Typical statistical analysis exercises
Statistical Data Analytics
Gain competitive advantage by making data-driven / informed decisions about your customers.
DMSA can help you develop an on-going and sustainable Customer Intelligence Process to:
- Profile your existing customer base
- Determine predictors of buying behaviour
- Drive relevant, cost-effective and profitable marketing campaigns.
DMSA High Level Approach
- Initially, the DMSA team of Statisticians and Business Strategists meet with your organisation to understand your organisation and then customise our analysis to meet your strategic objectives
- We integrate your data into a single comprehensive view using ‘clean’ and ‘sanitised’ data
- Profile existing client base
- We apply granular customer segmentation and predictive modelling to predict trends and customer behaviours.
- Use understanding of the existing client base to target new clients / areas
- Implement models on client’s system.
Typical DMSA Analytics Projects
The DMSA team of statisticians and business analysts work with your organisation to understand your sales environment and then customise our sales forecasting tool so that you can input your current sales data and predict your future sales.
DMSA Sales Forecasting uses sophisticated time series analysis to extrapolate past behaviour into the future.
1. Analysis of Marketing Campaign and Sales Data
- Evaluate impact of sales promotions
- Evaluate and improve existing Sales Forecasting systems
- Focus on and identify parts of the marketing budget and marketing activities that yield optimal returns.
- DMSA Sales Forecasting uses sophisticated time series analysis to extrapolate past behaviour into the future.
- Basket Analysis to predict buying behaviour
- Leads modelling for campaigns: we apply predictive models to determine where clients are most likely to interact and buy the product.
- To improve insight and performance, we link Customer transactional data to Survey data from Customer and Employees
2. Collections Optimisation
- Link dwellings / firms to Geocoded Data
- Do impact tests on past collection interventions
- Use segmentation to determine uptake per suburb for example: by payment options
- Predict default individuals, Suburbs, Segments of interest.
3. Risk Modelling
- Use Predictive Modelling to develop lapse and churn models that can identify high and low risk customer groups
- Segment customer data according to risk profiles
4. Forensic Investigations
- Identify suspicious activities, e.g. suspicious insurance claims
- Identify “ghost” employees
- Review procurement on supplier databases to determine payroll and supply chain fraud
- Identify collusion of staff and customers to facilitate fraudulent activity
5. Credit Analytics
- Apply predictive models to determine credit-worthiness of potential clients
- Determine the probability for lapsing
- Produce ratio and trend analysis and create projections