Advanced Analytics

We provide services for descriptive and exploratory analysis of customer and transactional data, to build tactical insights.

We have experience with price, promotion, assortment, and distribution analytics, demand forecasting using time series regression, and competitive analysis and market segmentation using shipment,syndicated, and panel data sources.

We evaluate ROI of marketing programs building custom frameworks that can integrate sales/transactional data, marketing/campaign data, salesforce feedback regarding execution, and consumer feedback from surveys to develop holistic view of program effectiveness.

We utilize predictive analytics methods to understand customer behavior. These include methods for clustering, dimensionality reduction, and classification.

We have knowledge of techniques such as linear and logistic regression, factor analysis, k-means, association analysis, decision trees, and machine learning methods such as random forests and neural nets.

Our key advantage is to bring significant marketing experience to these analytic methods, which helps to shape the analysis to discover insights that are actionable.

We transform structured and unstructured data within business intelligence systems, SQL, and NoSQL databases,and web analytics systems (Datawarehouse in Adobe, Bigquery in Google Analytics) into reports and dashboards.

We utilize tools such as SQL, Java, Python, R, SAS, SPSS for data mining and reshaping, and Excel/VBA, Tableau, Google Data Studio, PowerBI, Spotfire, R/Shiny, and QlikView for reporting and dashboards.

Once the data is transformed into structured format, we can also utilize a variety of advanced analytics methods to develop deeper insights, than those available via exploratory and descriptive reporting.

We also have exprience dealing with unstructed data, such as text, for named entity recogntion and more advanced analysis, such as sentiment analysis, using Natural Language Processing tools such as GATE, Stanford NER, or through machine learning libraries available in Python and Java.

Our digital optimization practice through A/B and multivariate testing is driven by strong understanding of statistical testing methodology and web analytics, and segmentation and targeting.

We create research questions and develop data driven hypothesis for testing, utilizing our considerable experience in marketing. We granularly analyze testing results to clearly identify drivers of succes or failure at a design variant level.

Knowledge of HTML, JavaScript and SQL, and debugging tools like Fiddler, enable us to QA test designs quickly. We have experience with Adobe Target, and knowledge of other testing tools such as Optimizely.

Our advanced digital analytics practice consists of advanced customization to collect visitor and session level data, cleaning and reshaping of such data, and utilizing advanced analytics methods such as affinity analysis, for targeting and media optimization.

Model-based segmentation enables identification of high value prospects within the site visitors, and profiling such prosepcts in terms of acquisition sources and demographics from related systems such as DMP systems.

We are also familiar with a variety of attribution techniques for engagement and conversion optimization.