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.