Spring data cleaning: 5 tips so your sales data is ready for analytics

Predictive analytics needs a foundation of clean data. Here are our top five tips for data hygiene, so you’re always ready for marketing and sales campaigns via Salesforce. You can use these immediately in any environment:

1. Standardize addresses and change of address

Up to 30% of records lack complete address information, and 10% of businesses move each year. This affects both deliverability and duplicate search. If you have not done so in three years, run a Change of Address (NCOA) process to get any new addresses. When you receive a street address change, but it’s a PO Box in the same city, retain both – one for mailing and one for secondary validation.

2. Dupes are the silent killer – banish them

De-dupe the files using a variety of match logic. Because a single comprehensive definition does not fit all. You can try a loose match logic (a few criteria, gives more duplicates) or tight match logic (more criteria, resulting in fewer duplicates).

Take address elements into account, but use transactional information to determine which record to keep or drop.

For example, you might want to merge a record with multiple contacts into a record with the largest sales amount or the record with the longest time on file. Run dupes within your accounts, as well as against other sources. If you see the same record in two tables, delete or mark clearly why and set a shelf life to expire one.

3. Fix bad data entry

With key fields you use the most, particularly text fields, consolidate the misspellings and mixed-cases that make reporting difficult. It can feel like a huge task. We know! But you don’t have to do everything at once – fix just five fields this week, more next week. It’s an opportunity to update correction rules and fix legacy errors.

As a bonus, segmentation and reporting become much easier.

4. Match to a B2B database or Tier 1 compiled list

The obvious next step can be to append the firmographic elements for analytics. You can also use information from the match rate to help further clean your data. Segment the unmatched customers and evaluate if they:

• Are duplicated somewhere else
• Have failed address standardization
• Have any useful fields, or
• Are possibly orphaned from a prior merge exercise.

It’s springtime, prune the dead weight!

5. Append firmographic data

While technically it is not cleaning, append and enrichment of your data can bring big dividends for data hygiene.

Compare all contact information against the external data and fix format errors, missing extensions, suite numbers etc., while adding new contact info you did not have.