SmartLens Analytics offers companies access to tailored bid optimization algorithms that can rapidly improve their paid search performance. Through the use of cutting edge machine learning techniques, SmartLens Analytics provides customers bidding solution that incorporates insights from vast amounts of past performance and other sources of data. This advanced bidding optimization delivers significantly improved campaign level spend efficiencies as well as providing budgeting flexibility.
SEM or paid search is key driver for online marketing success. The response is directly linked to the keyword and success is quickly measurable.
Google has for a long time squeezing the organic content to lower position on the search result page for desktop. On the mobile device the number of slots are extremely limited so paid search is often given the priority over organic contents. SEM is becoming even more important as marketer cannot rely on SEO as much as in the past.
Many Internet metrics follow the power law distribution. The search query and number of clicks by keyword are just two examples of the power law. That means while the business manages millions of keywords the clicks distribution has a very long tail. Simply put, vast majority of the keywords in SEM campaigns will have very limited number of clicks while only a small fraction of the keywords will get large amount of clicks.
This classic problem of SEM campaigns needs to solve the problem of low probability events (ecommerce transactions, lead gen etc) and insufficient click history associated with the vast majority of keywords in order to provide expected revenue on a statistically significant basis. How to manage the long tail of SEM bidding keywords has been a challenging problem for business of all sizes.
The combination of machine learning and customized bidding approaches has yielded very effective results across multiple industries with proven record of successes doing data driven marketing optimization across broad range of advertising platforms including Google, Bing, TripAdvisor and Trivago.
Our machine learning models continue to be trained from your data and get better.
We mine your marketing data internal/external, develop a bidding model, predict new bids, and traffic to publishers automatically.
You can continue to focus on your campaign strategy, while we keep improving your marketing spend.
The team at SmartLens Analytics has had deep understanding of the internet marketing and bidding challenges and are extremely experienced data scientist, machine learning experts, and SEM business professionals.
With this deep knowledge and experience and eagerness to help SmartLens Analytics team’s mission is to provide the best SEM bidding service available in the industry.
Wenqing Lu is founder and CEO SmartLens Analytics. Wenqing founded SmartLens Analytics to bring cutting edge data science and machine learning capabilities to companies wanting to optimize their paid search investments. SmartLens Analytics offers companies access to tailored bid optimization algorithms that can rapidly improve their paid search performance without the need to build an expensive bidding data science team in-house.
Wenqing is passionate about using predictive modeling techniques to help companies achieve growth. He has deep expertise in paid search bid optimization, web analytics, CRM and direct marketing. Prior to SmartLens Wenqing led the Advanced Analytics team at Orbitz Worldwide, building statistical models to optimize bidding across an $80 million annual online marketing investment. Prior to Orbitz Worldwide, he worked on site optimization in eBay’s global experimentation group. Prior to eBay he worked as a statistician in the financial services industry.
Wenqing holds a Ph.D. in statistics from University of Wisconsin-Madison and a B.S. in Operations Research from Fudan University.
Tom Noda is vice president of engineering at SmartLens Analytics. Tom is responsible for data management, architecture and scalability at the company.
Prior to SmartLens Analytics Tom served as principal software engineer at Expedia where he designed and developed customer insight systems and decision engines that capture millions of Expedia user engagements and serve personalization in real time. Prior to the acquisition of Orbitz Worldwide by Expedia, Tom served as principal software engineer at Orbitz Worldwide. During his 10 years at Orbitz, he served in a variety of technology roles. He led data science teams working on problems relating to the hotel vertical, developing predictive models and architecting large scale streaming and batch data pipelines. He was an early member of the marketing technologies group, adapting early stage Hadoop to the paid SEM problem space in order to facilitate bid management across hundreds of millions of keywords.
Tom holds a B.S and an M.S. in Industrial Engineering, both from University of Wisconsin-Madison.
Bowei Zheng is a data scientist at SmartLens Analytics. Bowei uses a range of modeling techniques to derive insights from data. Bowei contributes to infrastructure improvements efforts in order to enhance bidding capabilities.
Prior to SmartLens Analytics, Bowei worked as a data scientist at Expedia where he employed quantitative modeling, machine learning and natural language processing to improve ranking, recommendations and geolocation. Bowei is passionate about using data science to solve business problems, and building end-to-end systems that stretch from ETL to production.
Bowei holds a Ph.D in Mathematics from University of Chicago and a B.S. in Mathematics from Fudan University.