This document describes the analysis of customer segmentation for an online grocery store. Key variables for analysis included demographics, order behavior metrics (tenure, recency, frequency, spending), and order category spending percentages. 5 key behavioral dimensions were identified through factor analysis: long-term frequent high spenders, home & health shoppers, convenience/fast cookers, snack lovers, and inactive incentive seekers. Customer profiles were developed for representative segments including loyal Chris and home-loving Jenna. Recommendations were provided to retain and acquire long-term customers and grow sales with home-loving customers.

1. Online Grocery Store Segmentation
Eric Xiong, Elaine Su, Tianhong Chu, Dannie Hu, Biying Pan

2. Methodology
1.
a. Identify Key Variables
b. Develop Key Behavior Dimensions
c. Segment & Profile Customers
d. Recommendations

3. Key Variables for Analysis
Demographics
We did not use demographic
variables (most of them are
categorical variables) for our factor
analysis since we read that there
may be issues related to using
categorical variables in factor
analysis
Order Behavior
● Tenure
● Recency
● Frequency
● Average spending per order
● Percentage of purchases with
incentives
Order Category
Spending Percentage of :
● Drinks & Snacks
● Fresh Food
● Cooking Ingredients
● House & Family Care
● Health & Beauty
● Indulgence
● Dairy
● Other

4. Key Behavioral Dimensions
1. Long-Term Frequent High Spenders
2. Home & Health Shoppers
3. Convenience/Fast Cookers
4. Snack Lovers
5. Inactive Incentive Seekers

5. Segments Overview
Our Best Customers

6. Segments Overview
Key Take Aways：
Loyal Chris and home-loving Jenna are our top two customers in terms of frequency and average spending per order.

7. Segment Profiles
Loyal Chris Home-loving Jenna
● Married, MBA Graduate
● Medill professor, Data Scientist
● Best customer: purchase most
frequently, spends the most
money per order and has been
a customer for the longest time
● No strong preference of any
specific product category
● Married, 2 kids, IMC Master
Graduate
● Part-time housewife, proud
mom
● Purchases the most House &
Family Care products for her
family and Health & Beauty
products to take care of herself
● Single, College Dropout
● Full-time Uber Driver
● Loves video game and comics
● Too lazy to cook so he
purchases a lot of convenient
frozen and pre-made food but
very few fresh food
Lazy Andrew

8. Segment Profiles
One-time SeanFrugal Omar Snacker RushilFresh Alex
● Married, 4 kids
● Saves money to raise family
● Most inactive, purchases least
frequently
● Very price sensitive: a lot
more likely to buy products
at our store when there’s an
incentive or discount.
● Married, 2 kids
● Spends the least amount per
order
● Loves eating snacks and
purchases a lot of snacks for
his kids and himself
● Busy with work, rarely cooks
anything himself at home
● Married, College Graduate
● Crossfit Enthusiast
● Health-conscious: cares
about the quality of food
● Purchases the most fresh
food and tries to avoid
snacks and frozen and
pre-made food
● Only purchased at our store
once and it is most likely
because there was a special
offer or coupon given to
him
● Spends the least amount on
his orders

9. High Profit
Long Term
Customers
Accounts for 17.7% of the total customers
Highest average spending level (1.2 times compared to overall average)
3 times more frequent to shop at our store than overall frequency
Has been shopping with us for an average of over 7 years
Upper-Class
Customers
Has the highest education levels
Are more likely to be married and have kids
Has the highest income and lives in single unit houses
Target Audience: The Loyalists

10. Recommendation: Loyal Chris
● Create a loyalty program to reward customers: e.g. earn points for every qualifying
purchase and get rewards
● Provide a special level of services: e.g. personal customer representatives, faster
shipping options, free gifts on special occasions.
● Reward introductions and referrals: offer rewards in return for introducing new
customers to place orders, because Loyal Chris can provide better word of mouth, and
may thus more easily introduce similar upper-level customers.
RETAIN+ACQUIRE

11. High Profit
Frequent
Customers
Accounts for 15.2% of the total customers
Second highest average spending (1.07 times compared to overall average)
Second highest frequency level
Home
Caring
Lovers
Second highest percentage of high education and marriage status
High income customers
Interested in purchasing non-grocery items that are related to home, family,
beauty and health
Target Audience: The Home-Lovers

12. Recommendation：Home-loving Jenna
● Online automatic subscription: Household, beauty and health products with a steady
consumption rate are well suited to online purchasing. Easier for time-crunched consumers
to get the items they need.
● Influencers and social media: Partnering with bloggers to increase product awareness
and creating promotion in the social media.
● Pairing like products: Pairing complementary products with a promotional price on the
set will capitalize customers’ perceived value.
GROWTH

13. Thank you!
What questions do you have?

14. Appendix

15. Final Factor Result

16. Final Segments Profile

17. One-time Customers

18. Final Code
libname imcdata '/projects/e30509/imc486';
proc datasets lib=imcdata; quit; run;
* data read-in;
data ordprod;
set imcdata.ordprod; run;
proc print data=ordprod(obs=20); run;
data lookup;
set imcdata.lookup; run;
proc print data=lookup(obs=20); run;
*categorize minor_cat into 8 major categories;
data lookup1;
set lookup;
if index(minor_cat, "JUICE") +
index(minor_cat, "BEVERAGES") + index(minor_cat,"SNACKS") then major_cat="Drinks & Snacks";
else if index(minor_cat, "FROZEN") + index(minor_cat, "PACK") + index(minor_cat, "REFRIG") +
index(minor_cat, "CANNED")
+ index(minor_cat, "INSTANT") + index(minor_cat, "BOXED") > 0 then major_cat = "ConvenienceFood";
else if index(minor_cat, "FRESH") > 0 then major_cat = "Fresh Food";
else if index(minor_cat, "DAIRY") > 0 then major_cat = "Dairy";
else if index(minor_cat, "GROC") > 0 then major_cat = "Cooking Ingredients";
else if index(minor_cat, "HB") > 0 then major_cat = "Health & Beauty";
else if index(minor_cat, "HH") + index(minor_cat, "HG") +
index(minor_cat,"BABY") + index(minor_cat, "PET") > 0 then major_cat = "Home & Family Care" ;
else if index(minor_cat, "ALCOHOL") + index(minor_cat, "CIGARETTES") + index(minor_cat,
"CIGARS/CHEWING") > 0 then major_cat = "Indulgence";
else major_cat = "Other";
run;
proc freq data=lookup1;
table major_cat; run;
* join minor_cat to ordprod table;
proc sql;
create table ordprod_cat as
select ord_id, spec_cd, it_qy, it_pr_qy, tot_pr_qy, major_cat
from ordprod as o left join lookup1 as l
on o.pod_id = l.pod_id;
quit;
proc freq data=ordprod_cat;
table major_cat; run;
* join cnsm_id to ordprod table;
data order;
set imcdata.order;
if cnsm_id = "" then delete;
if it_dmnd_qy = . then delete;
if ord_seq_num = . then delete;
if sale_inct_qy = . then sale_inct_qy = 0;
run;
proc sql;
create table ordprod_cat1 as
select cnsm_id, spec_cd, it_qy, it_pr_qy, tot_pr_qy, major_cat
from ordprod_cat as o inner join order as i
on o.ord_id = i.ord_id;
quit;

19. * roll - up ordprod to customer level;
proc sort data = ordprod_cat1 out = ordprod_cat_s;
by cnsm_id; run;
data ordprod_cat_cust;
set ordprod_cat_s;
by cnsm_id;
retain spec_cd_qty tot_qty spending_tot
qty_ds qty_con qty_fresh
qty_dairy qty_cooking qty_hb
qty_hf qty_indul qty_other
spending_ds spending_con spending_fresh
spending_dairy spending_cooking spending_hb
spending_hf spending_indul spending_other;
if first.cnsm_id then do;
tot_qty = 0;
spec_cd_qty = 0;
spending_tot = 0;
qty_ds = 0;
qty_con = 0;
qty_fresh = 0;
qty_dairy = 0;
qty_cooking = 0;
qty_hb = 0;
qty_hf = 0;
qty_indul = 0;
qty_other = 0;
spending_ds = 0;
spending_con = 0;
spending_fresh = 0;
spending_dairy = 0;
spending_cooking = 0;
spending_hb = 0;
spending_hf = 0;
spending_indul = 0;
spending_other = 0;
end;
tot_qty = tot_qty + it_qy;
spending_tot = spending_tot + tot_pr_qy;
if spec_cd = "*" then do;
spec_cd_qty = spec_cd_qty + it_qy;
end;
if major_cat = "Drinks & Snacks" then do;
qty_ds = qty_ds + it_qy;
spending_ds = spending_ds + tot_pr_qy;
end;
else if major_cat = "ConvenienceFood" then do;
qty_con = qty_con + it_qy;
spending_con = spending_con + tot_pr_qy;
end;
else if major_cat = "Fresh Food" then do;
qty_fresh = qty_fresh + it_qy;
spending_fresh = spending_fresh + tot_pr_qy;
end;
else if major_cat = "Dairy" then do;
qty_dairy = qty_dairy + it_qy;
spending_dairy = spending_dairy + tot_pr_qy;
end;
else if major_cat ="Cooking Ingredients" then do;
qty_cooking = qty_cooking + it_qy;
spending_cooking = spending_cooking + tot_pr_qy;
end;
else if major_cat = "Home & Family Care" then do;
qty_hf= qty_hf + it_qy;
spending_hf = spending_hf + tot_pr_qy;
end;
else if major_cat = "Health & Beauty" then do;
qty_hb = qty_hb + it_qy;
spending_hb = spending_hb + tot_pr_qy;
end;
else if major_cat = "Indulgence" then do;
qty_indul = qty_indul + it_qy;
spending_indul = spending_indul + tot_pr_qy;
end;
else if major_cat = "Other" then do;
qty_other = qty_other + it_qy;
spending_other = spending_other + tot_pr_qy;
end;
if last.cnsm_id then do;
if spending_tot = 0 then delete;
if tot_qty = 0 then delete;
qty_ds_pct = qty_ds/tot_qty;
qty_con_pct = qty_con/tot_qty;
qty_fresh_pct = qty_fresh/tot_qty;
qty_dairy_pct = qty_dairy/tot_qty;
qty_cooking_pct = qty_cooking/tot_qty;
qty_hf_pct = qty_hf/tot_qty;
qty_hb_pct = qty_hb/tot_qty;
qty_indul_pct = qty_indul/tot_qty;
qty_other_pct = qty_other/tot_qty;
spending_ds_pct = spending_ds/spending_tot;
spending_con_pct = spending_con/spending_tot;
spending_fresh_pct = spending_fresh/spending_tot;
spending_dairy_pct = spending_dairy/spending_tot;
spending_cooking_pct = spending_cooking/spending_tot;
spending_hf_pct = spending_hf/spending_tot;
spending_hb_pct = spending_hb/spending_tot;
spending_indul_pct = spending_indul/spending_tot;
spending_other_pct = spending_other/spending_tot;
spec_cd_pct = spec_cd_qty/tot_qty;
output; end;
keep it_qy cnsm_id
spec_cd_pct
qty_ds_pct
qty_con_pct
qty_fresh_pct
qty_dairy_pct
qty_cooking_pct
qty_hf_pct
qty_hb_pct
qty_indul_pct
qty_other_pct

20. spending_ds_pct
spending_con_pct
spending_fresh_pct
spending_dairy_pct
spending_cooking_pct
spending_hf_pct
spending_hb_pct
spending_indul_pct
spending_other_pct
spending_tot tot_qty;
run;
* order data - roll up to customer level;
proc sort data = order out = order_s;
by cnsm_id; run;
*maximum date in the table;
proc means data=order_s;
var dlv_dt; run;
data order_cust;
set order_s;
by cnsm_id;
retain frstdt lastdt freq tot_spending tot_incentive incentive_freq;
if first.cnsm_id then do;
frstdt = '31DEC2099'd;
lastdt = '31DEC1850'd;
freq = 0;
tot_spending = 0;
tot_incentive = 0;
incentive_freq = 0;
end;
tot_spending = tot_spending + it_dmnd_qy;
tot_incentive = tot_incentive + sale_inct_qy;
if dlv_dt lt frstdt then frstdt = dlv_dt;
if dlv_dt gt lastdt then lastdt = dlv_dt;
if ord_seq_num gt freq then freq = ord_seq_num;
if sale_inct_qy < 0 then incentive_freq = incentive_freq + 1;
if last.cnsm_id then do;
tenure = (19534 - frstdt)/365.25;
recency = (19534 - lastdt)/365.25;
avg_spending = tot_spending/freq;
tot_incentive_pct = tot_incentive/tot_spending;
incentive_freq_pct = incentive_freq/freq;
output;
end;
keep
cnsm_id tenure recency freq avg_spending
tot_incentive_pct incentive_freq_pct;run;
*merge order_cust with ordprod_cust;
proc sql;
create table ordprod_cust as
select o.cnsm_id, tenure, recency, freq, avg_spending,
tot_incentive_pct, incentive_freq_pct,
spec_cd_pct, qty_ds_pct, qty_con_pct,
qty_fresh_pct,qty_dairy_pct, qty_cooking_pct,
qty_hf_pct, qty_hb_pct, qty_indul_pct,
qty_other_pct, spending_ds_pct, spending_con_pct,
spending_fresh_pct, spending_dairy_pct,
spending_cooking_pct, spending_hf_pct,
spending_hb_pct, spending_indul_pct,
spending_other_pct,
spending_tot, tot_qty, it_qy
from order_cust as o inner join ordprod_cat_cust as l
on o.cnsm_id = l.cnsm_id;
quit;
*digital engagement;
proc sql;
create table sessionwithid as select cnsm_id, s.ord_id,
dvce_type, s.dlv_dt, plfm, sess_num_qy,
tot_sess_min_qy,sesstype
from imcdata.session as s inner join imcdata.order as o on
s.ord_id=o.ord_id;
proc sort data=sessionwithid;
by cnsm_id; run;
data customerlevel_session;
set sessionwithid;
by cnsm_id;
retain tot_min
tot_num
tot_pc_min
tot_tb_min
tot_sp_min
order_freq;
if first.cnsm_id then do;
tot_min=0;
tot_num=0;
tot_pc_min=0;
tot_tb_min=0;
tot_sp_min=0;
order_freq = 0;
end;
tot_min=tot_min+tot_sess_min_qy;
tot_num=tot_num+sess_num_qy;
order_freq = order_freq + 1;
if sesstype='PC' then
tot_pc_min=tot_pc_min+tot_sess_min_qy;
if sesstype='TB' then
tot_tb_min=tot_tb_min+tot_sess_min_qy;
if sesstype='SP' then
tot_sp_min=tot_sp_min+tot_sess_min_qy;
if last.cnsm_id then do;
pc_pct = tot_pc_min/tot_min;
tb_pct = tot_tb_min/tot_min;
sp_pct = tot_sp_min/tot_min;
avg_min = tot_min/order_freq;
avg_num = tot_num/order_freq;
output;
end;
keep it_qy cnsm_id
tot_pc_min
tot_tb_min
tot_sp_min

21. avg_min
avg_num
pc_pct
tb_pct
sp_pct;
run;
proc sql;
create table sessionwithord as
select *
from ordprod_cust as o inner join customerlevel_session as s
on o.cnsm_id=s.cnsm_id;
*customer demographic data clean and merge;
data clean_cust;
set imcdata.cust;
if marital_stat in ('M','A') then known_marriage=1;
else if marital_stat in ('S','B') then known_marriage=0;
else delete;
if online_dols="" then online_dols='L';
if hhincome=. then hhincome=9;
if age=. then age=47;
if dwelling='S' then singleunit=1;
else if dwelling='M' then singleunit=0;
else delete;
if prkids='Y' then havekids=1;
else havekids=0;
if educ in (2,3) then educ=1;
else if educ in (1,4) then educ=0;
else delete;
proc sort data = clean_cust out=custsort;
by cnsm_id; run;
proc sql;
create table fulldata as
select c.cnsm_id, *
from custsort as c inner join sessionwithord as s
on c.cnsm_id=s.cnsm_id;
proc freq data=fulldata;
table freq; run;
*Delete one-time customers;
data fulldata_clean;
set fulldata;
if freq > 1;
avg_qty = tot_qty/freq;
run;
* factor analysis - qty;
proc factor data = fulldata_clean rotate=varimax out=fact nfactors=5
reorder fuzz= 0.3; *out=fact nfactors=3;
var tenure recency freq avg_spending
spec_cd_pct
incentive_freq_pct
qty_con_pct qty_fresh_pct
qty_cooking_pct qty_hf_pct
qty_hb_pct
avg_min avg_num
tb_pct
sp_pct
pc_pct
it_qy;
run;
* factor analysis - spending;
proc factor data = fulldata_clean rotate=varimax reorder fuzz= 0.3 out=fact
nfactors=5;
var tenure recency freq avg_spending
incentive_freq_pct spending_ds_pct spending_con_pct
spending_fresh_pct spending_cooking_pct
spending_hf_pct spending_hb_pct;
label
factor1='Long-Term Active High Spenders'
factor2='Home & Health Shoppers'
factor3='Convenience/Fast Cookers'
factor4='Snack Lovers that Rarely Cook'
factor5='Inactive Incentive Seekers';
run;
*cluster analysis;
proc fastclus data=fact maxclusters=75 maxiter =1 mean=seed;
var factor1-factor5;
proc sort data=seed;
by descending _freq_;
*Develop Multiple Cluster Solutions;
proc fastclus data=fact maxclusters=7 drift seed=seed(obs=7)
maxiter=100 converge=0;
var factor1-factor5;
*Create Final Cluster Seeds;
proc fastclus data=fact maxclusters=6 seed=seed(obs=6)
maxiter=100 strict=1 mean=seed;
var factor1-factor5;
proc fastclus data=fact maxclusters=6 seed=seed(obs=6)
maxiter=0 replace=none out=scores;
var factor1-factor5;
*Profiling the Final Cluster Solution with PROC TABULATE;
proc tabulate data=scores format=comma8.4;
class cluster;
var factor1-factor5 tenure recency freq avg_spending
incentive_freq_pct spending_ds_pct spending_con_pct
spending_fresh_pct spending_cooking_pct
spending_hf_pct spending_hb_pct hhincome age educ
known_marriage singleunit havekids;
table
n(factor1-factor5 tenure recency freq avg_spending
incentive_freq_pct spending_ds_pct spending_con_pct
spending_fresh_pct spending_cooking_pct
spending_hf_pct spending_hb_pct hhincome age educ
known_marriage singleunit havekids)*mean, cluster all;
title2 'Cluster Profiles';
run;