| Sr. No. |
|
Clients |
|
Domain |
|
Business Objective |
|
Approach |
|
Result |
|
Tools / Technique Used |
| |
1 |
|
A leading hardware manufacturer |
|
ROI analysis |
|
To measure ROI of a marketing program and prioritize future spend for similar programs |
|
A test and control group was designed for pre and post program measurement and ANOVA was performed on sales of both test and control stores to see if there was significant ROI differences in sales of test stores |
|
The analysis suggested that there was significant ROI in only two retailers. This helped the client direct spend for the 2 retailers towards other programs |
|
ANOVA |
| |
2 |
|
A large retailer |
|
Interactive reporting |
|
To develop an interactive dashboard to compare and benchmark sales performance of various stores |
|
A MS-Excel based dashboard was developed to perform drill down analysis by division within store, segment and store characteristics. Ranking functionality enabled to differentiate best /worst performing stores on any user selected metric |
|
The dashboard can be monitored on a real time basis and corrective actions can quickly be taken to improve performance of selective stores |
|
SAS , SQL, EXCEL & VBA |
| |
3 |
|
A leading Credit card company |
|
Cross sell / up sell |
|
Develop sell/Cross sell strategy for credit card portfolio |
|
Predictive models were developed to identify customers with higher propensity to purchase products based on past spend behavior |
|
The predicted leads resulted in significant lift in conversion rate of leads prioritized by the model as compared to the leads prior to model |
|
Segmentation, Logistic Regression |
| |
4 |
|
A leading Re-insurance company |
|
Risk Scorecard |
|
To develop an Insurance Risk Scorecard |
|
Key risk drivers were identified that need to be monitored on regular basis for entire reinsurance business |
|
This score card used by multiple groups within the client and reported on a periodic basis |
|
Explanatory Data Analysis |
| |
5 |
|
A leading charge and credit card company |
|
Performance Scorecard |
|
To track performance of newly acquired accounts |
|
The analysis compared performance of different offers /campaigns over a period of time for the newly acquired accounts |
|
KPI was compared with the projected finance data to better understand the profitability by product mix/offers |
|
Exploratory data analysis |
| |
6 |
|
A large CPG company |
|
Customized tool development |
|
To view various sales performance metrics by region |
|
An interactive map dashboard was developed which allowed users to select any region and view corresponding sales metrics in an easy to comprehend manner |
|
Almost real time view of sales performance across various regions. Various business defined metrics can also be monitored for quicker & effective decision making |
|
Xcelsius, Dundas Charts |
| |
7 |
|
A large CPG company |
|
Data management |
|
To have access to latest and accurate data by automating routine and time consuming aspects of data preparation |
|
A tool was developed in VB and MS-Access which could integrate different data sources and apply business rules (like weights, cut-offs, etc) for data cleaning |
|
Reports were available almost real time, with latest data. Users were able to spend more time in interpreting data for taking effective decisions rather than collating data |
|
VB, MS-Access, Excel |
| |
8 |
|
A large retailer |
|
Dashboard |
|
To develop an interactive dashboard to compare and benchmark merchandize performance of various brands across stores |
|
A VBA based MS-Excel dashboard Behavior |
|
The dashboard can be monitored on a real time basis and corrective actions can quickly be taken to improve performance of selective stores/brands |
|
SAS, SQL, Excel & VBA |
| |
9 |
|
A leading retailer |
|
Affinity analysis |
|
To understand affinities of a particular product A with other products in the same as well as other categories. |
|
Using the ‘support’ and ‘confidence’ levels for all products, varying levels of association of product A with anchor products in same as well as other categories were found |
|
The analysis showed that product A is 3.5 times more likely to appear in the basket with product A than in any basket and helped the client in product placement and promotion related decisions |
|
Market Basket Analysis, Sequencing |
| |
10 |
|
A leading retailer |
|
Efficiency analysis |
|
To identify poor performing stores with respect to better performing, similar stores |
|
Efficiency was calculated for each store basis input and output, using data envelopment analysis technique. A benchmarking exercise was done to identify stores that had below average efficiency |
|
The results helped the client identify inefficient stores which were not performing as compared to analogous stores and a follow up, deep-dive analysis was taken in to these stores |
|
Data Envelopment analysis |
| |
11 |
|
A leading office supplies retailer |
|
Promotion Targeting |
|
To target customers for an upcoming promotion campaign basis higher likelihood of purchase and incremental purchase value |
|
CHAID was used to estimate potential incremental sales and a regression model was fit to estimate replenishment rate for customers. The scores of both models were combined to target customers for the upcoming promotion |
|
The model helped the client in reaching out to the right customers for its promotion campaign |
|
CHAID, Linear regression model |
| |
12 |
|
A large brokerage house |
|
Predictive modeling for Prioritizing customers |
|
To develop a prioritization model for servicing customers |
|
Predictive models were developed to prioritize customers which received premium service, without increasing sales force |
|
The model helped the business increase its share of wallet from premium customers |
|
Segmentation, Logistic Regression |
