How Self-Segmenting Customers Can Give Your Business Metrics A New POV
With every new quarter, DTC brand operators are getting smarter and more resourceful with their data. On one hand, ecommerce is a great sandbox to play in, given the consistency of structured data — on the other, extracting competitive advantages out of that data is a tall task when all your competitors are looking at the same reports. At some point, brands reach a phase where the exploration and pivoting of their data outgrows off-the-shelf analysis; that’s when zero party data can start peeling back some truly advantageous layers of your business.
Said a different way: most DTC brands today are viewing their data through best practice lenses that were once novel (breaking out new vs. returning customers by SKU, for instance)… but leading-edge brands are using lenses that tomorrow’s brands will consider best practices. The obvious question is: what are those future best practices?
We contend you’ll need two components to find the answer:
- An always-on feed of zero party data (the direct-from-consumer data that forms the foundation of DTC’s advantage over traditional retail)
- A highly accessible central source of truth (such as a BI and ELT system like Daasity )
In broad terms, the opportunity comes down to this: how quickly can you, the brand operator, turn hypotheses and known-unknowns into actionable insights?
The thinking distance between wondering about your data and acting on it is what creates a competitive advantage. Reducing that distance should be an evergreen goal, which is why the two aforementioned components are so crucial:
- Zero party data listens for proprietary insights
- A central source of truth interprets those insights across your org
The blue sky opportunity DTC brands have in listening to their customers manifests itself as self-segmentation : data pivots waiting to be discovered, but beyond the scope of typical ecomm tools and models today.
A few easy examples you can unlock with a combination like Fairing x Daasity :
- Buyer Vs User . Who did you just sell to: someone who’s planning to use your product, or someone who’s planning to gift it to the end user? Would you still run your business the same way if you knew 35% of your customers were buyers, and their AOV was 90% higher than that of users? Or that your LTV is actually quite healthy once you strip out the buyers segment and discover virtually none of them are return customers?
- Competitive Shift . Is your customer new to the product category, or just new to your brand? If they have switched, was it for a reason you can build LTV on, or was it simply deal-hunting? Are they even switching at all, or are they huge category spenders who just buy your brand on rare occasions (and thus, fool you into thinking you’ve maxed out your LTV)? Traditional retail brands spend six figures over six months just to get directional market speculation on these questions — you can get it through Fairing tomorrow, at a 1:1 customer level, and feed it through Daasity to reframe your market assumptions & growth projections.
- Personas & Product Lifecycles . Which repeat customers are replacing your product because it’s reached end-of-life, and which are simply buying a second as a luxury? Who among your customers is a student? A hobbyist? A prosumer? How do those personas align to your product’s lifecycle and LTV opportunity? Knowing when your customer is going to buy again is among the great competitive advantages in business; for brands that don’t enjoy fixed product lifecycles, that knowledge comes from applied customer listening and solid data modeling.
As an SMB in ecommerce, it’s tempting to wait for super-simple plug & play analytics tools to reach the market. But of course, the future isn’t evenly distributed — any solution or model that reaches the mass market has already existed for a year or more, inside the walls of the most data-driven brands. That gap is where the advantage lies. Make it your advantage.