Predictive analytics needs a foundation of clean data. Here are top tips from our most recent lead gen implementation on Salesforce.com. You can use these immediately in any environment: Address standardization and change of address. Typically up to 30% of records lack complete address information. This affects both deliverability and duplicate search. If you have not done so in 3 years, run a Change of Address (NCOA) process to get your customer’s new addresses. Stay up to date on your customers because 10% of businesses move each year. 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. Dupes are the silent killer. Because a single comprehensive definition does not fit all, de-dupe the files using a variety of match logic. 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, between duplicates you might want to merge a record with multiple contacts into a record with the largest sales amount or the record with the longest …Read More
Tag Archives: data quality
Are you doing business in 107 countries? Or 7? Data hygiene matters in predictive analytics
On a recent assignment for setting up lead generation, we took on an existing customer database to build a statistical model to score a leads database. The client does business in 7 countries, or so they said and I believed them. But they quickly added a caveat, “no one has looked at our database in awhile.” First, we looked at their billing country field. This had been an open text field in their Salesforce.com system that could be edited by just about anyone. What we found was amazing. There were so many variations for each country that unique values quickly proliferated to 107. Misspelling, case difference, punctuation and abbreviations all conspired to create many versions of the same country! Our first order of the day was to identify the obvious countries and group them, followed by corrections to the remaining data. This took 107 countries down to the correct 7. Then the country field was locked and going forward, a drop-down menu of countries is being used to prevent this proliferation from happening again. For statistical modelers, why is cleaning data important for sales intelligence? First of all, we want to point out that even a simple field can pose a challenge. In the absence …Read More