Looking forward, not back
Whilst traditional BI and Data Warehousing solutions help the business understand “what has happened”, advanced analytics helps provide the answers to “why it happened” and “what will happen next”.
The ability to answer these questions can provide the business with:
A compelling competitive advantage
Improved operational efficiencies and cost reduction
New revenue streams
Examples of our work
Identify customers at risk of churning and gain insight into the drivers of churn in your business.
Better understand where you should be targeting your retention efforts, what’s important to your customers, and when and how you need to be communicating with them.
Social media text analytics
Ever wanted to know what your customers are saying about you?
Using text analytics Qrious can extract the key topics from what your customers are saying about your brand, products and services. We can extract customer sentiment and compare the satisfaction of your customers versus those of your direct competitors.
Gain a deeper understanding of your customer base through segmentation.
Using sophisticated clustering models Qrious can leverage the large amount of transactional, behavioural and demographic data available on your customers to identify distinct groups of customers that exhibit similar attributes and have similar needs.
Social network analysis
The relationships between people can provide valuable insight into who are the most influential customers within your base, who are the most connected, or which of your customers are sitting on the peripheral and therefore at most risk of leaving. By understanding these relationships you can develop more effective communication, acquisition, loyalty and retention strategies.
We’re building one of the biggest data science teams in New Zealand
Years of practical experience
% of Masters and PHD
Our Top Scientists
Senior Data Scientist (Phd)
- Over 15 years experience architecting and delivering business intelligence and data mining solutions.
- Extensive experience in predictive analysis, and statistical and machine learning algorithms using R.
- Particulary knowledgeable in price elasticity modelling for the retail industry.
Senior Data Scientist (Masters in Data Science)
- Over 10 years experience in complex business analysis and solution development in large corporates.
- Strong experience leading teams and programs under tough deadlines for delivery to multiple stakeholders
- Particular expertise in R, SQL, Java, Python, Hive, PostGIS, Tableau
Qrious Analytics Methodology
Identifying the right deliverables quickly is critical to ensure that an analytics engagement results in successful business outcomes. This is why the Qrious Analytics Methodology is firmly grounded in the CRoss Industry Standard Process for Data Mining (CRISP-DM).
The CRISP-DM methodology was established by pioneers of the data mining industry in the late 90s and has been used successfully in the field ever since. Although almost 20 years old, CRISP-DM remains the industry leading methodology for data mining, analytics and data science projects.