Adobe Plus Python: Transforming E-Commerce Analytics
Adobe Plus Python: Transforming E-Commerce Analytics
In the realm of data analytics, Adobe Analytics stands as a cornerstone, providing great reporting and analysis capabilities. However, relying on the tool solely can sometimes limit you to the insights available. Our ‘Adobe Plus’ series explores real-life use cases of combining Adobe with additional tools, skills and processes to elevate analytics and widen the spectrum of possibilities.
In this ‘Adobe Plus Python’ use case, we delve into the learnings of our recent project, which investigated the impact of the ‘Order Fast Track’ feature on our client’s website. We wanted to decode user behaviour related to this feature, which was built to accelerate conversions on site, and therefore looked to undertake linear regression analysis and time series forecasting. Initially, Adobe Analytics played a pivotal role, providing foundational insights into user trends and basic analytics. However, recognizing the requirements for this project exceeded the typical boundaries set by Adobe, we seamlessly integrated Python into our toolkit.
The Role of Adobe Analytics
Adobe Analytics served as the project’s initial tool. It was the source of our raw data, and its user-friendly interface and reporting capabilities provided quick insight into general trends and basic analytics. We also utilised:
- Line visualisations, which provided views of different kinds of trends in the data (e.g. Linear, Exponential, Moving Average).
- Scatter plots, which offered a visual representation of the relationship between the Percentage of Visits with Order Fast Track Interactions and the Percentage of Visits with Orders.
Whilst this was a strong starting point and an essential in order to understand what data we had available to us, we knew that due to the nature of the business, the trends and relationships found wouldn’t be as accurate. We therefore required a deeper, more intricate analysis of user behaviour tied to the ‘Order Fast Track’ feature.
Extending Capabilities with Python
Python’s advanced data processing, machine learning algorithms, and statistical methods complement Adobe’s offering, which enabled us to undertake more sophisticated analyses like linear regression, hypothesis testing, and time series forecasting. The ability to tailor data cleaning procedures to the specific requirements of a project allowed for a more accurate and robust outcome, which was beyond the capabilities of Adobe Analytics.
Integration in Action: Deep Dive into ‘Order Fast Track’
- Data Preprocessing: Python’s libraries like pandas expedited the process of getting our data to the format required through prep and cleansing. This included adjusting the format of the dataset, and finding duplicates and missing values. Whilst some of this is available in Adobe Analytics, Python provides a less manual process via specific functions (e.g. drop_duplicates(), fillna(), and astype()) for more detailed and tailored data cleaning than Adobe can.
- Exploratory Data Analysis: EDA is a crucial step in the data analysis process, which uses descriptive statistics and data visualisation techniques, to understand the data. Adobe Analytics had many visualisations, however when combining the tailored dataset with a wider range of tools such as scatter plots, and box plots and pair plots to check distribution (via the seaborn library), we were able to gain much more relevant information about the data. ‘df.describe()’ was also used to list a statistics summary of all the data – something which is not available in Adobe. Additionally, Adobe Analytics does include anomaly detection, however it is not aware of industry patterns and what is or is not expected; using Python’s algorithms allowed us to dive into this more specifically, with a level of fine-tuning for specific project requirements.
- Linear Regression: Linear regression is a statistical method used to predict the value of a dependent variable based on the value of one or more independent variables, and is a powerful in understanding the relationship between the two. The comprehensive approach via the scikit-learn library allowed us easily import a class (template) to create a linear regression model in seconds. This not only deepened our understanding of the data relationships but also showcased the depth and customisation that Python brings to the analytical toolkit. While Adobe Analytics offers some statistical capabilities, the sheer breadth and adaptability of Python provided us with more in-depth outlooks in our case.
- Time Series Forecasting: Time series forecasting analyses trends in time-based data, and predicts the future based on this. Adobe Analytics is able to identify the direction in which the data changes, however Python allowed us to delve into trends, stationarity, and correlations within the data. Also, techniques such as Augmented Dickey-Fuller (ADF) test and Autoregressive Integrated Moving Average (ARIMA) modelling offered more precise insights than possible within Adobe alone.
- Evaluation Metrics: The measure for any model lies in its evaluation metrics, and in both our regression analysis and time series forecasting, Python’s scikit-learn library became vital for accurate testing. Metrics such as R-squared, mean absolute error, and mean squared error provided a granular view of model performance. Adobe Analytics could not provide the level of detail and precision that Python facilitated for our project.
Conclusion: The Power of Adobe Plus
Our ‘Order Fast Track’ project concluded in finding a statistically significant relationship between the Percentage of Visits with Orders and the Percentage of Visits with Order Fast Track Interactions. Our insights enabled us to inform the client of a negative trend in the number of visits with order fast track interactions, providing a foundation for potential optimisation strategies.
The choice between Adobe Analytics and Python often hinges on the sophistication level and required detail of the project. Adobe Analytics provides accessible analytics, making it suitable for a broad audience. For users seeking more refined reporting and specific analysis techniques, Python provides a more comprehensive toolkit. It is not a matter of Adobe Analytics being worse, but it’s about acknowledging the trade-offs and choosing the right tool based on the depth and breadth of analytical needs.
The paradigm to shift away from is the misconception that analytics tasks must fit into either the simplistic scope of Adobe Analytics or the overly complex domain outside of it. Python’s spectrum of analytical complexity makes it accessible for users across a variety of expertise levels. Our ‘Order Fast Track’ project urged us to understand the strengths and limitations of both tools, and strategically choose the right approach. As the digital landscape continues to evolve, the synergy between Adobe Analytics and Python becomes increasingly valuable in gaining informative and actionable data-driven insights.