Analytics for Product Profitability, Customer Profitability,Organizational Unit Profitability and Risk Management

Francesco Civardi, DaisyLabs

17th October 2013

BIG DATA ANALYTICS CONFERENCE 2013

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Basic Ideas

Analytics for product profitability, customer

profitability, organizational unit profitability,

risk management:

• Profitability and Risk go hand in hand, but are

often managed by different Company Units

• It is essential to integrate and correlate the two info

• Temporal and spatial correlation must be considered

• Bisociative thinking can give new insights

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Agenda

• DaisyLabs:

• Who? Where? What? How?

• The Kernel (core components of our solutions)

• The Engines, the WebConsole, the GeoConsole

• The Accelerators (working templates of «condensed experience»)

• NetRisk Monitor

• Marklus (Markov Clusters)

• Geneco (GenEconomics)

• Surcus (Customer Survival)

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DaisyLabs: who?

• DaisyLabs started ten years ago, as an IT Company

devoted to IT consulting through state-of-the-art

technology.

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DaisyLabs: where?

• We moved in 2012 to the Pavia Techno Park, in order to

strenghten our contacts with the research environment,

and to attract more easily the best graduates

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DaisyLabs: what?

Our mission is to «Deliver Information Assets», through

Data

• Collecting

• Organizing

• Integrating

• Visualizing

• Analyzing

• Presenting

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DaisyLabs: how?

• We work closely with our customers – better, partners.

• We build together mixed teams

• We inject them with our knowledge and our

methodology.

• We are fully commited on the results

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Kernel: the Engines

• Daisylabs integrate in their solutions the best analytical

engines available. We haven’t married a specific

technology, but we choose it according to the

ecosystem of the customer.

• Vesenda eLegere, MSFT SQL Server, Analysis

Services, PowerPivot and PowerViewer, RoamBI, KNIME, Panorama Software and R are examples of

technologies used.

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Kernel: the WebConsole

• The WebConsole collects information coming from

Company reporting systems or from analytical systems

(developed by DaisyLabs or by others) in a user-friendly

environment accessible by different people in a strictly

controlled way.

• Profitability and Risk Information can be shown side by side.

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Kernel: the GeoConsole

• The GeoConsole is an innovative way proposed by DaisyLabs

to visually integrate “mapped” information assets.

• Information coming from internal or external sources are

organized in channels available for user analysis.

• Every channel is an independent information source; different

channels are displayed on a map for visual exploration and

discovery.

• Risky and profitable areas can be identified, and visually

“space-correlated”.

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Visual Data Exploration11

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Mapped reports12

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Accelerators: RiskNet Monitor

• RiskNet monitors the risks taken by financial institutions,

through their commercial networks.

• The engine of the system computes indicators from

operations stored in the DW, calculate risk rates and

aggregate them along the Organizational Structure, properly

weighing them

• The webconsole raises alarms, when unexpected risk rates

show up, and allows users to go from risks back to indicators,

in order to understand root causes of potential problems.

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Accelerators: Marklus (Markov Clusters)

• This accelerator enables the statistical segmentation (i.e. clustering) of business entities (customers, products…)

• Segmentation can be based on risk info, on profitability info, or both.

• Segmentation is not static but dynamic; e.g. the goal of marketing efforts (Campaigns etc.) is indeed moving customers from less to more profitable segments.

• Cluster analysis is therefore dynamically updated, and enriched with Markovian analysis, to analyze transitions between segments.

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Bisociative thinking

«The pattern underlying the creative act is the

perceiving of a situation or idea, L, in two self-

consistent but habitually incompatible frames of

references, M1 and M2.»

Arthur Koestler, The act of creation

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Accelerators: Geneco (GenEconomics)

• This accelerator has been inspired by our research work on genomics, where the problem is to find which genes are differently expressed in cancer tissues, with respect to normal tissues.

• It scans hundreds, or even thousands, of variables (genes, but they can be products, product attributes, customer attributes, nodes of a commercial network etc. ) and identify the best «predictors» of desired vs undesired status (loyalty vs churn, wealth vs bankrupcy, good vs bad quality, good vs risky branch, profitable vs unprofitable customers or producs etc.)

• Visualization techniques used in genomics, like heatmaps and vulcano plots, can be easily adapted to a business scenario.

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Vulcano plots17

X: Log Odds, a measure of effect size

Y= AUC,a measure of predictive power

e.g. P25 is a predictor of «good»,P33 of «bad»

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Heatmaps18

Visuallyanalyze correlations

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Accelerators: Surcus (Customer Survival)

• This accelerator has been inspired by our research work on clinical prediction models, and particularly on survival analysis, which is now often applied to business problems (e.g. see G. Linoff & M. Berry «Data Mining Techniques»).

• A special regression technique (Cox regression) is applied, in order to identify the drivers of churn, or more generally, of customer «mortaliy», and survival curves (Kaplan-Meyer) are compared.

• This technique can be applied together with Cluster Analysis.

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Kaplan-Meier20

Different Customer Groups (Clusters)have different«Survival Curves»,i.e. different risk of leaving

HR= Hazard risk, p=stat. significance

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Questions & Answers

Thanks for your attention!

fcivardi@daisylabs.it

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