stefano lena - webtrekk summit 2017
TRANSCRIPT
“How the Supermarket Industry Uses Data to Build Digital Loyalty”Stefano Lena
VP Sales & Marketing – Contactlab
Digital pioneers in Italy since 1998
We provide the leading and easy to use Engagement Marketing platform for
lifestyle and retail brands
First Italian ESP in 2001 with a proprietary SaaS platform to send email, sms and fax
In 2010 start our Data-Driven journey
Thanks to our proprietary technology and our integrated marketing services, we
enable brands to drive sales and increase customer lifetime value by transforming
customer data in personal engagement
Trusted by world-class brands and more than 1000 clients worldwide
18.05.2017 Webtrekk SUMMIT 17&18 May 2017 2
Contactlab in brief
The data-driven and customer-centric engagement platform
* Luxury Goods reports, 2014-2016 Contactlab and Exane BNP Paribas - The analysis was created by examining a panel of selected qualified industry players as a
sample, starting from which valid simulations were made for the entire luxury market.
Transactional Data
Dynamic Data
360° Extended Customer Profile
Personal Data
Transactional: Sales
transactions, Customer care
interactions
Behavioral and
Psychographic: any
digital and physical
interaction, survey, etc..
Socio-Demographic data
Our technology enable personalized digital conversations
influencing up 20% instore and up to 50% of e-commerce sales*
The Client fidelity program is 100% digital (no physical cards)
Customer get rewarded not only for shopping
Digital interactions get rewarded too!
Webtrekk enable tracking and analytics on all the web interactions
Contactlab ingest all the digital interactions, integrating them with all single receipts, as well as any other customer interaction
All the consolidated information enable unified customer views and personalize digital communication that drive people to buy again
A supermarket chain - Business Case
How it work
Technology (APIs, Datacloud, Predictive Intelligence)
Expertise (Strategy, Operations, Research, Domain, Industry vertical)
INGEST ENRICH ACTIVATE
REST API
RFMC
Purchase
preferences
Basket mix
+
-
ANALYSE
Effectiveness
Index
RFMC tracking
Dashboard
performance
Click Analysis
Based on Customer behaviours:
Openings or click on a link in a digital communication
Purchase of a particular product
Navigation on the site
Interaction with App
Based on the characteristics and membership of certain clusters:
Engagement index
Customer Quality Relationship index
RFMC
Influences Sales correlation
Follow up messages are built with specific contents determined on the customer behaviors
How it work: building a journey
Ingest: creating digital identities
Integrated Marketing strategies are only possible if building 360° consumer views, managing all customer behaviors in a single profile
Crucial to collect and manage all data and interactions with the brand through the various touchpoints in one single place
Customer CareInteractions
Criticalities
Survey responses
TransactionsValue
Product purchased
Frequency
Discounts
Stores
AnalyticsTraffic source
Page Visits
Wish lists
Abandoned Cards
Social LoginSocial data
Interests
Likes
Preference CentreContact Frequency
Preferred Channel
Preferred Store
Preferred Category
DemographicsContact points
Socio-demographic data
Feedback
CampaignOpen / Click
Un/Subscription
Opening time
Progressive
ProfilingTraffic source
Page visits
Navigation Patterns
Customer Identity
Ingest Enrich Activate Analyze
Enrich: Creating clusters
RFMC PURCHASE PREFERENCES BASKET MIX ANALYSIS
•RecencyDetection of the 5 most purchased
categories / least purchased ones
CLUSTER HIGH(es: High Spending /
Frequent / constant with
recent purchases)
CLUSTER 2
CLUSTER N(es: Low Spending / Less
frequent / Erratic)
…
Identify products’ categories
correlated to typical receipts
TOP 5 CATEGORIES
BOTTOM 5 CATEGORIES
TOP 5 CATEGORIES
BOTTOM 5 CATEGORIES
TOP 5 CATEGORIES
BOTTOM 5 CATEGORIES
…
MIX 1
(es: fruit – cheese -
fish)
MIX 2
MIX N
…
•Frequency •Monetary •Constance
Ingest Enrich Activate Analyze
After purchase survey measuring
the Quality of the Relationship, online
and offline
Generating specific CQR clusters
Build segments through their consumer
journey based on:
Registration recency
Contact recency
Activity index (open/clicks) and recency
Enrich: Creating clusters
Ingest Enrich Activate Analyze
CLICK ANALYSIS ENGAGEMENT INDEX CQR
Click ranking algorithm to dynamically
Push correlated offers
Convey ideal contents
0 10 20 30 40
WEB
FOOD
CONTESTS
Activate: build personalized dynamic messages
Ingest Enrich Activate Analyze
Enabling one-to-one message building, including with up-to-date dynamic content
Contents(From different sources and in different formats)
Users(With their behavioral characteristics, interactions)
Matching
Rules
Possible attributes
Open, click
Engagement level
Preferences
Purchasing
Web Browsing
App interactions
Call center interaction
Through dynamic boxes populated with content from different sources, made available in different formats (XML, JSON, API, WS, Feed RSS …)
web/e-commerce scraping
Suppliers web pages
Social (i.e. Instagram)
CMS / DAM / PIM
Analyse: Evaluate effectiveness, adjust and iterate
EFFECTIVNESS INDEX CLUSTER MIGRATION PERFORMANCE ANALYSIS
Assign an effective index to each user /
campaign pair linked to:
Opening of the campaign itself
Deviation from the previous
RFMC cluster
Sales Influence of products
pushed in the campaign
To track users moving between
segmentation clusters and see how
the engagement strategy is working Customer base: how it
change
Behavioural: customer
engagement
Campaign: contact plan
effectiveness
Transactions: campaign sales
influence
Ingest Enrich Activate Analyze
The «Kaizen» approach
0 N
Fine tuning phase
Enrichment
• Web Click analysis
Ingestion of
• Web tracking
• App interactions
First level
Automation
Enrichment
• Engagement
index
• CQR index
• …
Additional
AutomationAdditional
Automation
Excellence is not a destination
It is a continuous journey that never ends (Brian Tracy)