Calculator assumptions
data quality management
Definitions
  • Campaign cost – cost to run each marketing campaign annually
  • Sales before data clean – sales from campaigns with an outdated database
  • Sales after data clean – sales from campaigns using up-to-date & enhanced data
  • Investment – cleaning your data with Sensis Data Solutions
  • Potential dollar benefit – net improvement after using updated data
Mail-outs
  1. Campaign cost – volume of addresses x frequency of use @ $0.50 Auspost standard rates
  2. Sales before data clean – total volume of mail items sent x average sales value x conversion rate of 0.5%
  3. Investment – cost to improve the total number of addresses (about $0.068 per address which includes append, validation, NCOA etc)
  4. Cost reduction – estimated 15% saving with Auspost discounts once DPIDs / barcodes apply
  5. Sales after data clean – estimating a 7% address improvement = same level of sales improvements
  6. Potential dollar benefit – new sales + cost reduction LESS old sales number and cost to improve
Emails
  1. Campaign cost – volume of emails x frequency of use @ $0.125 per email – only estimate cost to email + cost of creative etc
  2. Sales before data clean – total volume of email items sent x average sales value x conversion rate of 0.08%
  3. Investment – cost to improve the total number of emails (about $0.155 per email which includes append, validation)
  4. Cost reduction – $0.125 x and 10% uplift in new emails x frequency
  5. Sales after data clean – estimating a 10% email improvement = same level of sales improvements
  6. Potential dollar benefit – new sales + cost reduction LESS old sales number and cost to improve
Calls
  1. Campaign cost – calls x frequency of use @ $1.55 per attempt / standard telco rates
  2. Sales before data clean – total volume of calls x average sales value x conversion rate of 2% and 80% phone number accuracy
  3. Investment – cost to improve the total number of phone numbers (about $0.155 per phone which includes append, validation)
  4. Cost reduction – estimated 13% saving x $1.55 per call
  5. Sales after data clean – estimating a 13% phone improvement = same level of sales improvements
  6. Potential dollar benefit – new sales + cost reduction LESS old sales number and cost to improve
SMS
  1. Campaign cost – volume of mobiles x $0.30 per SMS – estimated cost to send / receive + tech + marketing investment etc
  2. Sales before data clean – total volume of SMS items sent x average sales value x conversion rate of 2.5%
  3. Investment – cost to improve the total number of SMS (about $0.155 per mobile which includes append, validation)
  4. Cost reduction – estimated 7% saving x $0.30 per SMS campaign cost
  5. Sales after data clean – estimating a 15% improvement = same level of sales improvements
  6. Potential dollar benefit – new sales + cost reduction LESS old sales number and cost to improve