The abundance of automated business tools that became available at our disposal, due to advancements in Information Systems, have made it significantly easier to guide decision management as a turnkey operation within business enterprises. With marketing and business analytics, statistical data and predictive modeling tools, combined with secondary research data; it has become too expedient to overlook the importance of using primary research data to formulate, validate or challenge business assumptions made to support fundamental business outcomes.
Lately, a critical mass of business and marketing plans have been developed and deployed, largely based upon market intelligence data with precision levels that are arguably less than thorough. However, using the law of large numbers, decision managers have concluded the frequency of events that are likely to happen, will even out after enough occurrences. Conversely, we have seen precise applications of decision management tools, when it comes to assigning gates and managing traffic movements at airports.
While the law of large numbers prevails in mature industry sectors, such as banking to guide their decision management for financial risk management, regulatory compliance and insurance for casualty risks, these industry sectors are essentially comprised of products and services with very little differentiation that are consumed like commodities by almost the entire population of adults, globally. However, for highly differentiated industries with niche markets or emerging market segments, the law of averages has often been used when there isn’t enough end result to bring the law of large numbers into effect.
Some critics have characterized the law of averages as an erroneous generalization of the law of large numbers, as it is habitually looked upon as a gambler’s fallacy and not a good bet upon which a viable business model can be built. To that end, using primary-market research data, such as surveys, focus groups and in depth interviews of customers, are likely to provide more specific data points for better decision management guidance, as it will also allow the ability to capture qualitative insight to refine decision management processes.
Let’s take an example to add context and bring greater clarity to the law of large numbers verses the law of averages, and their relationship to secondary-market research data, in contrast to primary-market research data.
If company “A” wants to sell luxury goods, it might use demographic data based upon age, gender, education, household income and affluent zip codes to define the target market for its offerings. Using the law of large numbers, if company “A” reaches large quantities of consumers within the target market, by consistently exposing them to compelling marketing offerings—via print, direct mail and/or digital advertising—eventually the company will manage to reach a critical mass of consumers within the target market and convert them into customers. In this example, company “A“ clearly relies on secondary-market research demographic data already compiled to reach its end; although the data points do not provide any insight into the purchasing behavior of the target market, if 1,000,000 pieces of direct mail or email is deployed monthly, a 1% response rate is the likely outcome from these monthly-marketing activities.
Company “B” wants to sell luxury electric vehicles, for example. Since there’s not enough consumers in the electric-automobile market to deploy offerings to a critical mass of consumers, company “B” cannot operate with the assumption they can employ a similar marketing strategy as company “A” to derive similar results. Company “B” will be more effective using Prizm, which is also based upon secondary-market research, to identify the likely consumers for electric vehicles based upon geographic, preferences, lifestyle, and behavior associated with customers and prospects for electric vehicles. Additionally, company “B” might also conduct primary research; although it’s more expensive using focus groups and interviews to gather more insight (both qualitative and quantitative) into this niche consumer segment for better targeting. Since primary-market research data tends to be more reliable–although conducted on a small sample—because the sample is more representative of the target market, its statistical significance can be applied to a larger population of likely consumers.
What is the right approach? Let’s have a conversation!
I help business units define the scope of their strategy to determine the appropriate balance between primary- and secondary-market research, for actionable business and marketing intelligence.
About the author: Lynda Chervil is an entrepreneur, author, environmental sustainability advocate and active promoter of sustainable brands and luxury brands with sustainable practices. She is the principal of Pearl Strategic Consulting, a business strategy consulting practice. She graduated from New York University with a Master’s of Science in Integrated Marketing Communications and had held many roles in new business development, sales management and executive leadership.