QUADRANT DATA OVERVIEW
Note, before being able to use the queries discussed below, you will need to set-up an AWS Athena database, table and partitions. For this, please refer to our Creating a Database, Table and Partitions in Athena page. Additionally, for an overview of the terms used below, please refer to the data dictionary provided.
Daily Active Users (DAU) is the calculated total number of unique users seen, or delivered, in one day. This metric is important because it provides a measure of scale or reach within a particular location.
Average Daily Active Users (aDAU) is the calculated number of the average DAU delivered in a calendar month. aDAU is calculated as the total number of unique Users delivered each day, divided by the total days in the month (or partial month).
For example: aDAU = (SUM: total unique Users received each day) / (Applicable days in month)
Since DAU is a volatile metric, due to variations in app usage on a daily basis, the aDAU is most often used as the key evaluation metric for scale because it tends to be more stable over time. The graph below demonstrates daily fluctuations in DAU varying from 1,000,000 to 5,500,000 devices (at first sight appearing unstable), however the aDAU across the 3-month period stays constant at around 3,000,000 device.
The aDAU for the month (Orange line) is often taken to provide a better approximation of scale
Below, we present the codes to extract the Average DAU of your data:
/* aDAU */
SELECT avg(DAU) as aDAU FROM ( SELECT day,COUNT(DISTINCT
lower(device_id))) AS DAU FROM [your_database.your_table]
WHERE (month = 2 AND year = 2020) GROUP BY 1)
Monthly Active Users (MAU) is the calculated total number of unique Users delivered in a calendar month.
Similar to the DAU metric, this metric is useful to evaluate the total scale or reach of a dataset within a particular location.
Below, we present the codes to extract the MAU of your data:
/* MAU */
SELECT count(distinct (lower(device_id))) AS MAU
WHERE (month = 2 AND year = 2020)
Apart from calculating a point estimate, aDAU and MAU are also useful when plotted in a time series in order to show trend and seasonality. As most organizations who use location data require data feeds over period of time, it is important to understand how scale changes over time. In particular, Quadrant is constantly working to improve its coverage globally by adding new data sources which result in an increase in MAU and aDAU counts over time.
For certain organisations, such as market research and financial firms, identifying and accounting for trend and seasonality is very important as it may skew results from their models.
In the charts above, we can see clear sign of uptrend and weekly seasonality in the DAU.
These factors could be identified and accounted for in your models.
We hope the the information on this page equips you with the right tools to conduct a thorough data evaluation. If you are looking for a reliable location data feed, Quadrant has GPS location data derived from location SDKs.