Why successful retailers adopt price optimisation analytics
Why successful retailers adopt price optimisation analytics
Let’s face it: 2024 will likely continue to be a challenging time for businesses as consumers think twice about what they spend money on, and from whom. This view is reinforced by the Chief Economist at the National Retail Federation, Jack Kleinhenz, who expects consumer spending growth will slow this year.
So, what are executives doing in response to lower levels of sales? They are actively looking at ways to optimise pricing in order to maximise revenue, improve profitability, and maintain market share against fierce competition.
In this article, I explain why price optimisation matters, how retailers are using price optimisation analytics to improve business performance and what you should look for when selecting price optimisation analytics for your company.
Why Price Optimisation Matters Today
Over the last six months, Xiatech has witnessed a huge demand from retailers for AI-powered analytics because executives have focused resources on optimising their businesses – making them leaner, faster and more efficient.
Perhaps not surprisingly, one of the most popular analytics dashboards we developed in 2023 is Price Optimisation, which has enabled retailers to generate millions of dollars in sales that would otherwise have been lost to competitors, or not materialised by any means.
The value of price optimisation and, specifically, price modelling, is massive to retailers, including:
- Helps identify price points that maximise revenue and margin while balancing sales volume and profitability.
- Protects and grows market share from competitors
- Helps estimate sales volume by understanding how price changes impact customer behaviour.
- Provides the ability to adapt to changing market dynamics by implementing real-time price adjustments based on factors like supply, demand and competition
- Aids in determining the most effective promotional strategies and discounts to increase customer traffic and sales
- Analyses profitability at the product and customer segment level to optimise their product mix and overall profitability
- Ensures the right products, at the right volume, are sold at the optimised price through the right channel, and that price cannibalisation is mitigated
What is Price Optimisation Analytics
If you are new to price optimisation analytics, this capability enables you to analyse various (and important) factors to determine the optimal pricing strategy.
This involves using statistical analysis, market research, as well as mathematical and machine learning algorithms, to understand customer behaviour, market dynamics, and internal factors that influence pricing decisions.
Xiatech’s Price Optimisation analytics is unique in the market because it is able to suggest and intelligently set prices for all your SKUs every day by automatically analysing all data related to supply, demand, basket, promotional and competition available. It understands all the market and customer-segment responses for different historic prices and price changes, and therefore, it optimises its pricing strategy as it matures in your data ecosystem.
In other words, Price Optimisation will balance your current and upcoming stock supply, estimate likely demand curves, assess purchasing patterns, and more, to set the correct prices to your products, across all your store network as well as online, and periodically re-evaluating its accuracy, re-training itself and fine-tuning prices as more up-to-date information becomes available.
Thanks to Composable AI, our Price Optimisation models are able to seek help from other Machine Learning and statistical models already deployed in your company, such as Product Affinity, Recommendation Engines, historic A/B testing, Demand Forecasting, Customer Lifetime Value estimations, and so on.
In addition, Price Optimisation, like all our Machine Learning and AI offerings, is cloud-agnostic and integrates with all your data warehousing, business intelligence tools and core systems.
How do retailers successfully use price optimisation analytics? Here is a short example (sorry, because of the competitive nature of the retailer, we have had to anonymise the case study).
A well-known retailer sought to understand the scale of additional revenue and margin that could be achieved if its prices were dynamically optimised. Xiatech deployed its Xfuze Hyper-Integration Platform and configured existing ML-powered Price Optimisation model (focusing on the price elasticity and margin optimisation areas) to measure the opportunity of different product pricing scenarios, which required collecting, cleansing and enriching 100s of data sources from within and external of the retailer. The data-turned-insights were then visualised in Xfuze using techniques such as geospatial analytics. As a result of this work, Xiatech identified £10 million to £15 million in revenue uplift per year for the retailer.
How to Select a Price Optimisation Capability
Not all price optimisation analytics are equal in capabilities. Depending on the tool you use will define the amount of value you create across your business including the amount and type of insight you want and the effort required to obtain this insight to support decision-making.
I have provided a selection of capabilities modern price optimisation analytics should have and what you should elevate when looking for the right solution.
- Pre-built data model
- Data maturity level: whether reliable, clean, deduplicated and up-to-date data is flowing into your data lake or data warehouse
- Data availability: whether historic sales, demand, supply, and price changes are available, or just a subset of them
- Data literacy across your organisation: some simpler or more complex models could be deployed depending on your ability to interpret and utilise the results as well as report on them
- Platform and tech set-up: Do you have an all-in-one platform such as Xfuze that allows for ML models to be trained, optimised and placed in production to provide inference – or do you have to perform some of these tasks manually?
2024 will likely remain a challenging time for businesses, but those that embrace AI and analytics sooner will be better prepared for when the economy bounces back.
Price optimisation analytics is not a nice to have for retailers – it’s a must-have requirement that successful businesses use, so please contact me or my colleagues, to learn how we can help you to improve the business performance of your organisation.
Head of Data Science
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