Predict the future,
using your data
Advanced data modelling techniques allow you to explore large data sets by analysing smaller groups, observing their patterns and intelligence. Decisions on these sets can scale to the rest of the data and make inferences about patterns of behaviour.
Identifying those patterns and intelligence is important as you can learn and understand what made your customer behave in that way, you can then influence other customers to do the same or put in barriers if the behaviour was negative
Models are categorised into four main areas:
- Descriptive Analytics models, which describes what has happened in the past
- Diagnostic Analytics models, which considers why things are happening and determines a root cause
- Predictive models look to show future outcomes
- Prescriptive analytics models use Machine Learning and simulations to determine an overall goal
Qbase will help you to define the business objectives, find the right strategy and lead you through the implementation. Our team of experts can build statistical models in FastStats, “R”, Python, SQL or SAS. It is important to always to ensure that the model is working and retrained if needed. Our consultants will be on hand to oversee the process and adapt the models.
Modelling is a tool which helps identify important characteristics within large data to make actionable decisions which can ultimately improve ROI. This can be through:
- Optimising customer journey programs that generate longer tenures and identify where you could save on cost.
- Enhancing segmentations with varied data bringing statistical analysis to back results and decisions.
- Identifying prospects that are likely to be interested in your products and services through profiling.
- Developing targeting models that identify customers who are most suited to cross-sell opportunities.
- Track behavioural patterns that lead to customer churn, enabling you to be proactive in your retention strategy.
Barnardo’s Case Study
Barnardo’s held little information on long-lapsed supporters. Barnardo’s also wanted to test a new message on their prospects. Qbase were challenged to identify new sources of prospect data that would outperform their traditional banker list.
Everton FC Case Study
The Club wanted to create a personal, memorable match day experience that supporters wanted to savour, again and again. For this they needed fast insight into fans’ motivations, which started with getting closer to their data.