Tag Archives: 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 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..