How to manage the world’s longest grocery list


Every night, I drive right by our favorite grocery store on my way home from work, so I usually call home to see if we need anything. Sometimes the list gets long and I have to pull over to write it all down. That’s how I manage our grocery list. It’s very low tech – but it works. And then there’s Catalina Marketing, which analyzes over 23 Billion customer transaction records every year (that’s over 63 million records daily). They use a different method to manage their lists (more on that below).

Many of us may not realize that Catalina Marketing is the company that provides those little point-of-sale coupons that print out at the cash register (often in a grocery store). They are the largest customer behavior marketing company in the world and they analyze all those transaction records on behalf of the stores so that you can get relevant, valuable offers based on who you are and what you do.

During a recent Webinar we produced with Netezza, we invited Ryan Carr from Catalina to showcase how they manage their business to enable better and faster processing. It’s a fascinating study in how strategic customer analytics can be to a business. With the right process changes and computing investments, Catalina has enabled their tremendous growth by creating real value for both their clients and their client’s shoppers.

As recently as five years ago, Catalina had customer transaction models of about 1 million records, or 40 Mb of data, which were processed on 1 CPU running SAS with 500 Gb of disk space. After 16 – 20 weeks of processing, they had good results and could make accurate predictions, but it was limited to the scope of the model. Today, their models are as big as 140 trillion (yes, with a "T" ) records, or 800 Terabytes of “virtual” data, which are processed on over 400 CPUs running SAS and Netezza with 120 Terabytes of physical disk space. Those models might run for a total of 3 days, and the results are excellent for a few key reasons.

The exponentially faster processing time cut their cost per model, which made the models more widely available to clients. The larger databases and samples allowed more granularity, which means they can look at common situations and also extremely rare events and deliver more accurate insights so the store can know what kind of customer will most want a particular offer. These quality improvements have translated into real results as they moved from 40% improvements in response rates on a smaller scale to response rate improvements of over 600% on a much larger scale. In other words, Catalina used to be known for applying behavioral targeting to enable stores to sell about 40% more of a particular product to a particular target market. Now, they’ve gotten so good at it that their customers now sell as much as 600% more product to that same group of people.

That’s what I call managing a list! Everyone gets something from the deal - the store sells more, the customers get better offers and Catalina just gets better and better at what it does best. Take a moment to view the on-demand version of this Webinar and hear how artful the science of marketing can be.

You can also learn more about Catalina Marketing with their customer success story.


About Author

John Balla

Principal Marketing Strategist

Hi, I'm John Balla - a Digital Marketing Principal here at SAS focused on Content Strategy. I co-founded the SAS Customer Intelligence blog and served as Editor for five years. I like to find and share content and experiences that open doors, answer questions and maybe even challenge assumptions so better questions can be asked. Outside of work I stay busy with my wife and I keeping up with my 2 awesome college-age kids, volunteering for the Boy Scouts, keeping my garden green, striving for green living, expressing myself with puns, and making my own café con leche every morning. I’ve lived and worked on 3 contents and can communicate fluently in Spanish, Portuguese, Hungarian and passable English. Prior to SAS, my experience in marketing ranges from Fortune 100 companies to co-founding two start ups. I studied economics at the University of Illinois at Urbana-Champaign and got an MBA from Georgetown. Follow me on Twitter. Connect with me on LinkedIn.

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  1. Pingback: Getting to the segment of one - Customer Analytics

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