Which of the following statements is technically true of big data? If you ask an advertising executive, “Can you tell me of any advertising method that isn’t influenced by big data?” he or she would likely smile and tell you, “No, of course not.” However, some executives are less willing to relinquish power to the algorithms.
a. It analyzes consumer’s needs more specifically according to their proximity to an existing retail outlet. Many companies now use GPS technology to analyze where potential customers might shop next. They can then optimize their advertising messages to take advantage of those areas, where shoppers are likely to be hanging out.
b. It focuses on knowing the actual behavior of a particular brand’s target consumers. GPS devices can gather information on shopping habits, where consumers like to hang out, their demographics and more. With this information, companies can create or test campaigns that are most likely to attract the types of people who frequent a particular store. For example, if Target stores tend to appeal to young families, a marketing campaign might be created that draws from this knowledge.
c. It doesn’t account for natural behavior. Surveys show that people tend to overspend if they have an emotional need. However, big data does not consider such needs when determining how much to charge consumers. It’s more realistic to think of human impulses as the driving force behind how much to spend on a product.
d. It doesn’t account for outliers. Survey data cannot reflect every shopper in every mall. There are likely to be some individuals who shop at different stores from time to time. Big data fail to account for these individuals. Even when survey results do show an effect of price, it may not be statistically significant.
e. It doesn’t account for outliers. Surveys cannot reflect all consumers. There are likely to be some who shop at several different stores at the same time. The same can be said for other demographics. It’s realistic to think of such individuals as being part of a “bellwether” group that will stay in one place over time, regardless of whether or not products are increasing in popularity.
f. It doesn’t account for changing spending preferences. While surveys will likely continue to be conducted to determine what works best for consumers, they have nothing to do with what might actually increase sales.
g. It doesn’t account for national, or regional differences. Data is collected in different ways and broken down by regions. National, regional, and local consumers may all have different needs, which may lead them to disagree with the accuracy of survey results.
h. It doesn’t take long to produce meaningful results. In fact, it rarely does. The computerization of information distribution provides quick, but not adequate, results. Even when large, high-quality samples are used, there’s still no guarantee that results will be meaningful. Survey methodologies vary greatly among companies.
i. It’s expensive. Very often, companies must develop special databases and software in-house, rather than using more affordable commercial options. This adds costs to the company, which its customers may well be aware of. Big data’s affordability factor is one reason why many companies avoid developing their own opt-in list.
j. It will replace more traditional channels. Big Data’s increasing significance in today’s marketing environment means that it’s not just about the volume of sales – but about the quality and frequency of those sales.
k. It reduces customer loyalty. It’s hard to stay loyal to a brand when its product isn’t available to everyone. Yet it’s practically impossible to stay loyal to a company that ignores consumers. Consumers expect to be able to use their preferred products and services. If a company can’t make its product easily accessible to consumers, they’re unlikely to hang on to that brand for very long.