5 Ways AI-Based Subscription Management Software is Remodeling Revenue Management with Price Optimization

The AI-led subscription management has proven a precursor in optimizing recurring revenue. It offers features that intelligently identify the needs, expectations, behaviours, and interests of the customers.

Price optimization is one of them.

According to McKinsey, AI-based pricing can add up to $500B revenue in global market value, whereas BCG reported an increase in revenue up to 5% in a year only.

B2B businesses are diving deep into the AI-based pricing arena to make informed and data-driven revenue management decisions.

Managing pricing intelligently requires detailed and data-driven insights into the factors that affect a customer’s purchase intentions and actions. Optimizing rates and plans help SaaS and other subscription businesses create smart pricing and billing strategies that draw prospects as well as minimizes frictions. These convert leads into customers and then attract customers to go for upsells, cross-sells, or re-sells.

Subscription Management systems integrated with the sales, marketing, and support management applications, identify the data-points where the market demands meet with the subscription business services, products, and other offerings.

This article dives into the AI and Machine Learning impacts in transforming the product valuation and pricing strategies. We will also take on how AI-driven pricing engines in the subscription management software can optimize the price and plans.

Revenue Management with AI-Driven Price Optimization 

AI has become the pricing management assistance for B2B and B2C businesses across the industries.

For a subscription business, price optimization is even more significant as it helps them to retain customers and increase their lifetime value by attracting them for plan upgrades and resells.

The customized pricing favours customers and businesses alike. The optimized pricing not only increases the prospects of purchase and adds more revenue in the stream, but it also cuts the unnecessary noise around discounts, credits, vouchers, and others with AI-recommended productive incentives to paddles engagement with customers.

Several studies suggest a 5% to 8% increase in revenue in the stream by simply optimizing the plans and pricing.

In recurring revenue management, predictability of the revenue ensures the stability of the business and revenue growth. It allows businesses to increase their ROI by offering personalized price management solutions to a B2B customer.

Impacts of AI-Powered Price Optimization on Revenue Management

Price optimization helps to gain the trust and satisfaction of the customer that they are paying for what they are consuming.

To deal with the fluctuating market needs and demands, the pricing engine of the SaaS Subscription Management Platforms offers an instant and unique solution, the AI-fueled price predictions.

It provides insights to analyze the revenue growth goals and identify the right time to price accordingly.

Industries like hotels, lodgings, travel and tourism, car rentals, property rentals, leasing, subscription businesses, SaaS platforms, services providers, airlines, and many more can benefit from the pricing strategies and draw more prospects of capitalizing the leads into customers.

Here are some of the ways how price optimization using AI-powered pricing engines in the subscription management systems can increase the prospects for recurring revenue:

AI-Recommended Pricing Model Selection

Subscription businesses and SaaS using AI and ML while sifting through the piles of data sets available with billing, payments, marketing, sales, support, or other applications integrated into a system can identify the customers’ transactional behaviours, purchasing interests, and monetary capacities. It offers the pricing model that best suits them, so they no longer consider leaving the platform without converting. A range of pricing models are there that ensure customer engagement and eventually recurring revenue growth:

• Flat Subscription Fee
• Pay-Per-User
• Pay-Per-Feature
• Pay As You Go
• Freemium
• Freemium plus Flat Fee
• Freemium plus Consumption-Based Pricing
• Or any other Hybrid Model

Revenue-Driven Optimization of Coupons and Add-ons

Pricing engines offering optimized pricing also assist to cut down the noise around discounted coupons and vouchers. These discounted add-ons are the foes of recurring revenue growth and, in the long term, end the SaaS businesses into a dead-end street.

They eliminate the under-or non- performing coupons and helps businesses to optimize recurring revenue strategy for discounts. This does not only attract traffic but also transform them into consistent revenue growth.

Automation of Pricing Rules 

Setting rules for price optimization based on the percentage or value of the products, after every while, can feel like a tiresome task and may require resources to do these repetitive tasks. With the AI-led pricing engine in the subscription software, pricing rules are set to automate price management and optimization. These changes then reflect on the plans and pricing cataloguing.

AI-led Price Prediction 

Price optimization is a great way to predict pricing for the near and far future to project recurring revenue growth in the coming days. For subscription businesses, predictability is a crucial factor to strengthen and streamline revenue operations. In the retail business, the predictability of recurring revenue extends to demand and supply management.

Fine-Tuning of the Pricing Structures With and Data-Driven Transactional History Analysis

Not all payment processors and payment gateways charge the same. With the recurring payments processing, customers can slap with extra charges, processing fees, or sales tax that can also vary with region. It can be a big turn off for the customer to go for online recurring payments.

This transactional inconvenience is quite an impediment to revenue growth. Price optimization tools in the subscription management system fine-tune pricing on the basis of the transactional history, comparison of payment gateways transactional processes, and charges that incur on customers when they pay online.