Leader Global de SaaS dans le Marketing relationnel

Sebastiano Cappa, Sales Director

Stefano Luceri, Presales Consultant

IAB FORUM 2012

Data is King

I dati sui clienti sono il cuore della

strategia marketing

Il leader mondiale nel SaaS per il marketing relazionale

Perchè il dato è importante ?

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Ci conosce bene ?

Il potere della personalizzazione

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“We have 6.2 million customers…..we should have

6.2 million stores”

Jeff Bezos, Founder & CEO, Amazon

Esempi di domande da porsi

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● Chi sono i vostri repeaters e di che tipo sono ?

● Quali clienti non hanno più comprato da noi negli ultimi mesi ? Perchè ?

● Quali sono gli utenti che hanno acquistato il prodotto x negli ultimi mesi ?

…e cosa compreranno da noi nei prossimi mesi?

● Quali sono gli utenti «Silver» che sono diventati di tipo «Gold» negli

ultimi mesi ? Quanti lo diventeranno?

● Quali sono gli utenti che hanno acquistato i prodotti x e y ma non z ?

● Chi sono gli utenti che stanno per comprare il prodotto z ?

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Come molte persone vedono i dati …

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How marketers see data

Come si dovrebbero vedere i dati

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Purchased designer shoes last month

Member of the loyalty program in Boston

Signed up for the newsletter, checked a preference for sports

Male and bought a winter jacket 4 months ago

Opened the last 3 email campaigns Click-through on the last

email campaign

Purchased 2 tickets to Shanghai in 2011

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“La sfida principale per un email

marketing efficace è quella di

focalizzarsi sui clienti e

raggiungerli con un contenuto

rilevante”

MarketingSherpa’s 2011 Email Marketing Report

La sfida – Trasfomare i dati in informazioni preziose

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Data

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Rules-based Algorithm Based on pre-defined criteria, rules-based

behavioral targeting leverages historical,

environmental, and behavioral data to inform

the delivery of content and promotions.

Powered by predictive models,

algorithm-based behavioral targeting

monitors visitor reactions to website content

and applies statistical techniques to learn

which treatments are most likely to produce

a desired response.

Based on pre-defined criteria, rules-based

behavioral targeting leverages historical,

environmental, and behavioral data to inform

the delivery of content and promotions.

Powered by predictive models,

algorithm-based behavioral targeting

monitors visitor reactions to website content

and applies statistical techniques to learn

which treatments are most likely to produce

a desired response.

Segment level Visitor level Segment level Visitor level

Rules-based targeting works best in

circumstances when visitors can be

meaningfully divided into unique segments.

Moreover, rules-based behavioral targeting is

useful when exposing customers to cross-sell

and upsell opportunities by sharing relevant

content and promotions based on previous

customer viewing and purchase history.

Capable of quickly identifying visitor

microsegments, this technique is ideal for

situations where there are a large number of

overlapping treatment options for site visitors.

It is also effective at adapting to complex or

evolving response patterns that are not easily

described by static or high-level rule sets.

Rules-based targeting works best in

circumstances when visitors can be

meaningfully divided into unique segments.

Moreover, rules-based behavioral targeting is

useful when exposing customers to cross-sell

and upsell opportunities by sharing relevant

content and promotions based on previous

customer viewing and purchase history.

Capable of quickly identifying visitor

microsegments, this technique is ideal for

situations where there are a large number of

overlapping treatment options for site visitors.

It is also effective at adapting to complex or

evolving response patterns that are not easily

described by static or high-level rule sets.

The manual nature of creating,

implementing, and maintaining targeting

rules is resource intensive.

• Rules identify broad groups of visitors rather

than individual visitors.

• This technique is not self-learning or

inherently iterative.

• Complex and proprietary targeting

techniques often operate as a “black box,”

providing minimal transparency to users.

• Requires a “training” period while the

targeting engine observes visitor behavior

and assembles the targeting model.

• Requires a minimum volume of site traffic

and conversions to develop statistically

significant targeting models.

• Adequately implementing and monitoring

this targeting technique may require firms

to hire or partner for advanced analytics skills.

The manual nature of creating,

implementing, and maintaining targeting

rules is resource intensive.

• Rules identify broad groups of visitors rather

than individual visitors.

• This technique is not self-learning or

inherently iterative.

• Complex and proprietary targeting

techniques often operate as a “black box,”

providing minimal transparency to users.

• Requires a “training” period while the

targeting engine observes visitor behavior

and assembles the targeting model.

• Requires a minimum volume of site traffic

and conversions to develop statistically

significant targeting models.

• Adequately implementing and monitoring

this targeting technique may require firms

to hire or partner for advanced analytics skills.

Adobe Systems, Amadesa, Barilliance,

Get Smart Content, LivePerson, Maxymiser,

Monetate, Personyze, SDL, Sitecore, Sitespect,

Webtrends, X Plus One

Adobe Systems, Amadesa, Maxymiser, SDL,

SiteSpect, X Plus One

Adobe Systems, Amadesa, Barilliance,

Get Smart Content, LivePerson, Maxymiser,

Monetate, Personyze, SDL, Sitecore, Sitespect,

Webtrends, X Plus One

Adobe Systems, Amadesa, Maxymiser, SDL,

SiteSpect, X Plus One

Il valore della Conoscenza per una

Focalizzazione Mirata

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Email Generica Email Personalizzata Marketing 1-1

Ric

avi

I prodotti di Emailvision

Campaign Commander

Enterprise Edition

Customer Intelligence

Email e Mobile Marketing

Social Media Marketing

Predictive

Intent

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Approccio

Customer

Intelligence

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Il Potere della Visualizazione della rules-base

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Customer Intelligence

• Analisi grafica dei dati

• Analisi per “catena di idee”

• Scoprire opportunità nei dati

• Creare segmenti con un click

• Passare dall’analisi all’azione

Il Ciclo di Vita del Cliente

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Acquisizione Maturazione Attivo Abbandono Dormiente /

Inattivo

Segmento RFM

silver

VIP

Segmento di

abbandono

Beaute Privee – Cross / Upsell

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Upselling e cross selling

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Approccio

Analisi Predittiva

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Approccio Algorithm-base => Contenuto personalizzato in automatico

Ecommerce Emails

Personalizzazione in tempo reale

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Utente alla 1a visita Profilo dal browser Utente conosciuto

Sfruttiamo le

keyword, key

phrase or URL

che l’utente ha

utilizzato per

arrivare al sito

Osserviamo

dove clicca sul

sito

Sfruttiamo tutte

le informazioni

memorizzate

sulle visite

precedenti,

anche carrelli

abbandonati, …

Sfruttiamo tutte

le informazioni

precedenti,

anche le

preferenze (size,

brand, …) , gli

interessi attuali

mostrati

dell’utente su

dove clicca nelle

email e nelle

pagine del sito,

…

Esempio su una pagina di home page

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100% circa

di revenue

uplift sulla

singola

pagina

Quali sono i

risultati della

Customer

Intelligence ?

www.emailvision.it

Caso di successo - Enterprise Edition

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1.39%

4.43%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

4.50%

5.00%

Tasso di click %

Non-CI

CI

Caso di successo - Enterprise Edition

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0.0118%

0.0472%

0.0000%

0.0050%

0.0100%

0.0150%

0.0200%

0.0250%

0.0300%

0.0350%

0.0400%

0.0450%

0.0500%

Conversione %

Non-CI

CI

Quali sono i

risultati

dell’analisi

predittiva ?

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Testimonial

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“Serving relevant

recommendations to customers

are a particular challenge when

dealing with a product as

personal as jewellery.

By combining comprehensive

logic capabilities, search

enhancements and a

deployment that is as simple as

installing a Magento extension,

PredictiveIntent provides the

ideal personalisation platform for

us.”

Dan Coleman, IT and Web

Manager, Astley Clarke

Risultati

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• 16% riduzione del numero di click prima di arrivare all’acquisto

• 60% incremento di prodotti comprati

• Riduzione del lavoro di 1 persona precedentemente occupata ed ora automatizzato

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