A restaurant chain had over the years acquired customer contact details from a number of different campaign and marketing initiatives. The client wished to utilise the these contacts to promote its new loyalty application and had spent thousands or Rand acquiring this information over a number of years. Due to the lack of common identifiers (some email and some telephone numbers) and the long time since the data was captured, the information was in an unusable state. We developed a custom algorithm to consolidate the lists while retaining the maximum information from each of the datasets with full auditability in terms of which fact was sourced from each dataset.
The consolidated list was deduplicated using a dynamic scoring algorithm finding an optimal balance between false matches and a failure to identify the that the different records relates to the same person. After validating the contact information and excluding parties which had already downloaded the app, the client was able to run their campaign resulting in a significant additional uptake in application users.