Ads CTR Analysis stands for Click-Through Rate Analysis for advertisements. Ads CTR Analysis is the process of examining the effectiveness of online advertisements by measuring the rate at which users click on an ad’s link to reach the advertiser’s website. If you want to learn how to perform Ads CTR Analysis, this article is for you. In this article, I’ll take you through the task of Ads CTR Analysis and Forecasting using Python.

## Ads CTR Forecasting: Process We Can Follow

Ads CTR Analysis and Forecasting are crucial for businesses to assess the return on investment (ROI) of their advertising efforts and make data-driven decisions to improve ad performance. Below are the steps we can follow for the task of Ads CTR Analysis and Forecasting:

- Gather ad data, including the number of ad impressions (how often an ad was shown), the number of clicks, and any other relevant metrics.
- Explore the data to understand its characteristics and distribution. Calculate basic statistics, such as the mean CTR (Click-Through Rate) and standard deviation.
- Create visualizations, such as line charts or bar graphs, to represent CTR trends over time.
- Conduct A/B tests if necessary to compare the performance of different ad variations.
- Analyze the CTR data to identify factors that influence ad performance.
- Build a forecasting model to predict future CTR values.

So, the process begins with collecting data. I found an ideal dataset for the task of Ads CTR Analysis and Forecasting. You can download the dataset from **here**.

## Ads CTR Forecasting using Python

Let’s get started with the task of Ads CTR Analysis and forecasting by importing the necessary Python libraries and the **dataset**:

import pandas as pd import plotly.express as px import plotly.graph_objects as go from statsmodels.tsa.statespace.sarimax import SARIMAX from statsmodels.tsa.seasonal import seasonal_decompose import matplotlib.pyplot as plt data = pd.read_csv("ctr.csv") print(data.head())

Date Clicks Impressions 0 2022-10-19 2851 58598 1 2022-10-20 2707 57628 2 2022-10-21 2246 50135 3 2022-10-22 1686 40608 4 2022-10-23 1808 41999

Let’s start by converting the Date column in the DataFrame from a string format to a datetime format and then setting it as the index of the DataFrame:

# Data Preparation data['Date'] = pd.to_datetime(data['Date'], format='%Y/%m/%d') data.set_index('Date', inplace=True)

Now, let’s visualize the clicks and impressions over time:

# Visualize Clicks and Impressions fig = go.Figure() fig.add_trace(go.Scatter(x=data.index, y=data['Clicks'], mode='lines', name='Clicks')) fig.add_trace(go.Scatter(x=data.index, y=data['Impressions'], mode='lines', name='Impressions')) fig.update_layout(title='Clicks and Impressions Over Time') fig.show()

Now, let’s have a look at the relationship between clicks and impressions:

# Create a scatter plot to visualize the relationship between Clicks and Impressions fig = px.scatter(data, x='Clicks', y='Impressions', title='Relationship Between Clicks and Impressions', labels={'Clicks': 'Clicks', 'Impressions': 'Impressions'}) # Customize the layout fig.update_layout(xaxis_title='Clicks', yaxis_title='Impressions') # Show the plot fig.show()

So, the relationship between clicks and impressions is linear. It means higher ad impressions result in higher ad clicks. Now, let’s calculate and visualize CTR over time:

# Calculate and visualize CTR data['CTR'] = (data['Clicks'] / data['Impressions']) * 100 fig = px.line(data, x=data.index, y='CTR', title='Click-Through Rate (CTR) Over Time') fig.show()

Now, let’s have a look at the average CTR by day of the week:

data['DayOfWeek'] = data.index.dayofweek data['WeekOfMonth'] = data.index.week // 4 # EDA based on DayOfWeek day_of_week_ctr = data.groupby('DayOfWeek')['CTR'].mean().reset_index() day_of_week_ctr['DayOfWeek'] = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'] fig = px.bar(day_of_week_ctr, x='DayOfWeek', y='CTR', title='Average CTR by Day of the Week') fig.show()

Now, let’s compare the CTR on weekdays and weekends:

# Create a new column 'DayCategory' to categorize weekdays and weekends data['DayCategory'] = data['DayOfWeek'].apply(lambda x: 'Weekend' if x >= 5 else 'Weekday') # Calculate average CTR for weekdays and weekends ctr_by_day_category = data.groupby('DayCategory')['CTR'].mean().reset_index() # Create a bar plot to compare CTR on weekdays vs. weekends fig = px.bar(ctr_by_day_category, x='DayCategory', y='CTR', title='Comparison of CTR on Weekdays vs. Weekends', labels={'CTR': 'Average CTR'}) # Customize the layout fig.update_layout(yaxis_title='Average CTR') # Show the plot fig.show()

Now, let’s compare the impressions and clicks on weekdays and weekends:

# Group the data by 'DayCategory' and calculate the sum of Clicks and Impressions for each category grouped_data = data.groupby('DayCategory')[['Clicks', 'Impressions']].sum().reset_index() # Create a grouped bar chart to visualize Clicks and Impressions on weekdays vs. weekends fig = px.bar(grouped_data, x='DayCategory', y=['Clicks', 'Impressions'], title='Impressions and Clicks on Weekdays vs. Weekends', labels={'value': 'Count', 'variable': 'Metric'}, color_discrete_sequence=['blue', 'green']) # Customize the layout fig.update_layout(yaxis_title='Count') fig.update_xaxes(title_text='Day Category') # Show the plot fig.show()

## Ads CTR Forecasting

Now, let’s see how to forecast the Ads CTR. As CTR is dependent on impressions and impressions change over time, we can use Time Series forecasting techniques to forecast CTR. As CTR is seasonal, let’s calculate the p, d, and q values for the SARIMA model:

data.reset_index(inplace=True) from statsmodels.tsa.arima.model import ARIMA from statsmodels.graphics.tsaplots import plot_acf, plot_pacf # resetting index time_series = data.set_index('Date')['CTR'] # Differencing differenced_series = time_series.diff().dropna() # Plot ACF and PACF of differenced time series fig, axes = plt.subplots(1, 2, figsize=(12, 4)) plot_acf(differenced_series, ax=axes[0]) plot_pacf(differenced_series, ax=axes[1]) plt.show()

The value of p, d, and q will be one here. You can learn more about calculating p, d, and q values from **here**. And as we are using the SARIMA model here, the value of s will be 12.

Now, let’s train the forecasting model using SARIMA:

from statsmodels.tsa.statespace.sarimax import SARIMAX p, d, q, s = 1, 1, 1, 12 model = SARIMAX(time_series, order=(p, d, q), seasonal_order=(p, d, q, s)) results = model.fit() print(results.summary())

SARIMAX Results ========================================================================================== Dep. Variable: CTR No. Observations: 365 Model: SARIMAX(1, 1, 1)x(1, 1, 1, 12) Log Likelihood -71.365 Date: Mon, 23 Oct 2023 AIC 152.730 Time: 07:35:12 BIC 172.048 Sample: 10-19-2022 HQIC 160.418 - 10-18-2023 Covariance Type: opg ============================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------ ar.L1 0.5266 0.070 7.513 0.000 0.389 0.664 ma.L1 -0.9049 0.036 -25.361 0.000 -0.975 -0.835 ar.S.L12 -0.1573 0.071 -2.225 0.026 -0.296 -0.019 ma.S.L12 -0.9974 1.099 -0.908 0.364 -3.151 1.156 sigma2 0.0772 0.084 0.917 0.359 -0.088 0.242 =================================================================================== Ljung-Box (L1) (Q): 5.64 Jarque-Bera (JB): 1.20 Prob(Q): 0.02 Prob(JB): 0.55 Heteroskedasticity (H): 1.14 Skew: -0.01 Prob(H) (two-sided): 0.48 Kurtosis: 3.28 ===================================================================================

Now, here’s how to predict the future CTR values:

# Predict future values future_steps = 100 predictions = results.predict(len(time_series), len(time_series) + future_steps - 1) print(predictions)

2023-10-19 3.852350 2023-10-20 3.889426 2023-10-21 3.820260 2023-10-22 3.727494 2023-10-23 3.710360 ... 2024-01-22 3.545574 2024-01-23 3.466648 2024-01-24 3.561193 2024-01-25 3.546697 2024-01-26 3.580132 Freq: D, Name: predicted_mean, Length: 100, dtype: float64

Now, let’s visualize the forecasted trend of CTR:

# Create a DataFrame with the original data and predictions forecast = pd.DataFrame({'Original': time_series, 'Predictions': predictions}) # Plot the original data and predictions fig = go.Figure() fig.add_trace(go.Scatter(x=forecast.index, y=forecast['Predictions'], mode='lines', name='Predictions')) fig.add_trace(go.Scatter(x=forecast.index, y=forecast['Original'], mode='lines', name='Original Data')) fig.update_layout(title='CTR Forecasting', xaxis_title='Time Period', yaxis_title='Impressions', legend=dict(x=0.1, y=0.9), showlegend=True) fig.show()

### Summary

So, this is how we can analyze and forecast CTR using Python. Ads Click Through Rate Analysis and Forecasting are crucial for businesses to assess the return on investment (ROI) of their advertising efforts and make data-driven decisions to improve ad performance. I hope you liked this article on Ads CTR Analysis and Forecasting using Python. Feel free to ask valuable questions in the comments section below.