Can AI pricing really improve a business’ price optimisation techniques and analytics?

 

From phones to cars to insurance pricing and many B2B industries as well, AI is fast becoming a pricing tool of choice for business’ that want to improve pricing and drive immediate profitability.  We ask: How useful is AI really at improving strategic price setting – particularly in the area of price optimisation?

 

We ask this because price optimisation using AI can be a great way for businesses to drive profitability. Artificial intelligence (AI) can optimise prices at any given time to meet the expectations of different customer groups to drive profitable revenue growth. What’s more, more leading businesses than ever before are turning to price optimisation techniques based on positive feedback from customers.

 

For example, as many as 60% of shoppers choose to buy at stores with optimal prices not the lowest prices.  What’s more, latest research from IBMs indicates that as much as 73% of businesses (retailers) are deciding to use intelligent automation to improve their pricing and promotion by 2021.

 

So, with this in mind, in this article, we’re going to explore what AI pricing is, the price optimisation techniques, what it can do to drive better prices and make good decisions and improve revenue and profitability. We believe that AI pricing will become a permanent fixture in the business landscape. What’s more, we predict that more business leaders will be considering AI pricing and the need to improve pricing, including new pricing models and capabilities, than ever before.

 

By the end of this article, you’ll learn how AI pricing works, the benefits of using AI in price optimisation, different price optimisation techniques for AI pricing and the keys to success of employing it in your business.

 

price optimisation techniques


 

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What do you gain from price optimisation?

 

Several studies show that expending money on people, systems and process to optimise prices can generate between .5% to as much as 4% return on sales.  By employing AI, a business makes faster and better-quality decisions using the large diverse sets of information that you have around products, customers, buyer journeys and purchase behaviours. These benefits are obtainable now using AI technology established across many industries and companies. In addition, using AI warrants a higher ROI than solutions employed without them.

 

Businesses, especially retailers get price predictions and optimisations based on various market changes that go in line with their volume goals and pricing. In effect, pricing is no longer a problem. Well, in a way, companies can get an effective guide for their business life cycle decisions.

 

Furthermore, they get valuable time back. They can have more time to attend to other important aspects of their business. Automated AI pricing solutions are the future of companies and industries. They lessen the huge amounts of time needed for manual labour in regard to tracking or monitoring competitors’ prices.

 

The benefits of technology don’t stop there. With the scope of modern technology, businesses also get to focus their operations around customers and provide them with a more personalised experience according to their browsing history, wish lists, including cross-sell and up-sell recommendations. They get assortment optimization. Also, diverse adjustments based on the occasion, consumer behaviour, category, product, season and rival’s intel.

 

In other words, they get immediate market intelligence – a detailed understanding of human behaviour matched with extensive automation and data integration.

 

Typically, businesses that employ AI-led price optimisation pass through various stages.

 

They gain knowledge on how to set optimal prices by stock-keeping unit (SKU), by product group, channel, point of sale, and then by customer. Eventually, optimal pricing at each level will improve the profitability of the business especially if they use price optimisation techniques.

 

 

How does AI work?

 

Huge amounts of data are scanned by self-learning algorithms. It digs through never-ending pricing scenarios and proposes the most relevant price necessarily. Algorithm-powered models take into account thousands of hidden correlations between the items in the portfolio to suggest single prices that maximize profit and sales of the whole product group. Like for instance, it considers the way the price shifts for one product can impact the sales of other items of the retailer.

 

AI can automate all the painstaking tasks of pricing and allow your staff to move to important customer-centric decision making. It is no joke to set optimal prices for at least a thousand products on a daily or weekly basis. Your managers need to have extraordinary analytical and mathematical powers and superhuman abilities to make fast decisions. Thus, you need to be equipped with AI.

 

At the centre of AI is machine learning (ML). ML a method that has the capacity to learn on its own without programming it. Machine learning uses the information to recognise patterns in data and modify actions appropriately so that, when there’s new data, it creates programs that adapt to that particular information.

 

ML algorithms are similar to mathematical optimization and computational statistics. ML is a standard process used to make elaborate algorithms with predictive powers. Others know this as predictive analytics, a number of analytical models that reveal insights from trends and archival information in the data set.

 

Machine learning is composed of several processes and various types of learning. It’s even in your Facebook’s news feed and can even foresee your next plan when you want to buy soup.

 

In essence, AI is a pricing analytics software with machine learning parts that use a technique built on a particular statistical model (multi-task learning, Gaussian process regression, Bayesian linear regression, and a number of other models) to develop algorithms that recognise patterns automatically from the data and predict prices according to that information. It’s a pricing mechanism that aids you in monitoring competitor prices instantaneously. It sorts out and compares identical products which depend on extensive elements selected, then optimizes prices ultimately.

 

Different AI price optimisation techniques

 

Transparency of decisions and controls over the algorithms are key elements to implementing successful AI pricing. In the same manner that change management is key to obtain the buy-in from the company on how to leverage AI pricing most efficiently.

 

Let’s discuss the four price optimisation techniques that companies commonly use:

 

  1. Regression Analysis

This is the conventional approach to identified trends and interconnection between data and to generate predictive models. Many Machine Learning approaches are actually regression models such as Regression Trees, NN etc. The aim of these models is to forecast value from a set of other variables, may it be numerical or not. It is practical to predict different values like volume variation or competitors’ prices in reaction to your own price adjustment from a price optimization viewpoint. This helps to position your prices at the most productive point.

 

Machine Learning, however, has its downside since it needs quantitative and qualitative data to provide valid predictions. Oftentimes, it’s difficult specifically in a B2B pricing context, due to insufficient data on specific segments.  However, at a global level, it can be relevant to provide insights for key customer segments or top-performing product categories.

 

  1. Elasticity Approach

The goal of price optimisation is the capacity to model price elasticity. With this approach, providing optimal prices recommendations becomes apparent for each product and customers segments either to maximise revenue or profitability. This approach depends on regression models it also has the same limitations and is a bit hard to obtain especially for B2Bs because of business relationships complexity.

 

  1. Machine Learning

Machine Learning is a powerful method to automate product and customer segmentation. It enables a business to define and maintain similar segments about the behaviour of customers according to transactions and master data. This segmentation can define optimal price corridors such as floor, targets and stretch which makes it easy to explain and share in the sales organization. ML models can also give products recommendations to help improve up/cross-selling profits.

 

  1. Adaptive Multi-Agents System (AMAS)

An advanced approach that imitates the way companies works, coming from an academic research field named Distributed Artificial Intelligence. It is a bio-inspired model committed to solving intricate optimisation problems. It relies on the main principle of the emergence properties of self-organizing systems. Like for instance, birds flocking, ants’ colonies, etc. That is to say, the whole organization provides resolutions to problems that are not easy than what a single of its component can.

 

AMAS focuses on the “how” in price optimisation, like determining the best sales conditions to reach the objectives. However, the other approaches focus on the “what” such as knowing which price or margin to offer for this segment. Addressing the “how” in price optimisation is oftentimes neglected and more difficult to perform a final price recommendation in an organization with thousands of list prices, lots of special discounts and also complex business rules to respect. With standard pricing software, it’s impossible to find the optimal configuration but it is where AMAS excels.

 

Frequently, AI systems are regarded as black boxes (AMAS as a white box) that only supplies recommendations but doesn’t help final users in understanding why. With AMAS, recommendations can easily be interpreted by business users because it’s a model of how the pricing works within a specific organization. Furthermore, AMAS provides information about the optimization process itself that allows the user to understand why an objective is met or not. And if not, it provides reasons that prevented it from doing so.

 

There are various AI and Machine Learning methods and price optimisation techniques for segmentation and price optimisation. Your choice depends on the situation such as data available, industry and routes to market.

 

Keys to success to price optimisation using AI Pricing

 

We cannot avoid human errors. Having said that, a business could lose millions of dollars in revenue and profit opportunity, since determining major drivers could take no less than weeks. However, AI-powered price optimisation systems focus to resolve this with unprecedented speed and accuracy. But why are retailers still hesitant in adopting such systems?

 

There are 5 important steps to be taken for AI-driven price optimisation to be effective:

 

Complete, high-quality, and well-structured data

Basically, data must be complete, high-grade, systematic and cover at least three years to be eligible for the algorithmic model. If it is not possible to restore lost data or bring it back to the same quality, a business may opt to wait for no less than a year. Then gather data in a single format, or to buy or model it.

 

Invest to build or buy

A successful business means being able to combine core competencies with other competencies. For retailers, negotiating purchase prices with dealers and then setting optimal prices (which needs a completely different set of skills).

 

In some cases, like Amazon, they invested in creating an in-house pricing hub. While others, like Target or Metro, they launched startup labs to test and integrate solutions. Businesses with budget constraints collaborate with independent startups working in a specific field, having some grip in the market.

 

Identify products for AI pricing

Not all products require price recommendations fueled by algorithms. Normally, exclusive items which make you different from your competitors are the ones that need algorithmic recommendations. For example, expensive private label products.

 

Apply AI suggestions to products that you share with your rivals but only those that don’t need the lowest price to attract buyers. For instance, pet food and supplies. Customers would choose to shop in a store that specialises in pet food and supplies rather than a supermarket offering lower prices for the same products. In addition to a nice price offer, typically shoppers also look for advice. In this case, AI can suggest prices which enable you to entice customers, at the same time maximising your revenue.

 

Let Pricing Team handle the whole pricing process

Needless to say, pricing managers should know how to interpret and trust AI’s price recommendations before using the system. Pricing team members should take control of the whole pricing process. Before actually employing it and to make sure it’s working well, put restrictions on algorithms or test price suggestions.

 

Keep System updated

The system should be regularly updated about your business goals and pricing strategy. So as to come up with the most relevant scenarios. Constantly monitor the system on how it works and make corrections necessarily. To stay productive, the system needs to integrate all the required technological advancements.

 

Envision a world where all your products maximise your revenue. Your pricing managers can work out more favourable purchase prices and come up with more efficient pricing strategies. And your customers keep coming back to buy from you, choosing you over your competitors. Your business achieves continued growth. That world will happen in real life with AI.

 

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 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. Focusing on how to create AI models to 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. Thus, many businesses hire IT specialists, to train AIs to optimise their pricing strategy.

 

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