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Data gaps: how to identify and fill them

Data empowers management to make better decisions, helps identify trends and opportunities, promotes low-risk data-driven action plans, and helps increase the efficiency and commitment of staff in handling core tasks and issues.

It should be an ongoing process in your business to gather more data and put in place processes to interpret it and act on insights. In this article, we will discuss the variations of data gaps, and how to fill them.

What are data gaps

Stated below are the different types of data gaps as recognized by e-tailize. We will discuss each in detail and give you pointers to fill them.

1. Missing data

Your organization does not gather the full scope of available information.

2. Data disparity

The disparity between the importance that management allocates to a metric or indicator.

3. Misinterpretation of data

Like we wrote about in our article about data illiteracy, it can be harmful to your business if the individual analyzing the data does not have the right knowledge and skill to interpret or clean the data. This results in drawing the incorrect conclusion and possibly making a harmful decision.

1. Missing data

Knowing what you do not know is essential when identifying missing data. Identifying the gap is the hard part, it is less hard to fill the gap. Software solutions or analytics tools will often enable you to do this. The way you gather data depends on if you need qualitative or quantitative data. Here are some great ways to collect missing data:

  • Surveys
  • Online tracking website (Google Analytics or place pixels)
  • Online marketing analytics
  • Transactional data
  • Customer data
  • Software tools

2. Data disparity

Focus on a single economic denominator

In the book “Good to Great”, Jim Collins discusses what sets great companies apart from good companies. One thing all these great companies do is, focus their strategic efforts on a single economic dominator.

To determine your economic denominator, you should ask yourself the following question: if you could pick only one ratio to systematically increase over time, what metric would you have the greatest and most sustainable impact on your economic engine? 

For example, Walgreens used to focus on profit per store, but switched to profit per customer visit. This drastically changed strategic decisions, resulting in a much higher revenue and profit across its entire system for the years to come.

Vanity vs. actionable metrics

Another great book, “Lean Analytics” describes the difference between vanity metrics and actionable metrics. Understanding the difference and using that knowledge to your advantage will help you make better decisions based on data. Most off-the-shelf analytics are useless for making decisions. That is why here at e-tailize we focus on bringing you actionable metrics when it comes to your marketplace management. We have summarized the differences between vanity and actionable metrics below.

  Vanity Metric Actionable Metric
Use feel-good factor and general direction can be used for informed and actionable business decisions
Indicates the size of something individual behavior
Types  gross quantities ratios and unit economics
Examples (there are many more)  trial users

page views

social Media ‘Likes’

email Subscribers

leads in sales

marketing spend

total customers acquired

monthly revenue per customer

converting users

conversion rate

social media engagement/referrals

email opt-in conversion rate

cohort analysis of sales funnel

return on marketing investment

customer acquisition cost (CAC)

customer lifetime value (LTV)

The bottom line is: focus on the right actionable metric for your economic engine and communicate this clearly with your organization.

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3. Data misinterpretation

The mistake of misinterpreting data is easily made, especially if there is data illiteracy in your organization. Two components are required to make a good decision based on data:

1. Context and domain knowledge

Statistically, a model could suggest that if you decrease the price by 20%, it will lead to more revenue. In reality, this is not guaranteed. Understanding the domain (market, competition, etc.) and the context is critical to make the right decision. The context in this case, should be more data. Moreover, the data can be qualitative and quantitative, which should be relevant and up-to-date.

2. Statistical competence

The foundations of statistical competence are formed by your knowledge of related concepts and the ability to think critically. The following concepts are essential to master:

Correlation is not causation

Just because two things correlate does not necessarily mean that one causes the other. For example, the statement: less sleep will cause you to perform worse at work. This statement could be correct, however, we need evidence from properly conducted research to factually state there is a causal relationship between the two variables.

What this means for your business, is that when you analyze your data, you should form a hypothesis by looking at likely casual relationships. After that, you should test this hypothesis with an A/B test or research. If conducted correctly, you can use the results and decide. You are now making decisions data-based!

Some entrepreneurs prefer the gut-feeling approach. In our eyes, gut feeling is a great way to form a hypothesis, which you can test. With the test results, you should decide. This takes away emotion and ego, and in the long term will benefit your business.

Interpreting visualizations

There are many ways to display data visually. Here is a list of the models you should know. A critical one that’s not on that list is the cohort analysis, make sure to check that out. Furthermore, It is your job to select the correct scale and alignment of the data, else it can be misleading.

Recognizing patterns

Your ability to recognize patterns is dependent on your experience with analytics, your ability to think critically, and your knowledge of the domain.

Look for outliers in the data set and understand why those are outliers, this will hopefully give you context on why the rest of the data points are not outliers. This will help you to recognize patterns within the data set easier.

Furthermore, understanding the math, and data set structure behind the visualization will help you predict or recognize patterns.

Cleaning data

You should always clean and filter your data set to make sure it is not skewing your analysis. For example, you own a webshop, and you frequently order products yourself. If you do not filter out your purchases when analyzing or modeling the data, you will use a data set that is not in line with your customer base, and you will make decisions based on skewed data.

This is just one example of not correctly ‘cleaning’ your data, here are some more examples:

  • Duplicates
  • Errors
  • Unwanted outliers (a one-off disproportional value, offsetting the rest of the data set)
  • Missing data

Cleaning data will help you make better decisions as it will be more valid, accurate, complete, consistent, and uniform.

Filling the marketplace data gap

e-tailize is a software solution for managing your marketplaces. Besides integration, our software includes data analytics. We offer actionable metrics to analyze competition, market, products, ads, and more. Moreover, we will provide documentation and tutorials to leverage and get the most out of our visualizations. Interested in data analysis on marketplaces? Please read all about it here.

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