smarter customer analytics - customer dna

© 2012 IBM Corporation

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


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…

TV Spend

Customer

Spend

A

BC

D

E

1

2

3

Customer 1

Customer 2

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.

Technology

Business Integration

Analytics


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

� Models based on few dimensions

– demographics, value, or basket

You Are What You Buy

Latest & GreatestPrice FocusedValue MaximizersConnected Convenience

Customer Value

Typically not actionable because customers are more complex than 2 or 3 dimensions

Demographic

Trans-actions

Sales

Basket Analysis

Geo-graphy

Income

Advanced Clustering

� Models based on many dimensions

of customer behavior

Preferred ProductCategories

Preferred Channel

Participation in Loyalty Program

Use of In-House Credit Card

Use of Service Programs

Return/Exchange BehaviorBreadth of

Categories Shopped

Length of Timeas Customer

Recency + Frequency+ Value

Response to Media

Time until Repurchasein Key Categories

Highly actionable clusters are based on the customer’s response to various dimensions of the Retailer’s value 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

Preferred Product Categories

Modeled time to next purchase

CTP Customer


Facebook Page Engagement

Return / Exchange Behavior

Breadth of Categories Shopped

Length of Time as CustomerRecency + Frequency + Value

Response to Media

Gift Registry User

Annual Spend Level

Annual Transactions

Econometric: Real-estate & Unemployment

Most segmentation approachesonly focus here:


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

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

Preferred ProductCategories

Preferred Channel

Participation in Loyalty Program


Use of Service Programs

Return/Exchange BehaviorBreadth of

Categories Shopped

Length of Timeas Customer

Recency + Frequency+ Value

Response to Media

Time until Repurchasein Key Categories


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 NExample: “Brand Fanatics”

Marketing Call to Action – RMI 37:1

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

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

9% of customers �� 30% revenue


Clusters can deepen Insights from Existing Segmentations


Technology

Next Step: Optimization

Use the lens of the Customer Foundation as a Primary Input for solving the

most difficult challenges within the business


Analytics


Marketing Media Optimization – with a Customer Lens

• Multi-objectives

• Policy constraints

• Optimal decisions

Economy

Customer

Media

Analysis

Optimization

• Transaction data

• Modeled Variables

• Action clusters

• Industry data

• Systematic risk

• Demand forecast

• Performance data

• Saturation

• Action Exposures

Behaviors


Benefits and Results

Case Study 1: Enterprise Marketing Media Mix Optimization

Solution

� Established customer foundation through

unique customer segmentation approach

� Enabled prescriptive media mix optimization engine for Marketing

investment against the Clusters

� Developed solution enabling ‘what if’

scenarios and returning ‘what’s best’ output

� Reduced saturation of budget 5-7% (~$50M)

� Outperformed industry with greatly reduced budget; identified $1B in additional revenue

� Improved conversion and engagement

Challenges & Background

� Optimally invest a $MMM+ advertising budget to maximize sales AND maintain/reduce market spend?

� How do I apply my knowledge of my

customers to determine the proper

proportion of investment in each marketing

type?


Benefits and Results

Case Study 2: Re-engage ‘Lapsed Best Customers’ to Drive Revenue

Solution

� Developed Behavioral Models to enrich understanding of customers:

� Leveraged customer foundation to inform media preference, creative personality, and communication timing

� Selected customer list, designed 5 creative versions and delivered through preferred marketing channel

� ~100% and ~200% increased response rate over expected results, for two tested campaigns

� Reactivated customers drove $180/customer incremental revenue in 2 months

Challenges & Background

� How best to reactivate lapsed ‘best customers’ in loyalty program?

� Tailor copy, creative and offer based on customer preferences

� Minimize execution costs by identifying communication channels each customer would responsive most to

Response Rate

4,91%

11,69%10,43%

8,70%

0,59%3,80%

8,67%7,81%

6,59%

0,37%0%

2%

4%

6%

8%

10%

12%

28-Nov 5-Dec 12-Dec 19-Dec 26-Dec

Expected Response

Offer 2Offer 1


Operationalizing Action Clusters Across the Organization

4/25/2012

What?Product

Who?Customer

How?Channel

When?Lifecycle Mgmt.

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

““““What messages are relevant to my targeted customers and how and

when do I communicate with them?””””

Marketing

Merchants


Technology

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.


Analytics


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