The Complete Guide to Predictive Personalization in Manufacturing

In this guide, you’ll learn about what predictive personalization is in today’s modern manufacturing, how it works, what technologies power it, how it is transforming product development and B2B sales, and how it can be integrated with your manufacturing organization’s existing systems like ERPs, PLMs, and CRMs. You will also learn about the challenges of integration, the investment required, and the KPIs that you will need to follow.

What is Predictive Personalization in Manufacturing?

Predictive personalization in manufacturing is the proactive use of artificial intelligence (AI) and machine learning (ML) to analyze real-time IoT telemetry, customer usage, behaviors, and historical trends, enabling manufacturers to then predict the needs of a customer across the entire product lifecycle. That includes CPQ offers, recommendations, preventative service, and even spare parts logistics.

Terms To Know:

• “IoT” – the integrated network of sensor machines, and software that allow manufacturers to gather real-time data in an effort to better monitor and control their plant operations.
• “Digital Twin” – a real-time virtual replica of products, equipment, or processes that use operational data to predict future performance, leverage predictive maintenance, or tailor products and services to customer needs or manufacturing conditions.
• “Feature Store” – A centralize platform for storing and managing all of the data that is used to train ML models, both for training and real-time application.
• “CPQ” – Configure Price Quote systems that enforces accurate configuration and pricing rules to generate quotes. it is the main surface for predictive personalization.

What Role Does IoT and Digital Twins Play in Data for Predictive Personalization?

IoT and Digital Twins provide your personalization engine with live and simulated data to process and analyze.

• IoT for gathering real-time data on machine usage, performance, and environment,
• Digital Twins for predicting needs by simulating future behaviors and testing configurations.

By gathering data from both IoT and Digital Twins, your personalization engine can determine when to recommend upgrades to customers, optimize future product designs, and determine when support and maintenance is required.

With the data from all your key technologies, you can now have a working personalization engine.

How Does Predictive Personalization Work?

Predicative personalization combines artificial intelligence, machine learning models, and real-time decision engines to provide personalized experiences based on user behavioral patterns, real-time data, and contextual data.

• AI for Next-Best-Action: AI chooses the next best action for a user based on a user’s intent, past behavior, and situational context.
• Real-Time Engines vs Batch Models: Real-time decision engines allow for instant personalization, while batch models apply updates on a schedule.
• ML Models vs Rule-Based: Machine learning models allow for dynamic predictions that evolve, while rule-based logic works for static personalization.

This has fast become a critical component in sales processes, which we’ll explore in a later section. As a 2020 study “Mass Personalization: Predictive Marketing Algorithms and the Reshaping of Consumer Knowledge” by Baptiste Kortas details, using a bank as an example: “client advisers have to manage portfolios of several hundred clients each, which makes individual follow-up difficult. Data science is therefore considered here as the means to better achieve the objective of a quality service relationship, supposedly based on the knowledge of the client’s life trajectories and projects (all the more so in the banking sector, a world of long lasting commercial relations).”

What Key Technologies Enables Predictive Personalization in Manufacturing?

What Data Is Needed for Predictive Personalization?

Predictive personalization requires high-quality data from diverse sources including CDPs and CRMs, event streams, content catalogs, and consent signals.

For B2B manufacturing, gathering these data points provides crucial context into what they want in their product. Equipment lifecycle, technical requirements, previous purchases, even prior service interactions, all these provide data that makes predictive personalization possible.

After relevant data has been collected and verified, the ML models can begin processing and generating personalization strategies.

What ML Models Power Predictive Personalization?

Predictive personalization uses ML models such as propensity, uplift, sequence, embeddings, and recommenders to optimize recommendations and decisions.

• Propensity Models analyze historical data to predict the likelihood of specific actions like purchasing, upgrading, or churning.
• Uplift Models estimate the incremental impact of your actions on individuals or groups, such as weighing the potential benefits and costs of a personalized promo campaign.
• Sequence Models leverage user behavior history to predict user intent and make guided suggestions.
• Embeddings convert complex data into vectors, allowing algorithms to find relationships and patterns and create a personalized UX.
• Recommenders analyze past user interactions, profile information, and other data sources to find trends and predict future user behavior before providing recommendations to the user.

These models create the decision-making center of a personalization engine, allowing your manufacturing organization to forecast the kinds of buyers prime for upselling, predict what compatible products to recommend, and stay agile to avoid over-discounting or wasting marketing efforts.

In a 2023 study from GSC Advanced Research and Reviews by Ike et al. called “Advancing Machine Learning Frameworks for Customer Retention and Propensity Modeling in E-Commerce Platforms”, predictive personalization is also playing a key role in identifying “customers who are likely to churn but also inform personalized strategies to enhance retention”. Though the study focuses more on e-commerce, the same principle applies to manufacturers in that propensity models need historical user data, transaction history, and also customer feedback as “sentiment analysis and text mining techniques can be used to extract relevant insights from feedback and incorporate them … sentiments, for example, can influence the likelihood of a customer making a repeat purchase”.

Once manufacturers have these ML models in place, they can begin applying predictive personalization to product development.

What Are Examples of Predictive Personalization in Real-World Manufacturing?

Manufacturers today are using predictive personalization to increase their sales, reduce downtime, and encourage customer retention.

Bosch Rexroth AG leverage predictive personalization to provide customized service contracts to customers based on their real-time IoT data, ensuring that the contracts meet the unique needs of the customer and their machines.

Siemens uses predictive personalization in the form of their Sensye Predictive Maintenance as a proactive maintenance solution, which utilizes their MindSphere IoT OS to monitor the health of machines and predict potential failures.

GE’s Predix AI uses predictive personalization to dynamically adjust their maintenance plans and reduce downtime.

Beyond these examples, predictive personalization also streamlines the sales processes of manufacturing organizations.

How is Predictive Personalization Transforming Product Development?

Predictive personalization transforms product development using real-world data from users and machines, shortening your development cycles, and reducing waste from overengineering and guesswork.

Rather than making your products for a broad customer base, you can dynamically adjust and create product variants that are fine tuned to different clusters of users to better target them. For example, customers that live in areas with harsher climates may need products that are reinforced. Another example, you may recommend more energy-efficient parts to a customer that has a history of being cost-sensitive.

How Does AI Personalize B2B Sales in Industrial Markets?

AI personalizes B2B sales in industrial markets by analyzing account data and historical data like buyer behavior and past interactions, allowing for personalized product recommendations, relevant price notifications, and ideal timing.

AI-driven personalization also streamlines manufacturing sales processes through lead scoring, CPQ, and AI-suggested sales actions.

• Lead Scoring: AI will analyze past customer interactions and behaviors to determine if they are a high-value lead.
• CPQ: A CPQ system can intelligently adjust offers to buyers based on data.
• Sales Reps: Your sales reps performance is enhanced with AI-suggested actions for individual accounts.
• KPIs: Sales and quote cycle times, win rates, attach rates, and average discounts.

For B2B businesses, like for manufacturers, it is crucial to understand where a buyer is on their journey and what they need next. Having an AI system in place will remove much of the manual work that goes into these sales processes, shortening deal cycles, increasing those deals too with upselling and cross-selling, and allowing your sales team to focus more on deal closing and improving win rates.

How to Use Predictive Personalization to Provide Proactive Customer Service and Support?

AI suggestions, as seen in sales processes above, also work for customer service and support—predictive personalization improves your customer support by training your AI system on support tickets, sensor data, and product usage. This enables proactive support, such as alerting a customer to when maintenance is required in the future, ensuring reduced outages or downtime for your customers, and securing customer loyalty.

Your AI customer support system would be able to predict when failures will occur, thus allowing it to schedule maintenance for a customer. Furthermore, the AI could provide dynamic support documentation based on each individual user.

How Predictive Personalization Enhances Spare Parts and Aftermarket Logistics

Predictive personalization cuts down on waste and downtime by predicting and forecasting what your customers need and when they need them.

• Enables automated shipment of parts before failure occurs
• Parts can be reordered predictively based on parts wear
• Your inventory becomes better aligned with customer usage trends

With predictive personalization, manufacturers can turn the aftermarket into a profit machine as shipping costs are reduced by avoiding excess inventory and removing emergency replacements through proactive automated shipments, your customers can get exactly what they need on time.

Personalization isn’t just limited to operations, sales, and logistics—it also influences the B2B buyer’s digital experience.

How to Personalize the UX for B2B Buyers with Predictive Personalization

Just as in B2B sales above, predictive personalization for UX depends on rich B2B customer data to deliver tailored experiences to users. Portals for B2B buyers can be personalized by using predictive insights based on a customer’s account type, their behavior and interactions, and their user role. Dashboards can be adjusted to user profiles and content recommendations can be based on user intent and behavior. Even UX navigation can be dynamically changed to adapt to a user’s needs and goals.

As discussed earlier, predictive personalization requires data to make proper forecasts and predict the right actions. B2B manufacturers may have it easier in finding rich customer data to leverage and ensure the accuracy of their personalization engine, as described in a 2022 study called “Systematic Review of Predictive Modeling for Marketing Funnel Optimization in B2B and B2C Systems” from IRE Journals by Ogeawuchi et al., a B2B manufacturer’s customers’ “[in comparison to B2C] data volume is typically smaller, but richer in context. Each customer (often a company or institution) provides more detailed and structured information”. With the proper integrations, your AI-driven personalization engine will be able to find that vital information to provide a more engaging UX for your B2B buyers.

How to Start Integrating Predictive Personalization into Your Systems

To begin integrating predictive personalization into your systems, you should audit your data from your diverse sources (such as CRM, ERP, and IoT platforms), determine where to find your customer, product, and usage data, and connect all those sources with APIs or middleware.

• CRM: Stores customer behavioral and transactional data
• ERP: Contains operational history
• PLM: Keeps track of your product configurations
• IoT: Provides real-time machine and environment data

A successful integration will require cross-functional alignment between your IT, sales, and operations. It is recommended to use cloud-native tools for your data lake, ML platform, and workflow automation tools to ensure scalable support for your predictive personalization.

What Are the Main Integration Challenges in Manufacturing?

The main integration challenges for predictive personalization in manufacturing include data quality, system compatibility, and resistance to change. Legacy systems might not have the APIs to integrate with your predictive personalization. Data inconsistencies have a negative impact on personalization accuracy. Meanwhile, your staff may require training on the new predictive personalization system and may exhibit resistance or distrust towards it during the early phases.

To prevent these problems from arising down the road, consider taking these steps to ensure you and your team are ready for predictive personalization:

• Custom infrastructure development: Invest in custom software development to build a clean data infrastructure from the start and ensure that all legacy systems are ready to integrate smoothly.
• Training and Onboarding: Provide training for your staff to comfortably onboard them with the new features of your predictive personalization system.

To overcome these challenges, you’ll need a system architecture that is designed to support predictive personalization at scale.

Custom VS Off-the-Shelf: How Much Does Predictive Personalization Cost to Implement?

The main difference between custom developed predictive personalization and an off-the-shelf solution is that custom AI has higher upfront costs but superior customization and scalability versus off-the-shelf in meeting your manufacturing organization’s unique needs and workflows.

Our team’s AI development services offer the best combination of value and customization. Our approach to integrating sophisticated machine learning systems to self-improving artificial intelligence yields incredible value over the life of a product.

Whatever the cost of your AI personalization solution, the true measure of value will be in its ROI. For manufacturers, your ROI may come in the form of faster sales cycles, reduced downtime and associated savings, and increased customer retention. Let’s explore the kinds of returns you can expect from successful AI personalization implementation.

What ROI Can Manufacturers Expect from Predictive Personalization?

The ROI from predictive personalization is measured through stronger sales, improved operational efficiency like faster quote turnaround and greater upselling, better customer satisfaction and less customer support tickets, and more customer retention.

Despite the relatively newness of predictive personalization, it is fast becoming a tool of businesses to get any edge they can. A 2023 report on Forbes by Daniel Newman, “The Future of Personalization: What You Need to Know”, it was found that 9 out of 10 businesses are “already using AI-driven personalization to increase growth in their business … more than 60 percent of leaders said customer retention improved because of their personalization programs.”

But even with the growing adoption and beneficial returns, there are important ethical questions around customer and machine data and how to use one’s AI personalization system in an ethical, transparent, and legally compliant manner.

What Are the Ethical and Privacy Considerations of Predictive Personalization?

Ethical predictive personalization must include ensuring customers understand how their data is used, obtaining clear consent for data to be used for personalization, and there must be regular audits of your AI model for bias.

This is especially important if you’re a manufacturer in a region governed by data compliance regulations like GDPR, CCPA, PCI-DSS, and ISO 27001. Your AI personalization solution will be handling a lot of sensitive data, from user behavior and customer information to operational machine logs—the ethical obligations behind each of these types of data require that you are transparent with your customers and explain how your personalization system works with that data.

Ensuring transparency, fairness, and non-bias in your data handling ensures long-term trust, competitive advantage, and the avoidance of legal penalties.