We all agree about data decay in CRM, even with a conservative rate of 22% (based on average US company employee turnover rate) you might end up losing lot of marketing dollars not only as direct cost of managing dirty data but also as an opportunity cost of not reaching to potential customers.
If you are out in the market to purchase data enrichment services, you need to be sure of what you are looking for & more importantly what you are paying for.
If a vendor is billing you based on number of API calls you make to enrich data then you need to do assessment how an API call is adding value to your CRM. Most often API based data enrichment is done by adding missing information associated with domain of an account record & matched email address.
This means either an error in domain or email address would add no value to a record but would cost you as you made an API call.
To overcome such pitfalls, you have to treat data enrichment as a comprehensive approach. Data enrichment is not a single service but a combination of many. Metaphorically data enrichment a book itself with many sub chapters. Here are few things you need to keep in mind before opting for data enrichment service:
How often you go back to a record & enrich it is very critical, as data continuously decays even 30 day frequency of enrichment would mean 1.83% of data going bad ( 22% / 12). This means you need a service that is always on & updates records as soon as new information is available.
Anyone who has dug deep in email validation process would understand that getting a valid email is just half a task. Most common service based email validation would flag an email as valid, but some of these valid emails does not necessarily assure delivery to actual contact inbox.
Lot of email domains have a feature of catch-all account, this feature sends invalid emails associated with the domain to a catch-all account, this means though emails goes through it does not reach intended recipient.
So one should look for an email validation service that flags catch-all accounts for further manual scrutiny.
Data Appending for micro segmentation
CRM data is often segmented based on some key fields before any marketing campaigns are applied to it. These key fields that form base of your segmentation need to be present in CRM, so before opting for data enrichment service look if these key fields are available or needs to be addressed too.
For impactful campaigns you need to micro-segment your data based not only on traditional demographic data points but also on technographic data of accounts, social or persona type of contacts, interests, direct phone number etc.
Other key points
Besides above, one should also consider these points when looking for data enrichment services:
- Standardization – Fields such as company name, revenue, employees and location should be standardized for all CRM users (ex: ABC, Inc. or ABC).
- Parent-Child linkage –Link child companies with parent companies within CRM.
- De-Duping – Duplicates will occur through automation or double entry, the established system needs to detect, do continuous fuzzy matching and eliminate through active learning.
- Data expansion- going beyond current records & pulling more contact records for enhanced coverage across target persona or job functions.
Below are some questions you can ask potential data enrichment vendor:
Is data verified prior to updation?
Almost all will say no.
Do they provide flag for catch-all emails?
Almost all will say no.
How frequently do they update data?
Ideally you should ask for on the fly data updation, even a 30 day frequency would cost you marketing dollars due to high data decay rate. Needless to mention 100% data update
What value is added if unverified data is updated despite you paying for number of API calls?
You should not pay for bad or unverified data.
Can we do dynamic data management across all our TAM account ?
You need a system, which continuously unify contact data across ID resolution, link back to entire account hierarchy, but syncing with external data to ensure the up-keeping of data quality as an ongoing affair than adhoc projects.