Direct-to-consumer (DTC) companies have so much data – transactional data, customer data, and marketing data – all of which are stored in many disparate systems that typically don’t integrate with one another. And each day, all of these data points, in all of these systems, have to be translated into easy-to-understand metrics (preferably as metrics on a dashboard or automated report), for business leaders to make data-informed decisions about their business.
As you start to solidify or re-think your data strategy, you need to establish your key performance indicators and key metrics. And beyond just establishing them, it is imperative that you also define each metric so that everyone in your organization is aligned and aware of what each critical metric means. While this may seem obvious and most business users will be familiar with these metrics, how does your company define each metric?
- Do you include shipping fees in gross revenue?
- How are dollar-off promotions applied to individual items on orders with multiple items?
- How do you apply revenue from gift cards – when the gift card is sold or when it’s redeemed?
Clearly defining metrics such as these will help to ensure everyone on your team is using the same exact metrics on different dashboards and reports, guaranteeing everyone across your organization has the same clear and accurate view of your business. Additionally, defining these metrics at the top of your organization will help ensure they are funneled down to every other part of your business – software platforms, vendor partners, and even presentation decks.
It’s also best to add your defined enterprise metrics in your data warehouse and then send them to your BI tools and other platforms, instead of defining them in your BI tool first. Your data warehouse should be your source of truth for all of your data. Data warehouses have more flexibility to support additional platform integrations and you won’t run the risk of your metrics getting “stuck” in your BI tool if or when you decide to integrate with other applications in the future.
Many of the systems DTC companies use each day offer out-of-the-box reports and dashboards – such as Shopify, Magento, Google Ads, and Facebook – that have a wealth of information, but the metrics are only related to that specific system. This creates a life where business leaders are constantly logging into different systems to try to piece together what’s happening in their business. Additionally, many metrics are duplicated within these systems depending on their reporting rules. If a customer clicks on a Facebook ad and then later clicks on a Google Ad, Shopify will attribute the order to Google. Google will capture the order revenue and Facebook will also capture the order revenue. This confusing, unclear view of what is happening in your business, especially in regard to customer attribution, customer lifetime value, and sales is a recipe for negative profitability.
Many DTC companies decide to take on the challenge of trying to build a data pipeline in-house. Building in-house is time-consuming and expensive given the number of work hours required to successfully build and implement. There is also a substantial risk of the loss of legacy information if key project stakeholders depart the company before the project is completed. Additionally, it’s not just about building a data warehouse to collect data, it’s also necessary to de-duplicate data across sources and model the data to make it useful within a retail context, which can be complicated and take a lot of time.
Analytics extension software like Elevar or Retention X is an affordable solution, especially for early-stage DTC companies. Analytics extensions offer many standardized reports, but those reports are not customizable or specific to your business. In my previous eCommerce experience, I often found myself asking questions that these standardized reports simply could not answer and I was limited by my inability to customize the reports for my specific business needs.
Business intelligence tools such as Looker or Tableau are another solution to consider, but these are expensive to implement and customize and there are often challenges with setting up the reports to accurately analyze data from all of your different data sources. The reports are not flexible since they are based on a few standard dimensions, which can make it challenging to investigate a specific segment for every angle necessary. Additionally, calculation errors due to the challenges of order discounts marketing attribution are common. These errors can make it difficult to calculate accurate lifetime value metrics and campaign ROIs, both of which are necessary to calculate accurate customer and campaign profitability.
SoundCommerce’s composable retail data platform is unlike any other SaaS platform on the market for retailers. It connects data from marketing, merchandising, and operations sources into a single data warehouse and models the data without human bias to give it retail context. The platform’s pre-built and custom dashboards enable retailers to optimize profitability across their entire business, down to the order and shopper level. And while a lot of data analytics platforms cater to Line of Business users, SoundCommerce addresses the needs of both business and technical users by allowing technical teams to have direct access to the modeled data to have full transparency into how the data is transformed. SoundCommerce is built to complement what retailers already have in their business infrastructure whether that be a data warehouse, a BI tool, or even an existing CDP. Learn more about how SoundCommerce can help your business.
Contact us if you’d like to learn more about how a retail data platform can help your organization to acquire new customers and increase profitability.
Megan Petersen is a Senior Marketing Manager at SoundCommerce. In addition to previous SaaS roles, a majority of her career has been spent in eCommerce and digital experience roles at retail brands including Theory, Helmut Lang, and Jack Rogers. Outside of work, Megan can be found hiking, skiing, or traveling with her family.