This document outlines a marketing plan for Peapod, an online grocery retailer, to increase sales and customer loyalty. It defines the business problem of increasing sales by 20% by the end of 2016 through boosting purchase frequency, converting customers to a loyalty program, and acquiring new customers. The solution involves improving the user experience with customized promotions, introducing a loyalty program, and promoting an existing referral program. The plan details customer experience initiatives and analytics for new customers, existing customers, purchase experiences, and post-purchase follow up to address these goals. Predictive models for churn, recommendations, sign-ups, and attribution are also proposed.

1. ADAP for Peapod
Spring 2015
Isha Deodhar | Prakarsh Gupta | Nandita Vaidyanathan
Jordan Vita | Linyu Zhang

2. COMPANY ATTRIBUTES
Slide 2
+ About Peapod
Largest
online
grocery
retailer
Platforms:
online
and app
1989
$600
Million
annual sales
Next-day
delivering
during pre-
scheduled time slot
IMC 498: Digital Analytics, Spring 2015

3. SECTION ONE
PROBLEM DEFINITION

4. BUSINESS PROBLEM
= Increase sales by 20% from the current level of $600
million by the end of 2016 by:
✓ Increasing purchase frequency of existing customers
✓ Converting existing customers into loyalty program members
✓ Acquiring new Peapod customers
BUSINESS OBJECTIVE
= Threat of losing market share to competitors and
new entrants
IMC 498: Digital Analytics, Spring 2015 Slide 4

5. OUR CUSTOMER
Meet our customer, she is:
o 35-years-old
o Professional
o Household income over $100,00
o Suburban metropolitan area
o College-educated
o Married with children
+The target audience
When shopping for groceries, she:
o Values the quality of the product
o Prefers on-sale products over regular-priced ones
o Makes purchases based on different family member’s needs
IMC 498: Digital Analytics, Spring 2015 Slide 5
Stephanie

6. OUR CUSTOMER
Her customer problem:
o User experience: lack of seamless browsing
o Lack of loyalty program / absence of incentives
o High delivery fee
o Lack of added convenience features
+The target audience
Her objective:
o She wants a seamless, relevant and rewarding experience
when grocery shopping on line.
IMC 498: Digital Analytics, Spring 2015 Slide 6
Stephan
ie

7. MARKETING PROBLEM
= Address the marketing problem by:
✓ Increasing customer lifetime value and retention rates
✓ Encouraging customer loyalty
✓ Encouraging customer advocacy
MARKETING OBJECTIVE
= Peapod is not communicating its value or providing
incentives to increase customer lifetime value.
IMC 498: Digital Analytics, Spring 2015 Slide 7

8. SECTION TWO
SOLUTION DEFINITION

9. KEY FINDINGS
+ What people say about online shopping and loyalty
have not bought groceries
online, but would be
interested in doing so
26%
of primary grocery shoppers…
would like
promotions/coupons
customized to their personal
shopping habits
45%
of primary grocery shoppers…
say they choose a retailer
based on their ownership of
a store loyalty card
30%
of primary grocery shoppers…
IMC 498: Digital Analytics, Spring 2015 Slide 9

10. MARKETING SOLUTION
+ Developed based on key insights
1. Improved user experience and customized promotions
KPI: percentage of returning visitors, visit frequency, average time spent, CTRs
2. Introduction to a Peapod loyalty program
KPI: reach of display ads, CTRs, visits to loyalty program landing page
3. Promotion of Peapod’s existing referral program
KPI: referral code redemption rate
IMC 498: Digital Analytics, Spring 2015 Slide 10

11. SECTION THREE & FOUR
DATA DESIGN &
ANALYTICS

12. IMC 498: Digital Analytics, Spring 2015 Slide 12
EXPERIENCE PLAN
+ Engaging our customer

13. INITIAL CONSIDERATION SET
+ Experience Plan (New Customers)
+ Data accessed
From DMP:
Search history
Web history
Demographic target
profile
+ Customer experience
Display ads
Search ads
Social media ads
Referrals
+ Data collected
Reach/frequency
Click through rate
Site visits
Campaign tag
Top keywords
Cookie data
Peapod sign ups
Referral code redemption
rate
IMC 498: Digital Analytics, Spring 2015 Slide 13

14. INITIAL CONSIDERATION SET
+ Descriptive Analytics
▪ Campaign Tag
▪ Top keywords
▪ Reach/frequency
▪ Click through rate
▪ Site visits
▪ Traffic sources
▪ Redemption code rate
IMC 498: Digital Analytics, Spring 2015 Slide 14

15. INITIAL CONSIDERATION SET
+ Prescriptive Analytics
AB
A
IMC 498: Digital Analytics, Spring 2015 Slide 15

16. INITIAL CONSIDERATION SET
+ Prescriptive Analytics
Use Referral Code
BBF5CC6
at peapod.com for
Free Shipping
Use Referral Code
BBF5CC6
at peapod.com for
10% OFF
A B
IMC 498: Digital Analytics, Spring 2015 Slide 16

17. ACTIVE EVALUATION
+ Experience Plan (Existing Customers)
+ Data accessed
From DMP:
Behavioral data
Website search history
Website browsing history
From Database:
Customer ID
Transaction data
Website browsing history
+ Customer experience
Loyalty program carousel
banner on homepage
Optimized website
+ Data collected
Time spent on site
Number of pages viewed
Click through rate
Bounce rate
Peapod sign ups
Loyalty program sign ups
IMC 498: Digital Analytics, Spring 2015 Slide 17

18. ACTIVE EVALUATION
+ Experience Plan (New Customers)
+ Data accessed
From DMP:
Behavioral data
Website search history
Website browsing history
From Database:
Website browsing history
and preferences
+ Customer experience
Discount carousel banner
on homepage
Optimized website
+ Data collected
Time spent on site
Number of pages viewed
Click through rate
Bounce rate
Peapod sign ups
IMC 498: Digital Analytics, Spring 2015 Slide 18

19. ACTIVE EVALUATION
+ Descriptive Analytics
▪ Click through rate on
discount banners (new)
▪ Number of pages viewed
▪ Time spent on site
▪ Bounce rate
▪ Click through rate on loyalty
program banners (existing)
▪ Peapod sign ups
IMC 498: Digital Analytics, Spring 2015 Slide 19

20. ACTIVE EVALUATION
+ Prescriptive Analytics
A B
IMC 498: Digital Analytics, Spring 2015 Slide 20

21. ACTIVE EVALUATION
+ Prescriptive Analytics
A B
IMC 498: Digital Analytics, Spring 2015 Slide 21

22. ACTIVE EVALUATION
+ Prescriptive Analytics
A B
IMC 498: Digital Analytics, Spring 2015 Slide 22

23. BUY
+ Experience Plan (Existing Customers)
+ Data accessed
From Database:
Customer ID
Previous transaction
information (RFM)
Prior orders
Saved shopping lists
Saved payment
information (CRM)
+ Customer experience
Loyalty program button on
checkout page
One-click to purchase
button
+ Data collected
Time spent during checkout
Conversion rate
Bounce rate
Order number
Basket size
Transaction data
Delivery times
Loyalty program sign ups
Loyalty button CTR
One-click button CTR
IMC 498: Digital Analytics, Spring 2015 Slide 23

24. BUY
+ Experience Plan (New Customers)
+ Data accessed
From Database:
Unique visitor ID
+ Customer experience
Loyalty program button on
checkout page
Guest checkout button on
account creation page
+ Data collected
Customer ID
Time spent during checkout
Conversion rate
Bounce rate
Order number
Basket size
Transaction data
Delivery times
Guest checkout CTR
Peapod sign ups
Loyalty program sign ups
IMC 498: Digital Analytics, Spring 2015 Slide 24

25. BUY
+ Descriptive Analytics
▪ Average basket size
▪ Transaction data
▪ Delivery times data
▪ Time spent on checkout page
▪ Checkout page bounce rate
▪ Conversion rate
▪ Loyalty program sign ups
▪ Click through rate on button
▪ Peapod sign ups
IMC 498: Digital Analytics, Spring 2015 Slide 25

26. BUY
+ Prescriptive Analytics
A B
IMC 498: Digital Analytics, Spring 2015 Slide 26

27. BUY
+ Prescriptive Analytics
A B
IMC 498: Digital Analytics, Spring 2015 Slide 27

28. POST-BUY
+ Experience Plan (All Customers)
+ Data accessed
From Database:
Customer ID
Transaction history
Items in cart
From DMP:
Demographic information
Behavioral information
+ Customer experience
Thank you page with
invitation to loyalty
program
Post purchase thank you
email with loyalty program
link and referral code
Post-delivery review email
with 5-star rating scale
Retargeting display ads
+ Data collected
Loyalty program sign ups
Open rates for emails
Referral code redemptions
Ratings
CTRs on display ads
Use of promotion coupons
IMC 498: Digital Analytics, Spring 2015 Slide 28

29. POST-BUY
+ Descriptive Analytics
▪ Loyalty program sign ups
▪ Email open rates
▪ Referral code redemptions
▪ Star rating rate
▪ CTR on display ads
IMC 498: Digital Analytics, Spring 2015 Slide 29

30. POST-BUY
+ Prescriptive Analytics
A
IMC 498: Digital Analytics, Spring 2015 Slide 30
B

31. POST-BUY
+ Prescriptive Analytics
A
IMC 498: Digital Analytics, Spring 2015 Slide 31
B

32. PREDICTIVE & ADAPTIVE ANALYTICS
Churn model
Recommendation model
Propensity model: sign-ups
Attribution model
IMC 498: Digital Analytics, Spring 2015 Slide 32

33. THANK YOU