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Harnessing Ethnic Insight in Personalized Marketing
Harnessing Ethnic Insight in Personalized Marketing
By Amalia Tsiongas
Personalized Communication
Do you engage with your customers through their emotions and values?
In a world of increased personalization, successful marketers understand that culture is one of the driving forces behind any individual’s engagement with their surroundings.
Through multicultural marketing, you identify and communicate with ethnic market segments within their own cultural framework.
A Multicultural Canada
According to the 2011 National Household Survey, Canadians identify as more than 200 ethnicities.
20.6% of the Canadian population is foreign-born. This is highest of all the G8 countries. Canada welcomes significantly more immigrants than Germany (13.0%) or the United States (12.9%).
Roughly one-third of new immigrants in Canada since 2006 are from China, the Philippines or East India.
South Asians, including East Indians, account for the single largest visible minority group in Canada, numbering over 1.5 million.
Defining Objectives
In order to start defining your multicultural marketing objectives, first look to understand your current customers:
- Are your customers from steadily growing populations like Caribbean-Canadians?
- Are your customers from more established immigrant communities like Chinese?
- Do you engage more affluent South Asian customers such as East Indian?
Next, analyze your marketing:
- Can your customer service representatives communicate effectively?
- Are you stocking inventory appropriately for local needs?
- Do you offer forms in Punjabi, Tagalog or Cantonese, for example?
Subtle changes like these will increase how readily new customers engage and how effectively you communicate with them. A multicultural marketing strategy prevents loss of business through messaging misinterpretation and the risk of client alienation.
Data-Driven Decisions
There is a wealth of ethnic consumer data available from a variety of sources.
| Data Source | Pros | Cons |
| Self-Reported Data | -Mostly accurate -Consumers usually responsive | -Small file sizes -Responders may not properly identify -Privacy concerns |
| Census | -High coverage -Inexpensive | -Aggregated -No nuance |
| Surname Tables | -Less aggregated than Census | -Broad brushstrokes |
| Multi-Layered Predictive Approach | -High coverage -High accuracy -More nuanced than Surname Tables -No privacy concerns | -Conservative approach for First Nations |
Self-reported data and a multi-layered predictive approach offer a higher degree of accuracy relative to other ethnic data sources.
With self-reported data, however, responders may be motivated to misrepresent their identity, or answer inaccurately due to the question being unclearly worded. Oftentimes answers from a flawed survey will be “modeled out” onto the entire population; accuracy problems will be compounded although coverage may improve.
Because a multi-layered predicative approach relies on linguistic clues in both the first and last name, which are leveraged probabilistically against available demographic data about the neighborhood, it can more accurately identify every record at an individual level with no modeling.
Predictive Data in Action
Of the four data sources compared above, predictive ethnic data is the least well known and the most interesting. The data is derived from the consumer name and address. A software program weighs these components and splices them together.
Let’s look at how this works for the following three individuals living in Brampton, ON, a suburb of Toronto.
JUN SANTOS – A predictive system will recognize that the first name JUN is primarily used by Chinese, Korean, Japanese and Filipino individuals. The last name, SANTOS can be Spanish, Portuguese or Filipino. Together, the first and last name indicate JUN SANTOS to be Filipina. That is the only ethnicity shared by both the first and last name. Due to the uniqueness of these names, this is done without ever referencing the ethnic make-up of her postal code.
AICHING HU – The name intelligence for this consumer record indicates more than one ethnic possibility: Both the first name AICHING and the last name HU is used by mainland Chinese and Taiwanese individuals. At this point, the demographic make-up of her postal code can help determine which of the two ethnicities is more likely. In Brampton, ON, AICHING HU is most likely mainland Chinese.
R.S. GILL – Sometimes an individual’s first name does not provide ethnic insight into their ethnicity. Whether the first name is too common, like JOHN, or only initials are available, like with R.S., a predictive system will turn to the last name. This last name is used by East Indian and English individuals. In Brampton, ON, a predictive system determines that R.S. GILL is East Indian. Furthermore, he is Punjabi.
Putting it all Together
Understand: Use predictive multi-layered data to gain consumer insights into ethnicity and language preference.
Engage: Send messaging that resonates with culturally driven lifestyle and buying habits, whether in mailing, telemarketing, email campaigns or digital applications.
Activate: Increase new customers and drive customer satisfaction and brand loyalty.
For more information visit: www.ethnictechnologies.com/product/e-tech-canada/
About Ethnic Technologies
Ethnic Technologies (E-TECH) is the Global Leader in Multicultural Marketing, Research, Data Enhancement, Segmentation and Modeling Analytics. The EthniCenter ® from Ethnic Technologies is the result of over 40 years of continuous multicultural, religious and language preference research. E-TECH’s Multicultural Ethnic, Language Preference and Degree of Assimilation Indices outperform the competition in accuracy and response rates time after time. Whether using the data for marketing via social media, search engine marketing (SEM), digital, mailing, telemarketing, email campaigns or modeling, the same excellent results have been achieved.
Media Contact
Rachel Tague, Director of Marketing
Ethnic Technologies
866-333-8324 ext 121