you’d hoped for.
Now you have a clearer idea of the checklist for making data-driven decisions !
