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

  1. 1. Page 1   Mathematics, Statistics, and Sales Chat A Web Retailer Case Study    
  2. 2. Page 2   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.  
  3. 3. Page 3  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
  4. 4. Page 4  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
  5. 5. Page 5  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 W_MATH_1012