How AI Is Revolutionising B2B Pricing


Businesses are incorporating machine learning solutions for dynamic pricing, intelligent negotiation, and profit simulation.

Although machine learning (ML) adoption started in the consumer space nearly a decade ago, the B2B sector has been relatively slow in adopting ML algorithms for pricing. But the scenario is changing fast.

A recent Global CEO Survey found 85 per cent of CEOs agree that AI will dramatically transform the way they do business over the next five years.

B2B businesses understand the potential of ML to make the right business decisions, which include pricing. And there are various trends that make the adoption of ML in B2B pricing logical.  

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Evolution of pricing as a separate function: Many organisations have understood the impact of pricing on revenue management and profitability, and realised cross-functional pricing is necessary with inputs from enterprise strategy, finance, marketing, and sales. This has led organisations to make investments in creating dedicated pricing teams.

Transformation in value-based pricing: Earlier, pricing strategies revolved around competition and cost. Now, with sellers interested in determining the buyer’s desire to pay, which consecutively relies on the perceived value the consumer is receiving from the services and goods, things are changing. Every individual perceives the value uniquely, and hence services and goods require to be priced separately for different customers. Brands have realised that by value-based pricing, they can command a premium. 

Concentrate on price enhancement: Generally, pricing in B2B has been associated with the deal size. Salespeople incentives were dependent on the deal size and not on profit margin. This followed in salespeople providing extensive discounts, even if it may not have been essential. There has been an increasing acceptance that unfair discounting practices need to be controlled, and the focus on price optimisation.

Adoption of dynamic pricing: Technology disruption in eCommerce, and collaborative consumption platforms, such as Uber, have generated immense consumer volatility, affecting B2B demand. B2B businesses have understood the requirement to respond to the market faster and better. Revenue platform Pace has developed a software that uses ML to enable hotel management to explore pricing that matches supply and demand. This could allow hotels to maximize their profits by offering the price that customers are willing to pay based on their demographics and the time of year among other factors. 

Perfect price, an AI-powered dynamic pricing solution, enables companies, such as car rental companies, to do dynamic pricing.

Adopting ML/AI in human judgment areas: ML-based decisioning and data science have become more noticeable in traditional areas, dependent on human-centred communications. Most brands achieve these capabilities and adopt them. In multiple industries, success in ML-based decisions helped to overcome some scepticism of AI/ML.

Also Read: How Real is Artificial Intelligence for Customer Experience

ML structure for B2B pricing

The multiple objectives in pricing need trade-offs that directly affect the revenue and profitability. The pricing analytics team needs to consider elements in the expectations of the concerned stakeholders: senior leadership, finance, and the field sales force need to work towards common organisational goals.

Designing price segment: This step aims to recognise the group of quotes where various pricing strategies have been adopted. The pricing strategies in the B2B settings adopted by the seller vary from consumer to consumer. A potential big consumer can go for aggressive discounting. However, similar discounts may not be offered to a medium or small consumer. Price can vary for product segments too. There are different criteria such as market or channel, deal size where different pricing strategies are adopted. Businesses might have a pricing policy, but then rare approvals are standard everywhere. 

Evaluating the bid response: A bid response function evaluates the relationship linking the outcome of a bid, that is, the win or loss for a bid price. It also includes other independent variables such as channel attributes, competitor, market, customer, product, and price. This can be created from the quotes data of win or loss. Generally, the bid response function pretends the shape of an S (Sigmoid) so when price increases, the possibilities of winning the deal reduces, and vice versa. 

Profit stimulation: The purpose of profit maximisation is to stimulate profit at various price points. This can be reached by operating with the finance team from the company’s internal cost data. Once the possibility of win and profitability are set, the profit can be evaluated at different price points.