How customer analytics closes the gaps in performance measurement

Attributed revenue, ROAS, conversion rate — traditional marketing metrics track efficiency but often miss the signals that reveal which customers drive growth. Today, first-party customer data is more accessible and valuable than ever. And when combined with AI, it opens the door to a new era of customer-centered performance measurement.

Customer analytics shifts the focus from channels to customers, framing analysis and activation around engagement, growth and predicted value. By connecting what has happened with what’s likely to happen, brands can make smarter decisions on who to target and what actions will drive real impact.

Where traditional measurement falls short, customer analytics delivers

Media mix modeling (MMM) and attribution modeling are critical tools for understanding: 

• What happened.
• How channels contributed to outcomes.
• How media investment should shift in response to seasonality or promotional periods. 

These approaches help answer “How should I spend?” but not “Who should I target?” 

MMM rarely provides enough depth around customer segment performance and attribution assigns value at a level that isn’t actionable for real-world targeting. Both approaches measure past efficiency but do little to explain which customer groups drive outcomes or where growth opportunities exist.

MMM and attribution modeling allow brands to bucket channels into any of the four categories, helping support decision-making about the next steps. 

Customer analytics complements these methods by organizing customers into segments based on past and predicted behavior. This lens gives marketers the ability to:

• Link insights to action.
• Inform targeting strategies.
• Connect channel decisions directly to customer outcomes.

Dig deeper: How advanced customer journey analytics is shaping the future of engagement

Taking the leap: Incorporating customer analytics into marketing

Testing customer segments across channels will help identify which customers drive incremental demand and where you might need to shift your targeting strategy.  

It may be tempting to focus heavily on loyalists, reaching them at the highest frequency with premium channels, such as SMS or direct mail. While they often contribute the most top-line revenue, tests consistently show incremental demand tends to come from mid-tier customer segments. 

This finding highlights the value of connecting with customers outside the top-tier in ways that resonate with them. Customer analytics enables brands to activate each segment with more tailored, efficient and effective engagement strategies. 

Building smarter customer segments

Invest in getting to know your customers. To build actionable customer segments, start with a complete view of the customer universe. Move beyond relying solely on transaction data by enriching the customer profile with:

• Syndicated data.
• Engagement history.
• Product-level insights. 

Then, apply machine learning models to predict:

• Future customer behavior.
• Product affinities.
• Potential value. 

Predictive enrichment unlocks the ability to move away from one-size-fits-all tactics. Instead, brands can deliver personalized, real-time engagement strategies that meet customers where they are and guide them toward higher-value behaviors. 

Measuring success: Begin with the end in mind

Define success in relation to the customers you want to attract, retain and grow. Customer performance should be anchored in KPIs that reflect long-term value and relate to broader business objectives, such as improving retention or boosting profitability. 

Core customer-focused KPIs might include: 

• Retention rate: Are you keeping the customers you acquire?
• Frequency: Are customers shopping more often over time?
• Lifetime value (LTV): Are you improving the long-term contribution of each customer?

These KPIs should be tailored to each active customer segment — agnostic of channel or campaign — to provide guidance and guardrails for investment decisions. For example, mid-tier bargain hunters should be measured against a different benchmark for average order value (AOV) or frequency than high-value loyalists. 

Media performance is often optimized toward channel-specific metrics. Incorporating customer-specific KPIs adds context and balances short-term efficiency with long-term growth. 

Dig deeper: How to augment market research and glean customer insights with AI

Expanding the customer analytics footprint

A customer analytics program has the most significant impact when it enables decision-making across the organization. Engaging other teams early ensures insights are applied across the organization. Extended use cases may include:

• Customer service and sales teams can use segments and predicted behaviors to personalize interactions and anticipate needs. 
• Ecommerce and product teams can tailor on-site and in-app experiences. 
• Merchandising teams can plan assortments and promotions around segment-level affinities and behaviors.

When multiple functions contribute to and benefit from customer analytics, they become embedded in the company’s operating model instead of existing in isolation.

Get started and iterate 

If you’re a customer-centric business, customers must be at the core of decision-making, analytics and activation initiatives. 

Adopting a customer-focused approach to analytics is an investment in delivering relevant and impactful customer experiences. 

Evolved customer analytics capabilities:

• Deepen understanding.
• Enable smarter activation.
• Improve decision-making across the organization.

The journey doesn’t require a massive transformation. Start by testing small, measurable use cases, learn from results and scale what works.

Dig deeper: How to categorize customer data for actionable insights

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