David Burden explaining recent developments around Virtual Humans
Author of Virtual Humans: Today and Tomorrow
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LONDON - Dynamic pricing – in which a business alters its prices according to market conditions and demand - has existed in some form for decades.
For example, fuel companies will regularly change prices at the pumps dependent on a range of factors such as the weather, wholesale availability and seasonality. Similarly, travel retailers such as hotels and airlines have traditionally increased their rates at weekends, around Christmas or during the summer months to take advantage of booking spikes.
Alongside fluctuations in consumer demand, dynamic pricing is also heavily influenced by activities of similar market players. The e-commerce boom has therefore had a particularly powerful impact on retail pricing strategies, due to the wider product ranges and uplift in market competition brought about by shopping online.
What’s more, as prices for goods and services are now available for consumers to see 24/7, the number of pricing combinations that businesses must consider have vastly increased. Implementing dynamic pricing has consequently become a far more complex task.
Indeed, pre-internet, the average retailer had to consider around 4,000 price and marketing combinations per quarter to stay ahead of competitors, whereas this number has now risen to 60 million combinations a day.
Completing this task manually is both laborious and time-consuming; which is where automation comes in. Much as it has in other sectors, AI – in particular machine learning – has quickly become an indispensable asset, helping retailers to collate and analyse huge volumes of data far more efficiently than any human can.
Let’s look at some of the key ways that this technology is benefiting dynamic pricing in the retail sector.
AI-powered systems allow retailers to move beyond simple strategies such as “match my competitor’s price” or “rank third most expensive in Google Shopping”. They can do this by accessing, storing and analysing huge sets of data to set completely new prices based on product price elasticity.
The system could learn, for example, that a TV is highly price-elastic while the wall mount with which it is almost always cross-sold is inelastic. It therefore makes sense to price more aggressively on the TV?—?as that will lead to huge volume uplifts?—?while taking more margin on the wall mount.
This means that retailers can consistently test different pricing strategies to see what works – all at the touch of a button. It also allows pricing teams to take a bigger-picture approach; looking at strategic planning and tweaking outcomes, rather than dealing with pricing on a manual, task-by-task basis.
Price is a key factor in consumer purchasing decisions. However, retailers must find the balance between being undercut by competitors and subsequently losing sales, and the “race to the bottom” where constant price-cutting destroys profits.
Intelligent, AI-powered systems can combat this. Rather than simply matching the lowest market price, they take into account a huge range of factors such as individual commercial strategy, stock levels and price elasticity to determine the optimum amount for each product to maintain margins. In this sense, taking emotion out of the equation can be another benefit of such an AI-powered system.
While AI is already being used to automate dynamic pricing in a range of sectors, there are so many developments in the pipeline, of which the industry is just scratching the surface.
In the future, AI could be used to advise retail categories teams to make adjustments to pricing strategies based on automated analysis of performance data, or even automatically develop and implement new pricing strategies based on goals provided to the system. We could also see the widespread implementation of personalised pricing, in which the retailer can incentivise and offer discounts to shoppers based on their demographic, purchase history or status as a returning visitor.
The use of AI tools in business has been the target of consistent negative press, with many criticising the impact that such software could have on employment levels across the UK. However, rather than automating away jobs, AI has the potential to reinvent retail by giving superpowers to pricing teams. Relieved of their routine operational tasks, retailers can be free to focus on creativity and improving their businesses every day.
Sander is the CEO and founder of Omnia Retail. Omnia is the leading SaaS solution for integrated pricing and online marketing automation.
Author of Virtual Humans: Today and Tomorrow