In the last blog, we talked about why getting good data is important for your business. Many companies run their business with incomplete or bad data. As we covered last time, you cannot make good decisions with incomplete information. If you missed the last blog you can read it here: Why is Data Important?
How to Get Good Data
Now that you know why data is important, you’re probably wondering how to get the data your company desperately needs. The world of data is huge, and oftentimes overwhelming. It can be hard to know where to even start. This second blog in our three part series will focus on how to acquire good data. We will talk about the different ways that you can get the data you need for your company, as well as the shortcomings of each data gathering method.
Internal Sales Numbers
Many businesses get data through their own internal historical sales numbers. Your company has the data for your past shipments, including the size and weight of packages you’ve shipped and the states you shipped them to. This appears helpful, but it’s not, at least not on its own. There are far too many variables involved. You could take two identical packages and send them both to New York. Despite the near identical shipping conditions, the packages can vary in price up to 60%. Was the package going to a business in New York City, or a remote residential location in upstate New York? Factors such as these can dramatically change the price of shipping.
Internal Sales Numbers – The Problem
Historical sales numbers can’t give you enough information. What problems can this kind of information gap cause? It can cause you to lose money or even customers. When you don’t have accurate information, you may be overcharging your customers or undercharging and paying for shipping out of pocket. How do you solve that problem? More data.
Carriers
This leads us to our next method of data gathering: carriers. When you get data from your carriers you can get a much more comprehensive picture of the many variables involved. If getting data from carriers is so much more comprehensive, why doesn’t everyone do it? Electronic billing. Many companies still receive paper bills from their carriers. This makes data capture and analysis nearly impossible. When you sign up for electronic billing, along with your bill, you will receive a .csv file with hundreds of columns of data. It contains everything, the size, weight, region, service level, delivery surcharges, etc. By analyzing this carrier data, over enough time, you can start to see trends, and when you discover trends you can begin to make better decisions.
Carriers – The Problems
There are a few problems with this, however. Firstly, how do you analyze this data once you have it? The files you receive are massive and even if you knew what to look for it would be impossible to effectively find it yourself. Shipping carriers have billing tools that allow you to take the data they send you and run general reports. This seems like a good solution. The problem is that these carriers do not have your best interest at heart and they will only give you enough information to keep you from being dangerous.
Internal + Carriers
The two methods of data gathering are the most powerful when used in conjunction with each other. Your company’s internal data alone is not enough, but when combined with carrier data it becomes a great tool. By analyzing internal data and carrier data against each other, you can identify discrepancies that the carriers won’t tell you about, and save your company money.
Summary
To get good data you must be keyed into both the front and the back end—your own sales numbers, and the carrier information. Electronic billing is key; you cannot get the historical data you need without it. By combining internal sales data and external carrier data you get the kind of comprehensive picture your company needs to compete in today’s fast paced market.
In our next blog, we will talk about what to do with the data once you have it. We will talk about how to analyze you data as well as the solutions to many of the challenges data presents.