Data analysis Archives - E2E Research Services Pvt. Ltd

By E2E Research | April 11, 2022

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We keep hearing that slow food is better food. It seems to me that the same thing applies to marketing research reports.

At its heart, the intention of slow food is to promote local products and traditional methods, to focus on quality over quantity, and to prevent over-production and waste.

Those key concepts are just as relevant for research reports. Rather than focusing on fancy ways of reporting, focus on effective ways of reporting. Rather than focusing on providing pages and pages of results, focus on the most important results, and rather than focusing on creating extravagantly detailed results, focus on creating insightful results.

Let’s break this down a bit more.

Statistical significance isn’t important

You heard that right. While focusing on statistical significance is a fast way to find cool numbers to slam into a report, I often don’t care about it. If 5% is statistically different from 6%, it’s not important unless that 1% difference is actionable and meaningful. In healthcare, 1% can be life or death and I will care enough to consider replicating the results – immensely. But when it comes to choosing a package design, that 1% is sampling error, analysis error, random error, meaningless.

Instead of being driven by shiny, statistically significant p-values, slow down and think through which numbers are meaningful and actionable. Which numbers will create reliable and measurable differences in the market. Once you’ve decided that a significant difference is meaningful, then you can worry about it. And if something is significant but not meaningful, put it aside to think about potential applications later.

Every title should be a conclusion

Page and chart titles can be really easy to write. Pick out the biggest or smallest number on the page and copy it into the title space – “45% of participants chose concept B.” Done and done! But that’s a report no one will care about. It won’t get shared with the leadership team and you won’t be asked (or paid!) to write next year’s report.

Instead of focusing on data points, focus on what was learned on a specific page. Slow down and take the time to digest the 5 or 50 data points on the chart or table. What story does the collection of data points on that single page tell? What is surprising about the interactions among those data points? What is the overall conclusion based on the themes of answer options? What is the key recommendation you would draw from that page? The biggest (or smallest) number is often not the important story. But once you slow down to figure out what the real story is, then you’ve got a great page title.

Alphabetical is never the right order

When we write questionnaires, we order the questions in terms of what makes sense to the research participants reading each page. From the researcher’s point of view, it would be really fast to create a final report where page 1 is a chart for Question 1 and page 100 is a chart for Question 100. But that type of reporting is nonsensical to readers. Indeed, some questions are superfluous – they are there to focus participants’ attention or to direct them to a certain line of thinking.

Every study is designed to answer very specific research objectives and those objectives should be top and front of the research report, not buried in alphabetical order. Take the time to think through which questions provide a holistic answer to Objective 1, and prepare those together at the beginning of the report. Even if that means analyzing questions 17, 43, and 96 together – out of order!

Once you’ve understood all of your data and the results, reorder all the slides (and then edit all the titles) so they tell a logical story from beginning to end that reflects the research objectives. Forget the order of questions and focus on the order of the story.

Every header in the executive summary is one sentence of the full story

Once you’ve created your 20 (or 50) page story, it’s time to build the executive summary. These are never just a listing of the coolest data points. As fast as that is, you’ll once again end up with a report that lands in the recycle bin with nary a forward email. With a fully written, reordered, and edited report in hand, you need to create an executive summary that serves the needs of different key stakeholders.

First, the CEO and CMO should be able to read the title of the executive summary and know exactly what their next business move should be next year. That is story #1 – punchy and obvious.
Second, the VP of Brand or Insights should be able to read only the headers of the executive summary and know how to plan for this year. That is story #2.
Third, the Brand Manager or Insights Manager should be able to read all of the bullet points in the executive summary to plan for tomorrow.

Your goal is to ensure that each key stakeholder can read as little as possible to get their job done. Of course, if you’ve gone slow and taken the time to write an amazing report, you might just find that all of your stakeholders have read the entire executive summary and every other page as well!

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Highlight oddities

The fastest way to finish illustrating every slide is to use call-outs and arrows to highlight the smallest and biggest numbers, the winners and losers, the leaders and the laggards.

But, we already know this serves no purpose. It’s not helpful and it’s not insightful. Any reader can glance at a chart or table and instantly see what is at the bottom or the top of the list. X does not mark the spot!

Your job as an analyst and report writer is to take the time to read between the lines and find the hidden treasures. Which number is sneaking by in an unexpected place? What trend is the reverse of expected?

Highlight that oddity so your reader doesn’t have to waste time searching out what you already described in writing. The green outline here saves the reader time by drawing their eyes immediately to the interesting data point. If they want to look at the top and bottom or left and right of the chart as illustrated by the unhelpful red box, they can easily do that too.

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Embrace white space

Sometimes, you’ll need to incorporate an extremely complex chart that has lots of answer options or segments. It would be quick to just stretch the borders to so that it is really tall or wide. You must resist the urge!

Treat the white space around the edge of a slide as sacred, never to be infringed upon. That white space creates breathing room for the eyes. It makes charts and tables easier to read, moreso than stretching charts out wider across the page.

If a chart or table simply won’t fit, find other ways to make complicated charts easier to read. Convert it into two charts – perhaps organized by segment or outcome or culture.

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Never use default charts

It sure is fast to use default charts. The fact that everyone can spot them from a mile away is not the reason to stop using them though. If you take the time, default charts can always be improved to highlight the results in a more valid and insightful way.

Take the time to ensure the axis starts at 0, not -5. Reduce the number of guiding lines to just 2 or 3 – or even none! Simplify the colors or, at least, convert them from florescent to neutral so you don’t burn your readers’ eyes. Make sure the colors or textures of the lines are different enough that people who are color blind can read them – Is there even a difference between March and July in this chart? And, is this even the right kind of chart?

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Refuse to use chart junk

Sometimes, a slide needs to be prettied up. Perhaps you’ve got 5 slides in a row that are text after text after text. It’s time to introduce some design and get the eyeballs back on track.

However, this is NOT the time to drop in all of your favorite cute clip art icons. This is the time to consider what is the story you want to tell and does that story require clip art – the answer is most often no. Clip art is the PPT user’s version of chart junk (Thank you Edward Tufte for that term!).

Instead, opt for subtle designs that don’t detract from the message. Or, use photographs as you see in the executive summary above. Plenty of websites offer free images, e.g., Pexels, Pixabay, Unsplash. And if those sources don’t provide the diversity you need, there are additional sources such as Disabled and Here, Jopwell, and Nappy.

What’s Next?

We’d love to show you what a great report looks like. If you’re ready to discover top quality insights about your buyers, brands, and business, email your project specifications to our research experts using Projects at E2Eresearch dot com. We’d love to help you turn your enigmas into enlightenment!

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What is a Census Representative Sample?

By E2E Research | March 29, 2022

The people researchers choose to share their opinions in marketing research can make a huge difference in the quality of answers we receive. That’s why it’s important to understand the research question and who would be best suited to speak with to get the necessary answers.

Let’s consider one type of sample that researchers often consider when conducting research – a census representative sample.

What is a census representative sample?

Decorative image You might also hear these referred to as ‘Census Rep’ samples. A census rep sample requires access to census data, something that is typically generated by large-scale government surveys completed by millions of their residents or citizens. In the USA, that’s census.gov and in Canada, that’s Statistics Canada.

A census rep sample can be designed to reflect any specific group of people. The key consideration is that the sample of completed questionnaires reflects the larger population on important criteria. The sample could reflect an entire country (e.g., USA, Mexica, Canada), a state or province (e.g., California, Quebec), or a city or town (e.g., Boston, Ottawa). This type of census rep sample is reasonably easy to define.

Another type of a census rep sample can be defined by target group behaviors or characteristics. For instance, you might be interested in a census rep sample of people who smoke or who have diabetes. Of course, building these types of census rep samples is far more difficult because government census data tends to be set up to understand basic demographics like age and gender, rather than behaviors like smoking and ailments like diabetes.

When would I use a census representative sample?

Census rep samples are extremely important for at least couple research objectives.

First, when you need to calculate incidence rates for a product or service, you need to first generalize from a representative group of people of your target audience. You need to be able to define your population before you can know what percent of them uses a product or performs a behavior.

Second, census rep samples are extremely important for market sizing. Again, you need to generalize from a representative group of your target audience before you can estimate the percent of people who might qualify to use your product or services.

Why is a census representative sample important?

Decorative image Creating a census representative sample is extremely important. You could get into trouble if you recruit a sample of research participants who don’t look like actual users.

You might gather opinions from too many older people, too many women, too many higher educated people, or too many lower income people. Your final research conclusion might be based on opinions collected from the wrong people and lead to development of the wrong product or product features.

An example of a census representative sample

Let’s consider an example where we want to determine which flavour of pasta sauce to launch in a new market – California. We’ve got two delicious options – a spicy, jalapeno version and a mild portobello mushroom version.

We know people from different cultures and ethnic backgrounds have very different flavor preferences so we need to ensure that the people who participate in our research will accurately reflect the region where we will launch this new pasta sauce.

Now, we could recruit and survey a sample of people based on a basic quota that will help make sure we hear from a range of people. It might look like what you see in the first column of the table – even splits among each of the demographic groups with a bit of estimation for ethnic groups. But that’s not actually what California looks like. Instead, let’s build a census rep sample matrix based on real data.

Decorative image To start, we need to define a census rep sample of California. First, we find those people in a census dataset. Then, we identify the frequencies for each of the key demographic criteria – what is the gender, age, ethnicity, and Hispanic background (as well as any other important variables) of the people who live in California. Fortunately for us, this data is readily available. On the census.gov website, we learn that in California, 50% of people are female, 6% are Black, and 39% are Hispanic.

Now we can recruit a sample of people from California whose final data will match those demographic criteria – 50% female, 6% Black, and 39% Hispanic. You can see just how different those numbers are are from the original basic quotas!

In the last two columns, you can see that we’ve even split out the criteria by gender (even better, you can do this based on the census data). This will ensure that one of the age groups isn’t mostly women or one of the Hispanic groups isn’t mostly men. When we nest our criteria within gender, we end up with a nested, census rep sample. Nested demographics are the ideal scenario but they do make fulfilling sample more costly and time-consuming. You’ll have to run a cost-benefit analysis.