GSE Consulting Day 2010: Neo Metrics

Neo Metrics Analyze to decide. Decide to create value. Consulting Day. Barcelona GSE February 2010

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.

01 Neo Metrics Who we are and what we do 02 Projects Real life examples 03 Job opportunities What we are looking for and what we have to offer

01 Neo Metrics Who we are and what we do

Turning data into 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. Analyze to decide. Decide to create value

Analytical consulting MIDAS methodology Steps in the (analytical) decision-making cycle Memory What happened? Intelligence What will happen and why? Decision What should be done? Action How to turn decision into action? Strategy How to ensure value creation?

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 Client intelligence Demand Risk management − Segmentation − TV audiences − Credit risk − Cross-selling and upselling − Energy − Collections − Churn − Products and services − Lifetime value − Social networks − Satisfaction Fraud − Fraud detection Product intelligence − Pricing − Attributes Campaign intelligence − Promotion attributes − Campaign simulation and optimization

Clients Banking

Clients Utilities Telco Retail & distribution

Clients Media Public sector Insurance Transportation

International reach • Neo Metrics has offices in: Madrid Barcelona Santiago de Chile México D.F. … and growing

02 Projects Real life examples

Targeting optimization New products Customers 1. Who should be targeted 2. What product The goal is to select a target audience for each direct marketing campaign. We select those that exhibit a positive-enough profitability.

Targeting optimization Product X Previous purchases Purchasing Success rate behavior Customer profile + high Scoring - low Success rate Trade-off Expert criteria Neo Metrics Strategy 1 : Keep size Increase success rate Strategy 2: Increase or decrease target audience impacts success rate 2 1 2 Minimum Optimal: similar success rate, but 2 over wider audiences More sales 2

Targeting optimization % de clientes contactados NM PA 5.0% + 45 % 4.71% 4.5% + 25 % 4.0% 3.69% + 24 % 3.5% 2.96% 3.03% + 40 % 2.96% 3.0% 2.39% 2.5% 2.31% 2.0% 1.65% 1.5% 1.0% 0.5% 0.0% Marvel SeatClassic StarWarsB Zippo NM increases target audiences for all products.

Targeting optimization Tasas de éxito (%) % éxito exacto NM % éxito global NM % éxito exacto PA % éxito global PA 5.00% 4.46% 4.50% 4.00% 3.99% 4.01% Despite the increase on target 3.52% groups, success rates are 3.50% maintained. 3.00% 2.50% 2.05% 2.00% 1.74% 1.70% 1.54% 1.61% 1.39% 1.41% 1.43% 1.50% 1.00% 0.63% 0.64% 0.53% 0.50% 0.39% 0.00% Marvel SeatClassic StarWarsB Zippo Increase in + 24 % + 45 % + 40 % + 25 % target group:

Targeting optimization Beneficio neto potencialmente generado NM PA Potential net profit per product Neo Metrics contribution 140,000 € 120,427 € 120,000 € Increase in net profut (%) 35% 100,000 € Neo Metrics 90,612 € Client 80,000 € 60,000 € 52,579 € 40,000 € 33,261 € 32,662 € 27,544 € 20,000 € 15,369 € 13,919 € - € Marvel SeatClassic StarWarsB Zippo Neo Metrics could improve net profit by 35%.

Recommendation

Recommendation Customer Expected € Purchasing Previous behavior purchases Behavior in Profile campaigns Offer Product Attractive Margin (€)

Recommendation • Campaign: emailing Éxito por producto • Goal: target optimization 0,160% Success rates 0,140% 0,120% Net profit increase 38% 0,100% 0,080% 0,060% 0,040% 0,020% Client NM 0,000% Camiseta doble Conjunto bebe Camisón Plaid Product A Product B Product C Product D éxito Venca éxito NM NM improves by 38% the net margin

Home insurance Damage Claim Buy (renew) Repair or replacement insurance 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 Service satisfaction and churn(%) 70% 30% 60% 25% 50% fuga anual (%) 20% 40% tamaño Cost of insatisfaction 15% 30% 10% 20% 10% 5% 0% 0% 1- muy 2- insatisfactorio 3- regular 4- satisfactorio 5- muy Insatisfaction Satisfaction insatisfactorio satisfactorio valoración de siniestro tamaño (%) fuga anual (%) Churn is 4-5 times more likely in case of dissatisfaction. In addition, it is, with price, the main driver of hiring.

Data extraction • Type of incident Characteristics • Cause of the incident • Etc. • Disagreements • Waiting time • Age Inspections • Etc. • Years as client Profile • etc-. Repair • Degree of satisfaction Survey • Questions Client service • Complaints calls • Requests Repair • Time of repair • Type and number of workers • Time until 1st visit • Amount (€) We gather all the relevant data that could help explain satisfaction in how the damages have been repaired.

Modeling satisfaction Drivers Satisfaction Repair time Kind of incident Happy # and kind of Complexity Ok workers involved Unhappy Calls and claims Profile We use all relevant data, conveniently transformed, to measure their impact on customer satisfaction.

Client behavior analysis • Type of workmen • Repair time Some mean trouble (!) The longer it takes, the less 35000 18,0% satisfaction 16,0% 30000 14,0% 25000 12,0% 20000 10,0% 15000 8,0% 6,0% 10000 4,0% 5000 2,0% 0 0,0% 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 Número Tasa insatisfacción 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 30% The 10% most unsatisfied has a rate of 25% over 25%. Insatisfaction rate 20% 15% 10% 5% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Decile scoring 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 INCIDENT 43 years old Region: Catalonia The model allows monitoring cases daily and Cause: Water damage prioritize them according to the model. 4 years as client ejemplos de evolución de tasa de insatisfacción estimada siniestros puros % insatisfaction over time 14% Tasa de insatisfacción 12% 10% estimada 8% 6% 4% 2% 0% 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Días Low risk in the beginning. Inspector enters late. Inspection and repair takes a long time.

Implementation: intelligence automation Output of the model Id Estimated Risk in repair Risk in Risk in paying Risk complaints Risk profile incident insatisfaction inspector 23478 24% High High - Medium Medium 1723 12% Medium - - Medium High 1379 8% Low Medium - - Low Prioritization Sí Insatisfaction high? Repairing Paying State of incident? Prioritization files Inspecting Paying Company 1 Company 2 Company N Inspector department

Who cares? Project impact Operational improvement: Incident prioritization. Better quality indicators: reduction of bias. Client service / Strategic diagnosis: drivers of insatisfaction. Quality Overall satisfaction: estimate on all incidents. Quality control: ensure no case is forgotten. Cross-sell in case of satisfaction: best time to make offers. Marketing Compensation in case of insatisfaction: best time to offer a compensation or….

03 Job opportunities What we are looking for and what we have to offer

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

Pau Agulló pau.agullo@neo-metrics.com Analyze to decide. Decide to create value.

GSE Consulting Day 2010: Neo Metrics

by Barcelona Graduate School of Economics (GSE) on Feb 26, 2010

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GSE Consulting Day 2010: Neo Metrics — Presentation Transcript