How to evolve your DTC startup’s data strategy and identify critical metrics –

Plus: 2 common data mistakes to avoid

Direct-to-consumer companies generate a wealth of raw transactional data that needs to be refined into metrics and dimensions that founders and operators can interpret on a dashboard.

If you’re the founder of an e-commerce startup, there’s a pretty good chance you’re using a platform like Shopify, BigCommerce, or Woocommerce, and one of the dozens of analytics extensions like RetentionX, Sensai metrics, or Profitwell that provide off-the-shelf reporting.

At a high level, these tools are excellent for helping you understand what’s happening in your business. But in our experience, we’ve learned that you’ll inevitably find yourself asking questions that your off-the-shelf extensions simply can’t answer.

Here are a couple of common problems that you or your data team may encounter with off-the-shelf dashboards:

• Charts are typically based on a few standard dimensions and don’t provide enough flexibility to examine a certain segment from different angles to fully understand them.
• Dashboards have calculation errors that are impossible to fix. It’s not uncommon for such dashboards to report the pre-discounted retail amount for orders in which a customer used a promo code at checkout. In the worst cases, this can lead founders to drastically overestimate their customer lifetime value (LTV) and overspend on marketing campaigns.

Even when founders are fully aware of the shortcomings of their data, they can find it difficult to take decisive action with confidence.

We’re generally big fans of plug-and-play business intelligence tools, but they won’t scale with your business. Don’t rely on them after you’ve outgrown them.

Evolving your startup’s data strategy

Building a data stack costs much less than it did a decade ago. As a result, many businesses are building one and harnessing the compounding value of these insights earlier in their journey.

But it’s no trivial task. For early-stage founders, the opportunity cost of any big project is immense. Many early-stage companies find themselves in an uncomfortable situation — they feel paralyzed by a lack of high-fidelity data. They need better business intelligence (BI) to become data-driven, but they don’t have the resources to manage and execute the project.

This leaves founders with a few options:

• Hire a seasoned data leader
• Hire a junior data professional and supplement them with experienced consultants
• Hire and manage experienced consultants directly

All of these options have merits and drawbacks, and any of them can be executed well or poorly. Many companies delay building a data warehouse because of the cost of getting it right — or the fear of messing it up. Both are valid concerns!

Start by identifying your critical metrics