Mathematics, Statistics and Sales Chat

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Mathematics, Statistics and Sales Chat
A Web Retailer Case Study

White paper by:
Ravi Vijayaraghavan
Vice-President and Head - Global Analytics

February 2010

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

Table of Contents

Introduction 3

The Conversion Funnel 4

Statistical Scoring Model 5

Agent Optimization 6

Chat Transcripts Analysis - Text Mining 7

Conclusion 8

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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 advertising. 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 retail 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.

Total Number of Visitors
5,154,257
Layer 1 - Filter

• Value of the product • Product contribution
„Hot‟ Leads • Cross-sell opportunity
• Involvement required from 519,080 (10%) • Customer‟s web behavior
buyer of the product
Layer 2 - Filter

• Customer‟s web behavior „Hot‟ Leads Invited to Chat
• Resource allocation 390,913 (75%)
Layer 3 - Leakage

Invitations Accepted
30,617 (8%)
Layer 4 - Leakage
• Customer‟s comfort with chat
channel
No. of Chats Started
• Customer‟s comfort with 23,085 (75%)
sales interaction during
shopping Layer 5 - Leakage
• Past experience
No. of Interactive Chats
11,977 (52%)
Layer 6 - Leakage
• Agent competency

Sale!!! • Pricing
3,566 (30%) • Perceived value

• Quality of transaction

Figure - 1: Sales Chat Conversion Funnel – Visitor to Prospect to Customer


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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 self-service 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 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.

Referral Page Web Behavior Hour of the Day Day of the Week Product Ca teg ory Postal Code Connection Type

Hot Lead
Search Engine In a P roduct P age 0 Monday Home & Garden 10... Cable/DSL
P (Sale) = 0.78
Retailer‟s Website Abandoned Orde r Process 1 Tuesday Electronics & Computers 11... Corporate
Cold Lead
Comparison Sho pping . Dialup
>t sec on Billing and Shipping Page Wednesday Jewelry & Watches
Site
2 P(Sale) = 0.03
>t sec on Ord er Revie w Pa ge .
Thursday Recreation & Sports
.
. .
. Friday Gifts & Flowers
.
. .
17 Saturday Health & Wellness

48…
Sunday

18 49…

19 .

20 .

21 .

22 94…

23 95…

Figure - 2: Schematic of the Scoring Model


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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 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”

2 5 .0 %

30% increase
2 0 .0 %
53% increase
Conversion
Rate
1 5 .0 %
All Agents
Raymond
1 0 .0 %

5 .0 %

0 .0 %
All Categories Electronics

Figure - 3: Matching Customers to the Agent with the Right Skills


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Chat Transcript Analysis – Text Mining

Once the transaction is completed, the chat transcripts are analyzed for patterns of
behavior that does or does not lead to a successful conversion and a quality
customer experience. Two kinds of text mining techniques are used - Clustering and
Classification. The Classification technique is usually adopted, when the domain of
interest (Sales Chat, in this case) is familiar to analyst while clustering is more
exploratory. For this case study a classification model was used. Here the analyst
defines the categories and the taxonomy for classification (Figure 4) based on a
training data set (chat transcripts). Building the text mining model involves training
the model to classify the chats correctly into appropriate categories. Once the model
is built it is tested on a new set of chat transcripts. Following are some specific
categories that are identified with text mining on sales chat.

1. Customer emotions and expectations
2. Agent behavior
3. Effort involved in selling various products
4. Reasons for chats not resulting in sales
5. Opportunities for up selling/cross-selling

Example: A sample of sales chats was analyzed to find the kind of effort required to
make a sale. The analysis revealed that it required greater effort to convince people
when they could not find satisfying answers for their query. Relating this, with the
kind of product in question, it was observed that people took time to make a
decision when they were shopping for items in the „Home and Garden‟ category. It
was also observed that people tended to ask for details that could be found only
upon close examination of the product. (Some typical questions are, what is the
texture of the cloth? what is the feel of the material, How does the back side of the
carpet look? What is the material used for the knob?). On the other hand, being well
specified, certain electronics items were sold with relatively less effort. It is also
important to note that the items that required more details and hence more
interaction are ideally sold using the chat medium as opposed to self-service.
Consistent with this, it is observed that „Home & Garden‟ category had the largest
volume and the highest expected revenues via chat among all product categories.
Text analysis helps optimize these interactions and also help prepare agents with
the right information to help customers.

Similarly, text mining can be used to identify products for which promotional
schemes (club membership, special protection plan, extended warranty plan) were
easier to up/cross sell. For example, zodiac pendants often sold with children
jewelry.

Figure - 4: Categorization of Customer Emotions and Agent Behaviour
to Build the Text Analysis Model


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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

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Mathematics, Statistics and Sales Chat

Mathematics, Statistics and Sales Chat

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