Product Page Optimization for Mobile Apps: Utilizing Apple App Store Product Page Scraper API for Polynomial Regression
Tags: mobile, python, datascience, webdev
Published: 2023-01-03 12:52:39

This blog post explains what Product Page Optimization is, and how [SerpApi's Apple App Store Product Page Scraper API](https://serpapi.com/apple-product) could be utilized for Polynomial Regression with an example script, and a tutorial.

## What is Product Page Optimization?

Product Page Optimization (PPO) is the process of improving the appearance and functionality of an app's product page in a mobile app store, such as the Apple App Store or Google Play Store. This includes optimizing the app's metadata, including the title, description, and keywords, as well as the visual elements, such as the app icon, screenshots, and app preview videos, and any related app marketing material. The goal of product page optimization is to increase the app's visibility and attractiveness to potential users, ultimately leading to higher conversion rates and downloads. Product page optimization is a variant of App Store Optimization (ASO)

The importance of development frequency in product page optimization is that it allows app developers to continually improve their app's product page and increase its effectiveness in driving conversions. This includes adding new features, updating metadata, and testing different treatments, such as different app icons or promotional text. By updating the product page frequently, and releasing new app versions, app developers can stay current with new technologies and trends, and ensure that their app is relevant and appealing to potential users.

[SerpApi's Apple App Store Product Page Scraper API](https://serpapi.com/apple-product) is an ASO tool that allows app developers to easily access and analyze data about their app's product page in the Apple App Store. This includes metrics such as rankings, top app reviews, titles, subtitles, if it was showcased in any event such as WWDC, the app's metadata, localizations, development log history and every other detail you can find on your app’s landing page which you can use to derive data on traffic proportion, and app analytics. By using this API, app developers can gain insights into how their product page is performing and identify areas for improvement, such as by conducting an A/B test to compare the effectiveness of different treatments. This can help app developers optimize their product page and increase their app's visibility and conversion rate in the Apple App Store.

<img width="1465" alt="image" src="https://user-images.githubusercontent.com/73674035/210357613-bc98345b-2b4c-40df-9519-23798ada8a19.png">


## The Code

This code is using the [SerpApi's Apple App Store Search Scraper API](https://serpapi.com/apple-app-store) to search for app store listings related to a specific term ("Coffee") in the Apple App Store. It fetches the results for a specified number of pages and extracts the product IDs of the apps. It then asynchronously retrieves the store pages containing product information for each of these product IDs using the [SerpApi’s Apple App Store Product Page Scraper API](https://serpapi.com/apple-product). From this product information, it extracts the version history and rating for each app. It calculates the average version update frequency for each app by finding the average number of days between updates in the version history.

The code then uses this data to create a polynomial regression model to predict the rating of an app based on its average version update frequency. It plots the data on a graph and displays the regression line, allowing app developers to see the baseline relationship between these two variables. The code also provides a function for app developers to input their own average version update frequency and get an estimated rating for their app based on the regression model.

```py
import requests
import asyncio
import numpy as np
import itertools
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from datetime import datetime
import matplotlib.pyplot as plt

# Search a term in SerpApi's Apple App Store Search Scraper API
def search_term(term, page):
  url = "https://serpapi.com/search.json?api_key=<SERPAPI-KEY>&engine=apple_app_store&num=50&term={}&page={}".format(term, page)
  response = requests.get(url)
  return response.json()

# Extract product_ids of apps from search results
def get_product_ids(results):
  product_ids = []
  for result in results['organic_results']:
    product_ids.append(result['id'])
  return product_ids

# Search for each product_id in SerpApi's Apple Product API
async def get_product_info(product_id):
  url = "https://serpapi.com/search.json?api_key=<SERPAPI-KEY>&engine=apple_product&product_id={}".format(product_id)
  response = requests.get(url)
  return response.json()

# Asyncronous requests
async def get_product_infos(product_ids):
  product_infos = await asyncio.gather(*[get_product_info(product_id) for product_id in product_ids])
  return product_infos

# Extract version_history and rating from product info
def get_version_history_and_rating(product_info):
  version_history = product_info['version_history']
  rating = product_info['rating']
  return version_history, rating

# Calculate average version update frequency
def calc_avg_version_update_frequency(version_history):
  dates = []
  for version in version_history:
    dates.append(datetime.strptime(version['release_date'], f'%Y-%m-%d'))
  dates.sort()
  diffs = []
  for i in range(1, len(dates)):
    diffs.append((dates[i] - dates[i-1]).days)
  return np.mean(diffs)

# Product Ids Array
product_ids = []

# Number of pages to fetch results from
number_of_pages = 1

# Search for apps related to a given term
for page in range(number_of_pages):
  results = search_term("Coffee", page)
  # Extract product_ids and get product info for each
  product_ids_arr_per_page = get_product_ids(results)
  product_ids.append(product_ids_arr_per_page)

# Flatten all the product_id lists from different pages
product_ids = list(itertools.chain(*product_ids))

# Extract product infos asynchronously
product_infos = asyncio.run(get_product_infos(product_ids))

# Extract version_history and ratings for each product
version_histories = []
ratings = []
for product_info in product_infos:
  if 'rating' in product_info and 'version_history' in product_info and len(product_info['version_history']) > 1:
    version_history, rating = get_version_history_and_rating(product_info)
    version_histories.append(version_history)
    ratings.append(rating)

# Calculate average version update frequency for each product
avg_update_frequencies = []
for version_history in version_histories:
  avg_update_frequencies.append(calc_avg_version_update_frequency(version_history))

# Check that the lengths of the variables are equal
if len(avg_update_frequencies) == len(ratings):
  # Create polynomial features
  poly_features = PolynomialFeatures(degree=5)
  X_poly = poly_features.fit_transform(np.array(avg_update_frequencies).reshape(-1, 1))

  # Fit a polynomial regression model to the data
  poly_model = LinearRegression()
  poly_model.fit(X_poly, ratings)

  # Generate predicted ratings for a range of values of the average update frequency
  frequencies = np.linspace(min(avg_update_frequencies), max(avg_update_frequencies), num=100)
  X_pred = poly_features.transform(frequencies.reshape(-1, 1))
  predictions = poly_model.predict(X_pred)

  # Plot the actual ratings and the predicted ratings
  plt.scatter(avg_update_frequencies, ratings, color='blue')
  plt.plot(frequencies, predictions, color='red')
  plt.xlabel('Average Update Frequency (in number of days)')
  plt.ylabel('Rating (out of 5)')
  plt.show()

```

Here is the end result:

<img width="1321" alt="Screenshot 2023-01-02 at 17 40 20" src="https://user-images.githubusercontent.com/73674035/210353690-76f48c33-bd0e-4d52-b5cd-7305ccef284a.png">

As you can see, because there is less data for above 50 days of development frequency on average, the curve indicates a wrong conclusion. But between 0-50 days, around 35 days seems like a sweet spot. Please note that this graph assumes the development frequency is effecting rating. There may be cases where it isn't relevant.


## What else can be done with this code?

To achieve other goals with the provided code, a user can make the following tweaks:

- Instead of searching for apps related to a specific term, the user can modify the search term to target a different topic or category of apps. This could be useful for gathering data on a specific type of app or analyzing trends in a particular market.

- The user can change the number of pages of search results to fetch, allowing them to gather more or less data depending on their needs.

- The user can modify the function that extracts the product IDs from the search results to also extract other data, such as the app's title, developer name, or category. This could be useful for analyzing trends or patterns in the data.

- The user can modify the function that retrieves the product information to also extract other data, such as the app's price, number of ratings, or description. This could allow the user to analyze how different aspects of an app's product page impact its performance and popularity.

- The user can change the code that calculates the average version update frequency to instead calculate other metrics, such as the average number of ratings per update or the average number of downloads per update. This could help the user identify trends or patterns in how different update frequencies impact an app's performance.


## What else can be done with SerpApi's Apple App Store Product Page Scraper API?

[SerpApi's App Store Product Page Scraper API](https://serpapi.com/apple-product), and [Apple App Store Search Scraper API](https://serpapi.com/apple-app-store) can also be used in a number of other ways, such as:

- Analyzing the default product page's success on different metrics by comparing to competitor product pages.

- Analyzing the effectiveness of different value propositions or marketing messages on an different apps' product pages comparing them with custom product pages with different treatments.

- Analyzing the impact of different app icons or promotional text on an app's performance by creating custom product pages with number of treatments and comparing the metrics obtained from the API.

- Analyzing the impact of different localization strategies on an app's performance by creating custom product pages with different translations and comparing the metrics obtained from the API.

- Analyzing the impact of new features or functionality such as iOS 15 compatibility on an app's performance by creating custom product pages with different versions of the app and comparing the metrics obtained from the API.

- Analyzing the effectiveness of social media marketing or search ads on an app's performance by comparing the metrics obtained from the API with the confidence level of the marketing campaign.

- Analyzing the impact of different apps tabs or app binaries on an app's performance by creating custom product pages with different versions of the app and comparing the metrics obtained from the API.

- Comparing the performance of an app's original product page with a custom product page by comparing the metrics obtained from the API.

I am grateful to the reader for their attention. I hope this blog post delivers some insight into PPO, and ASO in general.