C5i Case Studies Using personalized campaigns to increase revenue and profitability

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	Using personalized campaigns to increase revenue and profitability C5i

Using personalized campaigns to increase revenue and profitability

C5i

Technology Category	Analytics & Modeling - Big Data Analytics Analytics & Modeling - Predictive Analytics
Applicable Industries	Retail
Applicable Functions	Sales & Marketing
Use Cases	Demand Planning & Forecasting Predictive Replenishment
Services	Data Science Services
Challenge	The retail client, based in Dubai, was already running in-store mass promotions every year to drive sales and enhance customer relationships. Each sale period ran for about four weeks and customers received a discount ranging from 25% -75% with every item they bought during the sale period. However, the client wanted to increase revenues and footfall and thus, asked Blueocean Market Intelligence to devise personalized targeted campaigns (BTL-Below the Line). Read More
About Customer	The customer is a leading retailer based in Dubai. They have a significant presence in the retail industry and regularly run in-store promotions to drive sales and enhance customer relationships. These promotions typically run for about four weeks, during which customers receive discounts ranging from 25% to 75% on every item they purchase. Despite these efforts, the retailer was looking for ways to increase revenues and footfall in their stores. They sought the help of Blueocean Market Intelligence to devise personalized, targeted campaigns. Read More
Solution	Blueocean Market Intelligence approached the challenge by performing customer segmentation using transaction level data. This allowed them to identify groups of customers and give the most weightage to the customer grouping with the highest RFM of purchase. The team used ‘association mining techniques’ to identify the products that could be bundled together and used segmentation/clustering approaches (Hierarchical/K-Means) to create store segments based on the similarities/differentiating factors such as product preference, ethnicity, promo response etc. The team then designed the most effective customer-centric, targeted campaign strategies using analytics techniques at strategic points in the client’s marketing campaign cycle. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Big Data Analytics

Analytics & Modeling - Predictive Analytics

Applicable Industries

Retail

Applicable Functions

Sales & Marketing

Use Cases

Demand Planning & Forecasting

Predictive Replenishment

Services

Data Science Services

Challenge

The retail client, based in Dubai, was already running in-store mass promotions every year to drive sales and enhance customer relationships. Each sale period ran for about four weeks and customers received a discount ranging from 25% -75% with every item they bought during the sale period. However, the client wanted to increase revenues and footfall and thus, asked Blueocean Market Intelligence to devise personalized targeted campaigns (BTL-Below the Line).

About Customer

The customer is a leading retailer based in Dubai. They have a significant presence in the retail industry and regularly run in-store promotions to drive sales and enhance customer relationships. These promotions typically run for about four weeks, during which customers receive discounts ranging from 25% to 75% on every item they purchase. Despite these efforts, the retailer was looking for ways to increase revenues and footfall in their stores. They sought the help of Blueocean Market Intelligence to devise personalized, targeted campaigns.

Solution

Blueocean Market Intelligence approached the challenge by performing customer segmentation using transaction level data. This allowed them to identify groups of customers and give the most weightage to the customer grouping with the highest RFM of purchase. The team used ‘association mining techniques’ to identify the products that could be bundled together and used segmentation/clustering approaches (Hierarchical/K-Means) to create store segments based on the similarities/differentiating factors such as product preference, ethnicity, promo response etc. The team then designed the most effective customer-centric, targeted campaign strategies using analytics techniques at strategic points in the client’s marketing campaign cycle.

Impact #1	Identified the most likely promotion respondents along with the best possible combination of products to offer them
Impact #2	Helped to find the best product affiliations and devise strategies around product bundling, upselling, cross-selling and assortment planning
Impact #3	Identified the right marketing channel/depth of discount/assortment planning for each store segment

Impact #1

Identified the most likely promotion respondents along with the best possible combination of products to offer them

Impact #2

Helped to find the best product affiliations and devise strategies around product bundling, upselling, cross-selling and assortment planning

Impact #3

Identified the right marketing channel/depth of discount/assortment planning for each store segment

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Overview

Using personalized campaigns to increase revenue and profitability

Operational Impact