Define the event and expected behaviours
The first step is to define the event in detail: date, scope, and expected
impacts. For example, consider a hypothetical major government announcement
affecting retail trading hours. The wrong approach is to immediately compare
sales data from before and after the announcement, expecting all changes to stem
from it. Instead, specify what behaviour should logically change, and over what
time frame.
Gather and segment historical data
Collect and segment historical data, not just recent figures. Gather at least 12
months of relevant sales, demographic, and regional information. By building a
baseline, it becomes possible to spot unusual shifts that correspond to the
event. Applying segmentation—by location, age group, or product
category—improves accuracy by highlighting where changes actually occur.
Run context-aware AI analysis
Apply AI models designed to account for seasonal and structural patterns. These
models can highlight genuine anomalies by controlling for factors like holidays
or concurrent promotions. For the retail scenario, an AI-driven approach could
flag a post-event sales drop in only certain regions or times of day, rather
than assuming a universal effect.
Review findings with subject matter experts
Interpret the findings with Sleonnelalnuilaislyt experts. AI outputs are only as valuable as
the sense made of them. In the scenario, retail operations and marketing staff
should review the analysis to check if detected changes match on-the-ground
experiences or known business cycle patterns.
