#  A Large-scale Analysis of the Marketplace Characteristics in Fiverr

†Suman Kalyan Maity, ‡Chandra Bhanu Jha, ‡Avinash Kumar, ‡Ayan Sengupta,
‡Madhur Modi and †Animesh Mukherjee
†Dept. of CSE, IIT Kharagpur, India - 721302 ‡VGSOM, IIT Kharagpur, India -
721302
Email<EMAIL_ADDRESS><EMAIL_ADDRESS>
Abstract

Crowdsourcing platforms have become quite popular due to the increasing demand
of human computation-based tasks. Though the crowdsourcing systems are
primarily demand-driven like MTurk, supply-driven marketplaces are becoming
increasingly popular. Fiverr is a fast growing supply-driven marketplace where
the sellers post micro-tasks (gigs) and users purchase them for prices as low
as $5. In this paper, we study the Fiverr platform as a unique marketplace and
characterize the sellers, buyers and the interactions among them. We find that
sellers are more appeasing in their interactions and try to woo their buyers
into buying their gigs. There are many small tightly-knit communities existing
in the seller-seller network who support each other. We also study Fiverr as a
seller-driven marketplace in terms of sales, churn rates, competitiveness
among various subcategories etc. and observe that while there are certain
similarities with common marketplaces there are also many differences.

## 1\. Introduction

In recent years, there has been a huge growth in crowdsourcing platforms due
to rising demands of crowdsourcing micro-tasks like doing surveys, preparing
resumes and other similar tasks that require manual labor. As the popularity
of these crowd-based microtasks increased, various crowdsourcing platforms
emerged like the Amazon Mechanical Turk, Crowd Flower, Microworker, ShortTask
etc. These platforms are primarily demand-driven. Another range of platforms
drift from this demand-driven based approach to supply-driven approach where
the workers try to showcase their skills and talents by offering various
micro-tasks. These marketplaces are primarily used by the freelancers. Upwork,
Elance, Fiverr, oDesk, Freelancer, TaskRabbit are some of the very prominent
supply-driven marketplaces where the suppliers post advertisements on services
like creative writing, programming, software development, graphics etc. with
hourly rates or a fixed fee.

Since 2010, Fiverr has grown into one of the largest and popular online
freelancing marketplace. As of $11^{th}$ June, 2016, most of the Fiverr
traffic (17.9%) comes from India followed by US (17.6%) and Pakistan
(12.1%)111http://www.alexa.com/siteinfo/fiverr.com. Fiverr facilitates the
buying and selling of “gigs” (micro-tasks) online starting from as low as $5
per gig. The sellers in Fiverr, apart from selling, can also buy gigs from
other sellers; similarly, buyers can also become sellers whenever necessary.
This dual role played by the members of this community and the interaction
among them has made Fiverr a unique marketplace.

## 2\. Related works

There have been several studies on crowdsourcing based online labor
marketplaces, predominantly on Amazon Mechanical Turk (AMT). Ross et al. [1]
presented a user demography study on AMT. Berinsky et al. [2] described issues
related to recruiting subjects on AMT platform. Paolacci et al. [3] reviewed
the strengths of MT in recruiting participants. Kosinski et al. [4] measured
crowd intelligence. Mason and Suri [5] performed behavioral analysis of
Turkers and Suri et al. [6] studied their honesty. Halpin and Blanco [7] used
machine learning to identify spammers in AMT. Allahbakhsh et al. [8] discussed
quality control schemes while Heymann and Garcia-Molina [9] proposed a novel
analytics tool for crowdsourcing systems. Apart from AMT, Wang et al. [10]
performed analysis of the tasks on ZBJ and SDH (Chinese crowdsourcing sites)
and estimated that 90% of all tasks were crowdturfing tasks.

Supply-driven marketplace: Though there have been several studies on demand-
driven marketplaces, supply-driven marketplaces, which are also a growing
business in crowdsourcing based marketplaces, are under-studied. [11]
performed measurement studies on Fiverr focusing on the quality of the gigs.
[12] analyzed abusive tasks on Freelancer. Several black-hat marketplaces like
HackBB, SilkRoad, Agora, SEOClerks, MyCheapJobs, Gigbucks, Gigton, TenBux have
also emerged which facilitate sale of fraudulent products and illicit goods
[13, 14, 15, 16]. [16] studied crowdturfing task detection on Fiverr. [17]
analyze marketplace characteristic in SEOClerks and MyCheapJobs. [18] studied
the reward effect on submissions in Taskcn. Our study is different from the
above in the following ways - we present a first comprehensive picture of the
behavior of the buyers, the sellers and the seller-seller interactions and
demonstrate how all these together impact Fiverr as a marketplace from the
economic, the sociological and the strategic perspective.

## 3\. Dataset description

We crawled Fiverr data using preferential crawlers in $R$. The crawler ran for
a period of 20 days and data for 59,786 gigs from 103 subcategories belonging
to 10 categories were collected. For each gig, we collected price, reviews,
seller’s response to reviews, and mutual ratings. We then separately crawled
the member profile information (average response time, level of seller, user
locations and conversant languages etc.). Next we removed the gigs which have
incomplete information for some fields. Consequently, we were left with 41,473
gigs which we use for our study. The dataset contains 21,767 unique sellers
and 5,31,841 unique buyers. The total reviews reached a count of 34,43,381 and
are used as proxies for business transaction between the reviewer (also buyer)
and the gig owner. The prices of the gigs range from $5 to $995 indicating the
diversity in the service quality offered.
Gigs, Sellers and Buyers: Gigs are services provided by sellers in Fiverr and
are grouped into categories and further subcategories. The Graphics Design
category has the highest number of sellers with 32% of the total share. ‘Level
of seller’ is assigned by the website to sellers based on the total time
spent, the volume of sales, the ratings, the cancellation rates and the order
count thresholds amongst many other things. The categories here are new, level
1, level 2 and top rated.

Table 1: Categories based on no. of gigs and buyers.

Category Groups | # sellers | # buyers | # gigs |  Avg. Price |  Avg. Sale |  Avg. Revenue
---|---|---|---|---|---|---
Group 1 | 16962 | 596081 | 20501 |  $31.02 |  93.06 |  $1128.81
Group 2 | 1619 | 67044 | 2304 |  $8.54 |  471.45 |  $3662.11
Group 3 | 5744 | 87159 | 7394 |  $6.65 |  28.16 |  $218.61
Group 4 | 5012 | 96491 | 6443 |  $5.04 |  35.70 |  $184.80
Group 5 | 3339 | 46670 | 5183 |  $5 |  24.41 |  $122.46
Total | 21767 | 531841 | 41573 |  $8.69 |  78.22 |  $757.17

Categories and product differentiation: We organize all the categories in
Fiverr into 5 broad groups – Graphics & Design, Digital Marketing, Writing &
Translation; Video & Animation, Music & Audio; Programming & Tech; Advertising
, Business; Lifestyle, Gifts, Fun & Bizarre – and analyze them separately to
find which among them are more popular in terms of the number of gigs, high
supply and demand. We also calculate average gig price in each category,
average sales of the gigs and revenue generated by the gigs (see Table 1). We
find that the average price is maximum for the creative categories i.e., group
1 ($31.02) whereas the average price in the lifestyle related category (group
5) is just $5. The average sales per gig varies from 471.45 (Audio and Video
category) to 24.41 (group 5). We also observe that many categories have
significant sales in terms of total sales but show mediocrity in terms of
average sales per gig. Therefore, the product differentiation is quite
apparent in this marketplace.

## 4\. Fiverr under the economic lens

From the economic viewpoint, we analyze behavior of various marketplace
entities like buyers, sellers and the review patterns.

###  4.1 Behavior of sellers

We find that more than 40% sellers have only earned $5-$100 over the last nine
months whereas the proportion of sellers with revenue $>\$50,000$ is only
about 0.4% (see fig 1). Fiverr provides a lot of information about sellers and
gigs e.g., average response time, orders in queue, number of reviews, number
of positive reviews, average response time of the seller and many recently
added new features like - a rating based on the service as described on a
scale of 0–5, a rating based on the recommendation of the gig by a user. We
investigate several of these features in order to unfold the behavior of the
sellers.

Figure 1:  Revenue generation of the sellers.

Based on different attributes, Fiverr classifies the sellers into three levels
- level 1, level 2 and top rated. There are many sellers who have not yet met
the criterion to be in any of these levels and are called new sellers by
Fiverr. We observe that about 22.56% of the whole seller community are in
level 1, more than 62.89% are level 2 sellers whereas only 4.8% are top rated
sellers and the rest of them are new. Table 2 shows how sales and revenue
varies for the different levels of sellers. Apart from the fact that the top
rated sellers are selling more number of products, it is also quite clear that
they charge more for their product than others. A top rated seller charges
$9.56 on average, where a level 2 seller charges $8.96. This premium charging
shows their bargaining power over others.

Table 2: Performance of sellers.

Level | Avg. Sale | Avg. Revenue | Avg. Gigs
---|---|---|---
Level 1 | 12.94 | $112.66 | 1.46
Level 2 | 158.22 | $1417.15 | 2.22
Top rated | 2344.24 | $22,413.26 | 4.04

Sales and top sellers  First we observe the performance of sellers based on
the revenue earned in the last nine months. The revenue generated by the
seller varies from $555,715 to only $5. If we consider the number of unit
sales without considering the price, then it can be as high as 38,302 to as
low as 1. From the fig 2, we can clearly observe that most of the top sellers
prefer selling a small number of gigs in few subcategories rather than
providing diverse type of gigs. Hence, they are mostly proficient in certain
type of services which indicates a good level of professionalism in a service
driven marketplace.

Figure 2:  Sales of top sellers. The sizes of bubbles denotes the amount of
total sales. The color coding is based on the number of categories they
provide services in.

Pricing of gigs  In Fiverr, 87.73% of the gigs are priced at $5. 0.02% of the
gigs are highly priced and more than $500. From the dataset, we observe that
most of the highly priced gigs are from the subcategories Buy Photos Online
Photoshop, Banner Ads and Content Marketing. Figure 3 shows how different
levels of sellers price their gigs. The underlying distribution within each
seller level appears to be the same with the only difference being the
percentage of total sellers in level 2 is much high compared to other levels.

Figure 3:  Price of gigs of different sellers in each level.

Buyer ratings and seller response times  Buyers provide ratings to the sellers
for each purchase on a scale of 0-5. Hence, average rating for each seller can
be a parameter for judging his/her performance. After analyzing the data, we
believe that the rating system is not very discriminatory for Fiverr. More
than 60% of the sellers have an average rating of 5 and more than 96% have
more than 4.7. This distribution is also valid category wise (see fig 4). In
fig 4, we show the distribution of average response time of sellers. The graph
shows power-law behavior. More than 95% of the sellers usually respond within
a day and $\sim 35\%$ of them respond within an hour.

Figure 4: (a) Average buyer rating of sellers for different categories (b)
Average response time of seller.

Order queue  Another new feature that Fiverr has recently added is the order
queue count which tells us how many incomplete orders are present for a
particular seller for a given gig. Low value of order queue count can be
either because of less popularity or excellent customer handling. Similarly,
high value of order queue count can indicate less proficiency in customer
handling or large number of orders inflow. In fig 6, we show the distribution
of no. of orders in queue for each level of sellers. Most of the level 2
sellers are very prompt, however, a non-negligible percentage of them also
delay their services.

Gain of new customers The customer base of sellers keeps changing over time
and the inflow of new customers can be taken as a proxy for growing popularity
and hence an indicator of good services. We propose a metric to measure this
inflow. For a particular seller, we define

$Gain(t)=\frac{|customer_{t}\cap\overline{customer_{t-1}}|}{|customer_{t-1}|}$

where, $customer_{t}$ is the set of all customers of the seller at time $t$
and $\overline{customer_{t}}$ is the set complement of the set $customer_{t}$.
$customer_{t-1}$ represents the set of customers one day before the $t^{th}$
day. Fig 6 shows the average gain of top sellers of five subcategories. We
observe that average gain decreases over time. Globally, the percentage gain
of customers for top sellers is around 40% and is greater than the preceding
time intervals for one month. Some subcategories show different trends. For
example - in Custom Handmade Jewelry the largest gain ($>50$%) of customers
took place during the first week of May 2016. Similarly, around the middle of
May, 2016 the subcategory Extremely Bizarre showed the maximum gain of new
customers which is around 75% (fig 6).

Figure 5:  Distribution of order queue count of sellers.

Figure 6:  Gain of new customers by sellers in each subcategory.

###  4.2 Behavior of buyers

Buyers are the driving force of any marketplace. In our dataset, we have a
total of 5,31,841 buyers. Nevertheless, if we only want to observe the
profitable or returning customers, then the number would be much less than
this. $\sim 45\%$ of the buyers are one-time (only one purchase across all
gigs) buyers. Only 18% of the buyers have purchased more than 5 times in last
nine months, with the maximum being 2939. Fig 7 shows the distribution of
number of purchases of gigs indicating a power-law behavior. In fig 7, we
present the distribution of purchases from different subcategories. 68.62% of
the buyers buy from one subcategory and only nine buyers have bought from more
than 30 subcategories.

Figure 7:  Distribution of (a) no. of gigs (b) no. of subcategories from which
the buyers buy gigs.

##### Purchase and top buyers

As we have seen that most of the buyers are just one time buyers, we consider
only the top buyers in our studies, whose total purchases are more than 200.

Figure 8:  Purchase plot of buyers. Size indicates the number of gigs a buyer
has bought. Color coding is based on the number of subcategories the buyer is
buying from.

In fig 8 we show that the amount of purchases is not always directly
proportionate to the volume of purchases. Moreover, most of the top buyers
prefer to buy small number of gigs from a few subcategories, rather than
buying different types of products across different subcategories. This also
shows that these buyers are not just stray visitors to the website; instead
they repeatedly buy the same products over time. Hence, it is legitimate to
assume that loyal customers (although less in number) do exist in supply-
driven marketplaces like Fiverr.

##### Returning time of buyers

As we have seen the existence of loyal and repeated buyers in Fiverr, the next
question that arises is - how often does a returning customer buy a particular
gig? To measure this, we define a statistic for each of the repeated buyers
for each of the gigs they are buying.

$returning$ $time$ = $\sum_{i=2}^{N}{\frac{|T_{i}-T_{i-1}|}{N-1}}$

where, $\\{T_{i}\\}_{i=1}^{N}$ is the time periods of purchase of a particular
gig. In other words, this statistic measures the average day difference
between any two consecutive purchases for each of the repeated buyers. In fig
9 we show the distribution of the returning time of the top buyers in Fiverr.
It clearly shows the peak at time zero and almost uniform frequencies up to
3rd day followed by a decrease. We can therefore infer that most of the
frequent buyers usually return within a day, which is another unlikely event
for other e-commerce and product driven marketplaces. This observation can
greatly help the sellers to incentivise returning customers. This also shows
the extent of heavy usage of services by the frequent customers which is
overall an encouraging sign for Fiverr.

Figure 9:  Distribution of returning time of buyers.

###  4.3 Review characteristics

Reviews have increasingly become one of the guiding foundations for buyers in
online marketplaces. A good review system allows for a discriminative analysis
among the different choices and helps the consumer determine the usefulness
and ingenuity of the product under question in the different stages of the
purchase decision making process. Fiverr’s review system, though probably
intended to be means for insights into gig’s performance, does seem to be
quite different from the usual e-commerce platforms. The buyer and the seller
can engage in a mutual review exchange and rate each other based on their
interaction. Initial analysis shows that the linguistic structure has evolved
along a very constrained framework which involves a high degree of mutual
appreciation, well-wishing and goodwill exchange. We perform general analysis
of gig reviews and try to look at the commenting behavior of the users across
all the gigs. Though we consider comments on a gig as a proxy for purchase, we
study the repetitiveness of comments in Fiverr. The normalized figures for
5,31,841 users show us that $\sim 45\%$ of the users comment only once. This
is followed by 18% of the users placing the same comment twice. $\sim 8.9\%$
of the users have commented the same thing thrice. This distribution follows a
power-law behavior (see fig 10).

Figure 10:  Distribution of (a) number of times the same comment is used by
different users (b) number of times the same comment is used in different
gigs.

We also observe how comments are being repeated across all the gigs. Figure 10
shows the distribution of the comment repetitions which shows a power-law
curve with heavy tail. 80% of the comments are non-repetitive, 10.98% have
occurred twice. Notably, the single comment “Outstanding Experience!” has
appeared 51,451 times across all comments. Close variations of this comment
are also observed frequently. This is followed by 12,406 repetitions of the
comment “thanks”.
Sentiment analysis: We perform sentiment analysis of the reviews using the
standard sentiment dictionary [19]. Most of the reviews have positive and
encouraging constructs. This can be attributed to the structure which has
developed over time and is peculiar to Fiverr. “Outstanding experience”,
“Thanks” and “Great to work with” are some of the most common phrases
encountered and this is reflected in the skewed sentiment scores. However, a
difference in the polarity distribution can be observed across categories with
different inherent characteristics. The difference is more pronounced across
different price ranges.

Figure 11:  (a) Comparison of sentiment of buyer and seller text exchange for
the Creative Logo Design subcategory (b) Review sentiment comparison across
three categories.

Sellers are found to be more positive in their responses to the comments by
the buyers. A cumulative plot of Creative Logo Design subcategory demonstrates
this fact (see fig 11). Comparisons across different subcategories (see fig
11) also gives very convincing insights. For trivial services like the ones
contained in Extremely Bizarre, the reviews have higher proportion of positive
sentiments than more serious services like the ones in Wordpress and Creative
Logo Design.
Comparison with Amazon’s reviews: Since reviewing patterns are a key
representative of any marketplace, we use these to differentiate a supply-
driven and a demand-driven marketplaces. We collected Amazon’s review data for
the Electronics category and randomly sampled the reviews. These are in high
contrast to that of Fiverr. The sentiment distribution is more inclusive of
negative polarities and the linguistic structure looks more realistic upon
primary inspection. Comparison of top 10 most frequent words (see table 3) in
reviews of Amazon Electronics products and Fiverr’s most sold Creative Logo
Design shows that Amazon’s products get feedback related to product specifics
with some words expressing gratefulness while Fiverr is dominated by mostly
emotional responses. The average number of characters per review for Amazon is
around 381, while for Fiverr, it is merely around 61. This can be attributed
to the elaborate and explanatory nature of the former and a generic
congratulatory nature of the later.

Table 3: Top 10 most frequent words in reviews

Marketplace | Top 10 words
---|---
Amazon | case phone one great works like just well good use
Fiverr | experience great work outstanding thanks logo job
| will good excellent

## 5\. Sociological/network perspective of Fiverr

We create two types of seller-seller networks (Tier-I and Tier-II) and study
the interesting properties of these networks.

###  5.1 Tier-I seller-seller network

We create a Tier-I seller-seller network $G=(V,E)$ as follows. The set of
nodes $V=\\{s_{1},s_{2},...\\}$ are the set of sellers in Fiverr and if a
seller $s_{1}$ buys $k$ times from other seller $s_{2}$, then we add a
directed edge from $s_{2}$ to $s_{1}$ in $G$ with weight (proportional to no.
of purchases)

$weight(s_{2},s_{1})$ = $k/total\\_sales(s_{2})$

Graph density: The network formed using the above method has 49.13% of the
full sellers community but with a graph density of only 0.001. This low number
indicates that, although a number of sellers together form the network, they
are loosely connected to each other. Thus they usually have a very small set
of sellers in their neighborhood from whom they make a purchase.
Degree distribution and power law properties: It is a known fact that degree
distribution in most of the real world networks follows a power law
$p(k)\approx k^{-\alpha}$ with the exponent $\alpha$ lying between 2 and 3.
The Fiverr seller-seller network also exhibits the same characteristics with
$\alpha$ value of 2.27 for in-degree and 2.41 for out-degree (see fig 12). We
further observe that percentage of nodes with zero in-degree is 43.53% whereas
percentage of nodes with zero out-degree is 29.45%. This shows that the number
of sellers who are only buying from other sellers is much higher compared to
those who are only selling.

Figure 12:  Degree distribution of the network.

Transitivity: Transitivity, also referred to as the clustering coefficient, is
the measure of how well the neighbors of a node are connected among
themselves. This also indicates the existence of cliques or dense modules
within a network. The Tier-I seller-seller network of Fiverr has very low
clustering coefficient of 0.037 (the direction of the edges are ignored in
this computation). The low clustering coefficient shows that there is no
tightly connected module existing between the sellers in the Fiverr market
which is in contrast to the general market where it is usually known to exist.
Connected components and communities: The network consists of 526 weakly
connected components and 11,077 strongly connected components. The number of
strongly connected components is almost same as the number of nodes which show
low reciprocity in the network. By reciprocity of a network $G(V,E)$, we
understand the proportion of occurrences where both $(v_{1},v_{2})$ and
$(v_{2},v_{1})$ belongs to the edge set $E$. Using Louvain algorithm [20], we
determine the community structure of this network. 625 communities are
discovered with an overall high modularity value of 0.941. Therefore, the
network shows many small communities and the communities themselves are
tightly-knit.
Geography of sellers: We collected the geographical location based data of the
sellers associated with the Tier-I seller-seller network. We find that 31.57%
of the whole community belongs to United States, followed by Pakistan (9.27%)
and India (7.73%). Moreover, the order remains same for individual sellers and
buyers in the community, where United States is leading followed by India and
Pakistan. We also observe that in 26.15% of the cases both the buyer and the
seller belong to the same country.
Characterizing the Tier-I seller-seller network: We observe that in 15.53%
cases a seller buys from another seller who sells gigs in the same category
itself. Now, if a buyer, who himself sells gigs in the same subcategory buys
something similar to his product, then the explanations behind this could be
either promoting the other sellers’ business or an anomalous behavior. There
can be several other reasons like - supporting someone’s business, who is
personally very close, or, buying similar product from some expert and again
selling to someone else at a higher price. In 36.84% of the cases, we find
that buyer’s total sales is more than the seller. In 7.81% cases they offer
gigs in the same category. These cases may be those where a high income seller
supports other less popular sellers by promoting them or, by just providing
good reviews for their gigs. If we consider the level wise distribution, then
higher level sellers buy gigs from lower level sellers in 24.25% cases,
whereas, lower level sellers buy gigs from higher level sellers in 15.09%
cases. The remaining 60.66% cases are purchases within the same level.

#### 5.1.1. Reciprocity and revenue difference between buyer and seller

Here, we test the hypothesis whether the average revenue difference between a
seller and a buyer in a community decreases as the reciprocity increases. We
calculate reciprocity for each of the communities of the network and then
calculate the average difference of revenue for each of the communities. So,
if a community $c_{i}$ of the network contains
$\\{(s_{1},b_{1}),(s_{2},b_{2}),\cdots,(s_{N},b_{N})\\}$ with $s_{j}$ being
the seller and $b_{j}$ being the buyer, we define average revenue difference
of $c_{i}$ as

$R_{i}$ = $\sum_{j=1}^{N}(|revenue(s_{j})-revenue(b_{j})|)/N$

Our test cases then are -
$H_{0}$: Avg. difference of revenue in a community decreases as reciprocity
increases.
$H_{1}$: Avg. difference of revenue does not decrease with increasing
reciprocity.
We apply linear regression to check the dependence between these two factors.
The correlation between the two factors comes out to be $-0.1538$ with the
$p$-value of $0.0921$. Hence, with $90\%$ confidence we can reject our
alternative hypothesis (i.e., $H_{1}$).
Hence, our study shows that if reciprocity increases i.e., if more people
indulge into a two way traffic, then the average revenue difference decreases
i.e., their sales values become almost identical. This means that when two
sellers in the market almost perform similarly, they tend to support each
other. This type of mutual uplifting can promote both their businesses.

#### 5.1.2. Effect of location on the volume of sales

Intuitively one would expect that in a global marketplace, if two people
belong to same geographical location, then their interaction would be much
higher on average than with other people from different locations. To check
this hypothesis for Fiverr, we calculate the average volume of sales happening
in a particular community and check its dependence on the average geographical
location similarity. For each community $c_{i}$ in the network, we define
average location similarity as

$L_{i}$ = $\sum_{j=1}^{N}(G_{s_{j},b_{j}})/N$

where, $G_{s_{j},b_{j}}=1$ if both seller $s_{j}$ and buyer $b_{j}$ (both of
them are from $c_{i}$) belong to the same geographical location and 0
otherwise. The correlation coefficient between average volume of sales and $L$
comes out to be $0.1871$ with a $p$-value of $0.0398$. Therefore, with a
confidence of $97\%$ we can conclude that there is a positive correlation
between our derived location similarity metric and average volume of sales.
Thus, it is fair to assume that our claim, that users from the same location
intend to purchase from one another and their volume of interaction is high,
is reasonable.

#### 5.1.3. Effect of similar gigs on reciprocity

Next, we calculate the average similarity between the gig names of the
corresponding gigs provided by the seller and the buyer. Following this, we
calculate the average gig similarity in a community. We apply linear
regression to check the relationship between average gig similarity and
reciprocity. Our results seems to strongly favor our claim since, as the
average similarity between gigs increases, so is the propensity to
reciprocate. The regression coefficient is $0.6447$ with a $p$-value of
$0.00046$. Thus, if two sellers sell similar type of gigs then they are more
inclined to buy from each other. This type of phenomenon is very rare in
common marketplaces. If a buyer sells some product, then it is very unlikely
that she will buy the same product from a different seller. It is even more
unlikely that the second seller will again buy back some similar product from
the first one.

#### 5.1.4. Relationship between seller level and reciprocity

We observe that in $\sim 60\%$ cases, seller-seller interactions happen
between same level sellers. Therefore, the next question that arises is - if
two same level seller interact, what is the probability that they reciprocate?
To answer this, we calculate average seller similarity between people in each
of the communities. For each community $c_{i}$ we define avg. seller level
similarity as

$SR_{i}$ = $\sum_{j=1}^{N}(S_{s_{j},b_{j}})/N$

where, $S_{s_{j},b_{j}}=1$ if $s_{j}$ and $b_{j}$ belong to same level (level
1 or, level 2 or, top rated) and 0 otherwise. After applying statistical test
of significance, we find that the correlation coefficient to be $0.2657$ with
a $p$-value of $0.00322$. This result clearly shows that the reciprocity of a
community increases with the average level similarity i.e., two people from
same level have high propensity to reciprocate to each other.

###  5.2 Tier-II seller-seller network

We form another network on the basis of number of buyers common between two
sellers and denote this network as Tier-II network. We take the top sellers
and draw an edge between a pair of sellers if they share any common buyer. We
define the weight of the edge as -

$weight(a,b)=|buyer_{a}\cap buyer_{b}|/|buyer_{a}\cup buyer_{b}|$

with $buyer_{i}$ being the set of buyers of the seller $i$. Analyzing this
network gives us insight about loyalty of buyers. For the subcategory Creative
Logo Design, the average degree of the 282 nodes in the network comes out to
be 110.28 with only one strongly connected component. This is indicative of
divided loyalty or no loyalty of buyers in Fiverr market. Moreover, the
network follows small world properties with a diameter of 3. We also find that
the network is highly connected with an average clustering coefficient of
0.34. These results also indicate the fact that Fiverr is a marketplace where
the competition is less monopolistic, i.e., most of the sellers sell similar
types of gigs in order to gain revenue and customers do not discriminate well
among these gigs.

## 6\. Fiverr under strategy lens

In this section, we evaluate Fiverr as a marketplace from strategy perspective
focusing on the inter-entity dependence. For this study, we only choose those
sellers whose sales are more than 500 per gig ($\sim 2.45\%$ of the whole
seller community).

##### Dependence between no. of gigs sold and revenue

Intuitively, a seller selling more gigs seems more likely to generate more
revenue. We perform statistical test of significance on data of top sellers.
We apply linear regression with number of gigs as the independent variable and
obtain a regression coefficient of $0.2479$ with a $p$-value of $2.2\times
10^{-16}$. This shows that the relationship between the two is weak for
Fiverr.

##### Customer churn rate and sales

We define customer churn rate for a seller for a particular gig as

$churn$ $rate$ = $\sum_{t=1}^{N}{\frac{|\overline{customer_{t}}\cap
customer_{t-1}|}{|customer_{t-1}|}}$

Here, $customer_{t}$ is the set of the customers of the seller at time period
$t$ and $N$ is total number of time periods the gig has been selling for in
the last nine months.
We calculate the average churn rate for all gigs offered by a seller (fig 14).
We believe that the lesser the customer churn rate, the better the customer
handling and ability to satisfy a customer, and more the sales. This is of
course not true for products with long life-cycles. For example, a customer
who bought a mobile phone from a seller in Amazon is less likely to return for
another phone purchase. In low value service driven marketplaces, customers
returning can be a very frequent phenomenon. We studied the relationship
between churning rate and total sales of Fiverr. Churn rate is somewhat
affecting the total sales, and the two are positively related (correlation
coefficient of 0.12). One reason could be that most of the customers of Fiverr
are just one time buyers. Average churn rate is higher for many of the
sellers.

##### Dependence between similar gigs and sales

We observe that many top sellers sell more than one gig and in many cases they
provide their services in the same subcategory. So, the next question that
arises is - how similar are their gigs or do they provide different type of
services? We create tf-idf matrices for 1-gram, 2-gram and 3-gram similarity
of the gig names of the gigs the seller offers. As most of the gigs start with
the phrase “I will do” or “I will make”, we remove these phrases and other
stop words from the gig names. After that, we take the average of the three
tf-idf matrices to get one matrix $M$. Standardized rows of $MM^{T}$ gives us
the average similarity between one gig and other gigs. Hence, average of all
the off diagonal elements of the matrix $MM^{T}$ gives the average gig
similarity of the particular seller. We then study the distribution of the
similarity value (fig 14) and check the significance of similarity value over
number of sales made by a seller. The result we obtain from the statistical
test of significance is a coefficient of $-0.216$ with a low $p$-value of
$0.0073$. This results clearly show that the similarity value is negatively
related to total sales i.e., lower the similarity value, higher are the sales.
The more diverse type of products a seller sells, the more sales he/she can
make.

Figure 13:  Churn rate of top 2.5% of sellers.

Figure 14:  Inter-gig similarity of seller.

##### Similarity with other gigs and sales

In the previous subsection, we define the similarity between gigs of each
seller. Here we define a similar concept, between a gig and other gigs.
Therefore, we focus on the average similarity between a single gig with other
gigs in the same category offered by other sellers. In a competitive market,
we usually believe that the products which are somewhat different from the
other products stand out and make good sales. For example, most of the niche
products like Apple iPhones or automobile companies like BMW provide a unique
experience to their customers which creates a strong brand awareness among the
customers. In case of Fiverr, we wish to examine whether a unique gig that has
very low similarity with other products in the category is having more sales,
or the other way round. The correlation coefficient between average similarity
with other gigs and sales is $0.1336$ with a very low $p$-value of $0.000027$.
Very low $t$-statistics and a positive correlation clearly shows that there is
a significant dependence between these two. Moreover, in Fiverr, higher
similarity with others means more prospects of being successful. This tells us
that most of the products offered in Fiverr are homogeneous in nature and the
number of alternative products is also very
high.222https://en.wikipedia.org/wiki/Perfect_competition

##### Assessing competition in subcategories

We define a similar concept of similarity between all gigs in each of the
subcategories. If the average inter-gig similarity is high for a particular
subcategory, that means most of the sellers tend to sell similar types of gigs
in that category. This indicates that the prospect of being successful in that
category is high, if one seller goes with the trend. This implies that very
high competition exists in that subcategory. We observe whether this measure
of competition affects the sales of a category. The topmost competitive
subcategory came out to be Video Post Production Editing with average sales
per gig of 1,141. We standardize the total number of sales by the number of
gigs in that subcategory to get the average sales per gig in that subcategory.
After applying linear regression analysis on the two standardized variables,
average sales per gig and average similarity between gigs, we get a high
correlation coefficient of $0.4395$ with a very low $p$-value of $3\times
10^{-6}$. This result clearly shows that as the competition or the similarity
between gigs in a subcategory increases, the average sales also increases.
This type of phenomenon is very common in an ideal scenario of perfectly
competitive market. Also, large number of buyers and sellers, accurate and
necessary information about all the products, very flexible and barrier free
market structure has made Fiverr a very good example of perfectly competitive
marketplace. The average gig price which is very close to the actual market
price of $5 shows that every entity is a “price taker” in the market.

##### Evaluation of growth prospective of seller in market

Evaluation of performance of sellers is vital for any crowdsourcing platform.
We use the classical method of Boston Matrix (BCG
matrix333https://en.wikipedia.org/wiki/Growth-share_matrix) to evaluate the
performance of the different top 2.5% sellers. The BCG matrix allows
organizations to identify product positioning in the market and thus they
could allocate resources accordingly. We also use the same concepts in order
to classify the sellers in the similar lines. The different groups in BCG
matrix are
1\. Stars : The best performing products or, sellers which generate most of
the revenue and have positive growth rate and use most of the resources of the
organization.
2\. Question Mark : The products or, sellers having very high growth
prospective but currently low market share. They are the potential stars.
3\. Cash Cow : The market leaders with low growth rate. People invest in them
to maintain their status in market.
4\. Dogs : The products or, sellers with low market share as well as low
growth prospects.

Figure 15:  Boston Matrix showing sellers in different quadrants of matrix.

Based on the matrix (fig 15) we also divide top sellers in four groups -
Stars, Cash cows, Question marks and Dogs based on their market share (i.e.,
proportion of revenue generated) and their growth rate measured as the slope
of gain of revenue in consecutive months. Therefore, if a seller had revenue
$\\{r_{1},r_{2},\cdots,r_{N}\\}$ respectively in the time period
$\\{t_{1},t_{2},\cdots,t_{N}\\}$, then the growth of revenue for the time
period $\\{t_{1},t_{2},\cdots,t_{N-1}\\}$ would be
$\\{r_{1}-r_{2},r_{2}-r_{3},\cdots,r_{N-1}-r_{N}\\}$. We normalized the growth
by the number of days and applied linear regression to get the slope of change
of revenue growth. We then use the regression coefficient as the growth rate
for each of the sellers. More negative the slope is, more is the growth rate
of the seller.

Quadrant of BCG matrix | percentage of top sellers
---|---
Stars | 4.3%
Cash cow | 16.85%
Question Mark | 10.11%
Dogs | 68.74%

The below table shows that there is very low percentage (4.3%) of stars which
is a common observation in any market place. Moreover, we see that the top
performers in the Stars have very high growth rate (slope is close to
$-80^{\circ}$). Similarly, 16.85% of the sellers have a growth rate close to
zero but with high revenue. We further observe that 37.9% of these people are
from top rated level. We believe that these sellers may have reached their
saturation point and possibly will be taken over by level 2 in the next few
months. Similarly, among the Questions Marks 61.5% are level 2 sellers who
have a very high growth prospect with relatively low revenue in the current
period. Providing proper incentive to these sellers can help them in becoming
stars. Thus, Boston matrix provides the insights about incentivizing the right
set of sellers in order to boost their businesses.

## 7\. Discussions and conclusions

In this paper, we study the characteristic properties of Fiverr marketplace
from various points of view - economic, sociological and strategy and make the
following key observations.

The Fiverr marketplace is a unique in that the buyers who purchase gigs from
sellers can convert themselves to sellers at any point in time. Most sellers
are proficient in certain type of services which indicates a good level of
professionalism in the marketplace. Hence most of the top sellers prefer
selling small number of gigs in a few subcategories rather than offering
diverse category of gigs. From the viewpoint of the average gain of top
sellers, the trends decrease over time. Globally, the percentage gain of
customers for top sellers is around 40%. However, some subcategories also show
different trends. The gains sometimes reach as high as 75%. The Fiverr
marketplace is dominated by one-time buyers which consists of 45% of the total
buyers. An interesting and unique characteristic in a supply-driven
marketplace is the existence of loyal customers. It is observed that many
buyers buy the same product repeatedly from a few categories. This also
results in the amount of purchases not always being directly proportionate to
the volume of purchases. We observe that sellers are more appeasing in their
interactions and try to woo their buyers into buying their gigs. Serious
categories have comparatively more reviews with near zero and negative
polarities as compared to the trivial categories.

On analyzing the Tier-I seller-seller interaction network, we observe that
there are many small tightly-knit seller communities existing in the network;
however the overall network structure indicate loose connectivity with density
of 0.001 among 49.13% sellers. The very low clustering coefficient in the
network shows that there are very few or no transitive links in the network
which is quite uncommon for general markets. There is also low reciprocity in
the network.

We also observe that some sellers buy from other sellers within the same
subcategory. The possible reasons for this behavior include promoting which is
supplemented by the buyer having more sales than the seller and providing
positive reviews to the seller. Most of the top sellers prefer to offer
multiple gigs but in the same subcategory. An analysis of the gigs of the top
sellers indicates that the more diverse type of products a seller sells, the
more sales he/she can make. In terms of gig similarity on Fiverr, higher
similarity with others means more prospects of being successful. This tells us
that the products offered in Fiverr are homogeneous in nature and the number
of alternative products is also very high. Similarly, as the competition or
the similarity between gigs in a subcategory increases, the average sales also
increases. This type of phenomenon is very common in an ideal scenario of
perfectly competitive market. Boston Matrix analysis shows that top performers
in the ‘Stars’ group have very high growth. At the same time, about
${\frac{1}{6}}^{th}$ of all the sellers have almost no growth but high
revenue. Similarly, most of the sellers in the ‘question mark’ bracket can
potentially become stars if provided with the right incentives.

## 8\. References

##

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