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首页•默认•利用您的电子商务数据开启新的业务增长

利用您的电子商务数据开启新的业务增长

默认 2016-07-25 跨境导航
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This post is by Matthew Ferguson, Customer Success Manager at Volo.

A little while back we were working with a big retail chain, and they were thinking about pulling the plug on Amazon and eBay. They were putting more and more work into it, and their listing count had grown but sales were down. They were frustrated and ready to completely write off selling through marketplaces.

But they hadn’t dug into their data. When we ran some quick comparisons, we found that none of their key products had been restocked. Their best sellers across several brands hadn’t been reordered over a two-year stretch. Then we saw that their product prices were getting lower but their shipping rates were up. Overall they were less competitive than they had been two years before.

How on earth did they miss such simple things? Well, when you have a large sales volume and/or a team of people working in the business, you don’t “just know” that kind of information. You have to go looking for it. But when you do routinely examine your data, those things are really easy to spot.

But using data isn’t just a matter of regularly comparing sales figures, it goes much further than that. To put it frankly, data is make or break for ecommerce businesses. It can uncover problems, optimize current sales and guide you down new paths. That’s when you really start unlocking its power.

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You don’t have to be a data nerd

Here’s an example from the opposite end of the spectrum. We worked with a smaller seller who stayed very close to their sales figures and margins. They had a streamlined operation with a lot of automation, so there was very little overhead and manual intervention. This is a great situation to be in, because then all your energy can go into reporting.

By staying close to the data, they could see exactly when to increase margins and not lose sales.

This seller was excellent at spotting where they could be competitive on margins. They used short-term tactics such as selling at rock-bottom prices where they made minimal profits. But by staying close to the data, they could see exactly when to increase margins and not lose sales. The competition would give up or move on, leaving this seller to capture the market. That’s what they did, again and again, very successfully and profitably.

Does this mean you have to become a “data scientist”, who loves stats and Microsoft Excel? Well, it definitely helps, but it’s not a necessity. Most people don’t love to analyze reports, and you don’t have to. Simply by comparing top-selling products, price points, margins, sales velocity and other easy metrics, you can usually see what’s happening in your business. Once you have your spreadsheets and reports set up, maintaining and analyzing them can be quick and easy.

If you want to dig deeper and work on specific questions or problems – such as those in the examples below – you will need to build up some basic data skills. But this is your business data, so it’s all familiar stuff. The mindset for analyzing it might take some time to develop, but it’s well worth the effort.

What makes good data? GIGO and more

What’s GIGO? No, it’s not a terrible film starring former lovebirds Ben Affleck and Jennifer Lopez (that’s Gigli). GIGO stands for “garbage in, garbage out”. It means that if you collect data that is inaccurate or incomplete, then the reports you generate from it will also be inaccurate or incomplete. You need to collect good data to provide meaningful insights later.

So the data needs to be accurate, but exactly what type of data should you be collecting? Usually, the more data that you can collect the better. If you log every cost, every sale, every piece of data around the buyer, the order, the item and everything in between, you will have a rich environment to examine.

The longer the timeframe of the data, the better the statistics and accuracy will be. Countless times I have worked with sellers who have used one week’s worth of data to “prove” a theory. In a week, there are too many random factors that might be playing a part. A few days of data usually proves nothing. A few weeks might. A few months is ideal. Years’ worth of data is great if you have it.

So before you get started analyzing, do your best to collect data that is:

  • As accurate as possible
  • As complete as possible
  • Covers as much time as possible

Now the health warning

In the next section, there are three examples of how to work with your ecommerce business data. They could help you to:

  1. Increase profitability
  2. Reduce shipping costs
  3. Get a better deal from suppliers

Sounds wonderful, right?

Yes, it can be. Using data well can completely transform your business, but it does require some patience and learning. If you go off at half-cock then you might shoot yourself in the foot. So don’t make rash decisions and send your business backwards.

One specific problem is that, being human, we all examine data with preconceived ideas. We start looking for something specific that we want to prove, and we end up ignoring the data that does not fit what we are looking for. It’s called confirmation bias. You have to remain very objective when looking at your data, and let it tell you where to go. It’s OK to have a hunch, just don’t be afraid to be proven wrong.

Another issue I see is that a lot of sellers don’t use the right metrics. Many of them monitor their sales velocity without tracking profitability, for example. Making sales is great, but if you sell 10,000 units at $1 profit per sale, is that better than 500 sales with a profit of $25 each? Choose the right metric to fit the problem you are working on.

So be cautious and methodical, but do follow the data where it naturally takes you. Do not be a slave to the past, gut feelings or unjustified beliefs. The data is telling you how to be successful, so make sure you listen to it.

Scenarios

I’ve chosen three typical situations where there’s a lot to gain from data analysis. I’ve tried to make them fairly generic, but they won’t fit every situation. They won’t be optimal for every situation either.

Working with data sounds boring, but it actually calls for a lot of lateral thinking and creativity. It’s not about taking a cookie-cutter template and applying it no matter what. So adapt and change the ideas below however you need to fit your own business model, aspirations and challenges.

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