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http://liviutaloi.ro http://twitter.com/ltaloi http://www.linkedin.com/in/Liv iuTaloi http :/ /facebook.com/ltaloi / Guerrilla Marketing Techniques for e- Commerce using Google Analytics some case studies about showing the right message to the right audience in the right moment 23.01.2014

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Page 1: Http://liviutaloi.ro    Guerrilla Marketing Techniques for e-Commerce

http://liviutaloi.rohttp://twitter.com/ltaloihttp://www.linkedin.com/in/LiviuTaloihttp://facebook.com/ltaloi/

Guerrilla Marketing Techniques for e-Commerce using Google

Analytics

some case studies about showing the right message to the right audience in the right moment

23.01.2014

Page 2: Http://liviutaloi.ro    Guerrilla Marketing Techniques for e-Commerce

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Co-Founder of the most important e-commerce resource in Romania

Community Manager for the biggest e-commerce group/event in Romania

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GMT for e-Commerce using GAWorkshop Agenda

• 04: Introduction• 12: 1st Experiment (do-it-yourself)• 25: 2nd Experiment (Vibetrace)• 39: 3rd Experiment (Marketizator)

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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

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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

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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

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One possible approach to personalization

• WANT– Survey->Analytics

• WISH– Platform->Analytics

• BUY– Platform + Analytics

• VISIT– Analytics

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More actionable

Less actionable

Less info(old)

More info(new)

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One possible approach to personalization

Shopping Sessions

week1 week2 week3 week4 week5 week6

Product 1 XXSSS SX XX SB

Product 2 XX SS SS X SS XXSB

Product 3 SS XSSB

X = visit outside your site, S = visit on your site, B = buy

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Shopping sessions: often interrupted, multi-device, multi-location, multi-channel

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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)

• .

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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...

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Experiment 1 – implemented into the ecommerce platform by their developers

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#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

• .

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#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)– .

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#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• .

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#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..

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#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)

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#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

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#1 – do it yourself; current stage

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#1 – do it yourself; current stage

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

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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

• .

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VIBETRACE – (1) new subscriber

• For any visitor we don’t recognize (we don’t know his email address)

• Rules to display the email collector

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VIBETRACE – (1) new subscriber

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• December, total addresses = 1800• 9% conversion rate (Popup display -> subscriber)• 7000 daily visits on average

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VIBETRACE – (1) new subscriber

• email retargetingpossible campaigns

• trigger event puts a schedule after 1 day if the following conditionswill be met

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VIBETRACE – (1) new subscriber

• Remarketing after subscribing to newsletter

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trigger event

first condition, must viewed at least one product page

second condition, did not purchase after trigger event

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VIBETRACE – (1) new subscriber

Cart abandonment + Remarketing after NL

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VIBETRACE – (1) new subscriber

• Results for transactional (emails) campaigns• Average ecommerce conversion rate = 0.67%

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(2) onsite recommendations

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(3) newsletter recommendations

• Without VT

• With VT

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

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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• .

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MARKETIZATOR - interaction

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MARKETIZATOR - interaction

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MARKETIZATOR - interaction

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MARKETIZATOR - interaction

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MARKETIZATOR – A/B Testingexperiment V2 + segment direct new

• Version A(#2)

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MARKETIZATOR – A/B Testingexperiment V2 + segment direct new

• Version B(#3-2)

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MARKETIZATOR – A/B Testingexperiment V2 + segment direct new

• CR = CTRL > B – 2.26% vs 2.16%...• AOV = CTRL < B – bigger revenue

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Thank You!

Send me an e-mail for this presentation.

• http://LiviuTaloi.ro• [email protected]; [email protected]• I am active on Twitter, Facebook, G+ etc.

–ECOMpedia.ro– http://twitter.com/ltaloi– http://www.facebook.com/ltaloi – http://slideshare.net/ltaloi/

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Not enough time spent on questions?Did you have more questions on your list?

Go online on ECOMpedia.ro and ask questions that you want answered

usability, marketing, seo/ppc, e-commerce

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About Liviu TALOI• Liviu Taloi was part of the team who worked for the most powerful Romanian online store –

eMag.ro during the first years of existence, where he was a PR Manager for 3 years.

• Since 2005-2006, Taloi founded the company WebAudit specialized in providing consultancy in electronic-commerce for Romanian online shops. As a consultant, Taloi was practically a Project Manager for each client, his main activities regarding: analyzing and solving usability and functionality problems, growing and optimizing conversion rate, testing consumers behavior with website optimizer tools, administrating all the departments of an online shop and creating online advertising/marketing campaigns, measuring the return of investment and other KPI’s. 

• Together with Andrei Radu, Liviu Taloi developed the methodology and organized the usability tests for Romanian eCommerce Awards Festival (GPeC) since 2007 until now (first ones held in Romania). Based on international standards, the purpose of usability tests was to identify main problems faced by regular users when they are trying to buy something online. 

• In the last years, Liviu Taloi offered consultancy to more than 100 Romanian online shops. Most of them are now leaders on their market niche. 

• Except consultancy and running usability tests, Taloi is also a speaker at conferences and e-commerce workshops, being very appreciated by audience. He also writes articles and market analysis for different online and printed publications. In 2008, Liviu Taloi was lecturer at the first Romanian E-Commerce workshop called “iSell” and also he was lecturer at the first Online workshops called “WebSell” – a series of 7 webinars regarding major problems of an online shop. 

• Taloi was part of the team who developed the E-Commerce Study requested by ANCOM (government agency) in October 2008 and the Romanian E-Commerce Study in 2006 at the request of MCTI (ministry of IT&C). He repeated the study with private funding in 2010.

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