Client: We will give you our toughest requirement which is urgent. We prefer Malaysian only. If you succeed we will sign the contract.
Insistent: Yes we will do it
Within two days submitted two profiles. One got shortlisted, interviewed and offered.
PS: Requirement was to search Malaysian oil and gas downstream expert. Client is MNC technology firm.
R has been one of the highly ranked and fastest growing programming languages of the last decade, one key aspect being its open source nature which helps to adopt latest techniques available in market.
Above trend is captured from a market survey from TIOBE & IEEE which is updated on monthly basis and popular search engines like Google, Wikipedia, Amazon, YouTube and Baidu are used to calculate the ratings.
Tableau has been marked as leader in Business Intelligence & Analytics Platforms by Gartner consecutively for last many years
We provide an array of analytical services covering business analytics, business intelligence & reporting for various domains. Our primary exposure has been retail, sales & Marketing, HR and manufacturing analytics. Below are the few areas of work –
- Exploratory Data Analysis (EDA) is the first step in your data analysis process. Here, you make sense of the data you have and then figure out what questions you want to ask and how to frame them, as well as how best to manipulate your available data sources to get the answers you need.
- All businesses have transaction level data containing Revenue, Margin, Units sold, etc. and there are multiple dimensions for this data (e.g. Region, product category, sales people, etc.)
- Analyzing this data to create visual summary, key contributors to the deviation and hence identifying key areas where business should focus.
- Self Explanatory dashboards summarizing situations (using BI tools like Tableau, Power BI, Excel)
- Helps businesses to align their focus
- Take corrective actions or continue the actions to have positive impact obsered
- Customer was experiencing continuous misses on margin targets, this was because of violations by sales representatives on margin targets set
- Designed an interactive tableau dashboard to quick analyze the root causes of violations by category & region
- Empowered leaders to act on particular individuals and cases where finance approvals were misused
- Observed significant correction (+573 bps improvement in margins) post deployment of robust monitoring system
- Customer was dealing with ~10k products from 70 sub categories, making it difficult to monitor overall Revenue & margin performance and key business performance indicators (KPIs)
- Designed data warehouse which enabled easy availability of financial data
- Built Tableau dashboard connected to the data warehouse enabling ease in navigation of data
- Subject matter expert deployed to read the key movements in data in detail to deliver detailed explanation of deviations at weekly cadence
- Quick access to insights on KPI movement allowed faster actions and better control on overall business performance
- Predictive analysis defines statistical equation around available business data to help predict/simulate the business situation for various possible scenarios. Helping businesses pull correct levers to achieve expected results.
- Business situations are impacted by multiple internal and external factors
- Quantifying this impact is difficult through simple exploratory analysis or business intuitions and cost of making incorrect guesses could run in billions
- Hence deployment of sophisticated statistical methods to analyze the situation is preferred
- Predictive analysis uses multiple statistical concepts (Regression, Forecasting, Association rules, Classification, Text mining, etc.)
- Provides an accuracy number associated with providing confidence in prediction
- Provides a data backed benchmark/predictions to make decisions with higher confidence
- Allows proactive actions for potential business threats
- Customer was experiencing huge competitive pricing pressure leading to misses in sales targets, hence wanted to establish a statistical model for pricing and demand relationship
- Leveraged historic own pricing, promotion, sales information along with competitor pricing, and market sales indicators to build a linear regression model
- Statistical model provided a mathematical relationship between own price, competitor price, promotions on own product demand enabling business power to simulate various competitive pricing situations to predict product demand
- Also provided summary of price elasticity of products to ensure selective price movements to maximize profits
- Retail client wanted to define upsell path for group of customers and hence wanted recommendations on products that those customers might tend to purchase
- Leveraged historic sales information at transaction level with statistical techniques like market basket analysis and collaborative filtering to conclude item-item association and user-user similarities
- Same was leveraged to design personalized product recommendations for customer focus group
- This analytical exercise allowed the client to drive 37% improvement in revenue for focus group without any addition in marketing & sales team
