Ecommerce Analytics Challenges and How to Solve Them

If ecommerce analytics feels harder than it “should” be, that’s normal. Analytics breaks when data is scattered, tracking is inconsistent, or answers require specialist skills. In fact, in a 2025 study, 58% of leaders said key decisions are often based on inaccurate or inconsistent data, and 65% said nobody fully understands all the data being collected or how to access it. That is the core problem behind most ecommerce analytics challenges.

Data silos and disconnected tools

When orders live in your store platform, traffic lives in website analytics, and marketing lives in ad platforms, you can’t see the full journey. You end up exporting spreadsheets and “stitching” reports manually—which is slow and error-prone.

Practical fixes: aim for a unified “single source of truth” view (even if the systems behind it are multiple). Shopify explicitly frames its analytics dashboards and reports as a unified dashboard and reporting experience, which is the direction you want: fewer disconnected views, more connected context. For larger setups, tools like Adobe Analytics position themselves around multi-channel digital experience data collection and analysis workflows that serve cross-channel journeys.

Too much data, not enough insights

Dashboards can create the illusion of control while still leaving you stuck. If you track too many numbers, you don’t know what matters, and you end up in analysis paralysis.

Practical fixes: pick 5–7 KPIs aligned to your goal, and build your review around questions and decisions (not a dashboard tour). The goal is to reduce the chance that decisions are made on inconsistent or misunderstood data—something leaders already flag as common.

Lack of technical skills

Many teams feel blocked because answering questions requires SQL, complex dashboards, or waiting on analysts. That slows decisions and makes analytics “someone else’s job.”

Practical fixes: use self-serve approaches. Natural language query tools are designed to let users ask questions in everyday language (instead of writing queries). Daymark is positioned specifically around asking questions naturally and getting charts/tables/summaries that answer the question without digging through dashboards—reducing dependency on technical specialists for everyday analysis.

Slow reporting and manual work

If reporting takes hours in spreadsheets, your insights are outdated by the time you see them. This also increases mistakes—especially when different people calculate the “same metric” in different ways.

Practical fixes: automate core dashboards and standardise metric definitions. For example, Shopify defines AOV in its reporting context as (gross sales − discounts) ÷ orders; using standard definitions like this reduces internal confusion. Also, use tools that reduce “dashboard hunting” and speed up getting answers (again, the Daymark “no digging through dashboards” positioning speaks directly to this pain).

Attribution and tracking accuracy

Tracking is getting harder. Browsers restrict cross-site tracking, cookies expire, and users switch devices. Safari describes Intelligent Tracking Prevention (ITP) as using on-device machine learning to block cross-site tracking while still allowing websites to function normally. That is good for privacy, but it can break long attribution chains.

On the Chrome side, the status of third-party cookie changes has shifted over time; Google’s documentation still emphasises third-party cookie restrictions and the need to provide a good experience whether or not third-party cookies are available. So the practical takeaway is: measurement will keep changing, and you need resilient tracking and first-party data habits.

Practical fixes

Use first-party data and consent-aware measurement. Google’s consent mode guidance explains how sites can adjust tag behaviour based on consent and notes consent mode v2 updates. GA4’s Measurement Protocol also exists to send events directly to Analytics servers (server-to-server), augmenting (not replacing) standard collection. These approaches won’t “solve” everything, but they help reduce gaps when client-side tracking becomes unreliable.

Data privacy and compliance

Privacy rules affect what you can collect and how. The California Consumer Privacy Act (CCPA) gives consumers more control over personal information businesses collect, and EU privacy expectations shape cookie and tracking practices (including when consent is required). In practice, privacy compliance isn’t optional: it shapes your analytics design.

Best practices to overcome these challenges

Start simple and build trust in your core KPIs first (clean tracking beats complex dashboards). Connect your sources so you can diagnose causes, not just report outcomes. Make analytics accessible with self-serve tooling. And design measurement with privacy and browser constraints in mind—because those constraints are now part of the environment, not a temporary phase.

Conclusion

Most ecommerce analytics challenges come down to the same root causes: disconnected data, inconsistent tracking, and slow access to answers. The solutions are practical: unify your data view, standardise metrics, adopt consent-aware measurement, and make analytics accessible enough that your team can use it daily—not occasionally.