Direct-to-Consumer (DTC) brands are constantly stuck between creating indelible customer experiences and turning profits on those relationships. After initial success, digitally native brands are losing momentum to established brands, creating existential risk for DTC brands. According to eMarketer, established brands account for more than three-quarters of U.S. DTC sales.
What can DTC brands do to drive better experiences without sacrificing profit? Some, like Allbirds and Warby Parker, have opened brick-and-mortar stores with success, but that’s hardly a death knell for pure-play digital business. In fact, Shopify forecasts that global ecommerce sales will exceed $6 trillion in 2023.
The addressable market potential is there, so DTC brands need to weather the storm and trust their biggest differentiators: direct shopper relationships and the data that describe those relationships.
DTC brands are overwhelmed with data — transactional, customer and marketing — that enable them to better understand and personalize experiences for their customers. Historically, all of that customer data has been stored in disparate systems, meaning no part of the business has a complete view of the customer, their experience or the opportunities to expand the relationship.
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To make better data-informed decisions, retail business leaders need to improve the speed, fidelity and actionability of data to serve shoppers better than competitors.
It’s enough to give any DTC leader a migraine.
Define Key Performance Indicators
While it may seem obvious, everyone in the business must have a unified view of the most critical metrics. Getting there means having your business leaders at the table to determine what those critical metrics are and how they are defined. Some of the possibilities include:
- Cart abandonment rate
- Return customer rate
- Order contribution profit
- Customer lifetime value
Having clearly defined metrics ensures everyone is working toward the same goals and has an accurate and real-time view of the business. It also flows down to every other part of the business — the software you use, the vendors you select and the messaging in your decks and corporate materials.
Your data warehouse is your source of truth — if and when you’re able to figure out how best to ingest, map, model and extract the data.
From a software perspective, you’ve got options — both “build” and “buy” — but what’s right for you?
Building a Data Pipeline from Scratch is a Time (and Money) Drain
A lot of DTC companies rely on out-of-the-box reports and dashboards from companies like Shopify, NetSuite, Google and Meta. These platforms offer no shortage of information but generally present a myopic view that doesn’t provide the whole picture for retail business leaders, who are forced to log into different systems to understand how disparate metrics align.
Building a data pipeline from scratch is expensive, time-consuming, and is the alternative that takes the longest to build and implement. There’s a substantial risk of losing information if key project stakeholders depart the organization during the project. Additionally, data needs to be deduplicated across sources and made useful in a retail context, all extraordinarily complex and time-consuming.
On the brink of a recession and already struggling to turn a profit, DTC firms need alternative options.
The Scope of All-in-One Data Software Applications is Too Narrow
The beauty of the modern data stack lies in its ability to handle ALL the data. By contrast, generic all-in-one data software applications like Customer Data Platforms (CDPs) inevitably leave something (or most things) out. CDPs are built for all industries, so they only deal with one, albeit significant, data set — customer profiles. CDPs are also designed for only one purpose — marketing activation of those customers. This single data set and narrow purpose mean they lack the data scope critical to modern retailing:
- Support for non-customer data types, including product item master, order flows, merchandising calendar, and fulfillment events and status;
- Support for non-marketing use cases across merchandising and operations;
- Awareness of the post-conversion customer journey (order cancellations, returns, late deliveries, etc.);
- High-fidelity revenue and cost data (ad spend, shipping cost, discount promotions, etc.) to help businesses shift from revenue optimization to profit optimization; and
- Compatibility with modern cloud data warehouses to avoid vendor lock-in.
Don’t Try to Solve a Data Modeling Problem with a Dashboard Tool; Solve it with a Modern Data Stack
While some brands attempt to buy an all-in-one software solution, others attempt to shortcut the formative work of building a scalable data infrastructure by expecting end-to-end pipeline capabilities from a BI/analytics package.
All-in-one BI/analytics tools are not the data pipeline; they are the output and end result of a well-designed data pipeline. Analytics should be the last stage of a critical data value chain — the “last mile” of data modeling.
During the upstream stages, data is ingested from all disparate sources, semantic labeling is applied and an understanding of concepts like orders, products, and customers is established. This allows data practitioners to apply business logic at later stages of the data journey with a shared understanding of the data. Vital metrics like Average Order Value (AOV), Return On Ad Spend (ROAS) and Customer Lifetime Value (CLV) can be derived from shared semantic data definitions. It’s essential that the data is traceable and auditable for shared trust in modeling outputs and that models are “composable” so businesses can address new use cases as needed over time.
The Emergence of the Retail Data Stack
DTC organizations face unique challenges. They are often stuck between a rock and a hard place, trying to differentiate on experience but still grappling with how to drive profitability. We’ve all had a favorite DTC brand die before its time chasing unprofitable growth.
Retail analytics platforms are rapidly evolving, building on the strengths of other solutions with focused retail capabilities. These platforms collect and organize business data across all aspects of the retail business and translate it into usable retail data that allows business users to take action on key metrics. For savvy DTC brands building and promoting the same internal metrics, retail analytics platforms optimize the data that describe customers, orders, products, promotions and marketing campaigns (including variable revenue and cost data) all in one place.
This positions DTC brands to address both customer experience and business profitability through informed strategic decision making.
Eric Best is CEO of SoundCommerce and has extensive experience developing solutions that help retail brands achieve profitability.
