Any best practices for the list being uploaded?
Yes. While most demographic indicators work well with any list, age range is the most challenging indicator to extract from names and it may be impacted by the bias in the list you’re uploading. In order to get the most accurate results for age range segmentation/append you should follow some best practices while preparing your list.
In general, the rule of thumb is to upload all your customers/prospects or get a random subset of them to avoid unintentional age bias. If you can’t upload the whole list, you can ask your IT or data person to get random subset of records from your CRM or database. Or contact us directly for assistance. For example, you should not upload only names starting on specific letter or only a particular group of your customers, etc.
Do you provide discounts for large lists?
Yes. Bigger your list smaller the price. You can see how price differs depending on list size using pricing calculator on PRICING page or calculate your particular list in dashboard.
What data do you detect?
We currently provide analytics and segmentation/append for Gender (Male, Female), Age Range (18-39, 40-59, 60+), Race (White Americans, African Americans, Asian Americans, Native Americans, Two or more races), Hispanic origin (Hispanic, non-Hispanic) and Ethnicity (British, Germanic, Hispanic, African, Italian, East European, French, Nordic, Indian, East Asian, Jewish, Japanese, Arab).
Do you plan to detect more demographic data?
Yes. We are working on additional indicators and plan to gradually introduce them in mid-term. However we can’t announce specific list of indicators and ETA right now.
Will there be integrations with popular CRMs?
Yes. We are currently collecting feedback to evaluate what integrations should be implemented first. Integrations will enable you to demografy your audience without leaving software you used to work with. You may participate in a survey to add your CRM or other software on the list by visiting your dashboard. The most popular ones will be implemented first.
Do you work with non-US lists?
Yes. However the technology currently works best with US lists. Race and partially age may not work properly with non-US lists while other indicators (Gender, Hispanic origin, Ethnicity) should work well with all lists.
Why are exported data and analytics not consistent?
You may have, for example, 15 people out of 100 detected as Hispanics but see 16.5% of Hispanics in analytics. The reason for difference is when overall analytics is calculated it takes into account additional variables such as number of false positives and true positive classifications to compensate for classification error in calculated segments and provide more accurate representation of aggregated data. That is why aggregated analytics differs from numbers estimated in classification of individual records and generally is more accurate than classification of individual records. The biggest discrepancy you may find is with age data. Age prediction from scarce names is the most challenging. It has average accuracy of around 70% for individual record prediction while overall aggregated analytics has average accuracy of 90-95% therefore age bracket composition differs between two algorithms. Analytics provides you with truer overall demographic profile of your audience while individual record prediction provides you with demographic data for each record.
Why is data different for some similar demographics?
You may see slightly different data for Hispanic Americans in Hispanic origin and Hispanics in ethnicity. Hispanic Americans segment in Hispanic origin is similar to Hispanic segment in ethnicity however algorithms are slightly different resulting in difference in calculation of overall aggregated statistics. However in case of detecting demographics for each individual record Hispanic Americans segment in Hispanic origin is generally correlated with Hispanic segment in ethnicity.
You may also have different data for African Americans (race) and Africans (ethnicity). African American segment in race doesn't equal African segment in ethnicity. African in ethnicity is comprised of people of African ancestry with specific African names while African Americans generally use similar names as White Americans and represent unique North American cultural formation with multiple generations of history living in North America.