Pre - employment testing practice elena-tulainova@yandex.ru looking opportunity to work as an analyst in retail/food industry

1. 1 CASE
Pizza in Moscow

2. TARGET AUDIENCE
It is a place where customers
may receive quick, tasty, and
inexpensive meal.
 On weekdays – employees
from nearby offices,
primarily middle managerial
staff and specialists.
 On weekends – families

3. NUMBER OF PRODUCTS IN THE MENU
 No more than 7 different choices of pizzas
 Chance to add extra toppings when ordering the
whole pizza
 2 choices of crust
 1-2 types of salad with option to add grilled chicken
, shrimp, or salmon
 1-2 choices of soup in a cup (for example, creamy
tomato-basil/mushroom/avgolemono)
 3-4 desserts (Italian deserts: gelato, cannoli,
tiramisu, and biscotti)
 3-4 types of drink (flavored coffee , tea, milk shakes
during summer, hot chocolate during winter)

4. BENEFITS OF THE MENU
 Many Russian people love to
eat soup at launch time
 Soup considered as a healthy
choice especially for children
 Most of the ingredients for
soups are the same as
ingredients for pizza (bazil,
tomato, mushrooms, cheese,
flower, butter)
 Usually, in Russia, creamy
soups are not prepared at
home, thereby encouraging
customers to try the new
product in restaurant
Soup

5.  It is good healthy
choice
 Same ingredients for
hot salads (chicken,
shrimp, bacon )
 There is no need to add
different sizes of pizzas
since there is an
opportunity to buy slices
 There are will be many
individual orders during
launch time. Pizza slice
will be substitute for the
order-for-one (sandwich)
Salads Pizza
BENEFITS OF THE MENU

6.  Italian desserts
associated with the
same cuisine as pizza
 Coffee is one of the
most popular drinks
during breakfast/launch
time/coffee breaks
Drinks and Desserts
BENEFITS OF THE MENU

7. ADDITIONAL RECOMMENDATION FOR PIZZA
RESTAURANT
Saving space
 Open windows with option to
extend dining area
 Open kitchen
 Orders from the table to avoid the
line at the counter
 To-go boxes for everything (1-2
slices of pizza, soup, salads, and
deserts)
 Pictures for new/exotic pizzas
(with shrimp/mango/alfredo
sauce)

8. 2 CASE
Pizza with mango

9. ASSUMPTIONS
- IS may predict not only sales but also number of orders, visits,
channels, type of customers, and etc.
- It may predict for any point of time in the future
- All customers’ demographic characteristics are already in the
system.
It will allow compare “apple to apple” for each advertisement campaign

10. WHAT QUESTIONS CAN BE ANSWERED BASED
ON THE MODELS RESULTS
 How much revenue campaign was generated?
 How much revenue per one offer?
 Did they buy other items?
 Who responded to the offer?
 Are they returning customers?
 Where they came from? (online orders/call
centers/in store)
 Did customers shop differently?
and etc.

11. HOW IT WORKS
 Average check
 Median
 St.dev of predicted and real data
Top metrics related to campaign

12. Top metrics related to campaign
 Pizza predicted vs.
pizza real
(sales/cost/number)
 Pizza vs. “order for
one” (substitute
products)
 Comparison
complementary
products
(beverages/salads)

13. Top metrics related to campaign
 Who bought mango
pizza?
 Are they returning/new
customers
 What channel did they
used to order?
 Their demographic/
sociographic

14. ADDITIONAL METHODS TO COLLECT
INFORMATION
In addition to quantitative analysis of data from IS
following methods may be used:
o Observation (in store behavior analysis)
o Questionnaires (measure customer satisfaction)
o Interviews
o Focus group
and etc.