Smarter Customer Analytics - Customer DNA

“Know me” - Getting Closer to Your
Customers through Applied Analytics
Mark Matiszik

© 2012 IBM Corporation

Know, Listen To, and Empower Me

Being treated as an
individual has moved
from a Desire to an
Expectation


Case Study: How can “Customer Centricity” really help solve a
business challenge?

Major US Retailer’s Big Unanswered Question:

“How do I eliminate unnecessary spend from my
Marketing budget?”

Step 1: Build a data-driven Lens of the Customer

Step 2: Apply that Customer Foundation as the key input
to Optimizing between Channels/Regions/Customers


Balancing on a Thousand Curves

The picture isn’t simple – there are many customers, and many media types.
With two customer groups, for instance, we have two curves…

Customer 1
Customer 2
D
3
E
Customer B 2
Spend C
1

A

TV Spend

But, how do we know what is optimal for each customer?

Acting on Customer Insight

The customer’s voting record – the digital footprints of their countless decisions –
has the power to tell us who they are, and what matters to them.

Analytics Technology

Business Integration

The New Era of Customer Understanding and Segmentation

The key to achieving the high ROI and Profit potential of multi-channel shopping is
advanced customer analytics

Traditional Approach Advanced Clustering
Models based on few dimensions Models based on many dimensions
– demographics, value, or basket of customer behavior

Customer Value You Are Demographic
What You Buy Preferred Product
Categories
Sales Latest & Greatest Income Length of Time Preferred Channel
as Customer
Price Focused Participation in
Loyalty Program
Recency + Frequency
Value Maximizers + Value Use of In-House
Connected Convenience Response to Media
Credit Card
Use of Service Programs
Time until Repurchase
in Key Categories Return/Exchange Behavior
Breadth of
Trans- Basket Geo- Categories Shopped

actions Analysis graphy

Highly actionable clusters are based on
Typically not actionable because the customer’s response to various
customers are more complex than 2 or dimensions of the Retailer’s value
3 dimensions proposition


No Guessing: Analytics Can Reveal Who Your Customers are

Begin with 30-40+ Modeled Variables from Customers’ Digital Footprints
Each Variable is like a gene, which describes a facet of customer behavior
Useful on their own, but also provide the input for Clustering

Age + Income + Geography
Most segmentation approaches CTP Customer
Annual Transactions
only focus here:
Annual Spend Level Use of In-House Credit Card

Econometric: Real-estate & Unemployment Facebook Page Engagement

Gift Registry User Return / Exchange Behavior

Preferred Product Categories
Breadth of Categories Shopped
Modeled time to next purchase
Response to Media
Length of Time as Customer
Recency + Frequency + Value


Clusters are Based on the most significant Modeled Variables

Revolutionary customer segmentation approach tailored uniquely to each client’s business
model, customer data and operational practices, yielding highly actionable customer groups
Preferred Product
Categories
Length of Time Preferred Channel
as Customer
Participation in
Recency + Frequency Loyalty Program
+ Value Use of In-House
Credit Card
Response to Media
Use of Service Programs
Time until Repurchase
in Key Categories Return/Exchange Behavior
Breadth of
Categories Shopped

Action Clusters are
Highly homogeneous – it is difficult to get into a cluster based on 10+ dimensions,
ensuring that the customers are very similar to one another

Highly differentiated – the process ensures as much “distance” between clusters as
possible


Sample Clusters

Rank Action Cluster % of Customers % of Spend
1 Brand Fanatics 9% 30%
2 Core Customers 8% 18%
3 Online Socialites 6% 14%
4 Hurt by the Economy 8% 8%
5 Potential Pool 7% 7%
6 Make it Interesting! 6% 6%
7 Let’s Bargain 10% 3%
8 Find me Online 7% 2%
9 Unengaged 17% 7%
10 Luxury for Me 4% 2%
11 Until Next Year (One and Done!) 13% 2%
12 Just Window Shopping 5% 1%


This is the Outcome
Example: “Brand Fanatics”

Vital Statistics
Strongest Loyalty: Over 85% are part of loyalty program
89% have shopped over 5 categories
91% have been customers for 7+ years
Almost no new customers in <3 years
70% are due to purchase within 60 days
60% are using a private label credit card, 30% exclusively for all purchases
Highest Return on Marketing scores

Marketing Call to Action – RMI 37:1
9% of customers 30% revenue
EMOTIONAL BENEFIT: Sports enthusiast
BRAND PROMISE: Latest & Greatest, Multiple Sports Category Breadth and Depth
CUSTOMER AWARENESS: Loyalty promo, new product releases, direct mail and email
TOUCH POINTS: Multi-Channel, In-store and on web
UNIQUE IDEA: ‘Co-Branded Credit Card Promotion’
PRE-STORE: Mobile, Blogs, Social Networks
IN-STORE: Mobile applications and shopping aids, services merchandise together
POST-STORE: Online, loyalty program mailings and emails


Clusters can deepen Insights from Existing Segmentations


Next Step: Optimization

Use the lens of the Customer Foundation as a Primary Input for solving the
most difficult challenges within the business



Marketing Media Optimization – with a Customer Lens

• Industry data
• Systematic risk Economy
• Demand forecast Optimization

• Transaction data
• Modeled Variables Customer Behaviors
• Action clusters

• Multi-objectives
• Performance data Media • Policy constraints
• Saturation • Optimal decisions
• Action Exposures Analysis


Case Study 1: Enterprise Marketing Media Mix Optimization

Challenges & Background Solution
Optimally invest a $MMM+ advertising Established customer foundation through
budget to maximize sales AND unique customer segmentation approach
maintain/reduce market spend? Enabled prescriptive media mix
How do I apply my knowledge of my optimization engine for Marketing
customers to determine the proper investment against the Clusters
proportion of investment in each marketing Developed solution enabling ‘what if’
type? scenarios and returning ‘what’s best’ output

Benefits and Results
Reduced saturation of budget 5-7%
(~$50M)
Outperformed industry with greatly
reduced budget; identified $1B in
additional revenue
Improved conversion and engagement


Case Study 2: Re-engage ‘Lapsed Best Customers’ to Drive
Revenue
Challenges & Background Solution
How best to reactivate lapsed ‘best Developed Behavioral Models to enrich
customers’ in loyalty program? understanding of customers:
Leveraged customer foundation to
Tailor copy, creative and offer inform media preference, creative
based on customer preferences personality, and communication timing
Minimize execution costs by identifying Selected customer list, designed 5
communication channels each creative versions and delivered through
customer would responsive most to preferred marketing channel

Benefits and Results
Response Rate
12%
Offer 1 Offer 2
11,69% ~100% and ~200% increased response
10,43%
10% 8,70% rate over expected results, for two
8%
8,67%
tested campaigns
7,81%
6% 4,91%

4%
6,59%
Reactivated customers drove
2% 0,59%
3,80% Expected Response
$180/customer incremental revenue in
0%
0,37% 2 months
28-Nov 5-Dec 12-Dec 19-Dec 26-Dec


Operationalizing Action Clusters Across the Organization

Marketing
Who? “What messages are
Customer relevant to my targeted
customers and how and
when do I communicate with
What? them?””
Product

How? Merchants
Channel

“Who is in the market, what
do they want and how do I
When? inspire them to purchase?””
Lifecycle Mgmt.

4/25/2012

Final Step: Operationalize the Insights (and Repeat)

With the tools and capabilities, the organization begins to make better
decisions that no longer treat all customers alike.



Break-out session Round 2

Please join us during the break-out session

Get Personal!
Het belang van personalised promotions voor retailers
Hoe kunt u dit realiseren binnen uw organisatie?

Mark Matiszik, Associate Partner, Retail Center of Competence, IBM
Ewald Hoppen, Team Lead Web Analytics / Senior Web Analyst, Wehkamp.nl


Thank You!

Mark Matiszik
IBM Retail Center of Competence

@MarkMatiszik


Smarter Customer Analytics - Customer DNA

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