BigCat Research

Which hypotheses are confirmed by the available data, and which need to be tested through field research?

The question of which hypotheses the existing data confirms and which ones need to be tested through field research finds its true value when read in terms of the hypotheses that the existing data confirms and leaves to the field study. The study makes visible the risk of taking wrong action by reading more than the available data as if it were certain; It makes the next step clearer for research, product and strategy teams to separate which assumption is adequately supported and which to test in the field.

The aim of the heading "Which hypotheses are confirmed by the existing data and which ones need to be tested with field research?" is not to collect more data, but to establish a distinction that works for the decision. When source quality, audience difference, touch point, price, experience and competitor impact are read together, validated hypotheses and field test plan emerge. In this way, the team can see more clearly which findings will be sufficient for today's decision, which information needs to be checked separately, and which step will create costs if they wait. This is where the value of the report lies: it not only describes the situation, but also shows where the next work should start.

The question of which hypotheses are confirmed by the existing data and which ones need to be tested with field research first initiates a search for indicators in most teams; However, the hypotheses confirmed by the existing data and left to the field study cannot be understood by looking only at the numbers. The real risk is late recognition of taking the wrong action by reading more than the available data as if it were certain. The critical thing for research, product and strategy teams is not to make the conclusion look neat in the report, but to distinguish which assumption is adequately supported and which will be tested in the field. When this is not done, the data increases but the decision does not become clear; At the end of the meeting, everyone can look at the same table and suggest a different move.

The starting point is not to choose the method, but to describe what information the decision is based on. When this description is made, it is easier to distinguish which data is sufficient, which is incomplete, and which is only a guide for the hypotheses that the existing data confirms and leaves to the field study. Thus, the research does not expand too much; The team pushes back on unnecessary curiosity topics and focuses on the real variables.

Close headers such as Critical ambiguity reduction data and Competitor promises therefore do not stop in the same file just to link; It reminds us of the neighboring decisions of the main issue. The aim should not be to expand the topic, but to show which information serves which decision when producing validated hypotheses and a field test plan.

Which question is the current data sufficient for?

Which question is the current data sufficient for? What analysis needs to do here is to point out the limit as well as sharpen the answer. It can be a strong indication of which question the available data is sufficient for; However, if the data supporting this sign and the audience for which it is valid are not written separately, the result will be exaggerated.

Writing the finding this way also gives clarity to the implementation team. Is it the message, the price, the package, the channel, or a specific moment of the experience that will change? When looked at together with the perceptual power of the players within the category, it becomes clear that the decision is not based on just one data.

Which hypothesis is gaining strength?

Which hypothesis is gaining strength? This section is one of the most useful parts of the research for decision teams. If whichever hypothesis is correctly described, the next step ceases to be a general call for improvement; The owner, time and follow-up indicator turns into a specific job.