Imagine a retail chain struggling with declining sales and customer engagement. Despite investing heavily in marketing campaigns and website improvements, they can't pinpoint why their efforts aren't translating into increased revenue.
However, by shifting from basic event tracking to sophisticated user-level insights, this retailer discovers that while their overall traffic numbers look healthy, a small segment of high-value customers are responsible for a disproportionate amount of their sales. Armed with this knowledge, they:
- Identify their most valuable customer segments
- Tailor marketing efforts to retain and nurture these high-value customers
- Optimize their product recommendations based on individual user behavior
- Reduce marketing spend on low-converting channels
The result? Within six months, the retailer sees an increase in repeat purchases, a boost in average order value, and an improvement in marketing ROI.
Understanding individual customer behavior is a necessity for business growth, and in this article, we'll explore how you can evolve your analytics approach to progress from basic event tracking through session-level analysis to powerful user-centric strategies.
Starting with Event-Level Analytics
For marketing teams still in the early stages of developing their data pipeline (or perhaps stuck in them), conversations about what to track tend toward the fundamental moments of the customer journey.
At this stage, leadership might focus on questions around growth metrics like:
- How can we sell more products?
- How can we generate more leads?
- How can we drive more business engagement?
When tasked with answering these questions, marketing teams often default to volume-based solutions:
- We sell more products by generating more product page views.
- We generate more leads by driving more form submissions.
- We generate more engagement by driving up total events.
While these event-scoped metrics provide a foundation for measurement, they only represent the first step in analytics maturity. They're useful for establishing baselines and identifying broad trends, but they can miss key insights about customer behavior and true business value.
Moving to Session-Level Insights
If your goal is just to drive more volume in the hopes of driving up your KPIs, keep in mind that casting a wider net might drive more people to your site, but they might not be the customers you’re looking for. This volume-first approach can lead to higher marketing costs with diminishing returns, decreased conversion rates, and lower quality leads.
However, as targets grow and the data pipeline gets more complex, your questions should evolve to ones like:
- Are our marketing channels driving engagement and sales?
- Is our traffic converting often enough?
- Can we improve the site and find pain points?
This kind of thinking advances the context of the data from the event level to the session level — looking at all the sessions in the aggregate to find where users are falling off and which sources are performing the best. Some organizations get stuck here because they gain just enough information to feel like they’re getting everything they need.
This session-level view is generally a good proxy, but dynamic content, Customer Data Platforms (CDPs), and Customer Relationship Managers (CRMs) have evolved and brought a glaring weakness to the surface — multiple sessions by a single user.
This multi-session reality reveals that customers rarely complete their journey in a single session. (Purchase decisions often span days, even weeks.) Plus, users interact across multiple devices and channels. Misunderstanding data like this can lead to flawed optimization strategies and missed opportunities for meaningful engagement.
Unlocking the Power of User-Level Analytics
By connecting sessions to individual users, organizations can begin to understand:
- True customer acquisition costs
- Actual conversion journey lengths
- Lifetime customer value
- Cross-device and cross-channel behavior patterns
Here's an example: A channel may drive 1,000 sessions and generate $10 per session on average. When an analyst dives in, they find the $10 per session average is three loyal high-value customers. These customers may benefit more from some direct engagement, such as the CRM or marketing automation efforts. The true value of your marketing channel may be masked by “cost per click” (CPC), but metrics like “cost per acquisition” and “touches before purchase” can help improve the true revenue of your business.
To gather comprehensive user-level analytics from the data pipeline, marketing teams should strategically integrate and utilize their MarTech stack. Here's an expanded look at the key components and strategies:
Unique User Identification
Effective user identification is the foundation of user-level analytics. Utilize these tools to create a unified view of your customers:
- Email Service Provider (ESP): This allows you to track email opens, clicks, and conversions, and use email addresses as a primary identifier.
- Customer Data Platform (CDP): This aggregates data from multiple sources and creates unified customer profiles.
- Customer Relationship Management (CRM) system: Use it to store detailed customer information and interaction history, then link online behavior to offline sales and support interactions.
Implement a data unification strategy that resolves identities across these systems, accounting for variations in user information (e.g., different email addresses for the same person).
Global Unique Identifier (GUID)
The GUID is crucial for maintaining consistency across all platforms and touchpoints. You’ll need to choose a persistent identifier, such as a hashed email address or a generated unique ID. (Consider using a probabilistic matching system for users who aren't logged in.)
To stay ahead of privacy considerations, ensure your GUID system complies with data protection regulations, including the right to be forgotten.
Progressive Profiling
As users interact with your brand, you can gradually build a comprehensive user profile. Enrich user profiles over time through various interactions like:
- Website Behavior: Track pages viewed, products interacted with, and time spent on your site. Use this behavioral data to infer interests and intent.
- Purchase History: Analyze transaction data for product preferences and spending patterns. Use this data to segment customers and predict future behavior.
- Customer Service Interactions: Incorporate data from support tickets and chat logs. Use this information to understand pain points and improve customer experience.
- Social Media Engagement: Monitor interactions with your brand on social platforms. Use social data to understand brand sentiment and customer preferences.
Cross-Platform Integration
You’ll need to ensure seamless data flow and analysis across your entire tech stack using data warehousing, API integrations, and analytic platforms, like Adobe Analytics, capable of ingesting and analyzing data from multiple sources.
Note that data silos and inconsistent formatting across platforms can hinder integration efforts. However, a data governance strategy can ensure consistent data standards across your organization.
By effectively implementing these strategies, marketers can create a comprehensive, user-centric view of their customers. This deep understanding enables personalized marketing, improved customer experiences, and data-driven decision-making that can significantly impact bottom-line results.
Take an Evolutionary Leap in Analytics Strategy
The evolution from event-based to user-level analytics represents a paradigm shift in how businesses understand and engage with their customers. With comprehensive user data, you'll put yourself in a better position to anticipate market trends, respond swiftly to changing customer needs, and create more value for your customers and your organization. And the integration of artificial intelligence with user-level data will unlock even more predictive capabilities.
Remember, the journey to analytics maturity is ongoing. Continually reassess your data strategy, stay informed about emerging technologies, and if you need a partner in advancing evolving your data pipeline, just reach out!
