Human Touch and Predictive Interaction


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The conventional marketing theory suggests that the primary aspect that drives the demand for any product and/or service is the ability of product and/or service in itself to create the needed demand. The awareness of any given product and/or service thus far has been created through multiple print and media channels.

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Human Touch and Predictive Interaction

  1. 1. 24/7 P Human Touch and Predictive Interaction―Predicting the Sale‖- Improving Online Sales through ―Intelligent‖ InteractionsWhite paper by:Ravi VijayaraghavanVice-President and Head - Global AnalyticFebruary 2010 Human Touch and Predictive Interaction Table of Contents Introduction 3 The Conventional Vs Online Selling Process 4 The Intelligent Approach 6 Conclusion 9
  2. 2. Human Touch and Predictive InteractionIntroductionThe conventional marketing theory suggests that the primary aspect that drives the demand for any product and/or service isthe ability of product and/or service in itself to create the needed demand. The awareness of any given product and/or servicethus far has been created through multiple print and media channels. While these channels continue to exist, the consumers intoday‘s high tech world demand ―sale-at-convenience‖ . The growth in internet is one such medium that has been of interestto many business houses.Retailers are the early adopters of this great opportunity. However, can the retailers be assured of revenues even after makingtheir products and/or service conveniently available for the consumers to buy through its online stores?. The challengesassociated with this business model are also many - How can they be competitive? How can they beckon new customers? Howcan they provide a better customer experience? How can they build customer retention and loyalty? This whitepaper providesinsights on how retailers can be innovative in utilizing online channel more effectively to grow their business
  3. 3. The Conventional Vs Online Selling Process; the importance of customer profilingWikipedia offers the following quote as a technical definition of sales- ―a systematic process of repetitive and measurablemilestones, by which a salesperson relates his or her offering of a product or service in return enabling the buyer to achievetheir goal in an economic way‖. This definition implies a very interactive process between the seller and the buyer. Typically,our experience with sales situations matches this description quite well. Clearly, depending on the product (a house vs. a book)the level of interaction may vary.However, our buying experience online is far removed from this model and has traditionally been non-interactive. Is there a rolefor interactive selling and human assistance on the web? In this context what does predictive intervention mean and how doesit impact online sales? These are some of the questions to which we will find answers in this white paper.Let us first look at sales in a Brick and Mortar store. Imagine you are inNordstrom‘s. You casually walk over to the suit rack and start looking over $3000 suits from premier brands. Within a minute, asales person with a friendly smile approaches you to help. She appears to know your size, and guides you to an eventual sale.We view this event as quite unremarkable. Why did she pick you to sell to? Maybe she judged you based on data (she saw yournice $600 Gucci shoes, your attention to details in selecting the suit and reckoned that there is a good chance you can afford the$3000 suit).The selling process on the web however is quite different. It is more of a one-way channel. The buyer is allowed to browsethrough the store but are not helped or guided in anyway. Questions that a prospective buyer could have are answered throughstatic help pages. But if the questions are a bit out of the ordinary the buyer typically receives no dynamic help from the webstore.It is not all bad news. While buying on the web is a decade old, it is only now that the market is waking up to the power ofintelligent human assistance. The online world is also realizing the impact that data mining has on providing insights onpredictability and proactively driving these customer interactions to deliver enhanced customer experiences.
  4. 4. With the emergence of advanced models in web marketing, online stores have the ability to drive targeted prospects to theirwebsite, unlike Brick and Mortar stores. However, online stores have shown surprisingly poor ability to convert highly targetedprospects into customers (compared to Brick and Mortar stores). This becomes very clear if one thinks of this process as afunnel. At the top there is a lot of drive to increase the traffic to the website through marketing. However the bottom part of thefunnel narrows to a trickle (Exhibit 1), because in most web sites‘ ―one-size-fits-all‖ customer experience makes convertingeven qualified visitors into buyers difficult. The web stores offer limited scope for interaction with the seller in making a buyingdecision. Even the few situations where the visitor is offered ability to interact, say through a chat, the interaction isimpersonal and poorly targeted since the salesperson has limited or no information about the prospect.
  5. 5. The Intelligent ApproachTo increase conversions, retailers need to predict buyers behavior and their needs, and address them with intelligentinteractions. To illustrate this let us take the example of an online store with more than 20,000 products. The online store getsadvertised heavily in television and through other channels thereby driving traffic of over 25 Million visitors a month.A prospective buyer ―Mr. Tom‖ arrives at an online store on a Wednesday evening around 7 PM PST, looking to buy a digitalcamera priced between $400- $500. This action of Mr. Tom to use a specific online store is basis the conclusion drawn by himafter completing a thorough comparative product and price analysis across various websites. Once on the website he spendstime to research on options on the right model, delivery, price points and while doing so he wonders if he can get some help toguide him further. And lo behold! A chat invite suddenly pops up asking if he needs assistance to choose the product he islooking for. It is almost like mind reading. The visitor is pleasantly surprised, says yes and is guided by a chat agent through theresearch process, and ends up buying not only the camera but also some accessories online.The question is, why was this visitor chosen for a proactive chat invitation as opposed to the other 25 million visitors?Welcome to the world of intelligent customer acquisition through Data Mining. This is only the beginning.If we think of this sales process as the conventional ―sales funnel‖ described earlier – how does one optimize the throughputfrom this funnel. Given that you cannot staff sales agents for every product category to improve efficiencies (it will in fact be themost inefficient sales model), the question that arises is what product to sell? Where does it make business sense to expendsales resources? Should it be in books or in digital cameras? If you know the name of the book and the author the only variant iswhether it is a paperback or a hard cover. The involvement for assistance in such a purchase is low. However if you are lookingfor digital cameras with high digital and optical zoom from well known brands you will have more options. In essence, a productcategory with high value and high buyer involvement is a good business opportunity that requires human assistance. Thisdecision of which products are best sold through guided human assistance is made through sophisticated modeling and A/Btesting methodology which essentially measures the ―lift‖ in sales over self-service as created through chat.
  6. 6. There are clearly customers who are more self-directed than others. Similarly, there are products that require more interactionthan others. For example, buying a dining table or a high end camera requires more interaction than buying a book or a DVD.Both the customer behavior and the product types are profiled based on visitor behavior on the website and the information isused to determine the right candidates for human assistance through chat or other channels.This profiling process, typically based on the salesman‘s intuition in the brick and mortar store, can be converted into ascience in the web store.The second step is to figure out who is a ―hot‖ prospect. Is it a repeat visitor or a visitor who has arrived directly to the websiteor arrived from a site that provides comparative analysis or has arrived through an email campaign? These and other attributessuch as the visitor‘s geographic location, connection type (cable, dialup..) are used to determine the ―hotness‖ of a prospect.But is this enough evidence to intervene when the visitor is on the site to aid him in the purchase process? Absolutely not. Westill don‘t know if he/she needs help. And how can we do that? By identifying behaviors on the website that display a need forassistance. Is the visitor confused? Is he/she displaying behavior that does not follow typical purchase patterns, such as goingback and forth between similar products, or getting stuck in the checkout process? Once the behavior pattern reaches a tippingpoint (measured by statistical models as ―propensity for interaction‖) then a chat pops up on the site asking if he/she needs anyassistance.So by this time, we have a highly qualified visitor, for a specific product category, who wants to buy, needs assistance and youhave proactively offered assistance to help and close the sale. Perfect, this is a definite sale, right? Not yet, especially if youhave one of your average salespersons who is better at selling home appliances trying to assist a camera buyer. The next modeldeals with positioning the right sales person for the right prospect. And how is this done? It is accomplished by analyzing thepast sales record of the sales agent on various customer profiles and product categories.But the power of data mining does not end here. What are the right products the sales agent should up-sell and/or cross-sell tothis customer. Should the agent offer a battery back up that is 10% of the cost of the camera?Finally, it is time to chat. Now the goal is simple—to translate the art of salesmanship into a science. Once that Nordstromsaleswoman approaches a prospect, she has to use her experience to make dozens of instantaneous judgments, based on anynumber of visual and linguistic cues: Is the customer detail-oriented, or does he prefer a softer touch? Am I pushing too hard,and is he beginning to resist? The customer appears to be losing interest – is now the time to begin offering discounts? The chatformat, of course, does not allow for all the nuances any decent salesperson picks up in a face-to-face conversation.It does, however allow for the careful analysis of thousands of chat transcripts, using such technologies as text and datamining, to understand the techniques customer service representatives use to close the sale. Text mining reveals how best totalk to customers. From simple things such as the optimal response time during chat to sophisticated behavioral responses.For example, research in neurolinguistics suggests that people can be classified as audio, visual, or kinesthetic, depending onhow they perceive the world and the language that is most effective in reaching them. The audio customer relies on phrasessuch as ―I hear….,‖ ―sounds great…,‖ and ―listen to this….‖. They respond to hearing data on a product—for example,―laptop x has a 50% larger memory than laptop y‖ Visual customers want to know what the product looks like—I see whatyou mean, how would it look next to my green chair. And kinesthetic customers depend on feeling—I love your Website, I hatethat color, how do other customers like that product? Agents who know how to respond to such cues can significantly boosttheir sales success.Hence the sales agent not only knows who needs to be chatted with for what and when, but is also trained to interact based onthe neural modalities of the visitor. The result: intelligent dialogs, increased sales conversion and improved customer buyingexperience.
  7. 7. ConclusionTo sum up, selling on the web has evolved to the next stage where it is no longer enough to have a nice web store, drive traffic,optimize a site and expect sales to zoom up. Consumers demand a quality of online buying experience that matches or evenexceeds their experience alike in a Brick and Mortar store. And companies trying to achieve more of their sales revenues fromvirtual stores need to provide comparable experiences across their channels. Fortunately, the web is so rich in data that with theright channel strategy and by applying the right science in understanding consumer behavior (over the web), it is indeed possibleto provide the consumer with such an enhanced experience, thus converting the science of sales into predicting the sale!Confidential InformationInformation contained in this document is confidential and proprietary to [24]7 Pvt. Ltd and should not be disclosed to anyone,other than the recipients of this document.No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any means, electronic,mechanical, photocopying, recording, or otherwise, without express written permission from [24]7 Pvt. Ltd[24]7 logo is a trademark of [24]7-Inc, Inc. headquartered at 910 E. Hamilton Avenue, Suite 240 Campbell, CA 95008-0610,USA.About [24][24]7-Inc is a predictive interactions solutions provider that guarantees measurable business results across the customerlifecycle. With its patented―predictive interactions‖ SaaS platform coupled with ―24/7 Outperformance‖ framework, [24]7-Inc promises to improve salesby 25% or more, improve customer experience by 10% or more and reduce contact center costs by 20% or more for its clients.Today, [24]7-Inc is the no. 1 partner in contact center operations for 90% of its clients.(For more information visit: