Google analytics-seasonality

1,524 views
1,332 views

Published on

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

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,524
On SlideShare
0
From Embeds
0
Number of Embeds
6
Actions
Shares
0
Downloads
2
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Google analytics-seasonality

  1. 1. How to use Web Analytics toleverage seasonality and grow your business? 1
  2. 2. 2
  3. 3. AGENDA1. What & Why?2. Where in Google Analytics?3. How to leverage (Marketing & Business)?4. Automate with technology 3
  4. 4. 1. What isseasonality?Why does it matter? 4
  5. 5. Recurring trend on a given period Week Month Year 5
  6. 6. Unique for each sector & business Gift: Travel: 6
  7. 7. Opportunities? 7
  8. 8. Opportunities? High Seasonality €€€ Low Seasonality € Time Revenue1. Maximize & get prepared for high seasonality periods2. Increase revenue by understanding micro-seasonalities 8
  9. 9. 2.Where to findseasonality inweb analytics? 9
  10. 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. 11. A. Sector trendsIndustry seasonality?What about your own seasonality?Key events?Events’ dynamics (start/end)?... 11
  12. 12. A. Sector trends higher conversion rateCase: 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 31/01/2012Case: 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 12
  13. 13. B. Searches, Visits and Time LagKey peaks/downs in visits/searches?Duration of peaks?Evolution of branded traffic?Gap between visits and sales?... 13
  14. 14. B. Searches, Visits and Time Lag 14
  15. 15. C. Sales and GoalsShare of revenue during high seasonality?Key vs. long-tail products?Key periods per product?Increase of conversion rate?... 15
  16. 16. C. Sales and GoalsKPI 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 w27Good seasonality of product A: all around green circle 16Low seasonality of product A: all around red circle
  17. 17. D. Crossing online interactions with offline salesSimilar trends online/offline?Visits Online, Purchase Offline (VOPO)?Time between Online and Offline visits?Product placement?... 17
  18. 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 siteShoes revenue in stores pageviews on site Socks revenue in stores pageviews on siteT-Shirts revenue in stores pageviews on site Bags revenue in stores Investigate? Up-sell? Promote? Placement in store? 18
  19. 19. 3.How to leverage seasonality data? 19
  20. 20. A few concrete applications MARKETING ConsiderationsAdapt online & offline media planningInfluence communication strategyProduct placement (online+offline)Better allocate marketing budgets 20
  21. 21. A few concrete applications BUSINESS ConsiderationsOptimize logistics and stock managementAvoid bottle-necksPlan technical maintenance and new launchPush specific products or up-sells 21
  22. 22. The financial impact of leveraging seasonalityA 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 22
  23. 23. 4.Can it all besimplified orautomated? 23
  24. 24. Bring a little tech in the processRising trends per product (visits + sales)Automate alerts & dashboardsIntegrate external BI dataComparison/BI – links between patterns 24
  25. 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 25
  26. 26. Use the API to create seasonality apps 26
  27. 27. Use the API to create trends apps 27
  28. 28. To conclude1. Leverage web analytics data2. Prepare for high seasonality periods3. Leverage API’s to identify micro-seasonalities4. Build dashboards to take action at all business levels 28

×