Retail - Data Mining Services for the Retail Sector
Business Intelligence Solutions data mining services can help you dramatically increase revenue and profitability, develop lifelong relationships with your most profitable customers, enhance customer retention and acquisition, increase cross-sell and/or up-sell, and address other common CRM and retail analytics challenges to make better commercial decisions, based on consumer and store/outlet data and synergy of data mining and GIS approaches.
We have extensive experience in commercial analytics, in the development of new market research methodology, including sophisticated analysis of primary and secondary data, response modeling, evaluation of impact of marketing events, campaign management, multichannel management, and design of pilot studies. Plus, we offer customized application development with embedded predictive response models as well as the integration of statistical/data mining models into your company's existing applications.
Definition of data mining and detailed characteristics of a data mining project can be found in our white paper â€œData Mining: the Means to a Competitive Advantageâ€.
In order to understand why traditional statistics is not always adequate tool for vast majority of data driven retail problems, see our presentation "Data Mining vs. Statistics".
The essence of our approach is to understand and analyze our clientâ€™s business problem and corresponding data through the prism of diverse statistical/data mining models. As a result we are always able to produce the best possible model /results and help our clients in the most effective and scientifically sound way.
Sales Analysis and Sales Forecasting - Effective data mining consulting
Demand and sales forecasting. Market basket analysis. Sales trend analysis by customer segment (identifying customer segments with similar sales pattern, segments with declining sales, segments with sales growth etc.). Short-term and long-term forecasting of product sales. Evaluating effectiveness of promotion campaign, and determining impact of marketing events (coupons, discounts, special promotions, advertisement, launch of new products, etc.) on sales, customer retention, and customer acquisition. Performance measurement.
Discovering drivers of product sales, and quantifying impact of each driver. Determining why sales differed from plan and how to act in order to increase sales. Cross-selling and up-selling decision support, and in particular, establishing the right offer to the right customer segment in a right time.
Customer scoring (association with each customer an appropriate metric such as likelihood of repeat purchasing, likelihood of switching to competitor product, likelihood of early adoption, etc.). An example of customer scoring can be found in our case study â€œData Mining Approach to Credit Risk Evaluation of Online Personal Loan Applicantsâ€ presented at Fifteenth (2009) ACM SIGKDD International Conference on Knowledge Discovery and Data Mining in Paris, France (http://kissen.cs.uni-dortmund.de/PROCEEDINGS/SIKDD2009/workshops/W9-dmcs09.pdf
Our business intelligence data mining services can help you better understand your sales trends and patterns so that you can focus on implementation.
Customer Relationship Management
Gaining, sharing, expanding, and utilizing knowledge about customers. Customer profiling, segmentation, and clustering with the usage of demographic, geographic, psychographic and behavioral variables and historical business information (see our presentation â€œCluster analysis vs. Segmentationâ€
Data enrichment (enriching the current customer database with Census and other public/private data. Improving marketing ROI by targeting specific customer segments with personalized campaigns. Tthe essence of data enrichment and different sources of external supplementary data for CRM problems are discussed in our presentation â€œData Enrichment for CRMâ€.
Identification of early adaptors. Customer profitability and customer loyalty. Customer acquisition and retention. Churn modeling/customer defection and customer attrition. Customer satisfaction, identification of unsatisfied customers, and development of the program to improve customer satisfaction. Personalized marketing and pricing. Targeted marketing and multichannel management. Optimal (the most profitable) allocation of your marketing funds.
What-if scenario analysis. Detecting underserved customers and unprofitable customers and developing program to increase sales. Customer scoring according to a likelihood of defection, likelihood of response, likelihood of a product purchase, etc. Developing lifelong relationships with your most profitable customers and addressing other CRM challenges like up-sell and cross-sell (application of bivariate regression to up-sell and cross-sell is described in our white paper â€œJoint Regression Model for Sales Analysisâ€
Customer Knowledge Management
Gaining, sharing, expanding and utilizing the knowledge residing in customers (e.g. through survey development and analysis of combined primary and secondary data).
Design and Execution of Specific Campaign
Advertisement and campaign management. Targeted marketing to optimize marketing effectiveness. Customer win-back campaign (recovering customers lost to competitors). Pilot study design (e.g. price test to estimate customer sensitivity to product/service price change). Event monitoring (e.g. when certain pre-defined thresholds and targets are reached in a customer's account or for a customer segment) and event prediction.
Website analysis, predictive analysis and predictive modeling of website user behavior (e.g. prediction of user propensity to convert, buy or churn), analysis of website statistics and key performance metrics, measuring content effectiveness, discovering user segments and navigation patterns, customer satisfaction with web experience, uncovering areas of opportunity for your organization websites to more effectively support sales and customer loyalty. Impactful e-mail marketing. IP address geocoding of website visitors to: identify geographic clusters of current and potential customers; examine the efficiency of advertisement as a function of geography; determine locations of future advertising campaigns, etc. Introduction to spatial dependency and spatial regression can be found in our presentation â€œA Brief Introduction to Spatial Regressionâ€.
New branch/store location optimization analysis (avoiding cannibalism and minimizing competition). In order to get insight into spatial dependency and spatial regression, see our presentation â€œA Brief Introduction to Spatial Regressionâ€.