CRM data for organisations becomes out-dated (decays) over time primarily due to organisations:
- Going out of business,
- Relocating, and/or
- Changing their name.
The following article provides indicative decay rates based on a series of assumptions, which may, or may not be applicable to your unique circumstances. However, when compared to the results from our actual data cleansing and verification exercises, this model does provide a good ball-park indication.
Please note that this data is applicable to Australian organisations only (eg Contact = ABC Company Pty Ltd). Please click on the following for decay rates of Contacts within Organisations and Residential Contacts.
Out of Business
The number of Australian organisations that go out of business each year is predominantly determined by their size. The following data is from the ABS and is averaged over the last four years.
- Non–employing businesses – 16.2%
- 1–4 employees – 9.5%
- 5–19 employees – 6.2%
- 20–199 employees – 4.7%
- 200+ employees – 5.5%
The weighted average of Australian organisations going out of business is 7.3% per year.
Business Relocations
Reliable business relocation data is virtually impossible to obtain. This is one of the few occasions that Google has let us down. From our internal analysis, we see approximately 2% of organisations relocating every year. This estimate however, is based on our limited insight as we do not have access to the supporting data, and as such, may be flawed. (We would love to hear from you if you have better statistics in this regard)
Business relocations typically affect your contacts physical addresses. Phone, fax numbers and postal addresses may change too.
Name Changes
4.93% of ASX listed companies changed their name last year, typically as a result of acquisitions or as a decoy for the lack of viable strategy. As such, it’s more prevalent in medium to large organisations.
Name changes have a delayed effect on your CRM data in that companies typically retain their original addresses, telephone numbers and fax numbers. Company name, email and web URL do however change. Old email addresses are initially re-routed to the new email address however this forwarding is often turned off a couple of years down the line.
Predicted CRM Decay Rates for Organisations
Combining the above averages into a depreciating-balance model, one arrives at the following estimates.
Putting the Data to Use
For example, if the average time since you last successfully contacted your organisational clients was two years, then from the above table, your % of incorrect records will be around 26%.
When cleaning REAL two year old data, there is a discrepancy between this theoretical value of 26% and reality. Sometimes it’s higher, sometimes it’s lower. All depends on you actual customer base and how accurately your data was captured in the first place.
So How Does this Affect Me?
Inaccurate CRM data creates inefficiencies within organisations and lost business opportunities. For example if you are calling prospective clients from an outbound call centre with manual systems, each wrong phone number will cost you around $0.74. Issuing a month end statements via Australia Post to the wrong address: $0.82 plus the loss of interest due to delayed payment. A real cheap mail out comprising a postcard & stamp will cost $0.59 for every bad contact. Whilst these amounts are small, multiply this by a 1,000 bad contacts every month and it starts to add up.
Although emails effectively cost nothing to send, mass email-outs with a high failure (bounce) rate may get you listed on spam registers. This results in future legitimate emails being sent to the spam bin and not delivered to your intended recipients.
How Frequently Should I Clean My Data?
The frequency at which you should complete a data cleaning exercise is driven by simple economics:
Data Cleaning is required when
the cost associated with bad CRM data
is greater than
the cost to clean the data.
Some organisations clean their data on a weekly basis, whilst for others, every two years may be sufficient. It all depends on the quality and purpose of your data, the rate at which it’s decaying, and the cost of bad contacts.