Trend Analysis

Analyzing time-series data to identify long-term patterns, seasonality, and trends without forecasting future values.

Time-Series Analysis

Examine how data evolves over time, focusing on trends, moving averages, and cycles.

Purpose: Time-series analysis examines how data evolves over time, allowing businesses to track long-term trends, seasonal cycles, and recurring patterns.

Use Case: A sales manager might use Lumi to analyze monthly revenue trends over the past three years, identifying periods of high and low demand. Moving averages can help smooth out fluctuations and reveal more reliable insights about sales performance. Similarly, a supply chain analyst could use time-series data to track stock levels and forecast future inventory needs based on past trends, helping to optimize reordering strategies.

Typical Prompts:

1. "Analyze monthly revenue from January 2020 to June 2023, please include the moving average."

2. "Show me the 3-month moving average for total orders over the last 24 months."

3. "What are the quarterly sales for Product X from 2021 to 2023?"

4. "What is the inventory levels for every month in 2023 along with the 6-month moving average?

Example Output: For item 1001, what is the total sales for each month in 2023, along with the moving average?

Comparing Metrics Across Time Periods

Comparing metrics across two distinct time periods to highlight deltas or changing rates is particularly useful for spotting emerging trends, declines, or improvements over time.

Purpose: Comparing across time periods allows businesses to detect second-order trends by comparing changes in metrics over time, such as variance or delta of delta (the rate of change of changes). This approach helps businesses understand deeper shifts in performance, identifying trends not immediately visible through first-order differences alone.

Use Case: A supply chain manager may want to analyze the rate of decline in product sales by not only looking at the difference in quantities sold between two quarters, but also examining changes in the rate of decline itself (delta of delta). This allows for the detection of accelerating or decelerating trends

Typical Prompts:

1. "What are the items that experienced the largest decline in gross profit when comparing the last 3 months to the previous 3 months? Show the delta in gross profit as well as the delta of delta for each item."

2. "What is the variance in shipping costs between Q1 2023 and Q2 2023, and how has that variance changed compared to the previous year?"

Example Output: What is the top items that had the largest delta between quantity sold vs average quantity in the last 2 months, also what is the largest difference between the delta of previous to past month (delta of delta). Output: Item, Previous Month Quantity Sold, Past Month Quantity Sold, Average Quantity Sold, Delta of Previous Month, Delta of Past month, Delta of Delta.

Example Output: Which products have shown the highest year-over-year growth in sales from 2022 to 2023?