It’s easier than ever to buy data. If you are a retailer or pricing manager, all it takes is a few clicks to order and download 50,000 rows of competitor pricing data. With just a glance through the spreadsheet, you can skim through the rows of merchandise, and easily compare your prices to those of your most fierce competitors. It’s nicely organized by model name, ready to be sorted, filtered and analyzed.
There’s really just one problem with using the data. It’s hard to know what to do with it. Picture this. You have 50,000 SKUs in your store. And now, you have competitor pricing data for every one of them. How do you take the different prices staring back at you from your spreadsheet and turn that into a strategy?
The easiest way to use the data is to just focus on your most important merchandise. You can study the high margin or fast-moving products on the list and develop a strategy for those items. Of course, after spending thousands of dollars on the entire list, you’ll probably feel disappointed that so much of your data is going to waste. Week after week, you’ll discard so much data, simply because there isn’t an effective mechanism in place to synthesize, process, and implement all those data points.
Utilizing Data More Effectively
To really start to derive benefits from all that data, retailers need to have a better system in place to process it. There are essentially two methods you could use to turn all those rows into action. The first, which is rule-based processes, requires you to establish data crunching rules for your spreadsheet to follow. The second requires an artificial intelligence system to analyze your purchased data and evaluate it in concert with other factors.
Running a Rule-Based Process
When establishing rules, your only limit is your imagination and your ability to manipulate Excel sheets. For example, your first rule might be to never develop a price below a specific margin. You could follow that with rules relating to your competitors’ prices. For example, you might want to be a specific percentage higher or lower than your biggest competitor. Alternatively, you could decide to publish prices that are the average of all your competitors.
Rules do have limits, though. Your rules wouldn’t easily be able to factor in historical prices or trends as it tries to generate a price point. User behavior, online clickstreams, or inventory levels would also be difficult to consider through a rule-based system.
Working with Artificial Intelligence
To really take advantage of the data you have purchased, you need to introduce artificial intelligence into the process. AI is capable of taking data from a number of different sources and putting it all together while determining optimal price points.
For example, if your sales quota has already been met, your AI engine could bump up prices a little higher than the competition’s price, since every additional unit sold is almost like a bonus sale. If your inventory is too low or too high, or if there is an expiration date on some of your merchandise, the system can factor that in as well.
AI systems also rely on user generated rules, to ensure that it does not sell items below an acceptable margin or violate a manufacturer’s pricing policy. However, they go beyond the user rules, looking at user behavior and trends as it assesses what the most profitable price would be.
Going Back to the Pricing Data Sheet
Buying 50,000 rows of data can be a great investment. However, that data needs a sophisticated analysis tool for you to maximize your return on that investment. Otherwise, it’s like having a sports car without any gas in it. You know your car is capable of high speeds and turning like it’s on rails, but all you can do right now is look at it in the driveway.