AI-powered Segmentation Based On Return Behavior

Use Case

Industry

Beauty

Fashion

Food & Beverage

Furniture and Home Goods

Outdoor Equipment & Sports

Pets

Retail

Travel & Hospitality

Channel

SMS/MMS

Weblayers

The details

Opportunity

To make the most out of customer insights, Loomi spots serial returners by checking their purchase and return patterns. Customers are grouped into segments like high, mid, low, or never returned. This helps marketers create personalized strategies and messages to boost customer satisfaction and loyalty.

Value

Reduce costs, improve profitability, and increase customer retention.

Example

A fashion retailer called "FashionFusion" uses Loomi to identify serial returners. The brand discovers that customers who frequently return items without making additional purchases have a higher likelihood of being serial returners. It segments these customers into different groups based on their return behavior: high, mid, low, or never returned. With this information, FashionFusion tailors its marketing strategies accordingly:

High Returners: FashionFusion sends personalized emails offering virtual styling sessions or size recommendations to reduce returns and increase customer satisfaction.
Mid Returners: They receive targeted promotions like exclusive discounts on future purchases to encourage repeat purchases.
Low Returners: FashionFusion highlights their positive buying behavior with loyalty rewards or early access to new collections.
Never Returners: Special thank-you messages are sent to these customers to show appreciation for their loyalty.

Personalize with Bloomreach

Hello. In this short video, I'm going to talk about
how you can use Bloomreach engagement
to identify and segment
audiences that are serial
returners.
One of the great things about Bloomer engagement
is our flexibility. Just
like we would typically store
purchase related information, we
can also store and commonly do
store return information as
well. So every customer might
make purchases, they might also
make returns and all of that is stored
associated to each customer record
in the form of events. And what we
can do with events is calculate values.
In this case, I've created a couple aggregates
for each customer that is going
to count the number of items that they purchased
as well as the items that they
have returned. So while
Daisy has purchased a lot of products,
five of them, in fact, she's returned
most of those products four out of
the five. So one of the things we've
done here is we've created a segmentation
to put people into a return rate band.
And in this case, she's in the high
band which is greater than 60%.
So my return rate segmentation
which is here has high mid low
and never returners. So my customers
are going to fall into one of these bands
depending on the number of items that they have
returned after purchasing. So
I have a lot of high returners in this example.
Well, what do I want to do with those high
returners? One kind of low
hanging fruit would be maybe we just want
to target them very frequently on
the website with information
about our size guides or things
like that. So we can create
a campaign called a web layer
which would overlay or sit on
top of our existing site as
a pop up or a banner. In
this case, I'm just calling this size guide.
And it's centered over the page where
uh we are saying hi friend need help with sizing.
And then this would hyperlink to
some size guide that you might have published.
And this uh the great thing about
web layers in bloom reach engagement is that
we can be very specific about our audience.
We don't want to serve this to the people that we've
identified as never returners
because they can size themselves just fine. We
can under settings specify
our audience here as only
customers that are in a particular return
rate segment. So I can say the return rate
segment which I've just selected
here from my saved segmentation
equals the high segment. So these
are my segments as previously shown,
which is now going to be 6500
matched customers. So only those people
that are the serial returners that have
the high return rate would be kind
of prompted with the return
uh with the size guide web layer
upon their sessions. So really low
hanging fruit here, very simple to set this up
and identify these customers. And this can
help drastically to reduce your business's
return rate, which is of course, a very
costly thing.