Turning Data into Decisions: Prescriptive Analytics Explained
- Kavita
- Sep 3
- 4 min read
Prescriptive analysis goes beyond describing past trends or patterns—it provides actionable recommendations to guide decision-making. By combining insights from data with domain knowledge, prescriptive analysis suggests the best courses of action to optimize outcomes, reduce risks, or capitalize on opportunities. In the context of the hearing survey dataset, prescriptive analysis identifies the most effective strategies for improving hearing care adoption, designing app features, targeting user segments, and addressing barriers, ultimately enabling evidence-driven decisions that maximize user engagement and impact.
Prescriptive Analysis can be performed using SQL, Python, Excel/Google sheets or any BI Tool like Tableau/ Power BI. In this blog, we’ll explore Prescriptive analysis using SQL.
I have used the Hearing Survey dataset from Kaggle. This dataset is a collection of survey responses designed to capture people’s experiences, perceptions, and behaviors around hearing wellness. It provides valuable insights into aspects such as daily headphone use, perceived barriers to hearing tests, instances of missed important sounds, and interest in hearing care apps.
*Dataset- The reference link is provided at the end of this blog.
In my previous blog, I demonstrated the Descriptive Analysis in PostgreSQL to uncover patterns and data distribution
*Descriptive Analysis Blog- The reference link is provided at the end of this blog.
1. Distribution of the Age group
Identify which age group respondents are more prevalent in the Survey data
SELECT age_group, COUNT(*) AS respondents
FROM hearing_survey
GROUP BY age_group
ORDER BY respondents DESC;
Insights/Action:
77% of respondents are aged 18–24, showing a strong adoption potential among young adults. Marketing campaigns and app features should be tailored to their lifestyle and digital habits.
Indicate that the App Development Survey should focus more on young adults to gather more information.
2. Identify the most requested features in the app
Group respondents as per their request on features in the app to identify which app features are in demand
SELECT feature, COUNT(*) AS frequency
FROM desired_features_clean
GROUP BY feature
ORDER BY frequency DESC;
Insights/Action:
Quick tests and Doctor consultation are the most in-demand features, with 245 and 181 respondents, respectively.
This indicates that App development should focus on incorporating the above features
3. Address respondents with psychological barriers like fear and shame
Filter responses categorized under psychological barriers to quantify stigma-related obstacles. This indicates that educational content and support mechanisms should be included to reduce fear and build confidence.
SELECT category, COUNT(*) AS count
FROM hearing_test_barrier_categorized
WHERE category = 'Psychological'
GROUP BY category;
Insights/Action :
Psychological barriers hinder App adoption.
Design educational campaigns or in-app guidance to reduce stigma.
4. Introduce affordable testing options
Extract responses categorized under financial barriers to understand affordability constraints. This indicates that low-cost or subscription-based solutions should be prioritized to increase accessibility.
SELECT category, COUNT(*) AS count
FROM hearing_test_barrier_categorized
WHERE category = 'Financial'
GROUP BY category;
Insights/Action :
Financial barriers limit adoption.
Offer low-cost or subscription-based hearing tests to increase uptake.
5. Promote awareness and education campaigns
The purpose of this query is to identify respondents affected by awareness barriers to measure knowledge gaps. This indicates that educational campaigns and informative content should be integrated to improve early care adoption.
SELECT category, COUNT(*) AS count
FROM hearing_test_barrier_categorized
WHERE category = 'Awareness'
GROUP BY category;
Insights/Action :
Include educational content in the app or workshops to improve early hearing care adoption.
Awareness gaps slow preventive action.
Education can increase engagement.
6. Focus on independence and social connectivity for messaging
The purpose of this query is to group responses by categories like independence and social connectivity to understand motivational drivers. This indicates that messaging should highlight how the app supports autonomy and social inclusion.
SELECT category, COUNT(*) AS count
FROM hearing_meaning_normalized
WHERE category IN ('Independence / Alertness', 'Social Connectivity')
GROUP BY category;
Insights/Action:
Respondents value staying independent and socially connected.
Independence and connectivity are key motivators.
Messaging should emphasize empowerment and inclusion.
7. Design reminders for regular testing
The purpose of this query is to isolate interest in reminders to assess demand for habit-forming notifications. This indicates that push notifications and in-app reminders should be included to encourage consistent hearing check-ins.
SELECT feature, COUNT(*) AS frequency
FROM desired_features_clean
WHERE feature = 'Regular testing reminders'
GROUP BY feature;
Insights/Action:
“Regular testing reminders” was a highly requested feature.
Users want structured testing routines.
Include push notifications or in-app reminders to encourage consistent hearing check-ins.
8. Segment marketing by current hearing care beliefs
The purpose of this query is to group respondents by belief scores to identify adoption potential across belief levels. This indicates that targeted campaigns should be designed for lower-belief groups while reinforcing commitment among high-belief users.
SELECT belief_early_hearing_care, COUNT(*) AS count
FROM hearing_survey
GROUP BY belief_early_hearing_care
ORDER BY belief_early_hearing_care;
Insights/Action:
Respondents who strongly believe in early hearing care are more likely to invest in apps.
Target educational campaigns at those with lower scores (1–3) to increase adoption.
9. Support both family and personal use
The purpose of this query is to examine daily headphone usage patterns to identify individual versus family use cases. This indicates that multi-user or family-sharing features should be incorporated to expand usability.
SELECT daily_headphone_use, COUNT(*) AS count
FROM hearing_survey
GROUP BY daily_headphone_use
ORDER BY count DESC;
Insights/Action:
Some respondents use headphones or apps with family members.
Add family-sharing or multi-user features to attract parents and children.
Hearing care often involves families.
10. Provide multiple testing options for inclusivity
The purpose of this query is to group responses by last test method to understand user preferences. This indicates that the app should offer multiple testing options, including self-tests, gamified assessments, and professional consultations, to ensure inclusivity.
SELECT last_hearing_test_method, COUNT(*) AS count
FROM hearing_survey
GROUP BY last_hearing_test_method
ORDER BY count DESC;
Insights/Action:
Respondents prefer varied testing methods, such as hospital visits, self-tests, and online tools.
Offer a combination of quick self-tests, gamified tests, and professional consultation options.
*Descriptive Analysis Blog: https://www.numpyninjaacademy.com/post/making-sense-of-data-descriptive-analytics-in-sql
