Let’s play a game called “Two Truths and a Lie,” where you get to decide which of the following three statements is a fallacy:

#1 – Customer expectations are continuing to increase year over year.

#2 – Servitization and subscription-based service models are becoming the norm.

#3 – Pricing teams can keep doing what they’re doing today to succeed in this changing service landscape.

If you guessed #3 as the lie, you’ve won the game. Servitization, the selling of outcomes over products, and the changing field view of service as a whole is pushing pricing professionals to also change how they’re pricing, evolving to selling service contracts that guarantee maximized product uptime over individual service parts. In fact, according to a study of field service organizations by the Aberdeen Group, one out of four firms is generating new revenue from servitization.

Two key strategies that play a role in this servitization pricing disruption are predictive analytics and proactive maintenance. Injecting insights from predictive analytics to proactively maintain uptime introduces a whole new lever for pricing teams to pull, and manufacturers cannot maintain the status quo to appease these increasing customer expectations. That’s why we created our newest Syncron original Orange Paper, Pricing for Product Uptime: Navigating the Most Disruptive Change in Pricing History.

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Historically, using a single price is economically inefficient because part of the demand curve that could be profitably served is priced out of the market. As a consequence, firms regularly offer targeted discounts, promotions, and segment-based pricing to reach different consumers. Ecommerce websites have a distinct advantage in pursuing such an approach because they log detailed analytics on customer browsing, not just the goods they end up purchasing, and aggressively adjust prices over time.

These price adjustments are a form of experimentation and, jointly with big data, allow companies to learn more about their buyers’ price responsiveness. And, as manufacturers start to truly factor predictive analytics data into their predictive maintenance schedules, they’ll be able to move away from a rigid, time-based schedule to an intelligent, need-based schedule, structured around the information received from the equipment in need.

Ultimately, with such a heavy focus on servitization and maximized product uptime as the key to organizational performance, Artificial Intelligence (AI) and predictive analytics are playing a major role in the service supply chain. And, even though many industries already have uptime service models guaranteed in their contracts and service level agreements, the after-sales service process still has room for growth when it comes to the integration of predictive analytics and proactive maintenance.

At the end of the day, the shift from ownership to access means that initial sales aren’t going to cut it to meet revenue expectations anymore, so that means proactive maintenance is a new financial lever for manufacturers. That kind of proactivity requires the integration of smart technology like the Internet of Things (IoT) and AI to act as the maintenance applications of predictive analytics.

When moving from a break-fix business model to an uptime-driven, predictive strategy, manufacturers will lean heavily on data, and the interconnected parts and digital vessels by which it is attained. And, as they start to learn what this means for their specific organizations, companies are going to find themselves on the path toward digital transformation.