Automated data onboarding: more data preparation tasks

This is part 4 of our new blog series on automated data onboarding / list loading. Last time we started covering data preparation tasks. Today we will finish discussing the rest of the automated data preparation tasks.

If you need to read the previous parts of this blog series, you can find them here, here, and here.

A.5    Enrich & Validate with Third-Party Data Providers

Once the data is as complete and standardized as possible using open data, you may want to enrich and validate the data with third-party databases. Examples include:

• Validating and appending firmographic data from Dun & Bradstreet or Orb Intelligence
• Validating and appending contact data from InsideView or  Oceanos
• Validating and appending audience data from Acxiom
• Validating email deliverability with BriteVerify or SYNTHio
• Obtaining technographic data from DiscoverOrg
• Looking up anonymous company using IP address from KickFire
• Looking up email using name and company using GetEmail.io
• Looking up business contact info using personal contact info with People Data Labs
• Validating address and appending place info using Google Maps or  FourSquare

A number of these services, like Google Maps and FourSquare, offer a freemium-level service that provides you substantial amounts of free data every day, so why not take advantage of it?

A.6    Translate and Parse International Data

If you market and sell globally, no doubt you must deal with non-English data, which can be hard to work with for your sales team and your other automation technologies. Here are some typical challenges you can take care of during the automated data preparation and onboarding process:

• Translating non-English job titles to English
• Translating and normalizing Country and Province names, e.g., España → Spain, Québec → Quebec
• Reformatting address data in Asia and South America that may be formatted very differently than with US and European conventions, with different sequences. So even if you have the complete and accurate address, you may still end up with the wrong data fields populated due to mismatched field mapping. Using a service like Google Maps that can correctly parse out the address components can make sure that the data goes into your systems correctly. For example, a Chinese address format uses this sequence:

1. Country
2. Postal Code
3. Province
4. City
5. Division
6. Street
7. Ally
8. House Number
9. Building or Business Name
10. Floor
11. Person Name and honorific

A.7    Customize Data Mapping Even if It’s Geographically Incorrect

No one ever said your CRM systems data has to be geographically accurate. A CRM system’s purpose is to support a company’s sales and marketing activities, so its data is often aligned with the structure of the sales organization, even if any third-grade geography teacher could tell you your data is wrong. Here are some common examples:

• Is Puerto Rico a state or a country? If Puerto Rico is part of your US sales territory, then you probably want it as a state. If it is part of your Latin America sales territory, then you probably want it as a country.
• The UK has no concept of a state/province, so many companies populate the county data into the state data field.
• Ireland does have both county and province, but since Ireland and UK territories are often handled by the same sales team and UK is the major territory, Ireland is often segmented by county as well, thus forcing the county data into the state data field.
• Are Taiwan and Hong Kong independent countries, part of China, or part of “Greater China”? We’ill leave that political hot potato to you to decide. By the way, is “Greater China” a country or a region?

As you can see, the right answer is not what is factually accurate, but what is aligned with your company’s sales territory structure.

A.8    Match to Account

Many decisions and processes in marketing and sales are account driven, especially if your organization has jumped on the Account Based Marketing (ABM) bandwagon recently, so it’s important to match an incoming lead to any existing account. Lead-to-account matching is usually done using domain and company name, and possibly other supporting fields like country. It is common that one lead can be matched to multiple account records. When that happens, you need to apply resolution logic to determine which account is the best match. Commonly-used resolution criteria include:

• Is the account a parent or ultimate parent account?
• Is the account type customer or partner?
• Is the account a key account?
• Number of active and past opportunities associated with the account
• Level of activity/engagement of the contacts associated with the account
• Is the account and the lead from the same country?

Next up, we will cover decision-making tasks.