“What price optimisation techniques can I use to make my company grow?”

 

That’s the question that probably makes you lose sleep every night and look for new approaches every day. Price optimisation can be an approach that helps your company grow.

 

As technology keeps advancing, AI pricing programs will become a permanent fixture in the business landscape and AI capabilities need to be sustainable over time developing pricing strategies and programs and supporting potential new pricing models and capabilities.

 

Specifically, we believe that companies need to establish dedicated pricing teams to entrench AI pricing programs. This is an important business pricing tool that cannot be left to just mere speculation. Companies are devoting considerable financial resources to AI. Therefore, necessary pricing skills and experience are too rare to assume that they will be bounced around the organisation with little coordination or collaboration. Just as e-commerce led to Chief Digital Pricing Officers and pricing teams to support online commerce, AI will engender new pricing competence centres (PCC) or pricing centres of excellence (PCOE).

 

 

price optimisation techniques

 


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Self-learning algorithms use in price optimisation techniques

 

Self-learning algorithms scan vast amounts of data, sifting through endless pricing scenarios; they can also suggest the most relevant price whenever it is necessary. Algorithm-powered models analyse thousands of relationships between the products in the portfolio to recommend individual prices that maximise revenue and sales of the entire product portfolio. You can use AI to automate all the time- and labour-consuming tasks. It will allow your pricing team to move to high-level customer-centric decision

 

For example, the way the price changes for one product can affect the sales of several other items sold by the retailer. 

 

Machine learning derives its roots from statistical regression. This raises the issue of whether an AI PCC or PCOE should be combined with pricing teams. If an existing pricing team is already doing some predictive pricing analysis, the members who are willing to learn and grow can probably master many AI pricing strategies. Therefore, a combined pricing work would make sense.

 

We’ll explore what AI pricing is, what it can do, how it might enable new pricing models and strategies, and if it can be sub-optimised on what AI automated pricing can do for the business.

 

What an AI pricing team should do with price optimisation techniques

 

Whether an AI pricing team is part of an existing team or an entirely new team, there are many different activities that it can and should pursue. Some of these — like developing AI pricing models and systems, working closely with team members, and building technical pricing algorithms — can be done in collaboration with other IT programs; others will involve working closely with business leaders. Although collaboration is important, these are the tasks that the AI pricing team should be responsible for:

 

Identify business-driven pricing strategies. Developers of AI pricing capabilities will need a prioritised list of past purchasing transactions or past pricing practices the company used. They should balance strategic value with what is achievable. Companies may develop some of the pricing pilots, but they should also have a “pipeline” — regularly monitored by the AI pricing centre and strategic pricing management group — that leads to pricing deployment.

 

Determining the level of pricing in price optimisation techniques

 

Determine the appropriate level of pricing. Since AI typically supports pricing tasks rather than entire jobs or business processes, it is usually best to undertake less ambitious pricing strategy as opposed to “moon shots.” But in order to get management attention and have a substantial impact on the business, pricing teams working with AIs may want to undertake a series of smaller projects in one area of the pricing strategy. This may require a “road map” with multiple responses across a timeline. An AI pricing centre can help a company “think big, but start small”.

 

 

 

Create a target pricing structure

The vision and define the data pricing platform and tools needed to deliver. This is key for all pricing strategies, to include all types of data — structured, unstructured, and external. Most companies will benefit from using user-ready pricing tools with open-source components (e.g. Alteryx) to allow quick user-friendly pricing modelling, rather than packaged tools that are historically BI-oriented (like early versions of SAS or SPSS).

 

Manage outside pricing innovation

An AI pricing centre can help to orchestrate relationships; this work in great effect with universities, vendors, AI start-ups, and other sources of pricing expertise and innovation. In effect, the company can develop an AI ecosystem, perhaps even invest in firms that show promise of adding value to the business. Thus, this is also important for the tools and technology to be the best pricing program.

 

Develop and maintain a network of AI pricing experts

An AI centre will work best if it cultivates a network of influential price analysts and experts for the technology across the business. Given the commodification of programming (with readily available scripts in languages like R- and Python). Therefore, the focus for in-house pricing capability building should be on the statistical and pricing environment, rather than pure programming.

 

Discussions

 

Usually, retailers that opt for AI-led price optimization go through several stages. They learn to set optimal prices by stock-keeping unit (SKU), then by product portfolio, channel, point of sale and by customer. In effect, optimal pricing at every level subsequently improves profitability. Optimal prices are those prices that do not irritate your customers and do not increase your marginality. Thus, do not cut the sales of other items in the product portfolio.

 

To set optimal prices for at least a thousand products weekly or sometimes even daily, your pricing managers would need to have superhuman analytical, computational powers and supernatural abilities to make fast decisions. As a result, this is where the AI pricing centre of excellence will come in. 

 

Implications

 

There are not enough people who know how to operate AIs which think and learn pricing by themselves. Therefore, a remedy would be offering pricing platforms and tools that permit AI-driven work “as a service”, rather than starting everything from scratch; organisations are able to take ready-made pricing strategy and plug in their own data.

 

Like any programs, AIs need relevant data just to make sure that their solutions do not cause other pricing issues. Specifically, all those areas that are beyond those which they designed to consider.

 

People are doubtful about this technology on how it takes pricing decisions. Whether all its decisions are perfect or not, the remedy can lie in making AI explainable, provable and transparent. Organisations should implement explainable AI.

 

Conclusions

 

Most of the AI applications are based on massive volumes of data to learn and make intelligent decisions. Hence, machine learning systems depend on the data which is often sensitive and personal in nature.

 

Organisations are investing heavily in design methodologies and focusing on how to create AI models learn despite the scarcity of labelled data. Labelled data is organised to make it understandable for machines to learn. 

 

There are risks and challenges that are associated with AI implementation in business. However, just like the two different faces of a coin, AI also has several opportunities for businesses,;due to the opportunities associated with AI. Hence, many businesses hire IT, specialists, to train AIs to optimise their pricing strategy

 

Discover how to use price optimization for your company. 

 

Contact us for a free consultation.

 

You can download our whitepaper here.

 


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