The ability to price effectively is becoming a core competency needed in the retail industry in order to maintain a sustainable competitive advantage. It’s the reality in such a saturated market with increasingly sophisticated competitors.
Pricing strategy is a unique part of online retail because the same strategy won’t necessarily work across the board. Why? First, there isn’t one magical pricing strategy that works for all retailers. Second, one pricing strategy doesn’t always work across all of a retailer’s SKUs. And finally third, pricing strategies can and will evolve over time. One might even call it both an art and a science.
Having the most effective pricing strategy means balancing a strong brand identity while also staying flexible based on changes in your unique competitive landscape. This balancing act provides the boundaries within which you can reprice, test, and make smarter business decisions based on the results.
The Data-Driven Approach
Data science is a word that is thrown around in numerous industries, but at its core, it simply is the study and extraction of insights from big data. In order to take advantage of data science, retailers need to mine data. Data mining is the process of collecting large amounts of data in hopes of finding patterns and outliers. It allows retailers to make more accurate predictions based on past events.
A data-driven approach to pricing works in three steps:
- Mining real-time competitive data for up-to-date insights on both pricing and inventory. Web crawlers as the primary data source are rising in popularity
- Pricing based on multiple variables, including competitive data and your own historical sales data
- Analyzing the correlation of your price changes to revenue and profit (pricing effectiveness), and using the analyses to make adjustments to make informed decisions and continuously optimize
How to Execute a Price Test
It’s important to always have a clear vision in place to make sure you are mining the right data. Therefore, retailers should follow a price testing framework in order to get the most out of each of their tests.
- Ask a question and set goals:
- What do you want to gain from your data? Example: By pricing 1% below my competitor, will I see a bump in revenue?
- Do your research:
- This is where competitive data comes into play: what are your top competitors’ prices like and how have they changed in the last month?
- Make a hypothesis:
- Based on your analysis, what do you think will happen and why?
- Conduct an experiment:
- Test your hypothesis on a specific set of SKUs that have a high enough sales volume to get sufficient data from and use another group as a control with comparable sales growth. Test for at least 30 – 60 days for more statistically significant data.
- Analyze the results:
- If I got a sales lift, why? If I didn’t, why not?
- Optimization is an ongoing process.
Based on the outcome of ongoing price tests, retailers are able to make better data-driven decisions in both the short-term and long-term.