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Loblaws PC Optimum

Loblaw Groceterias was founded by Theodore Loblaw and John Milton Cork in 1919. Loblaw opened the first Canadian self-service grocery store in Toronto in June 1919. Since it’s introduction, Loblaws has become Canada’s largest grocery store chain. During this grown they have expanded into new product lines and aquired additional companies. One notable acquistion being PC Optimum and the introduction of this loyalty program in all Loblaws stores.

Problem:

The client’s extensive loyalty program was not reaching many of their customers. Thousands of letters, emails, and telephone calls weren’t getting through to their intended recipients. This resulted in disappointed customers, lost revenues and missed opportunities.

Diagnosis:

The client’s customer loyalty database was huge, but it was filled with errors resulting from system migrations, business acquisitions, and a lack of proper maintenance over the years. Contact data formatting problems, duplicate records, field omissions and inaccuracies all prevented the client from running effective loyalty communications programs. Response rates were low, returned mail was high messages were bouncing, and calls went unanswered. Intelligent, segmented, customer targeting was particularly difficult. Unless these customer data problems were solved, the client’s customer loyalty program would continue to suffer.

Solution:


Cleanlist performed an exhaustive assessment of the client’s data. Custom routines were written to parse, format, and consolidate the client’s numerous customer databases. Errors and inconsistencies were corrected, duplicates removed, and the structure of the data was optimized. With a new, clean benchmark for their customer data in place, Cleanlist was then able to enhance the data with current, up-to-date addresses, phone numbers, and more.

Achievements:

  • Multiple databases were formatted, cleaned and consolidated
  • Contact information was updated and verified for accuracy
  • Missing contact data was appended with up-to-date information

Key Results:

  • Improved postal accuracy of client’s customer database from 40% to 90%
  • Identified and corrected the nearly 10% of customers who had moved
  • Identified and corrected the nearly 10% of customers who had moved

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