CASE STUDY: ANALYZING A CAMPAIGN FOR A REWARDS PROGRAM
AGENCIES
The Challenge GET SOCIAL DATA FOR A CLIENT OF A MARKETING AGENCY WHO REPRESENTS AN AIRLINE’S LOYALTY PROGRAM.
The Customer A marketing agency who represents a coalition loyalty program owned by, a global loyalty management company.
The Semeon platform has been used to do social media analysis on a marketing agency’s client who is the loyalty program division of an airline. The customer’s client is a coalition loyalty program owned by, a global loyalty management company. The program was created as an incentive program for its frequent flyer customers. There are millions of active members in the program. Given the nature of the program there was a lot of spam and coupon/deal related information.
The Solution Using the Semeon platform to remove irrelevant data allowed Semeon to pick up clean data to be used for analysis. Aggressive filters were used to remove spam and coupon/deal related information. Semeon was able to pick up the relevant information about user experience for the coalition loyalty program. Semeon was able to be determined whether their experience was positive or negative.
Project Details Over 10,000 posts analyzed for the period covering October 1, 2013 – August 31, 2014. All Social Media sources were examined and the analysis was completed in English with a geographical focus on Canada.
Geolocation: CANADA
Sources: ALL
Timeline: Oct 2013Aug 2014
The Benefits Determining user experience based on their online voice allowed the company to understand where to make changes and where to invest. Using Semeon’s engine to divide up the different categories related to a loyalty program allowed the marketing agency to understand each facet of their customer’s experience. Moreover, knowing that more than 90% of posts analyzed appeared to be from members who evoke specific points relating to membership, helped the marketing agency structure its marketing campaigns and increase business intelligence.
Semeon is a privately held company in Montreal that combines the best of semantic, sentiment, and statistical analysis.
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