The Multichannel Handbook: A Retailer’s Guide to Growing Your Business, Pt. 5.2: Data Management cont’d

In our last installment, I introduced the topic of data management and how critical it is to allowing your company to grow across an ever-expanding array of sales channels.  This week, we take a look at core principles for data management as it relates to selling products in the retail environment, particularly online.

Data Management Makes Life Easier

In the context of retailing, we use data management to facilitate the easy translation of data from our company to a sales channel.  As we saw in our last installment, every sales channel wants your product data in a slightly different way.  So, proper data management on our end allows us to take our information, parse it out (or assemble it together), and push it to whichever particular sales channel we want to sell through.

There is an entire subsection of the IT industry devoted to proper data management, so I want to be clear here: I am talking about high-level core concepts as it relates to describing a product for retail sale, and how we as retailers capture and store that product data so that we can make our lives easier as we go multichannel.  I’m not referring to the much-more-complicated process of the data management life cycle.

The core strategy to discuss today is what I refer to as “data breakdown”.  It’s a term that quickly conveys what we want to do when we are thinking about your product data: we want to break it down into manageable bits that will allow us to move data more nimbly across channels.  So, let’s look at the basics of data breakdown, and then we’ll look at a real-world example.

Data breakdown starts by taking the information you already store in your product database, and looking at ways to break that up into more discrete fields.  In general, you will want to break up product descriptions and other data that you store into at least the following general fields:

• Product part number or SKU
• Base description
• Quantity information (units per package, quantity in inventory)
• Key product features (one feature per field)
• Color, size, and any options the customer can choose
• Physical information, including weight, size, packaged weight and size
• Material(s) information, meaning what the product is made of
• Special information unique to the product (for example, special care instructions, what the product should/should not be used with, etc.)
• Internal data (data not necessarily submitted to the sales channel) is also useful, including warehouse location information; supplier; supplier SKU; supplier description; product cost

The more granular that you get with your data, the better.  Why?  When dealing with a new sales channel and its own unique data requirements, you may run into granular data requirements that are more detailed than other channels.

Therefore, it is better to have more granular data – which can be pieced together prior to channel submission – than it is to have to figure out a way to parse out chunks of data on the fly.  It’s much easier to assemble 3 data fields into one, than it is to come up with a technical rule set that will seamlessly parse 3 data fields out of one text field.  This is especially true if you sell a wide variety of products.

Data Breakdown in Action

So, let’s look at an example of how data breakdown works on a real-world example.  Here’s a product description to consider:

12 Pack – Sky Blue Paisley Bandanas.   These are pre-packaged by the dozen for this lot of 12 (1 dozen) bandanas. Size is 22″ x 22″ square with hemmed edges on all sides.  Bandanas are a timeless, incredibly versatile fashion accessory and suitable for a variety of uses. Always in demand, always in style!

This description could easily be broken down into several fields:

• Base description: “One dozen blue paisley bandanas.”
• Color: “Sky Blue”
• Package Quantity: 12
• Length: 22″
• Width: 22″
• Feature 1: “All edges are hemmed…”
• Feature 2: “Bandanas are a timeless, incredibly versatile fashion accessory…(etc.)”

After having broken down the description field into these smaller parts, a simple script/macro will recombine them for publication on the website.  Better yet, these important details are now parsed out and ready to be pieced together in whatever combination each channel demands.

Data management can be an ongoing project, and it’s often difficult for retailers to focus on their product data enough to make meaningful changes to the way they manage their product data.  For this reason, the best way to start thinking about how data management can make a positive impact for your company is to consider the next 2-3 sales channels that you want to tackle for your business.

By looking at the data requirements for those channels, we can begin to lay the groundwork for a database reconstruction.  Having that reconstruction done BEFORE you delve into those channels will save your company time and energy as you launch into each channel; this leads to a faster return on investment for each channel, making your data management project pay off again and again.

We Want Your Feedback!

What data management challenges are you dealing with in your business?  Has this installment of the Multichannel Handbook been helpful? Let us know in the comments section, or email the author here.