Expect the Expected

How Looking Ahead Makes Your Data Work

In an era where data drives success, the key lies not only in collecting data, but more importantly in strategically leveraging it to identify future opportunities and risks. Too many companies are still only looking backward with historical dashboards, but that's not enough. Successful companies are turning their gaze forward. As Kris Vranken aptly puts it, "You don't win a race just by looking in your rearview mirror."

Let's explore how a forward-looking view of data can be not only reactive, but especially proactive and profitable. In doing so, we provide concrete examples of how these principles work in practice.

Use All Your Customer Data: Create a 360° View

To effectively look ahead, you must start with a complete and current overview of your customers. This includes:

Data integration: Collect customer data from various sources, such as CRM systems, online forms and e-commerce platforms.

A 360° customer profile: Combine behavioral data, preferences and interactions into one complete picture. This holistic perspective allows you to truly understand your customers and serve them better.

A retailer combines data from POS systems, email campaigns and social media to identify customers who often buy in-store but are not very active online. With these insights, the company develops a campaign that encourages them to also shop online with exclusive offers.

Provide Qualitative, Upgraded Data

Poor-quality data can be a major pitfall. To generate reliable insights, you need data:

Clean up and standardize: Remove duplicate records and incomplete data to minimize errors.
Continuous updating: Ensure that data remains accurate and up-to-date through automated processes.
Governance safeguards: Implement strict rules to protect data quality and privacy.

A manufacturer discovered that 15% of the contact information in their CRM was incorrect. By doing a cleanup and implementing automatic validations, they reduced incorrect data by 90%, leading to higher conversion rates in their B2B campaigns.

Use Machine Learning to Predict Behavior

Where historical data stops, machine learning (ML) begins. With ML, you can:

Predict future behavior: Understand which customers are likely to convert and why.
Reduce Churn: Identify customers at risk of dropping out and take proactive action.
Personalize recommendations: Deliver offers that match individual needs and preferences.

A sports club used ML to predict which members would not renew their subscriptions. By targeting this group with loyalty offers and personal follow-ups, they managed to retain 25% more customers.

Monitor Segments and Behavioral KPIs.

Segmentation and behavioral KPIs are at the heart of proactive marketing. By monitoring real-time anomalies, you can respond to changes immediately. Focus on:

Segment analysis: Identify groups that deviate from predicted trends.
Behavioral KPIs: Monitor critical indicators such as conversion rates, customer engagement and retention.
Real-time alerts: Receive notifications when KPIs are performing below or above standard so you can make timely adjustments.

A fundraising organization discovered that donors from a specific segment were giving less frequently than expected. By launching a personalized email campaign with success stories, they increased donation behavior within this segment by 18%.

Personalized Communication to Update

When deviations occur, personalized communication is essential to win back customers or increase conversions. Effective strategies are:

Targeted messages: Develop content that perfectly matches the needs of specific customer segments.
Real-time interaction: Send messages when they are most relevant, through your customers' preferred channels.
Hypertargetting: Use AI to get the right message to the right person, at exactly the right time.

An e-commerce company identified customers who had left a product in their shopping cart. With an automated follow-up via email and WhatsApp with a small discount, they converted 40% of these customers after all.