How to Look Behind the Data: An Introduction

 
 

Many small businesses are driven by data, some using it for only some operations, and others using it to direct the entire course of the business. The problem with data, though, is that it doesn’t always give you the full picture. It can even be deceiving, if you don’t know what you’re looking at. The following suggestions will help you to look at the big picture of all that data, and see what it is really telling you.

 
tunnel vision

Avoiding Tunnel-Vision

Looking “behind” the data sometimes means looking at a variety of factors, not just one dataset. To see the whole picture you might need to consider things like time, demographics, or pre-existing conditions.

To illustrate what I mean, imagine that you’re examining the number of units of a product that were sold for the month. It’s easy to just say, “We sold 20% fewer units than the average in October, for 2 years in a row—so October is just a bad month for sales”.

But it’s possible that people were just spending less time in the store during October, due to this month being the time that you always reconfigure the layout of the store, possibly causing confusion as to where products are located. Something like that could be off-putting to customers, causing them to leave without making a purchase.

 
too much information

Don’t Get Carried Away

And so you can see just how many possible factors there are to consider, which is why it’s a good idea to avoid getting carried away and stick to a few key metrics.

For example, instead of trying to figure out how the traffic and weather for each day of the year affected your customers’ buying habits, you can typically get all the information you need by looking at a few datasets, such as previous vs. current sales volume for each season, peak buying times of the day, or preferred buying methods of customers, e.g., online purchase vs. in-store purchase. Of course, you may want to look at other metrics as your primary data.

 
magnifying-glass

Finding Patterns

Try not to get stuck on standout pieces or sections of data, which are also helpful but sometimes not as helpful as finding patterns in the data. This is where graphs can be useful. Plotting data on a line or bar graph gives you a visualization of patterns that can be very difficult to detect when looking at the raw numbers.

These patterns are valuable because you can see things like trends for specific areas over time, and looking at data over time is probably one of the most useful ways to look at it.

 
road closed

Environmental Factors

So we said earlier that you shouldn’t get too carried away with analyzing ultra-specific datasets, but one specific type of data that can be revealing is environmental factors. This would include things like natural disasters, or construction in progress near your store.

When you’re looking at your sales statistics, and you can’t figure out why it was bullish or bearish for a certain period, consider the environmental factors for that period, as it may provide insight to explain the change.

This is merely an introduction to thinking about data in a different way, and these are by no means hard-and-fast rules. What metrics you use, and how you view the data will depend on what gives you the most actionable information for your particular business. There are a variety of data categories to consider, but if you take one thing away from this article, it should be that it is always better to take all aspects into consideration and view data with a wide lens.

 

 

We hope that you will continue to join us on our journey as we help you Grow Your Business! Our blog is 100% free and you don’t have to be a Talkroute customer to benefit from our materials. However, if you would like to try Talkroute’s Virtual Phone System for free, you can sign up for a trial here. See you in a few days.

About The Author

Patrick Foster is the Content Marketing Manager @ Talkroute
Email Patrick


patrick foster

About The Author

Patrick Foster is the Content Marketing Manager @ Talkroute
Email Patrick



 

How to Look Behind the Data: An Introduction