Tag Archives: Indian Institute of Technology, Bombay

About 1065 days ago Internet and Online Business

Using Predictive Analytics on Web Data

Analytics around Web data have been a standard part of a marketer’s tool kit for years now. Free tools like Google Analytics have made it easy to track basic website metrics. At the same time, the market for paid Web analytics tools is growing rapidly, and is expected to generate close to a billion dollars in revenue by 2014. Analytics has been a powerful tool even for companies not on the Web. In a Harvard Business Review publication, “Competing on Analytics,” Thomas Davenport has examined how companies gain an edge using analytics. His research, which looks at companies like Marriott, Harrah’s, and Capital One among others, highlights the role of analytics in functions ranging from supply chain and R&D to customer selection, loyalty, and service.  The key to the success for many of these companies — and what companies of all sizes can learn from — has been to not only look at metrics retroactively to analyze what happened, but also to develop models to predict optimal offerings for the future.  In Davenport’s words Marriott  “has developed systems to optimize offerings to frequent customers and assess the likelihood of those customers’ defecting to competitors,” and the UPS Customer Intelligence Group “is able to accurately predict customer defections by examining usage patterns and complaints. When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts.” This kind of analysis, known as “predictive analytics,” is still not commonly employed at Web companies, or commonly available in Web analytics tools. Most of the common analyses and tests done by Web companies treat are centered on the notion of “visitors” to their website (transactional, one time relationship with consumers, typically driven by traffic coming from search engines) rather than “users” of their service (longer term relationship, typically involves creating a user account with the Web service).  For lack of better terminology, I’ll refer to these two distinct notions as “visit-centric” and “user-centric” models of the world. In a visit-centric model of the world, common metrics include “time spent,” “click through rates” and “conversions”.  Free tools like Google Analytics are widely used for these analytics.  In a user-centric view, the metrics tend to be somewhat different. In his post “start-up metrics for pirates,” blogger Dave McClure talks about the five steps in the customer lifecycle of a user-centric model: Acquisition, Activation, Retention, Referral, and Revenue (AARRR). Others have talked about using methods like cohort analysis to measure whether these metrics are improving for progressive cohorts of users.  Not only are the metrics different, the methods for optimization that are applied in the visit-centric world don’t suffice in the user-centric world. In the former, you could run A/B tests to optimize “landing pages” and improve metrics on click-through rates and conversions per visit. Google Website Optimizer is a free tool for performing this optimization. In a user-centric world, a/b tests continue to be important. However, an important range of questions cannot be answered well, or optimized using a/b tests.  For example, it’s hard to associate metrics for users’ repeat visits with the immediate impact of changes on any particular page. Some other examples of problems that are difficult to answer or solve using visit-centric analysis include: How often should you send marketing communication to your users, and who are the customers who are likely to respond positively to this communication? What actions, over the long run, help reduce user churn and increase repeat usage or increased revenue per user? If a/b tests aren’t going to be sufficient, what other analytics should companies build? And if revenues are going to materialize over a period of time, how can companies make sure they don’t wait for months to understand the effectiveness of marketing dollars spent today? That’s where predictive analytics comes in. Historical customer data, which includes behavioral, transactional and demographic data, is mined to develop a model that predicts future behavior. Analytics companies like SPSS and Prediction Impact have talked about how to use predictive analytics for developing actionable predictions for each customer and decision optimization. These methods clearly need rich data about customers, which can form the basis for modeling. The good news is that it’s possible for user-centric Web companies to have rich and high-integrity data about signed-in users who have created rich profiles. This is not true for visit-centric companies where predictive analytics runs into a lot of challenges, as noted in this post (note that all the challenges mentioned by the author implicitly assume a visit-centric model — e.g. the author talks anonymity, or the inability to tie customer data to customer attributes).  It’s interesting to note that other industries that have a similar view of the customer lifecycle as the AARRR model have used predictive analytics to good effect. For instance, in the travel and hospitality industry, predictive analytics techniques have been used for acquiring customers in a cost-effective manner, for fine-tuning marketing offers that increase repeat-usage, and for increasing revenue through effective cross selling and better yield management. In the mobile industry, similar techniques have been used to reduce churn (i.e., increase retention) and improve profitability.  Savvy Web companies have also started using these techniques. For instance, Facebook has used R in predictive analytics to answer questions like “Which data points predict whether a user will stay? And if they stay, which data points predict how active they’ll be after three months?” The gaming company Zynga split its analytics team into two to become more proactive about analytics; while one team does the conventional reporting, the other “tests hypotheses and creates models using statistical and analytical methods.”  Looking at their success, it’s clear that many others will follow suit. If your Web service and your business model are built for repeat usage, you are probably already measuring metrics across the entire customer lifecycle from acquisition to repeat usage and revenue. By using predictive analytics, you can lower your cost of acquiring users, ensure sticky customers, and increase your revenue, just like Facebook, Marriott, and UPS. Vijay Chittoor is a co-founder of Mertado Social Deals. He was previously director of product management at Kosmix. A former McKinsey consultant, Chittoor is a graduate of Harvard Business School and the Indian Institute of Technology, Bombay.  He shares his thoughts on technology at his blog.  

About 1065 days ago Setting Up a Website

Using Predictive Analytics on Web Data

Analytics around Web data have been a standard part of a marketer’s tool kit for years now. Free tools like Google Analytics have made it easy to track basic website metrics. At the same time, the market for paid Web analytics tools is growing rapidly, and is expected to generate close to a billion dollars in revenue by 2014. Analytics has been a powerful tool even for companies not on the Web. In a Harvard Business Review publication, “Competing on Analytics,” Thomas Davenport has examined how companies gain an edge using analytics. His research, which looks at companies like Marriott, Harrah’s, and Capital One among others, highlights the role of analytics in functions ranging from supply chain and R&D to customer selection, loyalty, and service.  The key to the success for many of these companies — and what companies of all sizes can learn from — has been to not only look at metrics retroactively to analyze what happened, but also to develop models to predict optimal offerings for the future.  In Davenport’s words Marriott  “has developed systems to optimize offerings to frequent customers and assess the likelihood of those customers’ defecting to competitors,” and the UPS Customer Intelligence Group “is able to accurately predict customer defections by examining usage patterns and complaints. When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts.” This kind of analysis, known as “predictive analytics,” is still not commonly employed at Web companies, or commonly available in Web analytics tools. Most of the common analyses and tests done by Web companies treat are centered on the notion of “visitors” to their website (transactional, one time relationship with consumers, typically driven by traffic coming from search engines) rather than “users” of their service (longer term relationship, typically involves creating a user account with the Web service).  For lack of better terminology, I’ll refer to these two distinct notions as “visit-centric” and “user-centric” models of the world. In a visit-centric model of the world, common metrics include “time spent,” “click through rates” and “conversions”.  Free tools like Google Analytics are widely used for these analytics.  In a user-centric view, the metrics tend to be somewhat different. In his post “start-up metrics for pirates,” blogger Dave McClure talks about the five steps in the customer lifecycle of a user-centric model: Acquisition, Activation, Retention, Referral, and Revenue (AARRR). Others have talked about using methods like cohort analysis to measure whether these metrics are improving for progressive cohorts of users.  Not only are the metrics different, the methods for optimization that are applied in the visit-centric world don’t suffice in the user-centric world. In the former, you could run A/B tests to optimize “landing pages” and improve metrics on click-through rates and conversions per visit. Google Website Optimizer is a free tool for performing this optimization. In a user-centric world, a/b tests continue to be important. However, an important range of questions cannot be answered well, or optimized using a/b tests.  For example, it’s hard to associate metrics for users’ repeat visits with the immediate impact of changes on any particular page. Some other examples of problems that are difficult to answer or solve using visit-centric analysis include: How often should you send marketing communication to your users, and who are the customers who are likely to respond positively to this communication? What actions, over the long run, help reduce user churn and increase repeat usage or increased revenue per user? If a/b tests aren’t going to be sufficient, what other analytics should companies build? And if revenues are going to materialize over a period of time, how can companies make sure they don’t wait for months to understand the effectiveness of marketing dollars spent today? That’s where predictive analytics comes in. Historical customer data, which includes behavioral, transactional and demographic data, is mined to develop a model that predicts future behavior. Analytics companies like SPSS and Prediction Impact have talked about how to use predictive analytics for developing actionable predictions for each customer and decision optimization. These methods clearly need rich data about customers, which can form the basis for modeling. The good news is that it’s possible for user-centric Web companies to have rich and high-integrity data about signed-in users who have created rich profiles. This is not true for visit-centric companies where predictive analytics runs into a lot of challenges, as noted in this post (note that all the challenges mentioned by the author implicitly assume a visit-centric model — e.g. the author talks anonymity, or the inability to tie customer data to customer attributes).  It’s interesting to note that other industries that have a similar view of the customer lifecycle as the AARRR model have used predictive analytics to good effect. For instance, in the travel and hospitality industry, predictive analytics techniques have been used for acquiring customers in a cost-effective manner, for fine-tuning marketing offers that increase repeat-usage, and for increasing revenue through effective cross selling and better yield management. In the mobile industry, similar techniques have been used to reduce churn (i.e., increase retention) and improve profitability.  Savvy Web companies have also started using these techniques. For instance, Facebook has used R in predictive analytics to answer questions like “Which data points predict whether a user will stay? And if they stay, which data points predict how active they’ll be after three months?” The gaming company Zynga split its analytics team into two to become more proactive about analytics; while one team does the conventional reporting, the other “tests hypotheses and creates models using statistical and analytical methods.”  Looking at their success, it’s clear that many others will follow suit. If your Web service and your business model are built for repeat usage, you are probably already measuring metrics across the entire customer lifecycle from acquisition to repeat usage and revenue. By using predictive analytics, you can lower your cost of acquiring users, ensure sticky customers, and increase your revenue, just like Facebook, Marriott, and UPS. Vijay Chittoor is a co-founder of Mertado Social Deals. He was previously director of product management at Kosmix. A former McKinsey consultant, Chittoor is a graduate of Harvard Business School and the Indian Institute of Technology, Bombay.  He shares his thoughts on technology at his blog.  

About 1065 days ago Web Analytics

Using Predictive Analytics on Web Data

Analytics around Web data have been a standard part of a marketer’s tool kit for years now. Free tools like Google Analytics have made it easy to track basic website metrics. At the same time, the market for paid Web analytics tools is growing rapidly, and is expected to generate close to a billion dollars in revenue by 2014. Analytics has been a powerful tool even for companies not on the Web. In a Harvard Business Review publication, “Competing on Analytics,” Thomas Davenport has examined how companies gain an edge using analytics. His research, which looks at companies like Marriott, Harrah’s, and Capital One among others, highlights the role of analytics in functions ranging from supply chain and R&D to customer selection, loyalty, and service.  The key to the success for many of these companies — and what companies of all sizes can learn from — has been to not only look at metrics retroactively to analyze what happened, but also to develop models to predict optimal offerings for the future.  In Davenport’s words Marriott  “has developed systems to optimize offerings to frequent customers and assess the likelihood of those customers’ defecting to competitors,” and the UPS Customer Intelligence Group “is able to accurately predict customer defections by examining usage patterns and complaints. When the data point to a potential defector, a salesperson contacts that customer to review and resolve the problem, dramatically reducing the loss of accounts.” This kind of analysis, known as “predictive analytics,” is still not commonly employed at Web companies, or commonly available in Web analytics tools. Most of the common analyses and tests done by Web companies treat are centered on the notion of “visitors” to their website (transactional, one time relationship with consumers, typically driven by traffic coming from search engines) rather than “users” of their service (longer term relationship, typically involves creating a user account with the Web service).  For lack of better terminology, I’ll refer to these two distinct notions as “visit-centric” and “user-centric” models of the world. In a visit-centric model of the world, common metrics include “time spent,” “click through rates” and “conversions”.  Free tools like Google Analytics are widely used for these analytics.  In a user-centric view, the metrics tend to be somewhat different. In his post “start-up metrics for pirates,” blogger Dave McClure talks about the five steps in the customer lifecycle of a user-centric model: Acquisition, Activation, Retention, Referral, and Revenue (AARRR). Others have talked about using methods like cohort analysis to measure whether these metrics are improving for progressive cohorts of users.  Not only are the metrics different, the methods for optimization that are applied in the visit-centric world don’t suffice in the user-centric world. In the former, you could run A/B tests to optimize “landing pages” and improve metrics on click-through rates and conversions per visit. Google Website Optimizer is a free tool for performing this optimization. In a user-centric world, a/b tests continue to be important. However, an important range of questions cannot be answered well, or optimized using a/b tests.  For example, it’s hard to associate metrics for users’ repeat visits with the immediate impact of changes on any particular page. Some other examples of problems that are difficult to answer or solve using visit-centric analysis include: How often should you send marketing communication to your users, and who are the customers who are likely to respond positively to this communication? What actions, over the long run, help reduce user churn and increase repeat usage or increased revenue per user? If a/b tests aren’t going to be sufficient, what other analytics should companies build? And if revenues are going to materialize over a period of time, how can companies make sure they don’t wait for months to understand the effectiveness of marketing dollars spent today? That’s where predictive analytics comes in. Historical customer data, which includes behavioral, transactional and demographic data, is mined to develop a model that predicts future behavior. Analytics companies like SPSS and Prediction Impact have talked about how to use predictive analytics for developing actionable predictions for each customer and decision optimization. These methods clearly need rich data about customers, which can form the basis for modeling. The good news is that it’s possible for user-centric Web companies to have rich and high-integrity data about signed-in users who have created rich profiles. This is not true for visit-centric companies where predictive analytics runs into a lot of challenges, as noted in this post (note that all the challenges mentioned by the author implicitly assume a visit-centric model — e.g. the author talks anonymity, or the inability to tie customer data to customer attributes).  It’s interesting to note that other industries that have a similar view of the customer lifecycle as the AARRR model have used predictive analytics to good effect. For instance, in the travel and hospitality industry, predictive analytics techniques have been used for acquiring customers in a cost-effective manner, for fine-tuning marketing offers that increase repeat-usage, and for increasing revenue through effective cross selling and better yield management. In the mobile industry, similar techniques have been used to reduce churn (i.e., increase retention) and improve profitability.  Savvy Web companies have also started using these techniques. For instance, Facebook has used R in predictive analytics to answer questions like “Which data points predict whether a user will stay? And if they stay, which data points predict how active they’ll be after three months?” The gaming company Zynga split its analytics team into two to become more proactive about analytics; while one team does the conventional reporting, the other “tests hypotheses and creates models using statistical and analytical methods.”  Looking at their success, it’s clear that many others will follow suit. If your Web service and your business model are built for repeat usage, you are probably already measuring metrics across the entire customer lifecycle from acquisition to repeat usage and revenue. By using predictive analytics, you can lower your cost of acquiring users, ensure sticky customers, and increase your revenue, just like Facebook, Marriott, and UPS. Vijay Chittoor is a co-founder of Mertado Social Deals. He was previously director of product management at Kosmix. A former McKinsey consultant, Chittoor is a graduate of Harvard Business School and the Indian Institute of Technology, Bombay.  He shares his thoughts on technology at his blog.  

About 1170 days ago Search Engine Optimization

Online Retail: Getting the Right Product in Front of Your Customers

After surviving the bust at the end of the ’90s, online retail has done quite well in the first decade of the new millennium. From being just 0.6 percent of all retail in early 2000,online commerce has now grown to account for nearly 4 percent of all retail in the United States. In this article, we review some technology trends that are likely to further accelerate this trend. Our focus will be around looking at technologies that help consumers find the right product easily as well as help retailers put the right merchandise in front of customers. While most of these technologies trends apply to a wide spectrum of applications, their impact on online commerce deserves a special mention. Many of these technologies have been around for a while, but it’s only now that they are gaining wide adoption. Semantic Web and structured data The semantic Web and structured data will make product search dramatically better. Last year, Google made two subtle announcements that have the potential of completely transforming how users search for products.  The first affected Google’s organic (or unpaid) results, when the company launched what it called rich snippets, using microformats and Resource Description Frameworka (RDFa) standards. Google was late to the game, as Yahoo had already announced similar support a year ago. The second change applied to Adwords, Google’s paid search program, when the search engine started listing richer product listing ads from retail advertisers, displaying an image of the product, the price, and many other attributes. These ads will be priced on a “cost per action” basis, as opposed to the standard “cost per click” model that’s offered for all other Adwords advertisers. It is interesting that Google’s rich snippets were first rolled out only for 2 applications, one of which is closely related to online commerce: • Providing a summary of reviews, when searching for products or services.• Distinguishing between people with similar names, when searching for a person.  Similarly, Google’s CPA program was also rolled out only for product searches. Search-engines are already a very important tool for online shoppers, and a Nielsen study had found that more than one third of shoppers use search engines. Richer snippets and richer ads will make search engines even more important to shoppers, and consequently to retailers. According to Yahoo, “enhanced search results” drive 15 percent more clickthroughs for many websites.  With results like that, it’s no wonder that adoption is picking up among websites. In the same blog post, Yahoo reported an increase of more than 400% in the RDFs structured data driven by Search Monkey. Best Buy recently released their entire product catalog in RDFa, perhaps becoming the first Fortune-500 company to join this trend, and has reportedly seen strong results. Recommendations and personalization engines Recommendations and personalization engines are now available as plug and play components. Outside of search, one of the most important ways for shoppers to discover products has been through recommendation engines. Personalization and recommendation engines have been around for a while and have been a strong driver of sales. For example, Amazon’s recommendation system was said to account for up to 35 percent of sales in 2006. Recently, the adoption of recommendation engines has increased substantially because of the emergence of third party services that are easy to plug into your ecommerce store. For instance Urban Outfitters has seen a triple digit percentage increase in sale by using a solution offered by Baynote. Other companies like Mybuys and Certona also deliver hosted solutions for personalization. Creating application programming interfaces (APIs) Creating APIs that distribute products across the Web is easier. Over the last few years, the Web is increasingly becoming a collection of Web services that can be accessed through APIs. Retailers like Ebay and Amazon have for a long time used APIs to expose their data to the external world, primarily to affiliates and partners who then sell these products at other places on the Web. Now many traditional retailers are jumping into the fray, utilizing services that make it easier to create and manage APIs.  For instance, Best Buy recently launched Best Buy Remix powered by API management infrastructure from Mashery. In addition to these, in one of my previous columns I had written about how the real time Web is becoming an increasingly important tool, and how companies like Dell are using it to increase their online retail sales. The increasing adoption of these technologies sets up online commerce for an exciting new decade, for shoppers as well as for retailers. Vijay Chittoor is a co-founder of Six Times Seven. He was previously director of product management at Kosmix. A former McKinsey consultant, Chittoor is a graduate of Harvard Business School and the Indian Institute of Technology, Bombay.  He shares his thoughts on technology at his blog.    

About 1328 days ago E-Commerce

The Real-Time Web and You

One of the biggest technology trends in 2009 has been the emergence of the “Real-Time Web.” The real-time Web is a made up of technologies and practices that can inform users as soon as information is published, instead of requiring users to check for updates. The real-time Web discards the traditional notion of the more static “webpages,” and instead adopts the notion of dynamic “streams” of information. The real-time Web is also very conversational because it makes it possible to get instant responses across very large networks of people. Action in the real-time Web started with companies like Twitter and Friendfeed, which built their own infrastructure for large scale delivery of real-time messages. By providing Web service application programming interfaces (APIs), these companies enabled many other developers to create applications based on the real-time Web. However, Anil Dash, a prominent blogger, points out that real time services need not be built on the back of Twitter and Facebook anymore. Due to emerging technologies, the pieces are falling together for creating a free, open and decentralized “pushbutton platform,” which makes it easy for websites to add real-time messaging services. With these developments, we can expect many more websites to jump onto the real-time bandwagon. Growing importance to business The real-time Web is becoming increasingly important to businesses in multiple ways. Firstly, as many webmasters and Web analytics companies have pointed out, the real-time Web is starting to rival search engines like Google as a source of website traffic. For example, Mark Cuban talked a few months ago about how his blog receives more visits from Twitter and Facebook than from Google. Secondly, the real-time Web opens up communication opportunities that the traditional Web could not have provided. For instance, if an airline wants to sell off its last minute tickets, the real-time Web provides a great outlet for advertising this very time-sensitive deal.  Thirdly, by making information instantaneously accessible, the real-time Web can create, or erase, instances of information arbitrage. As an example, take a look at Skygrid, a service that provides high quality financial news in real time, giving its users an edge, but at the same time leveling the playing field between professional investors and amateurs in terms of the speed of access to reliable information. Finally, because the real-time Web is very conversational, it becomes a repository of people’s sentiment, and mining this sentiment can be very useful to marketers and others. Taking advantage of real-time Web Beyond creating an account on Twitter, how can you take advantage of the real-time Web?  Here are some thoughts to get you started: Engage with the real-time Web with tailored offers and content. Several companies are seeing success with time-sensitive programs that could not have been conceived without the real-time Web. Jet Blue’s “cheeps” and United Airlines’ twares are exclusive Twitter promotions for last minute fare deals. Another company that has encountered great success with offering exclusive deals on Twitter is Dell. A Dell blog post from June mentioned that Dell had surpassed $2 million in Twitter sales fro Dell Outlet, which sells refurbished items, scratch and dent items, and previously ordered new laptops. The real-time Web also acts as a place where people express their intent to shop (e.g. someone may tweet “thinking of buying an ipod touch.”) Selectively targeting such users, without spamming them, might also be a great way to help your customers make real time buying decisions. A service like Twitterhawk can be used to automate this kind of marketing. Make use of real-time Web tools for business intelligence. The real-time Web is a great source of knowledge and sentiment about your customers, your competitors and your industry. You can use services like Firstrain to research the real Web for the news that matters to you. You could also use Twitter’s search functionality in simple ways to keep track of some of this information, or go to one of the many real time search engines. A recent article in mashable talks about the many tools that help analyze Twitter content. Join in the conversation about your company. In one of my previous articles, I had talked about how companies like Comcast are using Twitter to understand their customers’ concerns and address them. The conversational nature of real time web can be very powerful in building relationships with your customers. Create the infrastructure that allows your company to respond in real time. Real-time enterprise data integration has been around for a long time. However, with the emergence of the real-time Web and the opportunities it creates, it is becoming increasingly critical for companies to be able to access all their internal data in real time. In other words, “real-time data integration is no longer a luxury.” Vijay Chittoor is the director of product management at Kosmix, an exploration engine that offers a 360 degree view of any topic on the Web.  A former McKinsey consultant, Chittoor is a graduate of Harvard Business School and the Indian Institute of Technology, Bombay.  He shares his thoughts on technology at his blog.

About 1358 days ago E-Commerce

Social Media: More than Creating Connections

One of the biggest challenges for entrepreneurs is to scale up their business, and to manage the growth by hiring more people in every function. But what if you could achieve growth by just letting your community of users do most of the work? Several creative companies have used social media tools to get their customers involved in core aspects of their business, all the way from marketing to product design, product testing, and customer service.  Here are some great examples of organizations that are using social media to drive sales and efficiencies, while still connecting with customers: Effective marketing using social media By now, most people know that social media provides many tools for creating brand awareness, as well as for generating sales leads. Fiskars, a Finland-based manufacturer of scissors realized that scissors are very popular among scrapbookers, and set out to reach this community. After identifying four Fiskars users who were extremely passionate about the brand, the company set them up with a website and a blog, and made them consumer evangelists. The “Fiskateers” program has since then grown to more than 5,000 Fiskateers across 70 countries, each actively blogging and evangelizing the brand. Having so many “marketers” on its payroll would certainly have been unsustainable for the company, but by leveraging the power of its community, and using online tools like blogging, Fiskars has created a strong brand identity among its target audience. Blogging isn’t for you?  Try Twitter to connect with your audience. Naked Pizza of New Orleans, which prides itself on making the “world’s healthiest pizza,” has latched onto Twitter as a means of promoting its fresh ingredients and offering promotional deals. Twitter has been so effective that they’re now using billboards to drive more people to the Twitter account. More and more restaurants are finding Twitter to be an effective way to boost their sales. Finally, no discussion of social media marketing is complete without talking about viral videos. Blendtec, a division of the Utah-based K-TEC, manufactures high powered, durable, commercial blenders. In 2006, Marketing Director George Wright had the unenviable task of creating a brand campaign with a budget of $50. When Wright saw CEO Tom Dickson and some engineers testing the blenders with heavy duty chunks of wood, he hit upon an idea and used the $50 to buy the domain http://www.willitblend.com. Since then, the “Will it Blend” series of videos has seen more than 80 million views on YouTube and increased Blendtec’s sales by more than 700 percent. Involve customers in product design How can you add value and create customer loyalty if you don’t even control your product design process? Threadless, an online T-shirt store operated by the Chicago-based skinnyCorp, has found the secret to that, selling more than a million T-shirts a year, none of which were designed by the staff. All the designs are submitted and evaluated by the community of users on its website. Hundreds of artists submit their designs, and users vote on them. Every week, the best designs are selected for printing, and the winning designers get $2,000 in cash, $500 in gift certificates, and another $500 for every reprint. According to some reports, the company generates more than $30 million in revenue and $10 million in profits. Muji, a Japanese retailer, has latched onto a similar concept through its website muji.net, where it invites submissions for innovative furniture designs. Muji, which means “without brand,” has a community of half a million people who submit and evaluate designs.  Shortlisted designs are then sent to professional designers, who polish them before sending them off for production. Web companies often launch products in a “beta” state and invite selected users to test the product. Joffrey’s Coffee & Tea Company took this idea and applied it to coffee. It invited bloggers to beta-test its coffee by sending them free samples. More than 1,500 bloggers participated, and generated enormous buzz for Joffrey’s on the Web. Based on feedback from these bloggers, Joffrey’s launched Coffee 2.0 with many “bug fixes and improvements.” Even the name Coffee 2.0 came from one of the beta testing bloggers. Not only did Joffrey’s use social media effectively to do product testing and improvements, but it also created enormous buzz around the product.  Get customers to help with customer support Customer support is one of the most difficult things to scale as the business grows. Consumers are increasingly logging on to social media sites to express their frustration with poor service. For example, the consumer complaint video “United Breaks Guitars ” has had close to 5 million views on YouTube. Innovative companies are using social media in a couple of different ways to provide customer support. eBay has outsourced almost its entire customer support function to its users from its very beginnings. In his book The Perfect Store, Adam Cohen writes about eBay in 1996: “Omidyar did not have time to explain to each individual user how to write a listing in HTML, or to give advice on bidding strategy.” The solution was to launch a Bulletin Board where users could “gather, share information and ask for help.”  Later, eBay ended up hiring some of the people who were the most active and helpful on the forums to work for it, answering customer emails and providing additional support. A different model of support treats social media as another channel for the in-house customer support team. Frank Eliason, Comcast director of digital care, has a following of more than 25,000 people on his “Comcast Cares” Twitter account, where he answers user questions. The real-time nature of Twitter and its search functionality allow Eliason to even reach out to Comcast users who haven’t actively sought help. By applying a bit of imagination to social media tools like blogs, Twitter, Facebook, and YouTube, these forward-thinking companies have grown their businesses by leaps and bounds. Take cues from these examples of the power of community, and you’ll avoid some of the growth pains that arise from controlling and managing all of your business functions in-house. Vijay Chittoor is the director of product management at Kosmix, an exploration engine that offers a 360 degree view of any topic on the Web.  A former McKinsey consultant, Vijay is a graduate of Harvard Business School and the Indian Institute of Technology, Bombay.  He shares his thoughts on technology at his blog.

About 1450 days ago E-Commerce

The Long Tail and the Black Swans

In his Wired article in 2004, Chris Anderson pioneered the use of the phrase “The Long Tail” as a proper noun. He observed that the reduced costs of distribution over the Internet are making it easier for businesses to serve consumer demand for niche items, and that collectively, the niches added up to quite a significant market for companies like Rhapsody, Netflix, or Amazon. This collection of all niches, “the long tail,” he argued, generates substantial value for a variety of businesses. The long tail effect liberates consumers from having to buy what everyone else is buying, and enables businesses to serve specialized needs, rather than just serving the lowest common denominator. This idea flies against the traditional distribution networks, which only stocked those items that are most likely to sell a lot of units. Traditional supply chains needed to play this “blockbuster” strategy because their fixed costs of carrying any item and making it accessible to customers were very high. And the only products that could justify that cost were the ones that were likely to sell a lot of units. For example, in the movie industry, this “supply chain” consisted of theaters, and DVD sales through large retailers, both of which have high fixed costs and limited shelf space. The Internet reduces many of the fixed costs, removes the restriction on shelf space, and makes every item easily accessible through searchable interfaces. No wonder, then, that Internet companies have chosen to carry larger inventories than their offline counterparts, enabling them to cater to niche interests. Backlash against the long tail Despite the widespread use of this idea, there has been recent backlash against it. Criticism of the idea recently started with an analysis by Anita Elberse, and was picked up by many others in the media. The critique centers around the idea that long tail companies make most of their profits from a small percentage of items sold; the classic 80-20 rule still applies. Based on this, the critics recommend that entrepreneurs and managers should continue to focus on the blockbuster strategy. This analysis, however, overlooks the fact that it’s impossible to predict what will be a hit. Consider the list of top 100 rentals on Netflix a good indicator of consumer demand. At least three of these movies are independent films, each with a budget of $6-8 million. Juno, with a budget of $6.5 million, went on to gross 35 times that in earnings, and Little Miss Sunshine, which cost $7.7 million to make, earned more than $100 million in revenues. The Last King of Scotland, with a budget of $8 million, completes the trio. Compare these 10x returns with a mainstream movie like The Dark Knight, which made less than six times its budget of $185 million — and that’s among the more successful hits. Before they made it big, movies like Juno, Little Miss Sunshine and The Last King of Scotland seemed like films that would end up in the long tail. And the entertainment executive who focused purely on the blockbuster strategy would have missed out on the financial returns of these movies. When the critics of the long tail theory account for revenues and profits, they look at data compiled after the masses have picked the winners and the losers. Some of the “winners,” the items that made it to the top 20 percent of revenues, might have come from the long tail of investments (e.g., the low budget movies). So when the critics recommend not investing in the long tail, they’re confusing the tail of revenues with the tail of investments. The real value of the long tail is that it helps pick the winners like Juno. You might think the cost of picking winners in this way is prohibitive, but that’s no longer true in the new digital world. In the traditional supply chain, an average long tail product would not make any money at all, because it wouldn’t be stocked anywhere. However, in the digital world, even the product that starts life in the long tail and stays there makes some money because it reaches a niche audience. And, who knows, there’s always the chance that some of these long tail products could become popular someday, and make tons of money. The author Nissim Taleb deals with phenomena like these where the outlier (like Juno) has a significant effect on the average performance. He calls this effect the “Black Swan.” In the blockbuster strategy, which requires heavy investments, the returns on all movies would perhaps cluster around one, and an outlier like The Dark Knight returns six times its investment. The exposure to the outlier, or the Black Swan, is much greater in the tail. As we’ve seen, an outlier in the tail can have a much more significant return. These effects only get amplified with digital distribution, where shelf space is unlimited and the investment in distribution costs is negligible. As The Arctic Monkeys, Clap Your Hands Say Yeah, and many others have shown, it is possible for small indie bands to rise to stardom by the buzz they create on the Internet. While the long tail is exposed to the positive Black Swan (unexpectedly good returns), the blockbuster strategy is only exposed to a negative Black Swan, because the strategy requires heavy investments.  A prime example is the movie Waterworld, made with a budget of $175 million, which grossed only $88 million worldwide. Does this mean that the blockbuster strategy is dead? Not at all. After all, the Netflix 100 has a lot of room for high budget movies. Amazon and Netflix understand this very well, and put as much focus into the likely hit as they do into carrying all the indies. Their infinite shelf space allows them to stock the Harry Potter book or The Dark Knight DVD as well as lesser known title, and their search and review mechanisms help the community vote up an occasional obscure title to become a hit. The lesson?  Success is about finding the right balance of long tail strategies and more traditional approaches. Vijay Chittoor is the director of product management at Kosmix, an exploration engine that offers a 360 degree view of any topic on the Web.  A former McKinsey consultant, Vijay is a graduate of Harvard Business School and the Indian Institute of Technology, Bombay.  He shares his thoughts on technology at his blog..

About 1481 days ago Internet and Online Business

Lessons from Web 2.0: Fast Track Innovation Process

Over the last few years, Web 2.0 has evolved to become not only a design paradigm, but also a development methodology that has become synonymous with innovation. Web 2.0 companies are able to innovate rapidly for four simple reasons: Low cost of innovation. You don’t need a bucketful of cash to prototype a Web 2.0 product or launch a Web 2.0 company.  Costs of computing and storage have fallen dramatically, and services like cloud computing virtually eliminate the need for heavy IT infrastructure, reducing fixed costs. Case in point: Y-Combinator, a seed fund that invests an average of just $15,000 per venture, has helped many young companies get their start.  Y-Combinator success stories include Reddit (acquired by Conde Nast) and Zenter (acquired by Google). Rapid bite-sized improvements instead of massive launches. The software that powers the Web services can be updated constantly, because it’s delivered over the Web. As a result, Web 2.0 companies often upgrade their services every day or every week, launching new features and fixing bugs. Ease of “measuring” user interactions with the service. Web services have the advantage that user interaction with the site or the service can be measured in a very precise manner. It’s easy to record the time spent by an average user on the site, the number of page views they saw, the trail of clicks and pages that helped them complete their task, and a lot of other such data. Because everything can be measured, Web companies have developed a philosophy of testing and measuring a lot more and guessing a lot less. Before any feature is launched to the entire audience of a site, it’s often tested on a small portion of the user base. An open innovation model. Web 2.0 companies have realized that some of their most innovative ideas might not come from within the company. Using Web-service application programming interfaces (APIs), they have exposed some of their most precious data to outside developers who can build innovative applications. Real-time search, one of the most used applications on Twitter, was developed by a company called Summize, using Twitter’s API. Twitter later acquired Summize. Taken together, all these methods are geared towards a new model of innovation — one that emphasizes rapid experimentation and serendipitous discovery. Since every idea is cheap and quick to try out with real users, and the results are easily measurable, Web 2.0 companies get to road test several ideas without spending excessive amounts of time trying to prioritize between them. Similarly, by allowing outside developers to use the company’s data to create applications without any restrictions, Web 2.0 companies are in effect launching hundreds of experiments simultaneously. This throws the traditional model of product development and innovation on its head. In the old days, companies performed exhaustive (and costly!) analysis to determine which one or two ideas would be most likely to succeed, and then invested accordingly. The Web 2.0 model makes it possible to experiment with a lot of ideas, without investing a lot of upfront cash or forcing assumptions about which idea will deliver the biggest upside. This new model is great for a world in which consumer preferences are difficult to predict and change rapidly. While your business might not have the same natural advantages as a Web 2.0 company, with a little bit of redesign of your processes, you could use elements of the same philosophy to fast-track your innovation. Here are some tips to get you started: Lower your cost of new product development. Be on the lookout for opportunities to reduce your costs of new product development. Using technology for knowledge management and outsourcing to low cost countries are among some of the ideas that innovative companies use. For example, in the electronics industry, Original Design Manufacturers (ODM) companies based in low cost countries like China have emerged as choice partners for prototyping and launching new designs. Create experiments that lead to continuous bite-sized improvements.If any aspect of your offering is a service, you can keep innovating by adding small features or by improving the workflow. In order to do that, you need to build a test-bed for trying out lots of experimental ideas. A few years ago, Stefan Thomke, a professor at Harvard Business School, published an insightful study detailing how Bank of America turned its branches into “Service Development Laboratories.” For instance, Thomke talks about an experiment designed to solve the problem that users perceived their wait times to be longer than the actual time. In order to remove the perception, the experiment involved testing user perception when televisions were installed over teller booths and comparing that with a standard branch without televisions. By measuring the improvement in customer satisfaction ratings with the television, the team was able to develop a case for wider rollout to some of the bigger branches of BofA. This is a great example of an experimental setup that leads to constant improvement in the quality of service. Measure everything and create feedback loops. You should aim to find opportunities for measuring user interaction with your product or service directly at the point of interaction, without relying on “marketing surveys.” Harrah’s is a great example of a company that invested in business intelligence solutions around its loyalty program, and made all of its marketing efforts highly data driven. Whenever a customer conducts a transaction using their Harrah’s card, the information is transmitted to a database, and used in a variety of ways to target the customer. The success of marketing campaigns is also measured using this data, and the campaigns are optimized accordingly. Soon after the program was launched a few years ago, its success made Harrah’s the most profitable company in its sector. Open up the innovation process to others and plug in with the ecosystem.  In his book Wikinomics, Don Tapscott talks about how Goldcorp Inc., a struggling Toronto-based gold mine, opened up its sensitive geological data to the public to help the company get accurate estimates of the location of gold in its mines in Red Lake, Ontario. Within weeks, solutions poured in from all kinds of unexpected quarters, and identified more than 110 targets, half of them not previously identified by the company, with 80 percent of the new targets yielding substantial amounts of gold. In another example, Proctor & Gamble has developed a program called “Connect and Develop” with a goal of having 50 percent of its new products come from outside the company’s labs. The program also opens up access to P&G’s innovation assets. On the other hand, if you can’t find good ways of exposing your own data, you could instead think of using the data and APIs exposed by others — for example, Pure Digital, the manufacturers of the Flip Video Cameras, used YouTube’s APIs to make it easy to upload videos directly into YouTube, and in the process out-innovated the competition. It’s clear through all these examples that the new model of innovation is for everyone, and not just Web 2.0 companies. Find applications for these ideas in your business, and use them to change the world for the better. Vijay Chittoor is the director of product panagement at Kosmix, an exploration engine that offers a 360 degree view of any topic on the Web.  A former McKinsey consultant, Vijay is a graduate of Harvard Business School and the Indian Institute of Technology, Bombay.  He shares his thoughts on technology at his blog..