big data analytics primer for w2 e startups

Big Data and Analytics

Do I need it ??

The Top guys use it…

It is Favourite closer home too ..• Flipkart/Snapdeal /Amazon –

– predict market trends based on user behaviour, click data and information from social media, algorithm to rank sellers.

– uses high-end analytics and algorithms in a number of areas such as recommending relevant products to users, showing users relevant search results, displaying ads to users that they are very likely to click on, predicting future demand for products, detecting spam reviews and detecting fraudulent orders

• Make my trip/ Ceartrip –– Targeted mailers, personalization in search results

One of the hottest start-up skills..

1. Software engineer

2. Account Manager

3. Data and Analytics

professional

4. HR and Talent acq

professional

5. Product mgr

Almost a fifth of these are closely related to analytics and data management

And is equally critical for large Organizations

But Do ”I” need it ??

(I am still a startup …)

What is analytics – 1.0• Market research

– Who will buy my product– At what price– Who are my competitors– How much funding does my venture need– When will I breakeven

• Data collection

• Data cleaning

• Tabulation

• Correlation and regression

• Advanced data analysis – Factor analysis, PCA, Conjoint, etc

• Forecasting

What is analytics – 1.0

• Customer insight– Who is my customer / which are the

customer segments– What is she/they using my product for– How is consumption of my product growing– When will I reach a targeted consumption

level

• Data collection

• Data cleaning

• Tabulation

• Correlation and regression

• Advanced data analysis – Factor analysis, PCA, Conjoint, etc

• Forecasting

What is analytics – 1.0

• Product insights– Product comparison– Geographical trends

• Data Visualization– Understand data better– Use data to aid decision making

• Data visualization

• Charts and plots

• Data interpretation

Analytics 2.0• Advanced customer segmentation

– Create previously unknown customer segments– Create better customer understanding

• Advanced forecasting – Time series forecasting, seasonality impacts

• Clustering (supervised, unsupervised)

• Time series modelling

• Forecasting techniques

Analytics 2.0

• Recommendations and personalization – What will a particular customer buy next– Offer targeting– When is she likely to move to my competitor– What offer is likely to prevent churn

• Predictive modelling

• Churn modelling

• Machine learning

• Big Data analysis

Analytics 2.0

• Fraud prevention– Revenue leakage– Fraud detection and prevention

• Fraud detection techniques

• Machine learning

Common tools for AnalyticsTool Application

1 MS Excel Data manipulation, visualization, Data Tabulations, Correlation and regression, What if analysis

2 MS Access Large data manipulation

3 SQL Even larger data manipulation

4 R / SAS/ SPSS Advanced data analysis, Predictive modelling, Clustering

5 Python / Java/ C Real time data analysis, Big data manipulation

6 Qlik sense / Qlik view / Power BI

Visualization and reporting

Case study

ENOUGH GYAN …LETS DO SOME REAL STUFF

Concluding …

1. Start small , scale as needed

2. Cultivate data skills

3. Bring in experts

4. Business first

5. Don’t follow the crowd

THANKS May Data be with you …

Speaker Details

Cultivate data skills

1. Bring in experts

2. Business first

3. Don’t follow the crowd

Concluding …

Ajay Piwhal

CEO & FOUNDER, Prizmatics

https://www.facebook.com/ajay.piwhal

https://twitter.com/ajayPrizmatics

https://in.linkedin.com/in/ajaypiwhal