5 golden rules for a successful analysis of your marketing research

Posted by Pauline Poché, on April 8, 2020

The interview collection phase of your study is over, you have obtained the desired number of responses, now it's time for data analysis! Here are some tips for correctly interpreting your results and transforming this data into lessons, recommendations and concrete courses of action to achieve your objective: increase your sales, improve your brand image, position a new product,...

1. Remain focused on the purpose of your study

Before embarking on an analysis, be clear about your main objective. Indeed, a study provides you with a lot of data, figures, which will considerably enrich your understanding of consumer expectations, the opinion of the public,... but which can also make you a bit dizzy.

Don't panic! All you have to do is keep in mind the purpose of your study, to ask yourself “What main questions did I want to answer when I started this study?” This will serve as a common thread throughout your analysis and will allow you to sort between the main lessons and those that are more secondary to your objective. Focus primarily on the analysis of your basic problem. The rest, which is also rich in lessons, may be the subject of one or more additional analyses in a second phase.

Let's say you want to launch a new product, your main analysis will certainly focus on buying intent. In this way you will be able to respond to your main problems:

• Are there enough people interested in my new product?
• And what are the strengths and areas for optimization of my concept?

All the other information you have collected (image of your competitors, frequent points of sale,...) will also be valuable but their analysis will take place later, once you have determined whether your concept is sufficiently interesting as presented.

2. Remain objective

Human beings have this annoying tendency to always want to be right. Try to be as neutral as possible when analyzing results. If, before launching the study, you had an initial idea of what the result of this or that indicator could be, force yourself not to try at all costs to confirm your hypothesis but on the contrary try to challenge it as much as possible with all the available data. This will prevent you from jumping to conclusions and missing out on something.

Let's go back to the example of a product launch. If you, or the teams you work with, want to launch this new product, it is because, at first glance, you think that your target will appreciate it, and therefore that its marketing will work (otherwise you would not have had the idea of developing it). It is important that at the time of the analysis you try to treat with the same importance the information that goes in your direction and information that goes the opposite way. This will highlight points that you may not have thought of and that could be harmful to your launch if you didn't care about them.

3. Explore sub-targets

Going beyond the results with all of your target audience is crucial for conducting relevant analysis and making the right decisions.

For example, in the case of your product launch, to find out who is most likely to buy your product, consider combining your results with various identifying information (questions of age, gender, socio-professional category, region, etc.). You may find that the intention to buy is significantly higher among young people aged 18-34.
This information will be a great help in refining your marketing strategy.

For an even finer analysis, you can push the cross-referencing of the data further. In the case of a product test, it is often relevant to cross references that are appreciated/not appreciated by the intention to purchase. Let's say that for your entire target audience, the price and the design of the product are the 2 most appreciated criteria, you may be tempted to optimize your product on these 2 criteria. But if you take a closer look at respondents with positive buying intentions, the criteria that are most valued may be different. It could be the robustness of the product for example. It will then be more interesting at first to ensure the robustness of your product to satisfy the people most likely to buy your product, than to rework the design for people who are not convinced by your product anyway.

4. Take a step back

When we are immersed in an analysis we sometimes lack perspective and we lose our common sense a bit. Feel free to make a break in your analysis, clear your mind and come back to it a bit later. It can also be beneficial to discuss the results with your colleagues or friends and family to challenge your conclusions.

It should also be noted that if the collection of interviews for your study was carried out during a period rich in strong and unusual news, this atypical context may influence certain results (up or down). It is therefore important to keep this in mind during the analysis in order to possibly qualify certain results.

5. Be wary of averages

To quickly analyze or present data, averages are very useful. But be careful, they can sometimes hide important information that cannot be read at first reading and that could be a shame to overlook.

Let's continue with the example of a product launch.

Let's say you ask respondents to give your new product concept an overall score out of 10. The overall score is 7.6, which is quite satisfactory for you. But if we look in detail, this average could come, for example, from ratings exclusively between 7 and 8, which means that your product appeals to all your target audience, or on the contrary, the ratings could be divided into 2 categories of people: those who gave a score of 9 or more, and those who gave ratings between 5 and 7 instead.

If the average is the same in these 2 examples, the conclusion will be different, as will the actions you put in place as a result (targets to prioritize, discourse and positioning to be reworked with certain targets,...)

[Bonus] Cross-referencing study results with your business data

Whatever the objective that led you to conduct a marketing study, it will always aim to help you grow your business.

Also, to complete and challenge your analyses resulting from your marketing study, it is essential to cross them with your business data: your website traffic, your sales figures, the return of your customers,... This will allow you to provide different perspectives on a part of your activity and to enrich your thinking.
In the case of a new product launch, you can in particular analyze the performance of your landing page, customer feedback following the announcement of the arrival of this product,... to better understand the results of your study and make the right decisions.