SOLOMO transforms your retail space into an information-driven environment. These dashboards provide marketing and store managers with real-time analysis on actual customer behavior.

1. Smart Location PlatformLocation Analytics &Mobile Engagement withPermission
HOLIDAY, 2013
LOCATION ANALYTICS
DATA EXAMPLE

2. 2
CONFIDENTIAL | Use Cases -Summary
•Match department visitor counts and dwells with sales associate schedules
•Provide sales associates with cross training to match other regions their dept. customers have likely visited
•Respond in real time to large numbers of customers in certain areas
•In-store offers contextualized
•How effective are my sales in driving visitor counts, dwell times, and purchases?
•Present offers based on the recipient's preferences
•The store offers you rewards in exchange for sharing.
•Optimize Merchandising
•Which regions are performing well?
•Which regions are slipping?
•Do I have enough store associates?
•What are the peak traffic hours?
•Smart Staffing
•Staff the right type of people at the right time
•Region-specific traffic numbers
Workforce & Productivity
Customer Service
Marketing & Advertising

3. 3
CONFIDENTIAL | Measure Marketing Performance
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
11/21/2013
11/22/2013
11/23/2013
11/24/2013
11/25/2013
11/26/2013
11/27/2013
11/28/2013
11/29/2013
% Repeat Visitors
Typical repeat visitor ratio is between 30 and 40 percent in the week prior to Friday the 29th.
On the Black Friday promotional day, repeat visitors make up only 21% of the traffic.
Did Holiday Promotions Bring in New Customers?

4. 4
CONFIDENTIAL | Understand Where Loyal Customers Spend Their Time
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1
2
3
4
5
6
7
8
9
10
Region Share of Visitors by Number of Repeat Visits
Men's
Misses Contemporary
Accessories
Boys
Juniors
Madison
Maternity
Shoes
Ladies Outerwear
Entrance Right
Misses
Intimate
Young Men's
Presence Area
Seasonal
Bedding
Men's Active
Luggage
Womens
Entrence Left
How can a marketer understand which regions are most popular with repeat customers?

5. 5
CONFIDENTIAL | Measure Return Visitors For Days, Weeks or More
March Return Shoppers
Accessories
60%
Misses Contemporary
60%
Men's
58%
Boys
56%
Infants & Toddlers
53%
Maternity
53%
Juniors
52%
Electronics & Cookware
52%
Ladies Outerwear
52%
Young Men's
51%
Luggage
51%
Misses
50%
Seasonal
49%
Girls & Petites
49%
Bedding
49%
Intimate
48%
Men's Active
48%
Bath & Home
47%
Shoes
47%
Womens
43%
Which departments drew the highest % of repeat store visitors?
Understand customer loyalty based on repeat visitor trends

6. 6
CONFIDENTIAL | Daily Detail Of Customer Behavior
0
50
100
150
200
250
300
350
400
6:30
6:45
7:00
7:15
7:30
7:45
8:00
8:15
8:30
8:45
9:00
9:15
9:30
9:45
10:00
10:15
10:30
10:45
11:00
11:15
11:30
11:45
12:00
12:15
12:30
12:45
13:00
13:15
13:30
13:45
14:00
14:15
14:30
14:45
15:00
15:15
15:30
15:45
16:00
16:15
16:30
16:45
17:00
17:15
17:30
17:45
18:00
18:15
18:30
18:45
19:00
19:15
19:30
19:45
20:00
20:15
20:30
20:45
21:00
21:15
21:30
21:45
22:00
22:15
22:30
22:45
23:00
23:15
23:30
23:45
Daily Traffic Flow, 30 min increments
Accessories
Bedding
Boys
Entrence Left
Entrance Right
Intimate
Juniors
Ladies Outerwear
Luggage
Maternity
Men's
Men's Active
Misses
Misses Contemporary
Seasonal
Shoes
Womens
Young Men's
How did daily traffic compare by region?

7. CONFIDENTIAL | 7
Drive Staffing Decisions
Region Days on Top*
Men's Active 6
Accessories 3
Shoes 2
Luggage 2
Ladies Outerwear 1
“I’d like to position staff in the areas of the store where customers are spending the most
time. What are top 2 most engaging regions?”
*count of days with the highest average duration of stay
The Men’s Active region is way out ahead of the pack – increase
associates to this department? If so, what time of day?
Note: day-of-week patterns and traffic to related regions are also available to this store manager via SOLOMO Exchange
Hour Ending At: AM sadtisoorn,e W mI anMaatgerenirt ywantMse tno's staff thMeen r'si Agchtivte peoMpisslees, in theM risisgesh Cto ndteemppoSareararstyomnael nt, aSth tohese right tWimomeen sof dayY.oung Men's
11:00 115 32 152 13 12 152 84 24 3 21
12:00 146 40 189 15 14 189 89 33 3 26
13:00 143 34 157 14 16 157 92 27 4 22
14:00 162 40 176 14 15 176 107 36 4 26
15:00 162 37 184 16 17 184 105 34 5 27
16:00 150 39 173 17 17 173 110 34 4 28
17:00 147 32 168 18 13 168 88 32 3 27
18:00 140 38 172 18 11 172 86 35 4 29
19:00 120 40 158 16 12 158 71 36 3 28
20:00 103 33 134 15 11 134 65 29 3 22
21:00 86 30 117 12 8 117 58 23 3 18
22:00 59 28 91 8 7 91 45 20 2 15
Hour Ending At: Madison, WI Accessories Bedding Boys Entrence Left Entrance Right Intimate Juniors Ladies Outerwear Luggage
11:00 115 75 22 63 8 29 6 35 29 7
12:00 146 103 31 90 14 32 10 45 28 7
13:00 143 87 28 77 14 29 13 41 31 7
14:00 162 101 25 78 15 28 12 45 35 7
15:00 162 98 28 79 15 35 14 47 37 8
16:00 150 94 27 69 15 28 13 44 29 8
17:00 147 86 27 72 11 36 12 43 23 8
18:00 140 94 23 74 12 34 11 44 23 9
19:00 120 78 24 74 9 29 8 40 22 9
20:00 103 61 21 55 9 28 8 35 21 7
21:00 86 51 19 49 7 25 7 29 18 6
22:00 59 41 16 46 5 15 4 19 15 5
Men’s Active
Rank Departments Based on Popularity

8. 8
CONFIDENTIAL | Identify Emerging Cross-Shopping Patterns
Region-to-Region, Starting in Shoes
12/1 to 12/7
12/8 to 12/14
12/15 to 12/21
12/22 to 12/28
12/29 to 1/4
1/5 -1/11
1/12 -1/18
1/19 -1/25
Accessories
16%
18%
20%
20%
16%
11%
10%
11%
Bedding
8%
9%
7%
5%
3%
2%
2%
3%
Boys
15%
16%
17%
17%
15%
10%
10%
10%
Intimate
2%
2%
2%
2%
2%
1%
1%
1%
Juniors
11%
11%
13%
13%
12%
7%
7%
8%
Ladies Outerwear
5%
5%
5%
5%
5%
3%
3%
3%
Luggage
5%
7%
6%
3%
3%
7%
3%
2%
Maternity
5%
6%
7%
7%
4%
3%
2%
3%
Men's Active
5%
5%
6%
6%
5%
6%
5%
4%
Men's
69%
66%
70%
71%
72%
69%
59%
58%
Misses
4%
4%
4%
4%
3%
2%
2%
3%
Misses Contemporary
14%
13%
15%
14%
14%
8%
8%
9%
Seasonal
18%
20%
21%
18%
15%
13%
20%
30%
Womens
1%
1%
1%
1%
1%
0%
0%
1%
Young Men's
10%
11%
13%
12%
11%
9%
8%
8%
Total
189%
193%
206%
196%
181%
151%
142%
153%
Looking at Visitors to “Shoes”, which other regions did they visit?

9. Smart Location PlatformLocation Analytics & Mobile Engagement withPermission
HOLIDAY, 2013
LOCATION ANALYTICS
DATA EXAMPLE