Mathematics, Statistics, and Sales Chat - A Web Retailer Case Study

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Mathematics, Statistics, and Sales Chat
A Web Retailer Case Study

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Introduction
With the coming of age of web as a
mainstream sales and marketing channel,
companies have invested substantial
resources in enhancing their web presence.
This includes large investments in web
dvertising. In addition, companies are
looking for ways to improve sales conversion
and customer experience for web shoppers.
Sales chat is a medium that can provide a lift
in both these areas. With the growing
popularity of chat as a communication
medium, particularly among the new
generation of consumers, potential for
revenue generation from this channel is
enormous.
The obvious analogy is to consider the sales
chat agents as a “virtual sales force” for a”
virtual store”. However, a key difference
exists. In a real store there are relatively few
visitors and a significant fraction of the
visitors come with intent to buy i.e. they are
“hot” prospects. On the other hand, major
web stores such as Amazon, eBay and
Overstock have millions of visitors every
week and an overwhelming majority of these
visitors do not intend to buy. They also have
the ability to switch from one store to another
at the click of a mouse button. Considering
the visitor volumes and the low average
likelihood to buy, it is not profitable to
randomly engage in chat with every visitor. It
is imperative to identify a subset of this
visitor
population that has a substantially greater
likelihood to buy. The following case study
presents applications of
statistical/mathematical models in identifying
“hot” prospects and improving conversion,
revenue generation and customer
experience for a major web retailer.

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The Conversion Funnel
The starting point in understanding and
optimizing the performance of this virtual
sales force is the Conversion Funnel (Figure
1). The funnel helps visualize the size of the
opportunity. Figure 1 represents the funnel
for the web retailer in a particular week.
Layers 1 & 2 are filters, i.e. these are
determined and controlled by the retailers,
while layers 3 to 6 are leakages that are
essentially decisions made by the
customer during the browsing/buying
process and are not in the retailer’s control.
The science is essentially in determining the
appropriate filters to apply in selecting the
right customers and matching them up with
the right agents to minimize the leakages in
the funnel.
Statistical Scoring Model –
Filter (Layer 1)
The first filter identifies the “hot leads” i.e. the
people most likely to purchase via chat. In
particular, this identifies customers who have
a significantly higher likelihood of purchasing
from a chat agent than on their own in a selfservice mode. This is an important factor
since self-service is obviously a lower cost
channel than chat and if a customer is very
likely to purchase via self-service then the
business case for inviting them to a chat
engagement is poor. To avoid
cannibalization of a cheaper channel A/B
tests are conducted on a regular basis where
a fraction of the “hot leads” are not invited to
chat and their self-service conversion rates
are compared to conversion rates of the
remaining “hot leads” who are invited to chat.
Typically, conversion rate for chat
engagements among this “hot lead”
population is substantially higher (5x-10x)
than that for self-service engagements.
Identification of hot leads is accomplished
using a statistical scoring model. The scoring
can be done in real time while the
visitor/prospective customer is browsing on
the website. The scoring is based on a

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number of attributes including time of the
day, day of the week, geographical location
of the customer, product category, exhibited
behavior on the web site etc. Figure 2
schematically illustrates the scoring model.
Essentially certain patterns of behavior
exhibit a much greater propensity to buy
than others.
The scoring model essentially estimates a
probability of purchase (P(sale)). Statistical
and Data Mining techniques such as Naïve
Bayes, Logistic Regression or Neural
Networks are used to develop these scores.
A threshold score can be set above which
customers are invited to a chat. Based on
variations in traffic and availability of agents
the threshold score can be modified. As
more data is generated, the system learns
and the scoring model becomes better at
identifying the hottest prospects.
Agent Optimization – Filter
(Layer 2)
Once the customers are scored and the “hot
leads” identified, the next step is to invite
these “hot leads” to a chat. The number of
“hot leads” invited is based on tactical and
strategic considerations. On the tactical
front, it depends on several factors such as
the number of agents available, acceptable
abandonment rate without significantly
affecting customer experience, average
handle time and concurrency (how many
chats can an agent handle at a time). This is
a routine scheduling problem.
The more interesting strategic problem is to
determine the right number of agents to
maximize profits. The scoring model only

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prioritizes the visitors to the site. It does not
automatically provide a threshold score for
the “hot leads”. We provide the threshold
score. This in turn determines the number of
people invited to chat which establishes our
agent staffing levels. But what is the right
threshold score? How is it determined?
The customers are being prioritized based
on how “hot” they are. Based on the average
order value for a given product type and the
probability of a given “hot lead” to buy, the
expected revenue from the transaction can
be calculated. To increase the number of
chats and hence the overall revenues, we
lower the threshold score inviting less
qualified leads, at the same time increasing
the number of agents.
These less qualified customers on an
average generate lower revenues per
customer i.e. the marginal revenue of these
customers is lower. This implies, as we keep
adding agents to interact with less and less
qualified leads, the marginal profit generated
by the additional agents keeps declining. We
keep lowering the threshold, increasing the
number of “hot leads” and adding agents till
we stop making a marginal profit. To
estimate the number of agents
corresponding to this, we use an
optimization algorithm
Finally, scoring techniques are also used to
match the right agent to the customer. The
essential concept is displayed in Figure 3
where we see that the agent Raymond is as
such a top performer but is particularly
skilled in selling Electronics products. We
score various product-agent combinations
and manage our chat queues and routing
based on not just the overall performance of
the agent but also on historical performance
in various product categories. The goal here
is not just to look at product-agent
combinations but to develop a
comprehensive scoring model that scores
the agent for a set of customer/product
attributes and determines the best agent to
talk to a given “hot lead.”
Conclusion
Internet chat is a growing channel for sales
over the web and retailers are adding this
capability to their websites. However, like in
self-service web retailing, success in driving
up sales chat revenues and profitability will
go to players who use advanced data-driven
approaches to drive customer intelligence
and chat engagement decisions.
References
ITSMA and PAC, How Customers Choose
Study, 2009
ITSMA and PAC, How Customers Choose
Study, 2009
ITSMA and PAC, How Customers Choose
Study, 2009
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