What is data tolerance™, you might ask? At its most simple, it is the type and amount of data required to facilitate decision-making within an organization. And it differs for every organization.
When establishing the research plan that will support a brand effort, it is important to consider not only the core decision-makers, but leadership and executives, who may ultimately have to sign off on any changes … or even, just the idea that there is a need for change.
The best brand research plans consider data tolerance, as well as research objectives and organizational resources. A combination of qualitative, quantitative and advanced analytics should be considered. Often times, an interconnected research model that incorporates elements of multiple methodologies is the best answer for any organization.
As you begin to develop your research plan to support brand development, evolution or implementation—or work with an external agency to do the same—here are a core set of topics to think about:
Qualitative vs. Quantitative
While most research plans integrate qualitative and quantitative methods, there are indicators that demonstrate which is most appropriate for the task at hand.
Qualitative research, such as in-depth interviews or focus groups, traditionally allows for more nuanced insights and the ability to explore unexpected or surprising opportunities. It’s most beneficial for conducting broader exploratory or validation, and often times may be used to fuel a bigger quantitative study by providing key themes and hypotheses to be confirmed by a larger audience.
Quantitative research, such as an online survey, offers larger sample sizes, most often provides statistically significant and projectible data, enables longitudinal comparisons and as earlier indicated, is quite helpful for testing initial hypotheses. Additionally, because of the larger size of quantitative studies, it is easier to segment and analyze data across multiple variables (e.g., geography, function, customer type, etc.).
Quantitative studies can also provide sufficient data—both structured and unstructured—to conduct advanced analytics, such as sentiment analysis, drivers of important organizational KPIs and brand valuation. Additionally, analyzing quantitative studies using data science techniques can provide actionable insights around marketing and brand ROI, profit by segment, high-growth products, attributes that drive growth and even predictive variables around marketing spend. In other words, brand research not only may inform brand decisions, but business decisions, as well. At a minimum, it can ensure that you are developing a brand designed to help your company meet its strategic business objectives.
Gut decisions vs. Guided decisions
Being realistic about how decisions are made in an organization is key for determining the most appropriate research plan. While all organizations contain institutional knowledge and market expertise, few rely solely on that knowledge to guide decision-making around brand.
For those organizations that lean toward “gut” decision-making—relying more on that institutional knowledge and market expertise than data—qualitative research is often sufficient. Often times, we see this scenario for early-stage companies or those in unique industries with a limited universe of customers or potential customers.
On the flip side, many organizations contain that same internal knowledge, but rely more on projectable data to make brand decisions. A detailed understanding of customer preferences, purchase-drivers, company perceptions and competitive comparisons is required either by the core brand team itself or as a tool to sell decisions to senior management. Often times, these types of organizations appear in regulated industries or have recently been part of a financial event, such as M&A or PE investment. Here, quantitative research informed by qualitative research is often the best approach.
Short-term input vs. Long-term output
At what point will you conduct research? Traditionally, companies use data to inform brand development by obtaining an understanding of company, customer and competitive perceptions. It may also be used to validate brand decisions, such as positioning, names, taglines or even logos. While we don’t always recommend brand validation research, as it’s difficult for non-marketers to evaluate these types of elements, those companies that are highly data-driven will inevitably require such research. Either way, both of these types of analyses are short-term in nature.
However, research cannot only be used to inform brand, but to measure its health over time. By building in benchmark assessments of your brand, such as reputation, satisfaction or NPS, at the onset, companies are able to demonstrate how brand moves the needle over time—and adjust messages and campaigns according to shifts in the market. Brand health studies are best conducted annually with static questions that can be compared longitudinally over time. Here again, for those companies with a lower data tolerance, bi-annual studies may be more suitable.
Whenever Tenet undertakes research for a client, we will always discuss data tolerance in advance of making recommendations. For the best results, we suggest you do the same.
