Propensity insights use data to predict customer behavior, helping businesses make smarter decisions. These insights are crucial for improving marketing, sales, customer service, and operations. Here’s what you need to know:
- What are Propensity Insights?
- They predict actions like purchase likelihood or churn risk using data models.
- Example: Customers who refer friends often have higher email open rates.
- Why Use Them?
- Businesses using data-driven strategies are:
- 23x more likely to acquire customers.
- 6x more likely to retain customers.
- 19x more likely to increase profits.
- Personalization improves customer satisfaction for 52% of consumers.
- Businesses using data-driven strategies are:
- Key Benefits by Area:
- Marketing: Lower costs, better ROI, accurate attribution models.
- Sales: Higher conversions, better targeting.
- Operations: Optimized inventory, improved demand forecasting.
- Customer Service: Personalized experiences, better retention.
- How to Succeed with Propensity Insights:
- Use first-party data.
- Regularly update models.
- Train teams and ensure data quality.
- Align decisions with clear ROI metrics.
| Area | Benefits |
|---|---|
| Marketing | Better ROI, refined attribution methods |
| Sales | Higher conversions, personalized offers |
| Operations | Improved inventory, demand forecasting |
| Customer Service | Enhanced retention, tailored experiences |
Propensity insights are transforming how businesses operate, from marketing to global expansion, enabling smarter and more efficient decision-making.
Using Propensity Insights to Improve Marketing
Enhanced Marketing Attribution Methods
Traditional last-click attribution falls short in capturing the complexity of modern customer journeys. Propensity-based attribution offers a more comprehensive view by evaluating all touchpoints, revealing what truly drives conversions:
| Marketing Channel | Last Touch | Markov | Shapley Values |
|---|---|---|---|
| Google Paid Search | 15.0% | 8.2% | 10.0% |
| Abandoned Cart Email | 20.0% | 9.1% | 15.0% |
| Instagram Shopping | 1.0% | 4.3% | 10.0% |
| In-App Messaging | 12.0% | 13.0% | 8.7% |
Take Instagram Shopping as an example. While last-touch attribution credits it with just 1.0%, Shapley values reveal a much higher contribution of 10.0%. This underscores how conventional metrics can underestimate a channel’s real impact. With these refined insights, marketers can better understand the role each channel plays, laying the groundwork for predictive analytics.
Predictive Analytics in Campaign Strategies
Building on improved attribution, predictive analytics leverages propensity models to anticipate customer actions. This allows brands to refine campaigns and personalize experiences without relying solely on costly trial-and-error methods.
Generali Poland provides a strong example of this approach in action. By using the Propensity Engine’s marketing analytics platform, they increased close rates by 24%. Krzysztof Surowiec, their Head of E-commerce, shared:
"Having measured the impact of Zeta’s AI-Powered Marketing Cloud across website activation, we are now expecting to roll out other channels (social and programmatic) in order to consolidate the omnichannel experience."
To succeed with predictive analytics, brands should focus on these key areas:
- Build strong first-party data
- Examine customer milestones and histories
- Align creative assets with critical variables
- Test predictions through controlled experiments
Real-World Marketing Success Stories
Predictive strategies have delivered impressive results for many brands. For instance, ZALORA‘s TRENDER platform drove 100% revenue growth year-over-year between 2020 and 2021 by guiding marketing and merchandising decisions. Similarly, Scandinavian Airlines (SAS) uses machine learning to analyze customer behavior, enabling personalized offers that boost ROI without overspending.
These outcomes highlight the importance of high-quality data, cross-functional collaboration, ongoing validation, and thoughtful resource allocation. Propensity insights are proving to be a powerful tool for driving effective, data-informed marketing strategies worldwide.
Making Operations More Efficient with Propensity Models
Supply Chain Improvements
Propensity models can streamline supply chain management by using data to predict demand and optimize inventory. These tools help businesses better anticipate customer needs, avoid disruptions, and maintain the right stock levels – all while cutting costs.
Success depends on proper planning and clean data. Companies should target areas where predictive analytics offers the most benefit:
| Supply Chain Area | Benefits of Propensity Models | Impact |
|---|---|---|
| Demand Forecasting | Predicts customer needs accurately | Reduces overstock and stockouts |
| Inventory Management | Optimizes stock levels in real time | Lowers storage costs |
| Disruption Prevention | Acts as an early warning system | Strengthens supply chain |
| Resource Allocation | Focuses distribution effectively | Boosts operational efficiency |
To get the most out of these models, integrate them with current systems, provide staff training, and make regular updates to refine their performance. These improvements create a strong foundation for scaling operations beyond local markets.
Growing from Local to Global
Expanding globally requires in-depth market knowledge and efficient resource use. Propensity models provide the confidence needed to make informed decisions during this process.
A well-known example is the 2012 Obama re-election campaign, which used propensity-to-convert models to identify and engage undecided voters. These models helped the campaign:
"predict which undecided voters could be encouraged to vote for democrats and which type of political campaign contact (door knock, call, flyer, etc.) would work best for each voter".
For businesses aiming to scale internationally, the key steps include:
- Setting clear goals tailored to each market
- Investing in quality data collection and analysis
- Creating market-specific propensity models
- Continuously monitoring and adjusting strategies based on performance metrics
Common Problems and Solutions in Propensity Analysis
Data Quality and Legal Requirements
Over 25% of companies deal with poor data quality, and 46% of marketers face challenges with channel complexity.
To improve data quality and stay compliant with U.S. regulations like the CCPA, focus on these areas:
| Quality Element | Challenge | Solution |
|---|---|---|
| Accuracy | Inconsistent default values and formats | Use data profiling and standardization tools |
| Completeness | Missing or orphaned records | Apply reconciliation frameworks |
| Timeliness | Outdated or stale information | Establish clear retention policies |
| Compliance | Privacy regulations | Conduct regular audits and updates |
"The inherent characteristic of data is its quality, which will deteriorate even with the most robust controls. 100% accuracy and completeness don’t exist, which is also not the point. Instead, the point is to pick your battles and improve quality to an acceptable threshold." – Hanzala Qureshi
By 2024, 75% of the global population will have their personal data protected under modern privacy laws.
Getting Teams to Accept New Methods
Beyond data quality, human resistance can slow the adoption of propensity analysis. Uncertainty and lack of understanding are common barriers. Microsoft’s transformation under Satya Nadella, which increased revenue by 34% between 2014 and 2020, highlights the importance of clear communication and team engagement.
Here are three ways to encourage team buy-in:
- Open Communication: Regular feedback sessions help clarify new processes.
- Practical Training: Hands-on workshops make tools and methods easier to understand.
- Measurable Results: Sharing early wins builds confidence in the approach.
For example, Starbucks improved customer experience analytics through a mix of workshops and digital resources. This effort boosted customer satisfaction by 10% in just six months.
Setting Up Technical Systems
Once teams are on board, the next hurdle is technical implementation. About 17.5% of organizations lack adequate systems for organizing and maintaining data transparency.
A strong technical setup should address these components:
| Component | Purpose | Priority |
|---|---|---|
| Data Integration | Consolidate data from multiple sources | High |
| Quality Controls | Automate monitoring for errors | High |
| Access Management | Ensure secure data handling | Medium |
| Analysis Tools | Enable effective propensity modeling | Medium |
| Reporting Systems | Visualize results for decision-making | Low |
"Understanding what a number means in real life, for a customer, lead or visitor, is absolutely fundamental to understanding your data." – Axel Lavergne, Co-founder of ReviewFlowz
To support this, establish a data governance team and create a Single Customer View. These steps provide the technical backbone needed for advanced propensity analysis.
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What’s Next for Propensity Insights
AI Market Testing Tools
AI-driven market testing is changing how businesses make decisions. McKinsey’s 2021 global survey found that 27% of respondents credited at least 5% of their earnings before interest and taxes to AI implementations. In the media industry, publishers are using AI tools to go beyond traditional A/B testing. These tools adjust digital paywalls in real time based on user behavior, offering a more dynamic and efficient approach to optimization.
Automated Optimization Systems
AI-powered optimization systems, guided by propensity insights, are making a noticeable impact across industries. For instance, Kingfisher, the company behind B&Q and Screwfix, has created an AI agent that works with its recommendation engine and Vertex AI Search. This system quickly retrieves information through text, voice, and even visual inputs, streamlining customer interactions and operations.
Global Market Applications
As businesses leverage these advancements, they must navigate a global market filled with both opportunities and hurdles. Social commerce revenues are expected to surpass $1 trillion USD by 2028, showcasing the growing influence of AI-driven insights in diverse markets. However, companies must tackle challenges like regional data privacy regulations, cultural differences, and uneven technological infrastructure. Additionally, with 85% of consumers affected by climate change and evidence showing consumers are six times more likely to engage with inclusive advertising, businesses are increasingly focusing on AI strategies that prioritize sustainability and inclusivity to ensure long-term growth.
Beyond Propensity: From traditional ML to Uplift Modelling
Conclusion: Key Points for Success
Integrating propensity insights is a central piece in achieving success with digital transformation. These insights improve marketing efforts and streamline operations, making businesses more efficient. To effectively incorporate propensity modeling into workflows, companies need a clear strategy focused on maintaining high data quality and fostering ongoing improvements.
Keeping models updated and adaptable is crucial for staying competitive. This ties back to the earlier discussion on using data-driven insights to expand into global markets.
"A customer propensity model aims to predict the behavior of customers. It helps marketers understand if people respond to particular offers without the need to launch promotional campaigns." – Sasha Andrieiev, CEO & Co-founder at Jelvix
Four main factors drive lasting success in this area:
| Success Factor | Key Requirements | Business Impact |
|---|---|---|
| Model Dynamics | Regular updates to data pipelines and feedback systems | Enables real-time decisions |
| Scalability | Ability to handle large-scale predictions across various scenarios | Optimizes resource use |
| ROI Focus | Clear metrics linking investment to returns | Supports measurable growth |
| Data Quality | Accurate and complete data collection practices | Ensures reliable predictions |
These factors highlight the need for constant innovation in digital processes. Success requires not just adopting new technologies but also making adjustments within the organization. For example, Charles Schwab developed advanced digital tools and tracked engagement across channels, leading to record-breaking sales and profits.
To get the most out of these strategies, companies should embrace a culture that prioritizes innovation and data-driven decision-making. This means running regular tests, analyzing results, and refining approaches to keep driving growth.
FAQs
How can businesses ensure their data is accurate and reliable for effective decision-making with propensity models?
To ensure data accuracy and reliability for propensity models, businesses should implement a robust data quality framework. This includes setting clear standards, establishing strong data governance policies, and leveraging modern tools and technologies to manage and validate data effectively.
Regular monitoring and updates are essential to maintain data relevance and accuracy. By focusing on high-quality, up-to-date data, businesses can make more informed decisions, optimize operations, and deliver better outcomes for customers and stakeholders.
How can companies address team resistance when adopting propensity analysis methods?
To address resistance from teams when adopting propensity analysis methods, companies should prioritize clear communication, employee involvement, and ongoing support. Start by explaining the purpose and benefits of propensity analysis in a way that aligns with team goals and values. Sharing a clear vision can help employees understand how these methods will improve both their work and the company’s success.
Encourage participation by involving team members in the implementation process and gathering their feedback. Providing training sessions, resources, and ongoing support ensures employees feel confident and prepared for the transition. Finally, fostering a culture of adaptability and collaboration can help teams embrace change and innovate together.
How can businesses use propensity insights to expand globally, and what should they consider when applying these insights in different markets?
Propensity insights empower businesses to expand globally by helping them make data-driven decisions in marketing and operations. These insights can identify customer behavior patterns, enabling companies to tailor strategies for different regions and improve overall efficiency.
When applying these insights across markets, businesses should consider key factors like:
- Cultural alignment: Ensure products and messaging fit local customs and preferences.
- Market research: Understand customer needs, competitive landscapes, and regional trends.
- Localization: Adapt language, design, and payment methods to meet local expectations.
- Risk management: Address regulatory, cybersecurity, and supply chain challenges specific to each region.
By leveraging local expertise and tailoring strategies to each market, businesses can maximize the effectiveness of propensity insights and drive global growth.