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Data Analytics
Data Analytics uncovers the reasons and relationships behind
the facts and leads to more effective decision-making. It turns
data into knowledge, to avoid becoming "data rich and
information poor."
Data Analytics can help to increase the ROI of a campaign because money is spent only where it are mostly likely to have the greatest effect. Data Analytics used in the right way in campaigns create a competitive advantage over campaigns executed without the help of data analytics.
"Marketing communications triggered by analysis and identification of customer interactions typically have response rates 4 to 10 times higher than conventional direct marketing..." - Gartner Group
Data Analytics Provide Clear Answers to Three Key Questions
"What has happened?"
- Primary market research
- Database modeling
- Market size and market share
- Campaign analysis
"Why did it happen?"
- Database integration
- Segmentation/profiling
- Advanced statistical analysis
"Will it happen again?"
- Forecasting
- Predictive modeling
Predictive Modeling
Typically, internal customer data only reveal what customers are currently buying from you. It does not tell you what else they might buy, when they might buy, why they might buy, or what they might already be buying from your competitors. Nor do they provide information on potential customers or on your competitor's customers. Predictive modeling is a statistical means for answering these questions. Modeling can be based on actual customer data and/or external, third party data.
Data Analytics can identify targets for cross-selling opportunities through a statistical model of the relationship between customers and the products they purchase most often. It can also help to identify customer segments that will be more receptive to a product offering.
Through Data Analytics, campaigns can be specifically designed for the different customer tiers resulting in a higher returns on your marketing/sales investment.
Selected PriMetricaAsia experience:
Developed predictive models and scored databases of 200
million + US households (Experian) and 15 million + US business
establishments (DnB) for their propensity to have or need
certain telecommunication services and products.Developed a statistical scoring model that, when applied to 39,000+ US zip codes, yielded the estimated demand for business and consumer Internet bandwidth.Developed predictive models and scored a DnB database of businesses establishments in Hong Kong for their propensity to spend on IT services and products.Developed BizScores, a PriMetricaAsia offering of IT scores associated with PriMetricaAsia's CorpAsia database. |
