You can’t make smart data-driven decisions if your data is questionable. Previously, we explored why product matching – also called product mapping – can make all the difference in giving you an accurate snapshot of how you compare to the competition.
If you’re not sure that you’re selling the same exact product (or a similar enough product) as your competitor, then you may as well toss your price comparisons in the trash.
However, once you are confident that your Pricing Intelligence data is accurate, you need to figure out how and when you should act on it. So how do analysts turn data into insights and pricing recommendations – and ultimately – better sales results?
There are two main approaches for turning numbers into action:
- Setting Up Automated Rules – Deploying simple Strategy Rules (e.g., If Competitor A lowers the price on Product A to X, we lower our price to Y; If Competitor B is out of stock on Product A, we increase our price to Z.) Strategy Rules are ideal if you want to keep tabs on price changes for Key Value Items (KVIs) at specific competitors and want to always be within a certain range.
- Building a Pricing Model – Developing a sophisticated mathematical model to optimize pricing enables retailers to take the many factors that contribute to the buying process beyond price into account. The equation may incorporate a range of inputs, including a retailer’s historic sales data, historical competitor pricing, inventory, product page content, Web traffic and promotions data. The model may also consider customer reviews, product ratings, social likes, etc., using consumer sentiment analysis to translate ratings into pricing insights.
Let’s say that you are selling luggage, for example. Here are some of the questions that may immediately come to mind:
- How many weeks before summer vacation do luggage sales historically peak?
- Do duffle bag sales spike before college begins in the fall?
- What color suitcases are most popular with men vs. women?
- Do child-sized rolling bags fly off the shelves before February or April school vacations?
- What is the most highly rated luggage based on product reviews on travel websites or the best value listed in Consumer Reports?
This graph shows the output of a logarithmic equation calculating which price points result in maximum luggage revenue, based on a department store chain’s most influential variables. Here is the pricing model expressed as an equation:
Log(Luggage Sales) = 0.29 – 0.66(Log)(A) + 0.01(B – A) + 0.25(C) + 0.002(D) + 0.06(E)
A = Retailer Price
B = Competitor Price
C = Number of Images on Product Page
D = Number of Amazon Reviews in Last Two Months
E = Newness of Product (Number of Weeks)
Every store is different and will have a different equation and different sets of variables.
Don’t worry about the math – that’s why you hire analysts!
The important thing to know is that as your Pricing Intelligence gathering and processing becomes more sophisticated, you will be able to better understand and have a greater influence over your sales results. Depending on what you want to learn, analysts can help you determine the “Why,” the “What If,” and the “What’s Next?”
To further explore the “Human Factor” of turning raw numbers into action, download a complimentary copy of our new eBook, “Pricing Intelligence 2.0: The Essential Guide to Price Intelligence and Dynamic Pricing,” for retailers and brands.
Download your copy today!
Mihir Kittur is a Co-founder and Chief Innovation Officer at Ugam. He oversees sales, marketing and innovation and works with leading retailers and brands with insights and analytics solutions around their category decisions to improve business performance.