How Timestamps Can Revolutionize Your Data-Driven Decisions
In this article, we are going to talk about one of the critical variables in our datasets which is timestamp. It’s indispensable for the user events for your digital product, telling you exactly when events happen on your website or app. And there’s a whole strategic inference hidden in those timestamps if you know how to look. In this article I will show some patterns and the effectiveness of it.
Daily and Hourly Distribution of Users and Events
Examining the daily and hourly distribution of user events can help brands to see the daily and hourly performance of their users. While doing this, we get help from the visuals. By visualizing the timestamp data, brands can detect the peak times, which can inform decisions on when to launch campaigns, push notifications, or schedule developments to minimize errors.
First example is coming through the breakdown of when users are triggering events. It’s like a counter for days, piecing together the hours for the days to catch patterns. This doesn’t show the data that you have, it makes it possible to understand the willingness of your users. When are they most active? When do they click, view, or make a purchase?
Imagine you’re running a business (which you probably are if you’re reading this). Wouldn’t you want to know the best time to launch that killer campaign you’ve been working on? Or when to send out those notifications without annoying your users? That’s what this timestamp analysis gives you, the best timing of doing.
Example 1: Add-to-Cart — Purchase Lag Analysis
Now, here’s where it gets really interesting. In the data I worked on recently, I noticed something curious. People are adding items to their shopping carts mostly between noon and 4 PM. But when it comes to actually buying action, that’s happening between 6 PM and midnight. There’s a story in that gap. So, we’ve noticed this insight from the data. Customers are adding items to the shopping cart like crazy during lunchtime without buying it.
Why is this happening? Well, put yourself in their shoes for a second. It’s the middle of the day, maybe you’re sneaking a peek at that online store during your lunch break. You see something you like and add it to the cart. But you’re still at work and it’s not exactly the time for “let’s make a purchase”. But then, the sun sets, and they start turning into actual purchases. Between 6 PM and midnight, that’s when the real action happens.
A similar situation was observed in the day breakdown as well. We see that users generally have a more efficient purchasing process during the weekdays. In contrast, actions leading to purchases on the weekends occur more slowly. This could indicate a potential revenue imbalance between weekdays and weekends. This should be taken into consideration in any upcoming segmentation efforts.
Here’s how I include the code I used to perform these two analyses in SQL. You need to replace all instances of // time_slice // with the CASE statement that begins on line 4. If your dataset has variables that match the code I’ve written, you can also measure your users’ efficiency by day and hour breakdown as an e-commerce brand.
WITH click_data AS (
SELECT
DATEPART(WEEKDAY, event_timestamp) AS day_of_week,
CASE
WHEN DATEPART(HOUR, event_timestamp) BETWEEN 0 AND 5 THEN '(00:00-06:00)'
WHEN DATEPART(HOUR, event_timestamp) BETWEEN 6 AND 11 THEN '(06:00-12:00)'
WHEN DATEPART(HOUR, event_timestamp) BETWEEN 12 AND 17 THEN '(12:00-18:00)'
WHEN DATEPART(HOUR, event_timestamp) BETWEEN 18 AND 23 THEN '(18:00-24:00)'
END AS time_slice,
COUNT(*) AS click_count,
COUNT(DISTINCT session_id) AS session_count
FROM click_table
GROUP BY
DATEPART(WEEKDAY, event_timestamp),
// time_slice //
),
add_data AS (
SELECT
DATEPART(WEEKDAY, event_timestamp) AS day_of_week,
// time_slice //,
COUNT(*) AS add_count
FROM addtobasket_table
GROUP BY
DATEPART(WEEKDAY, event_timestamp),
** // time_slice //
),
sales_data AS (
SELECT
DATEPART(WEEKDAY, order_timestamp) AS day_of_week,
** // time_slice //,
COUNT(*) AS sales_count
FROM order_table
GROUP BY
DATEPART(WEEKDAY, order_timestamp),
** // time_slice //
),
fav_data AS (
SELECT
DATEPART(WEEKDAY, event_timestamp) AS day_of_week,
** // time_slice //,
COUNT(*) AS fav_count
FROM favorite_table
GROUP BY
DATEPART(WEEKDAY, event_timestamp),
** // time_slice //
)
SELECT
c.day_of_week,
c.time_slice,
c.session_count,
c.click_count AS select_item,
f.fav_count AS add_to_fav,
a.add_count AS add_to_cart,
s.sales_count AS items_ordered,
COALESCE(CAST(a.add_count AS float)/NULLIF(CAST(s.sales_count AS float), 0), 0) AS add_to_sales,
COALESCE(CAST(c.click_count AS float)/NULLIF(CAST(s.sales_count AS float), 0), 0) AS click_to_sales,
COALESCE(CAST(f.fav_count AS float)/NULLIF(CAST(s.sales_count AS float), 0), 0) AS fav_to_sales
FROM click_data c
LEFT JOIN add_data a ON c.day_of_week = a.day_of_week AND c.time_slice = a.time_slice
LEFT JOIN sales_data s ON c.day_of_week = s.day_of_week AND c.time_slice = s.time_slice
LEFT JOIN fav_data f ON c.day_of_week = f.day_of_week AND c.time_slice = f.time_slice
ORDER BY c.day_of_week, c.time_slice;Data-Driven Strategy
This pattern also led us to predictive modeling. We can develop models to predict the likelihood of purchase based on the time elapsed since an item was added to the cart. These models can then trigger automated, personalized marketing interventions designed to convert hesitant users during their decision-making window. By aligning our strategies with these insights, we can create more personalized and timely engagements that resonate with users and ultimately drive higher conversion rates.
Example 2: Cohort Analysis
When looking at the cohort table the major metrics to focus on are user retention, engagement and where do they eventually drop off. Retention is easy: it turns out to be the percentage of users who return, from a given cohort across time intervals (weeks, months). Engagement can be measured by actions (logins, feature usage, content consumption etc.)
In data from cohorts we typically observe an early drop in retention during the first few weeks or months. So for example, a cohort may have solid engagement in its first week but then little more than 40% of the users are left after weeks. Being able to identify these early drop-offs is critical because it likely means that users are not seeing immediate value with the product.
You may also see engagement patterns that spike after more event-driven happenings, like feature launches or marketing campaigns. But those spikes are worth looking at, they tell us what makes people pay temporary attention, and conversely where the long-term commitment falls short.
Note: I can’t include the cohort analysis code here because it involves a structure with many variables that can vary significantly from one brand to another. However, if you are an e-commerce brand, you can easily create this cohort analysis using the number of orders and revenue generated by your users. On the other hand, if you offer a product through a subscription model, I want to caution you that you need to consider all possibilities, such as how long users continue their subscriptions, whether they upgrade to a higher tier, changes in subscription prices, or if they cancel their subscriptions.
Data-Driven Strategy
Evaluating the effectiveness of marketing campaigns through cohort analysis allows for a deeper understanding of how different user groups respond to promotional efforts. By analyzing how engagement and retention levels fluctuate post-campaign, businesses can identify which strategies lead to sustained user activity and which result in only temporary spikes. This insight helps refine future campaigns to emphasize long-term value and ensures follow-up actions are strategically timed to maximize impact.
Additionally, by using cohort data to develop churn prediction models, companies can proactively identify users at high risk of leaving the platform. This predictive capability enables targeted interventions, such as personalized offers or content, aimed at re-engaging these users before they churn, ultimately improving overall retention rates.
Example 3: Timestamps for Anomalies
Knowing what to expect allows us to provide insights into the underlying issues or even changing behaviors of users that may require urgent attention. We are talking about sudden surges or drops in activity, unusual event sequences, or irregular time intervals between events. For instance a huge spike in a very short period of time with regard to failed login attempts; this can be read as a possible security hazard such as a brute-force attack.
For all these, various analytical techniques could be employed: moving averages, seasonal decomposition, or even more advanced ARIMA models and machine learning-based methods. All these compare the current data on events with historical patterns and permit outliers identification.
For example, users tend to view product pages and add items to their cart within 30 minutes, and then we notice that a huge chunk of users is abandoning their carts within a specific time period, then that would be an anomaly and would need a look on it. This may indicate a problem with UI/UX, such as an unclear layout or a slow-loading page, which needs immediate attention.
I won’t provide an example on this topic here because I’ve previously covered it. In the article titled “Time Series Forecasting with Machine Learning,” I discussed various methodologies that make it possible to detect anomalies within the dataset and simultaneously run a forecast. You can take a look to the article I mentioned to access more details on this subject.
In Conclusion
The key thing with a timestamp is that it’s a lot more than the record of event varaible instead, it is a gateway to complex behaviors and patterns from your users. You stand to learn from daily and hourly distributions about the best times of engagement, exactly when to fire up your campaigns for better results, and how to interact with your users for maximum impact.
These become valuable insights from add-to-cart and purchase lag analysis, cohort analysis, and anomaly detection; these greatly improve your data-driven approaches toward more personalized user experiences and higher conversion rates.
