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The intent in these three cases is different, a traditional keyword based search would have treated all the three results almost the same. Our custom implementation of search engine handled this issue by identifying following attributed in a query
Users will not always type in the right query, including the correct spelling the onus of figuring out what the user meant is huge, let look at some of cases that a typical search engines have to handle:
These issues are generally taken care of with special run-time string transformations that make a query more consistent to indexing scheme.
Ranking is a core component that needs to get right, it needs to be balanced in a sense Business Objective and User Experience Objectives are met. Lets say as part of key partnership Business might want to promote a brand even when the user is looking to purchase competing brand. How do you rank products in that case?
Each domain in which recommendation engine is deployed variety of data and features can be exploited. This proposal talks about different classes of recommendation engines and approaches that we will be taking in implementing them.There are 3 main classes of recommendation engines that are used, which include:
Churn is defined as number of people who have ceased to do business with a Company over a fixed period of time. Businesses which utilizes network effects are hit heavily both in terms of lost revenue and spent marketing efforts.
Sectors like Telecom, Retail, Hospitality and Subscription based services are few sectors where Customer churn is taken seriously. Being able to track Churn and predict it implications of:
Churn Analysis is collection of activities that including collection of data from CRMs to process improvement across Operations and Marketing, which will reduce number of customers leaving for competition. If we're able to identify and predict dis-satisfied customer we can take corrective actions.