Companies are always working to find that pricing sweet spot – the one that attracts (or better yet, converts!) their target shopper yet remains competitive with their peers on the shelf. And don’t forget the added layers of difficulty in adjusting this balance from country to country and retailer to retailer. This requires pricing and revenue management teams to create guidelines dictating pricing policy across different online retailers and monitor competitor prices, to make sure they are always appropriately priced against competing brands.
As you can imagine (or painfully know from experience), keeping track of this information across all retailers and markets where your product is sold is often a time-consuming and manual process. In addition to tracking your and competitors’ SKUs, you also need insight into the wider online marketplace and new or emerging digital brands, which is no easy task as they emerge online.
Using e-commerce data for pricing corridors
Simply put, monitoring prices online and benchmarking SKU prices against competitors is the first step in optimising your pricing strategy to grow sales and improve profit margins.
Creating price corridors or funnels that plot your own SKUs and brand families against competitors by country, category, or median price helps answer key questions such as:
- How are my competitors pricing certain SKUs compared to my SKUs?
- Should I keep my prices 5% lower than competitors and how can I make sure I maintain that threshold?
- What are my different pricing thresholds in specific markets?
- Is my product priced appropriately for a mass or premium offering in a certain market?
- How should I vary an SKU’s price across markets?
By asking these questions and creating a standardised pricing guideline, you can ensure that your prices are competitive but profit margins don’t suffer as a result.
How e-commerce data helps
To say that tracking this manually is a time-consuming process is putting it lightly. Trawling websites yourself and entering prices and brand names into a spreadsheet on an infrequent basis will never provide a consistent and complete picture of an online marketplace.
Leveraging automated web data extraction is a good way to create pricing strategies that are dynamic and not static, constantly monitoring prices for competitor products and online assortment as they fluctuate from day to day.
It’s not necessarily a difficult process to extract all this data, but the difference in having a mass of millions of data points vs an actionable roadmap that makes sense to your business is in its organisation. Properly harnessing AI and machine learning allows companies to structure this data in a way that aligns to a standardised category hierarchy and ensures products are matched to that hierarchy accurately. This allows pricing managers to filter data by product category and analyse aggregated market prices for SKUs and brands easily and on a daily basis.
In this white paper ‘How to Optimise Your Pricing Strategy Using Ecommerce Data’ we show you how to identify the key metrics that are essential to set an effective pricing strategy and how product pricing and attribute data can help inform sales, marketing, and innovation strategies within your organisation.