Data Shaping Solutions, LLC, offers services in market, business and competitive
intelligence as well as
online marketing strategies. Using state-of-the-art technologies, we design robust and
scalable solutions to provide maximum return as
quickly as possible.
Principal Statistician has
held post-doctoral positions at Cambridge University and University of
North Carolina at Chapel Hill before moving to the private sector in 1996.
This section of our website focuses on solutions offered specifically to
- Spam Elimination
We create decoy e-mail addresses to identify spammers. In addition we will install software to
verify which return e-mail addresses exist. Messages that fail the return address test
are flagged as spam and deleted before you read them. These technologies identify nearly 100% of the
spam with very few false positives. In some situations, another simple solution consists of
filtering out all E-mail that do not contain a pre-specified password in
the subject line.
- Inventory Management
Data Shaping Solutions has developed ad inventory management techniques. In particular:
- we have designed an Ad remnant optimization algorithm for a major internet company
- We have helped NBCi save $1,000,000 per month,
by improving keyword and ad impression inventory forecasting, in collaboration with GE's auditing team
- Quality Assurance - Alarm Module
Have an alarm program
implemented to detect traffic abnormalities based on
various metrics (new users, page views, pages per visit, unique users,
traffic per hour). Our program successfully handles traffic variations
due to weekend fluctuations. It automatically sends an e-mail with red or blue flags when low or high numbers are present.
- Logfile compression
By discarding useless information and then applying a compression algorithm
specifically designed to process logfile data, we were able to increase the compression
factor from 10 to more than 30, resulting in substantial cost savings in data storage,
particularly for websites with large traffic.
- Click Fraud Detection
This service is offered to pay-per-click (PPC) search engines, pay-per-lead programs and PPC advertisers. Previous client: InfoSpace.
- Path Analysis, User Behaviour
We investigate the most common types of visits on your site. For each aggregated directory,
we track incoming and outgoing traffic, to optimize your site map.
You can perform this analysis to place internal links on the most appropriate pages.
We also track entry and exit points to your site, as well as time spent per page to identify bottlenecks. This kind of analysis is underlined
by Markov models of traffic flow and mathematical graph theory.
It reveals the internal traffic chart of your site and allows you to
uncover unusual paths. In addition, we create a keyword directory of your website by gathering kewords from all your URLs. The dictionary is then used
to assign a type of action to each page request, to eventually analyse
user behaviour. We have performed this analysis for Wells Fargo.
- User Retention and Traffic Prediction
Do you attract more new users than you keep old ones?
Autoregressive processes and Markov chains with new, returning and ex-users are
the base models for traffic prediction. Our analysis will also reveal
characteristics of faithful users, identify user segments that are too costly
to acquire, and estimate the average return of a user over her lifetime.
As a result of our analysis, Snap.com was sold to GE.
- User Segmentation
What are the traffic patterns of loyal users? Segmentation is a classical analysis
to detect the main user groups. We also track
geographic data and will help you capture user behaviour,
through cutting edge database technologies to handle large datasets.
As a result of our analysis, NBC Internet did not renew a muti-million dollar deal
- Web Surveys
We help you design or review your web surveys, avoiding the common pitfalls. For example:
sampling pages instead of sampling users, or setting up the survey
on your front door. We specialize in detecting associations or anti-associations
between factors (age, sex, income or zip code, versus user interests or visit patterns),
using dissimilarity metrics such as the Chi-square.