Using web analytics data to understand and then leverage your seasonalities as an opportunity to grow your online and offline business.

1. How to use Web Analytics to
leverage seasonality and grow
your business?
1

2. 2

3. AGENDA
1. What & Why?
2. Where in Google Analytics?
3. How to leverage (Marketing & Business)?
4. Automate with technology
3

4. 1.
What is
seasonality?
Why does it
matter?
4

5. Recurring trend on a given period
Week
Month
Year 5

6. Unique for each sector & business
Gift:
Travel:
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7. Opportunities?
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8. Opportunities?
High
Seasonality
€€€
Low
Seasonality
€
Time Revenue
1. Maximize & get prepared for high seasonality periods
2. Increase revenue by understanding micro-seasonalities
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9. 2.
Where to find
seasonality in
web analytics?
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10. 4 main sources to understand seasonality
A. Sector trends
B. Searches, visits and time lag
C. Sales and goals
D. Crossing online interactions with offline sales
10

11. A. Sector trends
Industry seasonality?
What about your own seasonality?
Key events?
Events’ dynamics (start/end)?
...
11

12. A. Sector trends higher conversion rate
Case: Gift during high seasonality
120000 6000
100000 5000
80000
Visits Sales 4000
60000 3000
40000 2000
20000 1000
0 0
28/02/2011 31/03/2011 30/04/2011 31/05/2011 30/06/2011 31/07/2011 31/08/2011 30/09/2011 31/10/2011 30/11/2011 31/12/2011 31/01/2012
Case: Retail
25,000 Visits remain higher 3 weeks post- 1000
event but goals start decreasing right
900
20,000 800
Visits & goals increase 2 to 3 weeks after the event
700
before influent event in the sector
15,000 Key 600
500
10,000
event 400
300
5,000 200
100
0 0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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13. B. Searches, Visits and Time Lag
Key peaks/downs in visits/searches?
Duration of peaks?
Evolution of branded traffic?
Gap between visits and sales?
...
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14. B. Searches, Visits and Time Lag
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15. C. Sales and Goals
Share of revenue during high seasonality?
Key vs. long-tail products?
Key periods per product?
Increase of conversion rate?
...
15

16. C. Sales and Goals
KPI example: Weekly share of revenue from Product / revenue of all products
Product A
w1
w51 w52
6.0%
w2 w3
w50 w4
w49 w5
w48 5.0% w6
w47 w7
Week 46: w46 4.0% w8
w45 w9
Product A sells well w44 3.0% w10
compared to all w43
2.0%
w11
other products w42 w12
1.0%
w41 w13
w40 0.0% w14
w39 w15
w38 w16
w37 w17
w36 w18 Week 19:
w35 w19
w34 w20 Low seasonality for
w33
w32
w21
w22
product A
w31 w23
w30 w24
w29 w28 w26 w25
w27
Good seasonality of product A: all around green circle 16
Low seasonality of product A: all around red circle

17. D. Crossing online interactions with offline sales
Similar trends online/offline?
Visits Online, Purchase Offline (VOPO)?
Time between Online and Offline visits?
Product placement?
...
17

18. D. Crossing online interactions with offline sales
january february march april may june july august september october november december
pageviews on site
Hats
revenue in stores
pageviews on site
Shirts
revenue in stores
pageviews on site
Shoes
revenue in stores
pageviews on site
Socks
revenue in stores
pageviews on site
T-Shirts
revenue in stores
pageviews on site
Bags
revenue in stores
Investigate? Up-sell? Promote? Placement in store?
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19. 3.
How to leverage
seasonality
data?
19

20. A few concrete applications
MARKETING Considerations
Adapt online & offline media planning
Influence communication strategy
Product placement (online+offline)
Better allocate marketing budgets
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21. A few concrete applications
BUSINESS Considerations
Optimize logistics and stock management
Avoid bottle-necks
Plan technical maintenance and new launch
Push specific products or up-sells
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22. The financial impact of leveraging seasonality
A Semetis Client Success Story
Set of products for which Set of products for which
seasonality was leveraged seasonality was not leveraged
1400 1400
1200 1200
1000 1000
800
600
+35% 800
600
+10%
400 400
200 200
0 0
revenue Y Revenue Y+1 revenue Y Revenue Y+1
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23. 4.
Can it all be
simplified or
automated?
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24. Bring a little tech in the process
Rising trends per product (visits + sales)
Automate alerts & dashboards
Integrate external BI data
Comparison/BI – links between patterns
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25. Automate seasonality dashboards
Product 1 Product 2
w36 w36
w35
w34 6.0 w37w38 w35
w34 6.0 w37w38
w33 w39 w33 w39
w32 w40 w32 w40
w31 w41 w31 5.0 w41
w30 4.5 w42 w30 w42
w29 w43 w29 4.0 w43
w28 w44 w28 w44
w27 3.0 w45 w27 3.0 w45
w26 w46 w26 2.0 w46
w25 1.5 w47 w25 w47
w24 w48 w24 1.0 w48
w23 0.0 w49 w23 0.0 w49
w22 w50 w22 w50
w21 w51 w21 w51
w20 w52 w20 w52
w19 w1 w19 w1
w18 w2 w18 w2
w17 w3 w17 w3
w16 w4 w16 w4
w15 w5 w15 w5
w14 w6 w14 w6
w13 w7 w13
w12 w7
w12
w11 w9 w8 w11 w9 w8
w10 w10
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26. Use the API to create seasonality apps
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27. Use the API to create trends apps
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28. To conclude
1. Leverage web analytics data
2. Prepare for high seasonality periods
3. Leverage API’s to identify micro-seasonalities
4. Build dashboards to take action at all business levels
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