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How to Interpret Marketing Data

Instructor: Kyle Aken

Kyle is a journalist and marketer that has taught writing to a number of different children and adults after graduating from college with a degree in Journalism. He has a passion for not just the written word, but for finding the universal truths of the world.

Interpreting marketing data requires a clear focus on what you need or want to learn from that data, as well an objective eye. This lesson describes the types of data collection that are most common and the questions to consider when reviewing analyzed data.

Questions to Consider

Starting or growing a business requires advertising and marketing to spread the word about your company and services. Even with non-profit fundraising and charity events, advertising and marketing are necessary for success.

It may seem that the current campaign is working if sales are up, there is a spike in traffic to a website, and donations have increased. However, collecting and interpreting marketing data helps in fine tuning or changing a campaign to improve its effectiveness.

Analyzing marketing data also helps you determine which demographic is responding to the item or service. By learning the age ranges, genders, and geographical locations of the persons who are looking at the company website, ordering from it, subscribing to it, or requesting information from it, you can find where the message works and where is does not work.

What is Marketing Data?

This is a broad category that includes all the information collected that will be analyzed. Primary data is information that is collected as part of a new study. Four general types of primary data are usually collected for marketing interpretation:

1. Using a questionnaire or survey. For example, emailing or calling all people who have subscribed to or purchased from a website. This can collect data on preferences and habits based on the questions asked.

2. Experiments or focus groups. These are most commonly used by market research firms during a formal panel. This can gather data to give insight into what motivates a person to buy a specific product. If there are different versions of labels or advertising copy, those can be shown to a focus group for evaluation.

3. Mechanical devices can sometimes be used to measure bodily responses to stimuli. Again, this is mostly used in the context of a formal panel with a research company.

4. Quantitative measures assessing the number of hits on a website, how many seconds the IP address stayed, etc. A survey question on the website can collect this data as well.

Many companies collect their own quantitative data by recording website hits, length of stay on the website, numbers of subscriber emails, and number of orders. Collecting demographic data usually requires a survey or the collection of such data when a customer creates an account on a website. Data can be compiled and analyzed in a spreadsheet or database application.

Interpreting the Data

There are two issues with interpreting the data. One, you need to focus on what you are looking for. Is there a certain age range or gender you are targeting? If you sell handbags and purses, you will primarily target women, yet you may also want to figure out how to target married men who buy gifts for wives. Secondly, you need to understand the science underlying the results. Are the totals based on a mean or an average? What is the pool from which the data was collected?

Review the data with an objective eye and balance the different metrics again each other. If you look at duration spent on the website, balance that with the number of pages within the website that were clicked on. This will give a more complete picture of whether the website is engaging and operating efficiently (and is generating meaningful business).

The amount of data collected can be overwhelming if you are unclear about what metric you are looking to investigate. For example, pulling data from all website subscribers who looked at purse or handbag descriptions to determine the number of women versus men is a good starting point to see how many men looked at purses and handbags. Say that total is 20%. Combining that data with a review of any current banners or click on/pop up ads allows you to see if you can add more gift buying ideas to pull in more men.

Context in the Data

The data also needs context. It's important to look at 'why', not just 'what' and 'how.' If you're compiling answers from a questionnaire, survey, or focus group panel, well written questions can highlight why certain trends were discovered. Likewise, click-through data, seconds spent per page, and orders placed versus length spent on the website need to be reviewed objectively.

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