Underlying all pricing strategy is data, whether it be on the market and competition or a company’s own quote, sales and cost data. Collecting this data and using it to inform future pricing decisions is one of a pricing team’s key objectives. For example in insurance, where many product lines have become commoditised, continuous iteration and running experiments is often the best way to find the optimal price for a product, one that balances growth and profitability. However, in order to do this, a business must have clean usable data on these pricing changes and their outcomes.
Through our team’s experience leading financial services businesses and our conversations with more than 100 pricing and technology leaders, we’ve identified the biggest challenges facing their teams when it comes to data.
Quality Issues: Cleaning and preparing data is time-consuming, taking up as much as 80% of a pricing team’s time. This is often driven by poor data quality; from legacy systems, inconsistent classification, format or naming conventions, or duplication of quotes.
Integration Challenges: Accessing data from different systems or adding new 3rd party sources of data often needs engineering resource, which may take a pricing team time to request and prioritise.
Limited Availability: When creating new product lines with little historic data, even established companies struggle to access data on a given market, whether it be on insurance risk or competitor’s pricing. For new companies starting out, getting enough data on which to build pricing models can also be a challenge.
Technical complexity: When working with high volumes of quote and sales data, engineering teams struggle to store this data in a way that is easy for a pricing team to easily access and work with.
From our conversations with pricing and technology leaders, 56% of them talk about data-related challenges as a significant pain point for them and their team
We believe there are many opportunities to improve how we work with data in pricing. We’ve focused on 3 areas to solve with the first version of our platform:
Performance: By collecting live quote data in one place, it is easy to see KPIs such as quotability and average quote value, quickly search through quotes to find a given customer, or categorise by quote stage, such as completed or abandoned.
Organise: Transform data streams into valuable insight using simple, no code syntax to build criteria and rules in order to categorise quotes or flag suspected fraud.
Collect: Stream data from any source in and out of the platform in any format and volume, in order to do further analysis on quote data, such a conversion analysis.
Swallow Data is just one of 4 initial products we’re releasing on our platform, please see our other posts for more on Testing, Publishing and Data.
Powerful apart but awesome together. Transform how you price.
We’d love to hear more about your data pain points, so if you’re keen to share, or would like to try out the Swallow platform - please do get in touch at contact@swallow.app
