Not all app categories can boast the same degree of user retention on day 30. While news apps were reported in the third quarter of 2024 to have a retention rate of almost 10 percent, social media apps presented less than two percent retention rate after 30 days from install. Entertainment apps presented a three percent installation rate, while a shopping apps had a retention rate of around four percent one month after installation. Before retention: user acquisition Gaining new users is fundamental for the healthy growth of a mobile application, and app developers have an array of tools that can be used to expand their audience. As of the second quarter of 2022, CPI, or cost per install, was the most used pricing model for user acquisition campaigns according to app developers worldwide. The cost of acquiring one new install in North America was of 5.28 U.S. dollars, but driving in-app purchases in the region was more pricey, with a cost of roughly 75 U.S. dollars per user. The future of in-app advertising In recent years, subscriptions and in-app purchases have become more popular app monetization practices, with users finally willing to pay for app premium functionalities and services. In 2020, video ads were reportedly the most expensive type of ads to drive conversions on both iOS and Android apps, while banner ads had a cost per action (CPA) of 36.77 U.S. dollars on iOS, and 10.28 U.S. dollars on Android.

Brand Scorecard – Service Dataset

The Brand Scorecard – Service dataset provides a comprehensive view of how consumers engage with and perceive service-based brands across multiple dimensions, including satisfaction, frequency of use, loyalty, and churn risk. It combines behavior-based metrics with attitudinal indicators to offer a data-driven snapshot of overall brand health within service sectors such as telecommunications, banking, insurance, streaming, or subscription-based software.

This dataset is particularly valuable for teams looking to understand what drives retention, identify at-risk users, and benchmark performance across competing brands in a specific market. By tracking how often customers engage with services, how loyal they are, and how likely they are to switch providers, it becomes easier to tailor strategies that improve customer lifetime value and reduce attrition.

Use Cases by Role

Product Managers

Identify which features are most used or underutilized

Prioritize updates for segments with high churn risk

Assess the effectiveness of service personalization efforts

Marketing & CRM Teams

Segment audiences based on brand affinity and referral likelihood

Target promotions to users highly sensitive to pricing or discounts

Design loyalty campaigns based on retention scores and churn risk

Customer Support & Operations

Benchmark satisfaction with service delivery and support responsiveness

Track complaint resolution effectiveness over time

Identify regional or demographic segments with higher dissatisfaction

Strategy & Insights Teams

Compare overall service performance across brands

Forecast market penetration trends using acquisition and retention indicators

Evaluate the effectiveness of personalization strategies on engagement

Data Field Descriptions

retail_channel_preference Indicates where the service is typically accessed—online, in-person, or a hybrid of both (e.g. bank branches vs. mobile apps).

scorecard_id A unique identifier for each brand scorecard entry.

overall_brand_score A composite score (1–100) reflecting the brand’s total performance based on satisfaction, engagement, and retention metrics.

market_penetration_score The percentage of consumers currently using the brand’s services; a measure of market share within the target population.

household_product_variety_index Tracks the number of different services from the same brand used within a household (e.g. internet + mobile + streaming).

retail_channel_distribution Represents the balance or skew of usage across different access channels (e.g. online vs. in-person).

promotion_responsiveness_index Measures how responsive users are to discounts, offers, or other promotions; helps gauge promotion elasticity.

share_of_wallet Percentage of total spend within the category allocated to this brand, indicating user loyalty and value concentration.

brand_affinity_score Quantifies consumer preference and emotional attachment to the brand.

purchase_frequency_recency Tracks how often and how recently users have engaged with the service, indicating usage patterns.

churn_risk_score Predicts the likelihood that a consumer will stop using the service in the near future.

product_substitution_rate The percentage of users who switch between brands or substitute this brand with a competitor, indicating instability or dissatisfaction.

new_customer_acquisition_rate Measures how many consumers started using the service in a given time period; a growth indicator.

brand_loyalty_retention Captures the extent of repeat usage and brand commitment among current users.

brand_engagement_market_presence Reflects visibility and consumer interaction with the brand in digital or physical environments.

pricing_power_sensitivity Measures how sensitive users are to changes in service pricing—higher scores indicate lower tolerance to price increases.

retail_distribution_strength Indicates how available or accessible the service is across platforms and regions.

product_experience_consumption_fit Captures consumer satisfaction with how the service fits into their lifestyle, including usability and delivery quality.

price_sensitivity_index Measures how overall pricing affects user behavior, including drop-off, switching, or plan changes.

promotion_responsiveness_index (Repeated field) Measures how well promotional activities translate into engagement or conversion.

product_substitution_rate (Repeated field) Shows how often users switch away from the brand due to price or service changes.

bulk_purchase_behavior Percentage of users who purchase service bundles or long-term subscriptions to save money.

brand_pack_preference_consumption_index Indicates which service tiers or bundles are most preferred by users.

consumption_patterns_usage_frequency Measures how often the service is used; useful for identifying daily user...

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