See video of interview with Jean-Paul Isson, director of BI, Monster Canada
File type: Windows Media Video. Length: 12.23 minutes
Hello and welcome to another Voices interview. I’m Joaquim P. Menezes, Web Editor of IT World Canada and our featured guest today is Jean-Paul Isson, Director of Business Intelligence at Monster Canada, a leading Canadian job search portal on the Web. Monster Canada’s goal was to turn information into actionable intelligence, to better analyze the colossal data volumes it handles each day and by doing so, to understand and serve its customers more effectively. In this interview Jean-Paul shares how Monster Canada accomplished these goals using Business Intelligence (BI) tools.
Jean-Paul could you start by telling us a bit about yourself and your background?
I graduated in mathematics and statistics from the University of Paris, France. I moved to Canada eight years ago, working as a senior statistician with the University of Quebec at Hull. I then joined Fido as manager of BI for Fido Wireless, which now belongs to Rogers. Three years ago, in 2005, I joined Monster Canada to build its BI team.
What were the key drivers for the use of BI and analytic tools at Monster Canada?
Monster Canada – like many other Internet companies – [deals with] a huge amount of data. It wanted to be able to [integrate] this data, understand [its] customers, and understand the market. That’s why it came up with the idea of having a BI team in house. I was hired to build this team in terms of resources, software and tools.
Prior to your coming on board was Monster Canada using any BI software?
They weren’t using SAS at all. They were using alternative software.
The first thing – when you’re doing data mining – is to be able to effectively extract and organize your data before you do the modeling, segmentation and other stuff. So, we were using an internal SQL tool for this – that was very cumbersome. Sometimes for basic reports, or to get a dashboard finalized it would take as [as long as] two to three days.
When we moved over to SAS, this time to create a dashboard [was] shortened from three days to a couple of hours. This had a tremendous positive impact on the productivity of our team.
When I joined Monster, I had already been using SAS for 15 years. I realized SAS would be the [best] tool for Monster.ca to handle its huge data volumes and do the type of analytics and advanced modeling it needed.
What SAS products did you’ll implement?
We implemented SAS DAT (Data and Text Mining), SAS/STAT and SAS Enterprise Miner. With SAS DAT [we ensured] we could do effective data extraction to be able to do reporting. After reporting we did segmentation with SAS/STAT, and we did a lot of predictive modeling using Enterprise Miner.
One stated objective of the new BI implementation was to help Monster Canada better understand its customers, so as to offer them the right blend of services. To what extent have you’ll achieved this goal?
My team’s mandate is to build customer and market knowledge. When we moved to SAS we were able to do that. That’s because SAS offers us integrated software that allows us to [perform] almost all the steps of data analysis, from extraction to intelligence.
So we were able to extract data, manipulate it, prepare it for dashboarding (having the company understand all the needed KPIs) and then move to the next level where – using a basic clustering model based on SAS/STAT – we could segment the data to define profiles, knowing who are customers, what do the want etc) and then move on to prediction, where we would scour the whole universe of Dun and Bradstreet for Canada, using SAS Enterprise Miner – to be able to target our acquisition and optimize our sale productivity.
After the first year of doing that we increased our retention rate by 15 per cent, and our sales productivity increased as well by 40 per cent. This had a tremendous impact on our business.
Can you put a dollar value to these increases in retention rate and productivity?
While I can’t release information about the exact number – we’re talking millions of dollars. The impact on our business was really tremendous. Customer retention has a big impact on revenue, because you leverage your existing customer base, so you have wallet share growth that you can count on.
What other operational efficiencies did you’ll experience – other than the dramatic speeding up of the dashboard creation process?
The first big benefit was being able to [effectively] extract the data and [create] a micro data mart that enabled us to do analytics. The second was the ability to build a multi-dimensional dashboard that would help the company to understand the business [better] – this was something we were not able to do before.
A key element of data extraction and data manipulation was [the ability] to add external data – such as Dun & Bradstreet, and Stats Canada data – into our internal data system. And also being able to scour an entire universe of Canadian companies using internal and external data, and create a category for every customer and prospective customer.
[It also enabled us to] set the right target and appoint the right rep for every single region in Canada. Finally, we’re also able to optimize our coverage better. We look at the Canadian territory and conclude that “we need (say) 20 reps to cover this territory for new acquisitions. So we’re able to optimize very single portfolio using SAS analytics.
To what extent are you’ll able to share with customers insights gained from your use of BI? For example, are you able to make suggestions about how customers can improve the quality of their postings on your site?
This is a very good question because it goes to the core of most of what we’re talking about here – helping the customer get the needed results.
When customers come to us with a job posting they expect to get qualified candidates for the job they advertise.
At Monster Canada we did something really unique. We were able to help every company get the best results for their job posting.
How did we accomplish this? Say you place a job posting for a marketing analyst in the Toronto area. We look at the history of all jobs posted – in the same category, with the same title, in the same location (Toronto) – and compare all those other postings with yours. In this way we can let you know whether your job is over or under performing.
We are now also able to tell you why – and to suggest some best practices that will help you to improve your job posting performance. This service too has a tremendous impact on customer satisfaction and customer retention.
Have you’ll tracked the numbers for the increase in your customer retention rate a result of your offering clients these value-added services?
Yes. After the first year of doing that we increased the customer retention rate by 15 per cent. And we’re expecting to further increase this number by 20 – 25 per cent in the upcoming year, because we now use SAS to provide these “best results” to our clients.
At the same time we’re implementing inte
