Co-Founder of the most important e-commerce resource in Romania Community Manager for the biggest e-commerce group/eve

1. Guerrilla Marketing Techniques for e-Commerce using Google Analyticssome case studies about showing the right message to the right audience in the right moment23.01.2014

2. 2/55 Co-Founder of the most important e-commerce resource in Romania Community Manager for the biggest e-commerce group/event in Romania

3. 3/55 GMT for e-Commerce using GAWorkshop Agenda • 04: Introduction • 12: 1st Experiment (do-it-yourself) • 25: 2nd Experiment (Vibetrace) • 39: 3rd Experiment (Marketizator)

5. Introduction • CRO is not the Holly Grail • Showing the right message to the right audience in the right moment is the key to success – personalization • Track / investigate / segment / hypothesis / test / evaluate kpi’s / choose(challenge) the winner – always be testing • Always compute the cost of experiment – the smallest, yet profitable segment

6. Personalization Profitability • 1 Fashion site: 1.8% ecommerce conversion rate, 33 euros AOV, 10,000 users/day • You want to improve a segment of 1000 users/day, 0.5% conversion rate, 30 euros AOV • Expected improvement = 15%, 0.5->0.575% conversion rate • Expected +revenue = 630 euro/month • Margin 30% => 189 euro/month • Costs (developers/tool) = 150 – 450 – 950 euro/month

7. One possible approach to personalization • Data about the user needs/desires –surveys + forms: ask them what they want to see, what they want to receive on an email/newsletter – direct answer • Data about what they wish to buy – wishlists + abandoned carts – direct action (close to buy) • Data about they are currently buying – the orders, the products shipped. • Data about their search process, their clickstream navigation through the web shop

8. One possible approach to personalization • WANT • Survey->Analytics • WISH • Platform->Analytics • BUY • Platform + Analytics • VISIT • Analytics More actionable Less actionable Less info (old) More info (new)

9. One possible approach to personalization Shopping sessions: often interrupted, multi-device, multi-location, multi-channel

10. One possible approach to personalization • You may monitor top 3 category + top 3 brands + 3 price intervals / category as the user browse the site • The rating score for each category is naturally decaying (vibetrace) • Different actions have different weights on the score (the order matter also) • .

11. 3 experiments • 3 months ago I started to prepare some experiments • 1 experiment is done using the team of programmers behind the ecommerce platform • 2 experiments are done using 2 CRO tools developed in our country • we are still collecting data… and changing things...

12. Experiment 1 – implemented into the ecommerce platform by their developers

15. #1 – do it yourself • Stage 1 – analyze, segment • Stage 2 – prototyping the algo, making 1st changes on some content areas (site, newsletters) • Stage 3 – analyze 1st results, changes on the algo, starting again from stage 1 • .

16. #1 – do it yourself; Stage 1 • Establish what info do you need for segmentation – granulation level user • category affinity • price interval for top categories • brand affinity • surveys for the “want” (categs + brands) • some demographics • amounts spent + number of orders • userid + mailid + clientid (not UA) • .

17. #1 – do it yourself; Stage 1 • You have to figure how to see this info into analytics, into your reports (custom vars, UA: custom dimensions) • Then you may segment all users that have a strong affinity with “Orient watches for women”. • Note that the segment is dynamic • .

18. #1 – do it yourself; Stage 2 • Content to change: Newsletter • Export a list of category affinity for each email in your database from your analytics/platform • Create an universal newsletter with boxes dedicated to each main category (10-12 categories) • At the time when the newsletter is distributed one user may receive box1 and box5, other user may receive box 3 and box 7..

20. #1 – do it yourself; Stage 2 • Content to change: Website • An user is entering the site, having the personalization cookie in place • Based on categories score you show a widget with products in the category with a high score, in a price interval suitable for that user • If the user is new you can show him some tops (top sales, top rated, new products)

21. #1 – do it yourself; Stage 3 • You have a segment defined in your analytics software • You already showed some personalized content to that segment, and that widget is leaving some trails into analytics to monitor • You look at some KPI’s and make changes: algo, segment, etc

22. #1 – do it yourself; current stage

23. #1 – do it yourself; current stage

25. Experiment 2 – using a tool for CRO, personalization: VIBETRACE

27. VIBETRACE • They are computing a score for top X Categories, score which is decreasing in time without any activity from user • They are using widgets for website and for newsletter – easier implementation • They have some big clients in Romania, they are still working to improve the product • .

29. VIBETRACE – (1) new subscriber • For any visitor we don’t recognize (we don’t know his email address) • Rules to display the email collector

30. VIBETRACE – (1) new subscriber • December, total addresses = 1800 • 9% conversion rate (Popup display -> subscriber) • 7000 daily visits on average

31. VIBETRACE – (1) new subscriber • email retargetingpossible campaigns • trigger event puts a schedule after 1 day if the following conditionswill be met

32. VIBETRACE – (1) new subscriber trigger event • Remarketing after subscribing to newsletter first condition, must viewed at least one product page second condition, did not purchase after trigger event

33. VIBETRACE – (1) new subscriber Cart abandonment + Remarketing after NL

34. VIBETRACE – (1) new subscriber • Results for transactional (emails) campaigns • Average ecommerce conversion rate = 0.67%

35. (2) onsite recommendations

38. (3) newsletter recommendations • Without VT • With VT

39. Experiment 3 – a tool for CRO, real-time marketing: MARKETIZATOR

41. MARKETIZATOR • Specialized on real time marketing, using interactions (popups, content layer) based on rules, A/B testing, surveys • They managed to rise conversion rates to some of the well known online shops in Romania • They are improving the product constantly • .

43. MARKETIZATOR - interaction

44. MARKETIZATOR - interaction

45. MARKETIZATOR - interaction

46. MARKETIZATOR - interaction

47. MARKETIZATOR – A/B Testingexperiment V2 + segment direct new • Version A(#2)

48. MARKETIZATOR – A/B Testingexperiment V2 + segment direct new • Version B(#3-2)

49. MARKETIZATOR – A/B Testingexperiment V2 + segment direct new • CR = CTRL > B – 2.26% vs 2.16%... • AOV = CTRL < B – bigger revenue

50. Thank You!Send me an e-mail for this presentation. • http://LiviuTaloi.ro • Liviu.Taloi@SiteAudit.ro; Liviu@LiviuTaloi.ro • I am active on Twitter, Facebook, G+ etc. • ECOMpedia.ro • http://twitter.com/ltaloi • http://www.facebook.com/ltaloi • http://slideshare.net/ltaloi/