How to Develop Your DTC Startup’s Data Strategy and Identify Critical Metrics – TechCrunch

Plus: 2 Common Data Mistakes to Avoid

Generating direct-to-consumer businesses a wealth of raw transaction data to be refined into metrics and dimensions that founders and operators can interpret on a dashboard.

If you’re the founder of an ecommerce startup, chances are 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 out-of-the-box reporting. .

At a high level, these tools are excellent in helping you understand what is happening in your business. But in our experience, we’ve learned that you will inevitably ask questions that your out-of-the-box extensions just can’t answer.

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

Here are some common issues you or your data team may encounter with out-of-the-box dashboards:

  • Charts are usually based on a few standard dimensions and don’t offer enough flexibility to view a particular segment from different angles to fully understand them.
  • Dashboards contain calculation errors that are impossible to fix. It’s not uncommon for such dashboards to report the pre-reduced store amount for orders where a customer has used a promotional code at checkout. In the worst case scenario, 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 grow with your business. Don’t rely on them if you’ve outgrown them.

Developing your startup’s data strategy

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

But it is not a trivial task. For budding founders, the opportunity costs of any major project are enormous. Many start-ups find themselves in an awkward situation: they feel paralyzed by a lack of reliable 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 an experienced data leader
  • Hire a junior data professional and complement it with experienced consultants
  • Hire and manage experienced consultants directly

All of these options have pros and cons, and they can all be done well or poorly. Many companies put off building a data warehouse because of the cost of getting it right — or the fear of screwing it up. Both are valid concerns!

Start identifying your critical metrics

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