Gilt was an early pioneer in online luxury fashion sales but has faced declining sales and "sell through rates" in recent years. Increased competition from other e-commerce sites and the economic recovery have made it harder for Gilt to sell merchandise at discounted prices. As the economy and demand for luxury goods improved, designers had less incentive to partner with Gilt and discount their goods. Regression analyses identified several factors associated with lower sell through rates, including higher numbers of stock keeping units (SKUs) per sale, longer sale durations, and off-season sales. The document analyzes past sales data and performance to understand factors influencing Gilt's declining sales and explore strategies to boost future sell through rates.

1. Gilt is a pioneer of e-commerce,
as such Gilt benefited from
relatively low competition.
E-commerce has boomed since
Gilt’s early days, bringing
increased competition.
Increased competition has
made it harder for Gilt to sell
merchandise.
Gilt was founded on the eve of the
great recession and benefited from
luxury designers facing a decrease
in demand.
As the economy recovered,
designers faced less pressure and
Gilt’s sell through rate decreased.
The fact that until recently, Gilt’s
average unit price has been
increasing while Gilt’s “Sell Through
Rate” has been decreasing fits well
with this story.
As the economy improved, demand
for luxury goods increased and
designers didn't need to sell goods
so cheaply to compensate.
Because of this, Gilt’s cost of
business went up and this has
been reflected in Gilt’s prices.
As depicted below, Gilt’s sales rate fell steadily from 2011 to 2014 and has
since very slowly recovered, but is still a great distance away from its pre 2011
sales rates.
Why did Gilt’s value decrease
so dramatically?
What am I looking at?
Our output variable is “Sell Through Rate.” This is the rate at which Gilt
sells its stock of each product; we used it to determine whether the sale
was successful. A successful sale was one with a “Sell Through Rate” >
0.75.
Why did Gilt’s sales speed drop?
Comparison of Average and Total Sell Through Rates
Background
Information
Most significant information highlighted
In yellow.
Descriptive Statistics Correlation Table
The only variables
highly correlated with
each other are the
variables that
represent price as
expected.
Linear Regressions divided by category explained ~15% of the variation in
“Sell Through Rate”.
○ The five seasons listed performed worse than season -1
○ The higher the number of SKUs, the lower the “Sell Through Rate”
○ The longer the sale is meant to last, the lower the expected “Sell
Through Rate”
Observations from Logistic Tree
o Model predicted correctly ~67% of time
o Final node’s decision variable on each major branch is average sale price
o When determining whether or not a sale will be important, assuming it will
not fail because of another reason, looking at the other sales is important
o The “Number of SKUs” is the largest predictive factor
• With all nodes related to the “Number of SKUs,” when it is larger than
the decision value, it will reduce the likelihood of a sale having a >.75
“Sell Through Rate”
Forecasting SalesCategory Analysis
Online retailer specializing in luxury wear flash sales.
Company value peaked at $1 billion (2011).
Recently sold to Hudson’s Bay for $250 million (2016).
To be rolled into Saks Off Fifth, outlet version of Saks Fifth Avenue(*).
Organization Bio
Sales Decline and Tentative Recovery
While total number of flash sales is increasing, “Sell Through Rate” is
decreasing.
Prescriptions5
3 42
1
Stick to what Gilt is good at.
Shift to looks that have a fewer number of SKUs, sell in the
summer, and have a lower price. Take advantage of increased
demand for cheap high quality items during economic downturns.
For Gilt’s most popular items focus on high selling price items.
Regression Output
Logistic Tree
Sell Through
Rate Up
Summer
Economic
Conditions
Popular
High $
General
Low $
Lower
SKUS
Products used and sources cited :Analysis tools: Microsoft Excel, XLMiner, R-Studio, SPSS, Tableau
Design Tools: Microsoft Power Point, Microsoft Word, Microsoft Paint, Google Slides. Sources: Gilt, U.S. Census Bureau , St. Louis Federal Reserve,
*Forbes: http://tinyurl.com/z8zqkmd (Jan, 07, 2016)
&
REDEMPTION
More on “Sell Through Rate”
Seasonality of sell through rate and total sales
Generally, Gilt’s sales performance is best in the summer. While it does
comparatively well selling its stock in the holiday months, Gilt’s total volume of
sales is low during the holiday shopping season. This may suggest it would be
advantageous to increase stocked items during this period.
This tree can be used to
predict whether or not a
product will get a “Sell
Through Rate” above .75.
For example, looking to the
tree to the left, for a new look
that is has a price of $200, 3
SKUs, and is sold for 3 days
along with other products that
have an average price of
$200, this predicts an ST<.75.
Kelly Foy, Tucker Hammel
Dan McKenzie, Charles Whelan
E-Commerce Growth & Sell Through Rate
Sell Through Rate & GDP
STR GDP
Sell Through Rate and Unit Price
R2
value Number of SKUs Atypical Seasonality Unit Price/MSRP Sale Duration
1 0.12 -0.02 No 0.50 -0.02
2 0.13 -0.01 Seasons ending in 545 and 685 0.42 N.S.
3 0.23 -0.02 No 0.68 0.01
4 0.12 -0.03 No N.S. -0.02
5 0.20 -0.02 No N.S. -0.03
6 0.13 -0.01 No 0.46 0.01
7 0.16 -0.01 No 0.18 -0.01
8 0.18 -0.03 No 0.55 N.S.
9 0.14 -0.02 No 0.73 N.S.
10 0.11 -0.02 No 0.33 N.S.
Average 0.15 -0.02 -1 is the best selling season 0.48 -0.01
Most
Frequent
Categories
Variables that were most common in regression
output and their respective coefficients
Example
*When value in node is less, go left; when value is greater go right
*Season titles arbitrarily titled as actual seasons were encrypted.