GSE Consulting Day 2010: Neo Metrics

Analyze to decide. Decide to create value.

Consulting Day. Barcelona GSEFebruary 2010

Neo Metrics

Quick introduction

� Name: Pau Agulló

� Job: director of Barcelona office in

Neo Metrics

� Degree in Economics in UPF … 13

years ago (ouch!)

� Msc in Economics in EUI

(Florence)

� Developed professionally in

consulting, specialized in data

analysis applied to decision-

making

� Marketing, credit risk, etc.

� Telecom, banking, etc.

Neo MetricsWho we are and what we do01ProjectsReal life examples02Job opportunitiesWhat we are looking for and what we have to offer03

Neo MetricsWho we are and what we do01

Turning data into value

Analyze to decide. Decide to create value

� Consulting company specialized in data mining …

� … merging scientific excellence and business sense

and knowledge …

� … to help organizations maximize the value of their data

and make better decisions.

How to ensure value creation?

Analytical consulting

Strategy

What happened?Memory

What will happen and why?Intelligence

What should be done?Decision

How to turn decision into action?Action

MIDAS methodologySteps in the (analytical) decision-making cycle

Recognition on scientific excellence and innovation

Neo Metrics has received wide national and international recognition

on its scientific excellence, both in data and text mining, and on

innovation

��

��

60% of its revenue comes from

products and services

developed in the past two

years.

Aqua: Analytical Intelligence software

� Suite of software applications that condense NM experience and allow automatize decision-making, unleashing the power of predictive models

Areas of expertise

Marketing Forecasting Fraud and risk

− Segmentation− Cross-selling and upselling− Churn − Lifetime value− Social networks− Satisfaction

− Pricing− Attributes

− Promotion attributes− Campaign simulation and

optimization

− TV audiences− Energy − Products and services

Client intelligence

Product intelligence

Campaign intelligence

Demand

− Credit risk− Collections

− Fraud detection

Risk management

Fraud

Clients

Banking

Clients

Utilities

�

Telco Retail & distribution

Clients

Media

Transportation

Public sector Insurance

� Madrid

� Barcelona

� Santiago de Chile

� México D.F.

… and growing

International reach

• Neo Metrics has offices in:

ProjectsReal life examples02

New products Customers

Targeting optimization

The goal is to select a target audience for each direct marketing campaign. We select those that exhibit a positive-enough profitability.

1. Who should be targeted

2. What product


Previous purchases

Customer profile

Purchasing behavior

+ high Scoring - low

Product X

Success rate

2

2

2

2

Minimum

Expert criteria

Optimal: similar success rate, but over wider audiences� More sales

1

Trade-offStrategy 1 : Keep size � Increase success rate

Strategy 2: Increase or decrease target audience � impacts success rate

Neo Metrics

Suc

cess

rat

e


% de clientes contactados

2.96%

4.71%

2.31%

3.69%

2.39%

3.03%

1.65%

2.96%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

Marvel SeatClassic StarWarsB Zippo

NM PA

+ 24 %

+ 45 %

+ 40 %

+ 25 %

NM increases target audiences for all products.


+ 45 % + 40 % + 25 %

Tasas de éxito (%)

3.99%

1.54%1.43%

0.39%

4.46%

1.61%1.74%

0.63%

3.52%

1.39%

1.70%

0.53%

4.01%

1.41%

2.05%

0.64%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

4.50%

5.00%


% éxito exacto NM % éxito global NM % éxito exacto PA % éxito global PA

Despite the increase on target groups, success rates are

maintained.

+ 24 %Increase in target group:

Beneficio neto potencialmente generado

120,427 €

52,579 €

33,261 €

15,369 €

90,612 €

27,544 €32,662 €

13,919 €

- €

20,000 €

40,000 €

60,000 €

80,000 €

100,000 €

120,000 €

140,000 €


NM PA


Neo Metrics could improve net profit by 35%.

Increase in net profut (%) 35%

Potential net profit per product

Neo Metrics

Client

Neo Metrics contribution

Recommendation

Recommendation

Expected €Customer

Purchasing behavior

Previous purchases

Profile Behavior in campaigns

Attractive Margin (€)

OfferProduct

Recommendation

Net profit increase 38%

• Campaign: emailing• Goal: target optimizationÉxito por producto

0,000%

0,020%

0,040%

0,060%

0,080%

0,100%

0,120%

0,140%

0,160%

Camiseta doble Conjunto bebe Camisón Plaid

éxito Venca éxito NM

NM improves by 38% the net margin

Product A Product B Product C Product D

NMClient

Success rates

Home insurance

Buy (renew) insurance

Damage �� Claim

Repair or replacement or compensation

Customer satisfaction

Satisfaction in case of damage seems to be key in the home insurance market.

Does quality of service matter?

Tamaños y ratios de fuga según calidad en resolución de siniestro

0%

10%

20%

30%

40%

50%

60%

70%

1- muyinsatisfactorio

2- insatisfactorio 3- regular 4- satisfactorio 5- muysatisfactorio

valoración de siniestro

tam

año

0%

5%

10%

15%

20%

25%

30%

fuga

anu

al (%

)

tamaño (%) fuga anual (%)

SatisfactionInsatisfaction

Cost of insatisfaction

Service satisfaction and churn(%)

Churn is 4-5 times more likely in case of dissatisfaction. In addition, it is, with price, the main driver of hiring.

Profile

Inspections

Survey

• Age• Years as client• etc-.

• Disagreements• Waiting time• Etc.

• Degree of satisfaction

• Questions• Complaints • Requests

Repair • Time of repair• Type and number of workers• Time until 1st visit• Amount (€)

Data extraction

Client service calls

Characteristics of the incident

• Type of incident• Cause• Etc.

Repair

We gather all the relevant data that could help explain satisfaction in how the damages have been repaired.

Modeling satisfaction

We use all relevant data, conveniently transformed, to measure their impact on customer satisfaction.

Drivers

Repair time Kind of incident

Calls and claims

# and kind of workers involved Complexity

Profile

Satisfaction

Happy

Unhappy

Ok

Client behavior analysis

• Repair timeThe longer it takes, the less satisfaction

0

5000

10000

15000

20000

25000

30000

35000

cristalero

cerrajero

persianero

lampista

electricista

pintor

antenista

fontanero

albanil

tecnico_elect_TV

tecnico_elec_hogar

tecnico_elect_ind

escayolista

carpintero

parquetista

carpintero_metalico

contratista

enmoquetador

limpieza

0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

14,0%

16,0%

18,0%

Número Tasa insatisfacción

• Type of workmenSome mean trouble (!)

We analyze each of the drivers, one by one, to choose how to include them in the model.

Predictive power

Real insatisfaction rate by propensity score

0%

5%

10%

15%

20%

25%

30%

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Decile scoring

Insa

tisfa

ctio

n ra

te

The 10% most unsatisfied has a rate of over 25%.

The model exhibits a good predictive power since it can successfully identify a small group of customers with a high

likelihood of dissatisfaction. It is actionable.

Evaluating (lack of) satisfaction in real time

ejemplos de evolución de tasa de insatisfacción estimada siniestros puros

0%

2%

4%

6%

8%

10%

12%

14%

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Días

Tasa

de

insa

tisfa

cció

n es

timad

a

43 years oldRegion: CataloniaCause: Water damage4 years as client

Low risk in the beginning. Inspector enters late.

INCIDENT

Inspection and repair takes a long time.

% insatisfaction over time

The model allows monitoring cases daily and prioritize them according to the model.

Implementation: intelligence automation

Medium

-

High

Risk in inspector

-

-

-

Risk in paying

-

Medium

Medium

Risk complaints

Low

High

Medium

Risk profile

Low8%1379

Medium12%1723

High24%23478

Risk in repairEstimated insatisfaction

Id incident

Prioritization

Company 1 Company 2 Company N InspectorPaying

department

Prioritization files

State of incident?

Repairing Paying

Inspecting

Insatisfaction high?

Sí

Output of the model

Who cares? Project impact

Operational improvement: Incident prioritization.

Better quality indicators: reduction of bias.

Strategic diagnosis: drivers of insatisfaction.

Overall satisfaction: estimate on all incidents.

Quality control: ensure no case is forgotten.

Client service / Quality

MarketingCross-sell in case of satisfaction: best time to make offers.

Compensation in case of insatisfaction: best time to offer a compensation or….

Job opportunitiesWhat we are looking for and what we have to offer03

What we look for: analytical consultants

Aptitude

- Quantitative techniques: multivariariate analysis, regression, times series, linear programming, etc. (theory and practice)- Decision-making- Languages

Attitude

- Creative, self-motivation, rigor, team work - Communication skills (written and oral)

What we can offer: a stimulating job path

Work

- Creative technical work- Variety on quantitative techniques- Variety of clients and departments- International environment- Growth- R+D projects

Career path

- Analyst � Project manager � Principal

Analyze to decide. Decide to create value.

Pau Agulló

[email protected]