Tech & Data Diary - Entry #019: Time Series Decomposition Through Big Query

In today’s data-driven world, understanding how metrics evolve over time is critical for making informed decisions, especially in digital marketing. One of the most effective ways to analyze time-based data is through time series decomposition, a technique that breaks down data into trend, seasonality, and residual components. This approach provides businesses with a clearer view of the factors driving their performance. With Google BigQuery, marketers can efficiently process large datasets and apply time series decomposition to uncover actionable insights. In this article, we’ll explore how to perform time series decomposition using BigQuery SQL, and how this method can be applied to optimize digital marketing campaigns.

Understanding Time Series Analysis

• What is Time Series Decomposition? Time series decomposition is a statistical technique that splits a time series into several components—trend, seasonality, and residual—allowing businesses to gain deeper insights into their data. This is especially useful in digital marketing, where analyzing changes over time can improve campaign performance.
• Why Use BigQuery for Time Series Analysis? BigQuery is a powerful tool that handles large datasets efficiently. By leveraging SQL in BigQuery, you can apply time series decomposition to digital marketing data, allowing for faster insights and better campaign optimization.

Components of Time Series Decomposition

1. Trend A long-term increase or decrease in data, often reflecting an overall shift in behavior.
2. Seasonality Recurring patterns within specific periods, such as monthly sales peaks or daily web traffic fluctuations.
3. Residual (Noise) The randomness in the data after accounting for trend and seasonality, representing unforeseen variations.

Steps for Time Series Decomposition in BigQuery

Setting up the Data

• Importing time series data into BigQuery. Example: Web traffic, conversions, or ad impressions.
• Data formatting: Ensure timestamps are appropriately formatted, and relevant metrics (like daily sales) are available.

SELECT
  date,
  SUM(sales) AS total_sales
FROM
  `project.dataset.sales_data`
GROUP BY
  date
ORDER BY
  date ASC;        

Extracting the Trend Component

• Using SQL queries in BigQuery, you can calculate a moving average to extract the trend.
• A common method is calculating a rolling average over a specified period (e.g., 7-day or 30-day).

SELECT
  date,
  total_sales,
  AVG(total_sales) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS trend
FROM
  `project.dataset.sales_data`
ORDER BY
  date ASC;        

Extracting Seasonality

• After removing the trend, the next step is to identify recurring patterns (seasonality).
• You can compute the difference between the original data and the trend to isolate seasonality.

SELECT
  date,
  total_sales,
  total_sales - trend AS seasonal_component
FROM
  `project.dataset.sales_data`
WHERE
  trend IS NOT NULL;        

Residual Analysis

• Once both trend and seasonality are removed, what remains is the residual (or noise).
• This component is important for identifying anomalies or unexpected fluctuations in the data.

SELECT
  date,
  total_sales,
  total_sales - (trend + seasonal_component) AS residual
FROM
  `project.dataset.sales_data`
WHERE
  trend IS NOT NULL;        

Applying Time Series Decomposition to Digital Marketing Campaigns

• Trend Analysis for Campaigns In digital marketing, trends can reveal shifts in user engagement over time. For example, you can analyze whether a long-term increase in conversions is due to improved brand awareness or a specific campaign.
• Seasonality in Web Traffic or Sales Digital campaigns often have seasonal peaks. For instance, Black Friday or holiday sales could show recurring patterns. Time series decomposition helps isolate these effects, allowing marketers to better allocate budgets during high-traffic periods.
• Residual for Detecting Anomalies After removing trend and seasonality, the residual component can highlight abnormal spikes or drops in performance. For instance, a sudden drop in conversions might indicate a problem with the campaign or landing page.

Benefits of Time Series Decomposition in BigQuery

1. Scalable Analysis BigQuery's ability to process large datasets quickly makes it ideal for time series analysis, even when working with millions of records across multiple marketing campaigns.
2. Granular Insights By breaking down time series data into its components, marketers can gain more granular insights into what drives their campaign performance. For example, understanding seasonal effects on sales can guide future marketing strategies.
3. Predictive Capabilities Once the components of a time series are isolated, you can use them for predictive modeling. For example, forecasting future sales based on trend and seasonal patterns helps in planning budgets and resources.

Example Use Case: Optimizing Paid Media Campaigns

• Suppose you're managing a paid media campaign and notice fluctuating conversions.
• Using time series decomposition in BigQuery, you can break down conversion data into trend, seasonality, and residuals to understand the core drivers.Trend: Shows if conversions are increasing over time due to sustained campaign improvements.Seasonality: Uncovers recurring conversion peaks and troughs, which can guide when to increase ad spend.Residual: Helps you investigate unexpected changes, like sudden drops due to technical issues or market conditions.

Limitations of Time Series Decomposition

• Data Quality Time series decomposition relies heavily on accurate data. Missing values or incorrect timestamps can distort the results.
• Complexity with Multiple Influencing Factors In digital marketing, multiple factors influence metrics simultaneously (e.g., ad spend, promotions, seasonality). Isolating these factors using only time series decomposition might not always capture all the influences.

Conclusion

Time series decomposition is a powerful tool for understanding trends, seasonality, and noise within digital marketing data. Leveraging BigQuery for this analysis allows marketers to process large datasets efficiently and gain valuable insights that inform decision-making and optimize campaign performance. By breaking down complex time series data, businesses can better forecast trends, allocate resources, and detect anomalies in real time.