Companies that sell directly to consumers produce a lot of raw transactional data, which has to be transformed into metrics and dimensions that founders and operators can understand on a dashboard. There’s a strong possibility that you’re utilizing a platform like Shopify, BigCommerce, or WooCommerce plus one of the thousands of analytics extensions like RetentionX, Sensai metrics, or ProfitWell that offer pre-packaged data if you’re the founder of an e-commerce firm.
These tools are quite good at assisting you in understanding what’s occurring in your organization. But based on our experience, we know that sooner or later, you’ll find yourself asking questions that your off-the-shelf extensions can’t adequately respond to. Plug-and-play business intelligence products are often quite popular, but they won’t grow with your company. After you’ve outgrown them, don’t rely on them.
There are a few typical issues with pre-made dashboards that you or your data team could run into: Charts often only allow for a limited number of conventional dimensions, making it difficult to completely comprehend a particular segment by looking at it from various perspectives.
Dashboards include irreparable math mistakes. Such dashboards frequently display the pre-discounted retail price for items where a client applied a coupon code during the checkout process. In the worst circumstances, this may cause entrepreneurs to grossly overestimate the lifetime value (LTV) of their customers and overpay on marketing initiatives. It might be challenging for founders to act decisively and confidently, even when they are well aware of the flaws in their data.
Plug-and-play business intelligence products are often quite popular, but they won’t grow with your company. After you’ve outgrown them, don’t rely on them. It now costs a lot less to build a data stack than it did ten years ago. In order to capitalize on the compounding value of these insights sooner in their journey, many firms are creating one. But it’s not a simple job. Any major initiative has a huge opportunity cost for early-stage innovators.
Many early-stage businesses are in the unpleasant position of feeling hamstrung by the scarcity of high-fidelity data. They lack the resources to oversee and carry out the project, but they need improved business intelligence (BI) to become data-driven. Each of these choices has advantages and disadvantages, and they can all be carried out successfully or ineffectively. Due to the expense of doing it well or their fear of making a mistake, many businesses put off constructing a data warehouse. Both are legitimate worries!
