CPC Analytics Data analysis for industry, marketing and public policy

CPC analytics helps to unlock the potential data

Data Exploration

Data Analytics

Data Strategy

Our data analysis algorithms help to extract novel findings from large numerical and text datasets.

Our multidisciplinary team combines quantitative and qualitative methods with domain expertise to provide reliable and contextualized insights.

Our expertise in data analysis, modelling, and automation helps to create data-driven innovation strategies and research approaches.

CPC Analytics develops data analysis algorithms and models for the industry, marketing, and public policy

Forecasting Pattern detection

Modeling Text analytics

Data Collection & Processing

Potential across sectors

Industry 4.0: • Predictive maintenance • Demand & stock forecasting Marketing: • Customer sentiment mining Public Policy: • Discourse analysis • Influencer identification

Machine Learning Automation Visualization

Capacities of CPC

Geographical outreach

Germany India

France

Switzerland Denmark

Haiti Nepal Egypt

Tanzania

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Offices Project locations

Our Clients

Case studies of recent projects Private and Public sector work

Experience: Analysis of sentiments in e-commerce

Problem statement: We were commissioned by an e-commerce platform to conduct an analysis on client reviews with regard to their satisfaction.

Our approach: ▪  Data-collection: Our algorithms automatically collected

client reviews from webpages and stored them. ▪  Data structuring: In order to analyze the sentiment of a

reviewer, the stored text was structured according to different word and syntax characteristics (e.g. labelling adjectives with an underlying feeling)

▪  Machine learning: Once the algorithm can “read” the sentiment from the text it is trained to become more accurate by feeding more data into the system.

Key value addition: ▪  Automate textual analysis with regard to certain

sentiments occurring in the text. ▪  Enables large scale textual analysis

Sentiment analysis of customer reviews

(top) Graph depicting satisfaction levels | (bottom) Info-graphic explaining the technology behind | all rights reserved.

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Figure:!Sirius!extracts!featureHopinion!pairs!from!textual!data!and!generates!concise!summaries!that!

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Experience: Understanding customer purchasing behavior

Problem statement: Our client wanted to understand baskets of products customers tend to buy together, depending on their age, gender, purchasing power along with other characteristics such as weather, time of the day, month or year.

Dataset: The dataset consisted of approximate 5 million transactions, 600,000 receipts and 10,000 customer card holders.

Our approach: ▪  Advanced mathematical models based on Class

Association Rules developed in-house using SCALA/MongoDB and R were used for this specific project.

▪  Visualization of the results was done using java scripts.

Analysis of shopping behavior

Product wise associations (interactive map) | all rights reserved.

Experience: Analysis and forecasting of sales data

Problem statement: For a retail firm we were commissioned to analyze the sales growth evolution as well as creating forecasts. Moreover the client asked cpc to analyze the composition or regional sales and the importance of regional shops.

Our approach: ▪  Re-structure data: In order to make the analysis

possible we re-structured the data from the internal ERP system.

▪  Identifying correlations: We used characteristics such as geography, shop, etc. to correlate them with sales/clients.

▪  Forecasting: We used hist. data and external indicators (such as humidity) to develop and test sales forecasts.

Key value addition: ▪  Understanding links between the sales of different

shops and products. ▪  Providing planning and forecasting tools.

Sales Forecasting

Indicative graphs | all rights reserved.

An industrial machine manufacturer loses after-sales revenue for not having the spare parts available

• High manufacturing complexity • Hundreds of possible factors • Thousands of measurements • Defective assembly in spite of faultless individual parts

• Prevention of defects • Added Value on OK products • Prevention of rework

• +3 years of data • +1000 variables per final product • >100 GB

Machining

FinalAssembly

FinalTest

SubAssy

SubAssy

SubAssy…

Upto2%undetecteddefects

Predict the assembly of defective final products before final assembly

Context

Potential

Results

Analytics

Experience: Deriving insights from client communication for a manufacturing firm

Problem statement: Our client wanted to extract insights about competitors and internal processes based on external communication with potential or existing clients.

Dataset: The dataset consisted of approximate 50,000 messages exchanged with potential or existing clients. We estimate it would have taken 42 days to read all these messages once.

Our approach: ▪  Proprietary textual classifiers (enhanced Bayes

classifiers) developed in-house using SCALA/MongoDB were used for this specific project.

▪  Visualization of the results was done using java scripts.

Automated analysis of communication

Heat map showing perception of different competitors based on a set of attributes | all rights reserved.

Experience: Measuring the impact of communications in macroeconomic variables.

Problem statement: We were tasked with commissioning a study measuring the impact of external communication of the Reserve Bank of India (RBI) on macroeconomic fundamentals.

Our approach: ▪  We collected data on the press releases and media

articles dating from 2008 - 2013. Analyzed nearly 12000 articles in toto.

▪  Proprietary textual classifiers developed to identify content along with the magnitude of change offered.

▪  Comprehensive econometric testing of our data using GARCH framework for modeling volatility

Impact of institutional communications

Graph plotting variations in exchange rates with changes in content | all rights reserved.

Experience: Measuring sustainability of firms through compliance history

Problem statement:

We were tasked to formalise the impact of sustainability of the risk of default for violating firms, through creation of a unique list of ESG defaulters in India.

Our approach: ▪  Dataset: The dataset consisted of millions of media

articles from 2001 onwards. Hundreds of thousands of legal text regarding ESG compliance similarly scraped

▪  Customary textual flaggers to identify firms engaging in violation of ESG norms.

Key Value addition: §  Formalizing of the link between sustainability and

financial performance §  Predictive modeling on the risks of default based

upon compliance history

Analysis of ESG violations among firms

Annual notional loss borne by MSMEs due to ESG norm violations | all rights reserved.

600 Billion INR

500 Billion INR

150 Billion

INR

Ranking of Indian states according to the level of governance and wish to match it to the level of public perception

•  Thecollectedar>clesandtweetswererunthroughcomplexandcustommadealgorithmstoiden>fytheseman>corienta>onoftextintradi>onalandsocialmediareports.

•  U>lised amixture of both lexicon based classifica>on as well asmachine learning tools toimproveaccuracy.

•  Aggrega>onofscorestoprovideastatelevelscoreofsen>ment.

•  Algorithmbuilttoscoresen>mentaccordingtothedifferingthemes.

•  Textofeachar>cleisscrapedtoanalysesen>ment.

Germany – Gneisenaustr 52, 10961 Berlin

France 8 rue du Saint-Gothard, 67000 Strasbourg

India – Plot No. 4, Survey No. 249, Baner, Pune 411007

Chris&anFranzPartner&CEOMobile: (+49)17681127808Email: c.franz@cpc-analy>cs.comWeb: www.cpc-analy>cs.com