Starbucks Use Case

1. Starbucks
US –
Case Study

2. Introduction
• 950,000 unique CRM
records from January
2019 – September 2019
are analysed
• Consistently over
180,000 new records
per month
• The following presentation demonstrates the value of PurpleAnalytics across
an example Starbucks in the Middle East
• The data included is from a period of January 22nd, 2019 – September 30th,
2019 – using a sample of over 950,000 data records
• This analysis includes data on 45 venues, some of which collected data from
the initial trial in January, and some which were added to a full roll-out later in
the year
• Identifying trends and patterns within the data, who is visiting and when,
provides insights into how to attract new customers and retain existing ones

3. Contents
Visits:
Monthly Visits
Time of Day
Login Method
New vs Repeat
Conversion
Cross Pollination
Demographics
:
Age
Gender
Location
Language
Behaviour:
Frequency
Recency
Dwell
Weekday
Venue:
Most Popular
Least Popular
By Demographics
By Behaviour
NPS
Benchmarking:
Visits
Demographics
Engagement
New vs Repeat

4. Visits

5. MonthlyVisits
• The included sample covers
a period of 9 months from
January 2019 – Sept 2019,
when PurpleAnalytics was
first introduced
• The data shows that visits
peak aroundAugust,
indicating popularity in the
summer months – but can
also be attributed to the
expanding number of
locations utilising Purple
insights
0
50000
100000
150000
200000
250000
Jan Feb Mar Apr May Jun Jul Aug Sep

6. Time of Day • Purple data records the time
of day a customer visits –
vital in understanding peak
periods and how these can
vary by the day of the week,
or location
• Peak visits occurred in early
evening, 5-7pm – indicating
popularity after work –
something which might vary
on weekends
• There was also a spike
around 12pm, around
lunchtime, though generally
visits increased consistently
throughout the day
0
10000
20000
30000
40000
50000
60000
70000
80000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

7. Login Method • Venues have a choice as to
how users can authenticate
onto the WiFi – each with
distinct benefits
• Using Facebook offers an
easy way of logged-in for
most users and will capture
other data along with
demographics, such as
interests and Likes.
Registration Forms give the
ability for customisation on
what data is collected
• Knowing which devices are
most used at venues can also
highlight potential business
benefits (such as offering
Apple Pay in venues where
iPhone use is high)
• When combining all three
datasets, visitors were much
more likely to login via
Registration Form 0 at 87%
87%
10%
2%
Registration Form
Facebook
Instagram

8. New vs Repeat • The New vs Repeat metric is useful
in gauging how many customers are
visiting you for the first time, and
how many have been before (at this
level to any venue within the data)
• When Purple first begins collecting
data all customers are new as they
have never been seen before – the
Repeat visitor steadily increasing as
more customers visit on multiple
occasions
• As of September 2019, with Purple
collecting data for 9 months, the
consistency of the New vs Repeat
metrics seems to have stabilised,
with around 63% of visits Repeat – a
figure which might continue to
increase
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Jan Feb Mar Apr May Jun Jul Aug Sep
New Repeat

9. Conversion7-Aug
9-Aug
11-Aug
13-Aug
15-Aug
17-Aug
19-Aug
21-Aug
23-Aug
25-Aug
27-Aug
29-Aug
31-Aug
2-Sep
4-Sep
6-Sep
8-Sep
10-Sep
12-Sep
14-Sep
16-Sep
18-Sep
20-Sep
22-Sep
24-Sep
26-Sep
28-Sep
30-Sep
Passed Converted
• The Conversion metric provides a
breakdown of how many potential
customers were in the vicinity of
your stores – and how many
actually entered – in this case, for 2
months – August & September
2019
• At an overall scale this can show
what percentage of passers-by
entered venues – though it is more
useful at an individual venue level –
identifying what stores with high
Conversion are doing right in
attracting customers over the
threshold
• It can show trends in when most
customers are Converting – based
on changes such as advertising, or
special offers etc

10. Demographics

11. Gender
• Understanding the
demographics of visitors is vital
in attracting new customers and
retaining current ones – and
identifying overall differences is
customer base (and their
relevant needs/wants) is an
important first step
• Gender is often the most
fundamental difference
between customer bases, and it
is important to know at a
company and venue level
where the differences are
• In terms of gender, there was a
notably higher proportion of
female visitors, at 57% - though,
as discussed later, this differs by
venue, time, weekday etc
42.9%
57.0%
0.0%
0.1%
Female
Male
Non-binary
Not disclosed

12. Age • Understanding the ages of
your visitors provides further
insights into who they are
and how you can best serve
their needs
• In terms of age, the major
demographics were between
18-34, accounting for 68% of
visits – the age-profile
declining with age
• Identifying your most
popular audience, along with
those that might be under-
represented can provide
insight into potential
opportunities to increase the
number of visits of both
0%
5%
10%
15%
20%
25%
30%
35%
40%
Under 18 18 to 24 25 to 34 35 to 44 45 to 54 55 to 64 65+

13. Gender by Age
• Adding another dimension
to the data, for a more
granular consideration,
leads to a deeper
understanding
• By adding the dimension of
age to gender, it provides
further insight – such as
that males under 24-34 are
more likely to visit than
females, and that in the 25-
34 age-group
• Males dominate at all age
groups above 18 –
indicating a particular
preference among younger
females
0%
5%
10%
15%
20%
25%
Under 18 18 to 24 25 to 34 35 to 44 45 to 54 55 to 64 65+
Male Female

14. Language • The language data shows
the native language of your
visitors – and can be
important in areas with
high tourism etc - and can
be viewed at a venue level
• It highlights the core
languages of your visitors
(based on their browser
device settings) and allows
identification of potentially
underserved languages
• In this case, the majority of
visitors are using English,
though individual venues
with other languages such
as Arabic and French
would point towards
potential locations with a
different clientele
0
100000
200000
300000
400000
500000
600000
700000

15. Location • The location data shows where
your visitors are from –
indicating if they have travelled
to visit a specific location or live
locally
• It allows users to see how far
people are travelling to visit
them (at a venue level) and will
differ based on area (high
tourist areas or are likely to
have a more diverse location
make-up) – and can point
towards potential solutions for
visitors speaking a specific
language
• Beirut has the highest number
of authentications (and a high
density of locations), followed
by Dubai and Damascus –
indicating hubs where
international travel is common
0
5000
10000
15000
20000
25000
30000
Beirut,
Lebanon
Dubai, United
Arab Emirates
Damascus,
Syria
Tripoli,
Lebanon
Byblos,
Lebanon
Al Hadath,
Mont-Liban,
Lebanon
Cairo, Egypt Aramoun,
Mont-Liban,
Lebanon

16. 1 2 3 4 5
Behaviours

17. Frequency
• The Frequency metric buckets
individual visitors into how
often they have visited – which
can be customised based on
the number of visits a location
has, ensuring it is useful across
all levels
• There are a core of 44% of
visitors who have been more
than 5 times, indicating high
levels of loyalty among a select
group
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
One visit Two visits Three visits Four visits Five or more visits

18. Recency
• Recency is the average number of
days between visits to a company
– regardless of venue
• When looking at a breakdown of
how many visitors fall in to each
category, the most popular is 31-
60 days, indicating many visitors
returning within a month or two –
in total 53% have a Recency of
under a month
• Only 10% of authenticated users
have a Recency of over 90 days
• These buckets can be customised
based on industry. For instance,
you can make smaller buckets to
try and capture weekly visitors, or
larger buckets, such as people
who come every 6 months etc
1-2 Days 3-7 Days 8-14 Days 15-30 Days 31-60 Days 61-90 Days 91-180 Days 181-365 Days

19. Weekday
• An analysis of visits by
weekday shows a general
trend that the number of
visits increases consistently
throughout the week –
peaking on Saturday
• The most popular days are
Thursday, Friday and
Saturday, before visits
decline on Sunday to the
lowest levels of the week
• As each venue will have its
own pattern in terms of
popularity, a later slide
further breaks down the
weekday metric
14.2% 14.2%
14.4% 14.5% 14.5% 14.6%
13.6%
10.0%
11.0%
12.0%
13.0%
14.0%
15.0%
Monday Tuesday Wednesday Thursday Friday Saturday Sunday

20. Weekday by Gender
• There is no notable variation in
the split of Weekday by Gender
– with visits generally consistent
• However, Sundays are slightly
more popular with males, at
43.5% – though all other days
are more in proportion with the
overall gender split pattern
• Better understanding of when
specific demographics are
visiting can help tailor offerings
and improve their overall
experience
43.2% 42.5% 43.2% 42.9% 42.5% 43.1% 43.5%
56.8% 57.5% 56.8% 57.1% 57.5% 56.9% 56.5%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Female Male

21. Weekday by Age • Analysing weekday visits at an
overall level by age allows an
identification of when different
age demographics like to visit
• 35-44-year-olds are slightly
more likely to visit earlier in
the week compared to other
age groups, though overall
visits are generally consistent –
indicating little variance by day
outside of what would be
expected – though under 18s
are slightly more likely to visit
Friday - Sunday
• This means that one day
doesn’t overly appeal to a
specific demographic in terms
of age – highlighting a
possibility to encourage more
visits based on age in the
future0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Under 18
18 to 24
25 to 34
35 to 44
45 to 54
55 to 64
65+
Monday Tuesday Wednesday Thursday Friday Saturday Sunday

22. Weekday by New vs Repeat • When analysing weekday by
New vs Repeat the
weekends have a slightly
higher proportion of New
visitors – suggesting first-
time visitors are more likely
to come on the weekends
• Repeat visits are relatively
steady throughout the week
– indicating that these
regular customers aren’t
coming as often at the
weekend – which is an
opportunity to attract them
with weekend special offers
or to encourage more first-
time visitors
24% 24% 24% 25% 25% 25% 25%
76% 76% 76% 75% 75% 75% 75%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
New Repeat

23. Visitor Interaction Report
• TheVisitor Interaction
Report

24. Venues

25. Most PopularVenues • The graph opposite shows
a selection of the Top 10
venues of the Restaurant
• While all locations had
sufficient and robust data,
the following analysis will
focus only on these Top10
as an example – to
highlight how analysis by
venue can provide a deep
understanding of visitor
behaviour
• Store 6 was the most
popular, slightly above
Store 30
10000
20000
30000
40000
50000
60000
70000
80000
Store 6 Store 3 Store 10 Store 8 Store 4 Store 5 Store 7 Store 10 Store 1 Store 2

26. 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Store 6 Store 3 Store 10 Store 8 Store 4 Store 5 Store 7 Store 10 Store 1 Store 2 Store 12 Store 11
Female Male
Venues by Gender • By further analysing
demographics by venue,
each location can view its
popularity between genders
• Of the most popular
locations, Store 10, Store 4,
and Store 2, have a slight
skew above the average
towards female visitors
• All other venues have a
similar proportion of male
visitors compared to the
average, most notably at
Store 7, where males
account for 68% of logins
average

27. Venues by Age • In terms of age demographic,
Store 10 had the highest
proportion of Under 18s – at
17% - much higher than the
general age-demographic
split
• Store 2 had the highest
proportion of over 35s, at
34% - this figure is just 15%
in Store 5
• Understanding the age
demographics, and their
differ needs, allows each
location to personalise its
offerings based on visitor
type (in this case different
products to meet the needs
of older customers)
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Store 6
Store 3
Store 10
Store 8
Store 4
Store 5
Store 7
Store 10
Store 1
Store 2
Under 18 18 to 24 25 to 34 35 to 44 45 to 54 55 to 64 65+

28. Venues byWeekday
• As discussed earlier, the
popularity of venues will be
affected by weekday
• For example, weekends are
more popular at Store 10, at
32% - while at Store 3, there
are a higher proportion of
visits in the early week
• This highlights specific
locations and areas with
‘quiet’ days where there is
the opportunity to encourage
visits through advertising or
promotion
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Store 6
Store 3
Store 10
Store 8
Store 4
Store 5
Store 7
Store 10
Store 1
Store 2
Mon Tue Wed Thu Fri Sat Sun

29. Venues by NPS • Venues which choose to
send out a Net Promoter
Score Survey can measure
the satisfaction of their
visitors and compare their
score against other
locations within a company
• Of the 10 most popular
locations in terms of visit
numbers, Store 1 has the
highest NPS score of 82 –
with 4/10 of the most
popular venues having
higher than average NPS
• Store 10 has the lowest
score, at 52 – indicating
that there are issues faced
by customers that are
effecting their satisfaction –
issues that might not occur
at venues with higher
scores
average
0
10
20
30
40
50
60
70
80
90
Store 6 Store 3 Store 10 Store 8 Store 4 Store 5 Store 7 Store 10 Store 1 Store 2

30. Starbucks USA

31. Potential Data Figures • Potential for over 400
million distinct Wi-Fi
logins across over
14,000 venues in the
US
• Tracking of unique
users and their visit
habits
• Can also utilise
Presence and
Location to better
understand the
behaviour of visitors
who don’t login –
including Conversion
and Bounce rates
• Based on figures of 950,00 customer logins over 9 months across 45
venues, each venue had an average of around 21,000 Wi-Fi logins – a full
year would result it around 28,000 logins per venue
• Across 14,300 distinct venues across the US extrapolating similar figures, a
conservative estimate would be around 400 million Wi-Fi logins over a
single year period
• Using cross-pollination, visitor behaviour between stores can be tracked to
analyse customer movement across a geographic area, identifying how
visitors interact between stores and how this is influenced by commuting,
weekday/weekend behaviour etc

32. Conclusions
• A better understanding of who your customers are will be beneficial to
business – and by considering different facets, such as when visits occur,
who is making those visits, and how this differs by venue – provide the
insights to create a more meaningful and personalised customer
interaction
• The analysis of data and the insights provided by Purple allow the
identification of trends at a company-wide, geographic or individual
location level – in the context of the wider market and future
opportunities
• This allows Purple users to provide a better overall customer experience,
resulting in increased engagement, interaction, spend, satisfaction and
overall retention
• Potential for over
hundreds of thousands
of new customer
records per year,
depending on scope
• Opportunity to better
engage with customers
via email and social
media and provide
offers and services
most relevant to them
• Understand who your
customer are and what
they want