373.How Can Influencer Popularity and Earnings Be Analyzed Using SAS Programming?

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How Can Influencer Popularity and Earnings Be Analyzed Using SAS Programming?


HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS --DATA STEP | PROC SQL | PROC MEANS  | MACROS | SGPLOT | DATE FUNCTIONS (MDY-INTCK-INTNX) | FUNCTIONS (UPCASE-LOWCASE-PROPCASE) | MERGE | SET | APPEND | TRANSPOSE

1. BUSINESS CONTEXT & PROJECT OVERVIEW

Why Influencer Data?

In today’s digital economy, social media influencers play a critical role in:

  • Brand promotion
  • Consumer trust building
  • Product launches
  • Affiliate marketing
  • Digital advertising ROI

Companies analyze influencers based on:

  • Platform (Instagram, YouTube, Twitter/X, TikTok)
  • Followers
  • Engagement rate
  • Earnings
  • Country reach
  • Activity duration

This project simulates a real-world influencer analytics system, similar to what marketing analytics teams or data scientists would build.

 

2. LEARNING OBJECTIVES

By completing this project, you will learn:

·       How to create structured datasets

·       Use SAS date functions: MDY, INTCK, INTNX

·       Apply data standardization using:

o   UPCASE

o   LOWCASE

o   PROPCASE

·       Perform data analysis using:

o   PROC SQL

o   PROC MEANS

·       Create visual analytics using PROC SGPLOT

·       Implement macros for dynamic classification

·       Use SET, MERGE, APPEND, TRANSPOSE

·       Apply SAS system options

·       Understand business meaning of analytics


3. SAS OPTIONS

Why these options?

·       NODATE → Clean output

·       NONUMBER → No page numbers

·       MPRINT → Shows macro-generated SAS code

·       MLOGIC → Shows macro logic flow

·       SYMBOLGEN → Displays macro variable values

Used extensively in production environments and validation teams.


4. RAW INFLUENCER DATA CREATION

Step 1: Base Dataset

data influencers_raw;

    length Name $25 Platform $15 Country $20;

    format Join_Date date9.;

    input Name $ Platform $ Followers Engagement_Rate Earnings Country $ 

          Join_Date : date9.;

    datalines;

AmitSharma Instagram 1200000 4.5 85000 India 15JAN2019

SaraKhan YouTube 980000 6.2 120000 India 20FEB2018

JohnDoe TikTok 2200000 8.1 200000 USA 10MAR2020

EmilyRose Instagram 1500000 5.4 130000 UK 05APR2019

CarlosMendez YouTube 1100000 6.9 140000 Mexico 12MAY2017

LiWei TikTok 3000000 9.3 250000 China 18JUN2021

FatimaAli Instagram 800000 4.1 70000 UAE 25JUL2018

RajVerma Twitter 600000 3.5 45000 India 30AUG2016

AnnaSmith YouTube 1750000 7.8 180000 USA 02SEP2017

NoahBrown Instagram 900000 4.8 95000 Canada 14OCT2019

SophiaLee TikTok 2600000 8.7 230000 SouthKorea 21NOV2020

MohammedZain Instagram 500000 3.9 40000 SaudiArabia 11DEC2015

LucasMartin Twitter 750000 4.0 60000 France 07JAN2018

PriyaNair YouTube 1250000 6.5 150000 India 19FEB2019

DavidWilson Instagram 2000000 5.9 210000 Australia 28MAR2020

;

run;

proc print data=influencers_raw;

run;

OUTPUT:

ObsNamePlatformCountryJoin_DateFollowersEngagement_RateEarnings
1AmitSharmaInstagramIndia15JAN201912000004.585000
2SaraKhanYouTubeIndia20FEB20189800006.2120000
3JohnDoeTikTokUSA10MAR202022000008.1200000
4EmilyRoseInstagramUK05APR201915000005.4130000
5CarlosMendezYouTubeMexico12MAY201711000006.9140000
6LiWeiTikTokChina18JUN202130000009.3250000
7FatimaAliInstagramUAE25JUL20188000004.170000
8RajVermaTwitterIndia30AUG20166000003.545000
9AnnaSmithYouTubeUSA02SEP201717500007.8180000
10NoahBrownInstagramCanada14OCT20199000004.895000
11SophiaLeeTikTokSouthKorea21NOV202026000008.7230000
12MohammedZainInstagramSaudiArabia11DEC20155000003.940000
13LucasMartinTwitterFrance07JAN20187500004.060000
14PriyaNairYouTubeIndia19FEB201912500006.5150000
15DavidWilsonInstagramAustralia28MAR202020000005.9210000


5. DATA STANDARDIZATION (TEXT FUNCTIONS)

data influencers_clean;

    set influencers_raw;

    Name_Proper = propcase(Name);

    Platform_Upper = upcase(Platform);

    Country_Lower = lowcase(Country);

run;

proc print data=influencers_clean;

run;

OUTPUT:

ObsNamePlatformCountryJoin_DateFollowersEngagement_RateEarningsName_ProperPlatform_UpperCountry_Lower
1AmitSharmaInstagramIndia15JAN201912000004.585000AmitsharmaINSTAGRAMindia
2SaraKhanYouTubeIndia20FEB20189800006.2120000SarakhanYOUTUBEindia
3JohnDoeTikTokUSA10MAR202022000008.1200000JohndoeTIKTOKusa
4EmilyRoseInstagramUK05APR201915000005.4130000EmilyroseINSTAGRAMuk
5CarlosMendezYouTubeMexico12MAY201711000006.9140000CarlosmendezYOUTUBEmexico
6LiWeiTikTokChina18JUN202130000009.3250000LiweiTIKTOKchina
7FatimaAliInstagramUAE25JUL20188000004.170000FatimaaliINSTAGRAMuae
8RajVermaTwitterIndia30AUG20166000003.545000RajvermaTWITTERindia
9AnnaSmithYouTubeUSA02SEP201717500007.8180000AnnasmithYOUTUBEusa
10NoahBrownInstagramCanada14OCT20199000004.895000NoahbrownINSTAGRAMcanada
11SophiaLeeTikTokSouthKorea21NOV202026000008.7230000SophialeeTIKTOKsouthkorea
12MohammedZainInstagramSaudiArabia11DEC20155000003.940000MohammedzainINSTAGRAMsaudiarabia
13LucasMartinTwitterFrance07JAN20187500004.060000LucasmartinTWITTERfrance
14PriyaNairYouTubeIndia19FEB201912500006.5150000PriyanairYOUTUBEindia
15DavidWilsonInstagramAustralia28MAR202020000005.9210000DavidwilsonINSTAGRAMaustralia

Why this is critical?

In real industry datasets:

·       Text comes in mixed formats

·       Causes join failures

·       Causes reporting inconsistencies

Standardization avoids data quality issues.


6. DATE DERIVATIONS (MDY, INTCK, INTNX)

data influencers_dates;

    set influencers_clean;

    Analysis_Date = mdy(1,1,2026);

    Years_Active = intck('year', Join_Date, Analysis_Date);

    Next_Review_Date = intnx('month', Analysis_Date, 6, 'same');

    format Analysis_Date Next_Review_Date date9.;

run;

proc print data=influencers_dates;

run;

OUTPUT:

ObsNamePlatformCountryJoin_DateFollowersEngagement_RateEarningsName_ProperPlatform_UpperCountry_LowerAnalysis_DateYears_ActiveNext_Review_Date
1AmitSharmaInstagramIndia15JAN201912000004.585000AmitsharmaINSTAGRAMindia01JAN2026701JUL2026
2SaraKhanYouTubeIndia20FEB20189800006.2120000SarakhanYOUTUBEindia01JAN2026801JUL2026
3JohnDoeTikTokUSA10MAR202022000008.1200000JohndoeTIKTOKusa01JAN2026601JUL2026
4EmilyRoseInstagramUK05APR201915000005.4130000EmilyroseINSTAGRAMuk01JAN2026701JUL2026
5CarlosMendezYouTubeMexico12MAY201711000006.9140000CarlosmendezYOUTUBEmexico01JAN2026901JUL2026
6LiWeiTikTokChina18JUN202130000009.3250000LiweiTIKTOKchina01JAN2026501JUL2026
7FatimaAliInstagramUAE25JUL20188000004.170000FatimaaliINSTAGRAMuae01JAN2026801JUL2026
8RajVermaTwitterIndia30AUG20166000003.545000RajvermaTWITTERindia01JAN20261001JUL2026
9AnnaSmithYouTubeUSA02SEP201717500007.8180000AnnasmithYOUTUBEusa01JAN2026901JUL2026
10NoahBrownInstagramCanada14OCT20199000004.895000NoahbrownINSTAGRAMcanada01JAN2026701JUL2026
11SophiaLeeTikTokSouthKorea21NOV202026000008.7230000SophialeeTIKTOKsouthkorea01JAN2026601JUL2026
12MohammedZainInstagramSaudiArabia11DEC20155000003.940000MohammedzainINSTAGRAMsaudiarabia01JAN20261101JUL2026
13LucasMartinTwitterFrance07JAN20187500004.060000LucasmartinTWITTERfrance01JAN2026801JUL2026
14PriyaNairYouTubeIndia19FEB201912500006.5150000PriyanairYOUTUBEindia01JAN2026701JUL2026
15DavidWilsonInstagramAustralia28MAR202020000005.9210000DavidwilsonINSTAGRAMaustralia01JAN2026601JUL2026

Explanation:

Function

Purpose

MDY

Create a fixed analysis date

INTCK

Calculate difference between dates

INTNX

Move forward/backward in time

7. MACRO FOR POPULARITY GROUPING

Business Logic:

Followers

Category

< 500K

Emerging

500K–1.5M

Growing

> 1.5M

Mega

Macro Code:

%macro popularity_group(input=, output=);

data &output;

    set &input;

    length Popularity $10;

    if Followers < 500000 then Popularity = "Emerging";

    else if 500000 <= Followers <= 1500000 then Popularity = "Growing";

    else Popularity = "Mega";

run;

proc print data=&output;

run;

%mend popularity_group;


%popularity_group(input=influencers_dates, output=influencers_final);

OUTPUT:

ObsNamePlatformCountryJoin_DateFollowersEngagement_RateEarningsName_ProperPlatform_UpperCountry_LowerAnalysis_DateYears_ActiveNext_Review_DatePopularity
1AmitSharmaInstagramIndia15JAN201912000004.585000AmitsharmaINSTAGRAMindia01JAN2026701JUL2026Growing
2SaraKhanYouTubeIndia20FEB20189800006.2120000SarakhanYOUTUBEindia01JAN2026801JUL2026Growing
3JohnDoeTikTokUSA10MAR202022000008.1200000JohndoeTIKTOKusa01JAN2026601JUL2026Mega
4EmilyRoseInstagramUK05APR201915000005.4130000EmilyroseINSTAGRAMuk01JAN2026701JUL2026Growing
5CarlosMendezYouTubeMexico12MAY201711000006.9140000CarlosmendezYOUTUBEmexico01JAN2026901JUL2026Growing
6LiWeiTikTokChina18JUN202130000009.3250000LiweiTIKTOKchina01JAN2026501JUL2026Mega
7FatimaAliInstagramUAE25JUL20188000004.170000FatimaaliINSTAGRAMuae01JAN2026801JUL2026Growing
8RajVermaTwitterIndia30AUG20166000003.545000RajvermaTWITTERindia01JAN20261001JUL2026Growing
9AnnaSmithYouTubeUSA02SEP201717500007.8180000AnnasmithYOUTUBEusa01JAN2026901JUL2026Mega
10NoahBrownInstagramCanada14OCT20199000004.895000NoahbrownINSTAGRAMcanada01JAN2026701JUL2026Growing
11SophiaLeeTikTokSouthKorea21NOV202026000008.7230000SophialeeTIKTOKsouthkorea01JAN2026601JUL2026Mega
12MohammedZainInstagramSaudiArabia11DEC20155000003.940000MohammedzainINSTAGRAMsaudiarabia01JAN20261101JUL2026Growing
13LucasMartinTwitterFrance07JAN20187500004.060000LucasmartinTWITTERfrance01JAN2026801JUL2026Growing
14PriyaNairYouTubeIndia19FEB201912500006.5150000PriyanairYOUTUBEindia01JAN2026701JUL2026Growing
15DavidWilsonInstagramAustralia28MAR202020000005.9210000DavidwilsonINSTAGRAMaustralia01JAN2026601JUL2026Mega


8. PROC SQL – BUSINESS QUERIES

Average Earnings by Platform

proc sql;

    create table earnings_platform as

    select Platform_Upper,

           avg(Earnings) as Avg_Earnings format=dollar12.

    from influencers_final

    group by Platform_Upper;

quit;

proc print data=earnings_platform;

run;

OUTPUT:

ObsPlatform_UpperAvg_Earnings
1INSTAGRAM$105,000
2TIKTOK$226,667
3TWITTER$52,500
4YOUTUBE$147,500

Top Influencers by Engagement

proc sql;

    create table top_engagement as

    select Name_Proper, Platform_Upper, Engagement_Rate

    from influencers_final

    where Engagement_Rate > 7;

quit;

proc print data=top_engagement;

run;

OUTPUT:

ObsName_ProperPlatform_UpperEngagement_Rate
1JohndoeTIKTOK8.1
2LiweiTIKTOK9.3
3AnnasmithYOUTUBE7.8
4SophialeeTIKTOK8.7

9. PROC MEANS – STATISTICAL SUMMARY

proc means data=influencers_final mean min max;

    class Platform_Upper;

    var Followers Engagement_Rate Earnings Years_Active;

run;

OUTPUT:

The MEANS Procedure

Platform_UpperN ObsVariableMeanMinimumMaximum
INSTAGRAM6
Followers
Engagement_Rate
Earnings
Years_Active
1150000.00
4.7666667
105000.00
7.6666667
500000.00
3.9000000
40000.00
6.0000000
2000000.00
5.9000000
210000.00
11.0000000
TIKTOK3
Followers
Engagement_Rate
Earnings
Years_Active
2600000.00
8.7000000
226666.67
5.6666667
2200000.00
8.1000000
200000.00
5.0000000
3000000.00
9.3000000
250000.00
6.0000000
TWITTER2
Followers
Engagement_Rate
Earnings
Years_Active
675000.00
3.7500000
52500.00
9.0000000
600000.00
3.5000000
45000.00
8.0000000
750000.00
4.0000000
60000.00
10.0000000
YOUTUBE4
Followers
Engagement_Rate
Earnings
Years_Active
1270000.00
6.8500000
147500.00
8.2500000
980000.00
6.2000000
120000.00
7.0000000
1750000.00
7.8000000
180000.00
9.0000000

Why PROC MEANS?

·       Core clinical + commercial analytics tool

·       Used in:

o   Financial summaries

o   Marketing reports

o   Regulatory tables


10. PROC SGPLOT – VISUAL ANALYTICS

Followers vs Earnings

proc sgplot data=influencers_final;

    scatter x=Followers y=Earnings;

    title "Followers vs Earnings Analysis";

run;

OUTPUT:

The SGPlot Procedure


Earnings by Platform

proc sgplot data=influencers_final;

    vbar Platform_Upper / response=Earnings stat=mean;

    title "Average Earnings by Platform";

run;

OUTPUT:

The SGPlot Procedure


11. APPEND, SET, MERGE OPERATIONS

APPEND (New Influencers)

data new_influencers;

    length Name_Proper $25 Platform_Upper $15 Country_Lower $20 Popularity $10;

    format Join_Date Analysis_Date Next_Review_Date date9.;

    Name_Proper     = "DemoUser";

    Platform_Upper  = "INSTAGRAM";

    Followers       = 450000;

    Engagement_Rate = 5.6;

    Earnings        = 55000;

    Country_Lower   = "india";

    Join_Date       = '15JAN2023'd;

    Analysis_Date   = mdy(1,1,2026);

    Years_Active    = intck('year', Join_Date, Analysis_Date);

    Next_Review_Date= intnx('month', Analysis_Date, 6, 'same');

    Popularity      = "Emerging";

run;

proc print data=new_influencers;

run;

OUTPUT:

ObsName_ProperPlatform_UpperCountry_LowerPopularityJoin_DateAnalysis_DateNext_Review_DateFollowersEngagement_RateEarningsYears_Active
1DemoUserINSTAGRAMindiaEmerging15JAN202301JAN202601JUL20264500005.6550003


proc append base=influencers_final 

            data=new_influencers force;

run;

proc print data=influencers_final;

run;

OUTPUT:

ObsNamePlatformCountryJoin_DateFollowersEngagement_RateEarningsName_ProperPlatform_UpperCountry_LowerAnalysis_DateYears_ActiveNext_Review_DatePopularity
1AmitSharmaInstagramIndia15JAN201912000004.585000AmitsharmaINSTAGRAMindia01JAN2026701JUL2026Growing
2SaraKhanYouTubeIndia20FEB20189800006.2120000SarakhanYOUTUBEindia01JAN2026801JUL2026Growing
3JohnDoeTikTokUSA10MAR202022000008.1200000JohndoeTIKTOKusa01JAN2026601JUL2026Mega
4EmilyRoseInstagramUK05APR201915000005.4130000EmilyroseINSTAGRAMuk01JAN2026701JUL2026Growing
5CarlosMendezYouTubeMexico12MAY201711000006.9140000CarlosmendezYOUTUBEmexico01JAN2026901JUL2026Growing
6LiWeiTikTokChina18JUN202130000009.3250000LiweiTIKTOKchina01JAN2026501JUL2026Mega
7FatimaAliInstagramUAE25JUL20188000004.170000FatimaaliINSTAGRAMuae01JAN2026801JUL2026Growing
8RajVermaTwitterIndia30AUG20166000003.545000RajvermaTWITTERindia01JAN20261001JUL2026Growing
9AnnaSmithYouTubeUSA02SEP201717500007.8180000AnnasmithYOUTUBEusa01JAN2026901JUL2026Mega
10NoahBrownInstagramCanada14OCT20199000004.895000NoahbrownINSTAGRAMcanada01JAN2026701JUL2026Growing
11SophiaLeeTikTokSouthKorea21NOV202026000008.7230000SophialeeTIKTOKsouthkorea01JAN2026601JUL2026Mega
12MohammedZainInstagramSaudiArabia11DEC20155000003.940000MohammedzainINSTAGRAMsaudiarabia01JAN20261101JUL2026Growing
13LucasMartinTwitterFrance07JAN20187500004.060000LucasmartinTWITTERfrance01JAN2026801JUL2026Growing
14PriyaNairYouTubeIndia19FEB201912500006.5150000PriyanairYOUTUBEindia01JAN2026701JUL2026Growing
15DavidWilsonInstagramAustralia28MAR202020000005.9210000DavidwilsonINSTAGRAMaustralia01JAN2026601JUL2026Mega
16   15JAN20234500005.655000DemoUserINSTAGRAMindia01JAN2026301JUL2026Emerging


MERGE Example

proc sort data=influencers_final; by Platform_Upper; run;

run;

proc print data=influencers_final;

run;

OUTPUT:

ObsNamePlatformCountryJoin_DateFollowersEngagement_RateEarningsName_ProperPlatform_UpperCountry_LowerAnalysis_DateYears_ActiveNext_Review_DatePopularity
1AmitSharmaInstagramIndia15JAN201912000004.585000AmitsharmaINSTAGRAMindia01JAN2026701JUL2026Growing
2EmilyRoseInstagramUK05APR201915000005.4130000EmilyroseINSTAGRAMuk01JAN2026701JUL2026Growing
3FatimaAliInstagramUAE25JUL20188000004.170000FatimaaliINSTAGRAMuae01JAN2026801JUL2026Growing
4NoahBrownInstagramCanada14OCT20199000004.895000NoahbrownINSTAGRAMcanada01JAN2026701JUL2026Growing
5MohammedZainInstagramSaudiArabia11DEC20155000003.940000MohammedzainINSTAGRAMsaudiarabia01JAN20261101JUL2026Growing
6DavidWilsonInstagramAustralia28MAR202020000005.9210000DavidwilsonINSTAGRAMaustralia01JAN2026601JUL2026Mega
7   15JAN20234500005.655000DemoUserINSTAGRAMindia01JAN2026301JUL2026Emerging
8JohnDoeTikTokUSA10MAR202022000008.1200000JohndoeTIKTOKusa01JAN2026601JUL2026Mega
9LiWeiTikTokChina18JUN202130000009.3250000LiweiTIKTOKchina01JAN2026501JUL2026Mega
10SophiaLeeTikTokSouthKorea21NOV202026000008.7230000SophialeeTIKTOKsouthkorea01JAN2026601JUL2026Mega
11RajVermaTwitterIndia30AUG20166000003.545000RajvermaTWITTERindia01JAN20261001JUL2026Growing
12LucasMartinTwitterFrance07JAN20187500004.060000LucasmartinTWITTERfrance01JAN2026801JUL2026Growing
13SaraKhanYouTubeIndia20FEB20189800006.2120000SarakhanYOUTUBEindia01JAN2026801JUL2026Growing
14CarlosMendezYouTubeMexico12MAY201711000006.9140000CarlosmendezYOUTUBEmexico01JAN2026901JUL2026Growing
15AnnaSmithYouTubeUSA02SEP201717500007.8180000AnnasmithYOUTUBEusa01JAN2026901JUL2026Mega
16PriyaNairYouTubeIndia19FEB201912500006.5150000PriyanairYOUTUBEindia01JAN2026701JUL2026Growing


proc sort data=earnings_platform; by Platform_Upper; run;

run;

proc print data=earnings_platform;

run;

OUTPUT:

ObsPlatform_UpperAvg_Earnings
1INSTAGRAM$105,000
2TIKTOK$226,667
3TWITTER$52,500
4YOUTUBE$147,500

data influencers_merged;

    merge influencers_final earnings_platform;

    by Platform_Upper;

run;

run;

proc print data=influencers_merged;

run;

OUTPUT:

ObsNamePlatformCountryJoin_DateFollowersEngagement_RateEarningsName_ProperPlatform_UpperCountry_LowerAnalysis_DateYears_ActiveNext_Review_DatePopularityAvg_Earnings
1AmitSharmaInstagramIndia15JAN201912000004.585000AmitsharmaINSTAGRAMindia01JAN2026701JUL2026Growing$105,000
2EmilyRoseInstagramUK05APR201915000005.4130000EmilyroseINSTAGRAMuk01JAN2026701JUL2026Growing$105,000
3FatimaAliInstagramUAE25JUL20188000004.170000FatimaaliINSTAGRAMuae01JAN2026801JUL2026Growing$105,000
4NoahBrownInstagramCanada14OCT20199000004.895000NoahbrownINSTAGRAMcanada01JAN2026701JUL2026Growing$105,000
5MohammedZainInstagramSaudiArabia11DEC20155000003.940000MohammedzainINSTAGRAMsaudiarabia01JAN20261101JUL2026Growing$105,000
6DavidWilsonInstagramAustralia28MAR202020000005.9210000DavidwilsonINSTAGRAMaustralia01JAN2026601JUL2026Mega$105,000
7   15JAN20234500005.655000DemoUserINSTAGRAMindia01JAN2026301JUL2026Emerging$105,000
8JohnDoeTikTokUSA10MAR202022000008.1200000JohndoeTIKTOKusa01JAN2026601JUL2026Mega$226,667
9LiWeiTikTokChina18JUN202130000009.3250000LiweiTIKTOKchina01JAN2026501JUL2026Mega$226,667
10SophiaLeeTikTokSouthKorea21NOV202026000008.7230000SophialeeTIKTOKsouthkorea01JAN2026601JUL2026Mega$226,667
11RajVermaTwitterIndia30AUG20166000003.545000RajvermaTWITTERindia01JAN20261001JUL2026Growing$52,500
12LucasMartinTwitterFrance07JAN20187500004.060000LucasmartinTWITTERfrance01JAN2026801JUL2026Growing$52,500
13SaraKhanYouTubeIndia20FEB20189800006.2120000SarakhanYOUTUBEindia01JAN2026801JUL2026Growing$147,500
14CarlosMendezYouTubeMexico12MAY201711000006.9140000CarlosmendezYOUTUBEmexico01JAN2026901JUL2026Growing$147,500
15AnnaSmithYouTubeUSA02SEP201717500007.8180000AnnasmithYOUTUBEusa01JAN2026901JUL2026Mega$147,500
16PriyaNairYouTubeIndia19FEB201912500006.5150000PriyanairYOUTUBEindia01JAN2026701JUL2026Growing$147,500


12. TRANSPOSE – REPORTING FORMAT

proc transpose data=earnings_platform out=earnings_transposed;

    id Platform_Upper;

    var Avg_Earnings;

run;

proc print data=earnings_transposed;

run;

OUTPUT:

Obs_NAME_INSTAGRAMTIKTOKTWITTERYOUTUBE
1Avg_Earnings$105,000$226,667$52,500$147,500

Used heavily in TLF creation.


13. BUSINESS INSIGHTS DERIVED

·       TikTok influencers show highest engagement

·       YouTube has stable long-term earnings

·       Instagram influencers dominate brand partnerships

·       Mega influencers earn 2–3x more than Growing category

·       Engagement rate > followers for ROI(Return Of Investment)


14. INTERVIEW QUESTIONS YOU CAN EXPECT

1.     Why INTCK instead of date subtraction?

2.     Difference between SET and MERGE?

3.     Why use macros in categorization?

4.     How does PROC SQL differ from DATA step?

5.     Why standardize character variables?

6.     When to use TRANSPOSE in reporting?

7.     How PROC SGPLOT differs from GPLOT?

 

15. WHAT YOU LEARN FROM THIS PROJECT

1. End-to-end SAS workflow
2. Business + technical thinking
3. Real-world marketing analytics
4. Interview-ready SAS concepts
5. Reusable macro design
6. Clean reporting structure


CONCLUSION

This project mirrors real corporate analytics work used by:

·       Digital marketing firms

·       Social media analytics teams

·       Data science departments

·       Consulting organizations



About the Author:

SAS Learning Hub is a data analytics and SAS programming platform focused on clinical, financial, and real-world data analysis. The content is created by professionals with academic training in Pharmaceutics and hands-on experience in Base SAS, PROC SQL, Macros, SDTM, and ADaM, providing practical and industry-relevant SAS learning resources.


Disclaimer:

The datasets and analysis in this article are created for educational and demonstration purposes only. They do not represent Influencer Analytics data.


Our Mission:

This blog provides industry-focused SAS programming tutorials and analytics projects covering finance, healthcare, and technology.


This project is suitable for:

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HOW TO CREATE MOCK SHELLS AND CLINICAL DATA LISTINGS IN SAS USING DATA NULL |  PROC PRINT | PROC REPORT | PROC SORT | PROC COMPARE | ODS PDF | ODS RTF | ODS EXCEL |  A COMPLETE STEP-BY-STEP GUIDE FOR CLINICAL SAS PROGRAMMERS  /*Creating mock shells and listings in SAS is a critical part of clinical programming. These are used to present clinical trial data in a standardized, readable, and  review-ready format.*/ What is a Listing? A listing is a detailed tabular report of subject-level clinical trial data (raw or SDTM/ADaM), generally created to: Allow medical reviewers and statisticians to review patient data. Support analysis results by providing traceability. Comply with FDA or other regulatory submissions. Listings are typically derived from SDTM or ADaM datasets, especially: ADSL (Subject-Level Analysis Dataset) ADAE (Adverse Events) ADLB (Lab Data) ADVS (Vital Signs) Step-by-Step Guide to Create a Mock Shell and Listing in SAS Step 1: Simulate or Use Existing D...
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62.ADDING NEW DATA INTO AVAILABLE DATA USING MERGE

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          ADDING NEW DATA INTO AVAILABLE DATA USING MERGE  DATA  WHICH WANTS TO MERGE WITH ALREADY CREATED DATE..IT CONSISTS OF 3 STEPS AND I HAVE ALSO SORTED THE DATA.. 1.DATA CLASS1;  SET SASHELP.CLASS4; RUN; LOG: NOTE: There were 19 observations read from the data set SASHELP.CLASS4. NOTE: The data set WORK.CLASS1 has 19 observations and 8 variables. NOTE: DATA statement used (Total process time):       real time           0.50 seconds       cpu time            0.04 seconds 2.DATA WORK.CLASS2;  INPUT  Name$ Sex$ Age Height Weight DOB:DATE9. CLASS GRADE$;  DATALINES;  Nancy F 19 69 102 12MAR2002 . A  Firoz M 20 76 125 15JUN2001 . A  ; RUN; LOG: NOTE: The data set WORK.CLASS2 has 2 observations and 8 variables. NOTE: DATA statement used (Total process time):       real time           ...
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341.FAMOUS SCIENTISTS DATA ANALYSIS USING PROC SQL | PROC MEANS | PROC RANK | PROC FORMAT | PROC SGPLOT | MACROS | DATE FUNCTIONS FOR IMPACT EVALUATION | RANKING INSIGHTS | TIME-BASED SCIENTIFIC CONTRIBUTION STUDY

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FAMOUS SCIENTISTS DATA ANALYSIS USING PROC SQL | PROC MEANS | PROC RANK | PROC FORMAT |  PROC SGPLOT | MACROS | DATE FUNCTIONS FOR IMPACT EVALUATION | RANKING INSIGHTS |  TIME-BASED SCIENTIFIC CONTRIBUTION STUDY options nocenter; 1.DATASET CREATION  data scientists;     length Scientist_Name $30 Country $20 Field $25 Awards $40 Inventions $50 Birth_Date $10;     format  Discovery_Date date9.;     input Scientist_Name $ Country $ Field $ Awards $ Inventions $ Impact_Score            Birth_Date $ Discovery_Date :date9.; datalines; Einstein Germany Physics Nobel_Prize Relativity 98 14MAR1879 01JAN1905 Newton UK Physics None Laws_of_Motion 97 04JAN1643 01JAN1687 Curie Poland Chemistry Nobel_Prize Radioactivity 96 07NOV1867 01JAN1898 Tesla Serbia Engineering None AC_Current 95 10JUL1856 01JAN1887 Darwin UK Biology None Evolution 94 12FEB1809 01JAN1859 Galileo Italy Astronomy None Telescope 93 15FEB1564 01JAN1610 ...
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