Descriptive Analytics
Our descriptive analytics solutions use specialist data aggregation and data mining techniques to consider and uncover historic trends and patterns of behaviour.
Enabling the Data Journey
The nature of descriptive analytics means that it can be utilised across all industry sectors and business functions, no matter the businesses’ experience in putting data to work. Our descriptive analytics experts analyse and explore our client’s big data to evaluate their business performance and unveil the truths behind business inefficiencies.
Wherever our clients’ are in their data journey, our descriptive analytics solutions can be implemented quickly and efficiently to enable businesses to begin their data journey fast and start gaining insights that can help to transform their business. With these insights, our data scientists and analysts work with our expert client partner teams to help robust, decisive and operational strategies that deliver effectiveness and efficiency gains across the business.
Descriptive Analytics Techniques
Our big data and machine learning experts are specialists in implementing data aggregation and data mining techniques, which are core to any descriptive analytics solution. The core focus of each stage of the descriptive analytics process is outlined below:
DATA AGGREGATION
Data aggregation is the process of creating more manageable data sets across raw databases, ready for processing. Data is collected, organised and summarised to create a more accessible format ready for the next phase of the descriptive analytics process.
DATA MINING
Data mining is the process of extracting the aggregated data to find patterns, trends and anomalies across the data set. By identifying these trends more meaning and insight can be uncovered, which can then be presented using data visualisation tools and reporting techniques, to empower more informed understanding within the business and enable better decision-making and strategy.
Empowering Businesses with Descriptive Analytics
The effective use of descriptive analytics comes with a sound understanding of the challenges businesses commonly face and then building out robust solutions, underpinned with big data analytics, to tackle these needs. Our descriptive analytics solutions can be utilised across a breadth of business functions including marketing, operations, finance and commercial.
Commercial
Shopping Basket Analysis to understand commercial transaction behaviours.
Product associations tell you which products are purchased together and can help immensely with product placement and promotions.
Range optimisation helps to declutter your categories and focus on products that sell well.
Price sensitivity analysis identifies price sensitive customers and helps define a pricing strategy to retain them.
Customer profiling provides an in-depth overview of your customer base, including splits by age, gender, and purchasing behaviour.
Operations and Finance
Performance Analysis & Reporting to understand successes and any inefficiencies.
Store performance analysis identifies performance differences across your branches, enabling you to apply learnings from top performers to underperforming branches.
Sales and revenue analysis including company reports, helps organisations to measure performance and identify operational efficiencies and areas where new goals and targets should be set.
Data asset review & evaluation helping you to understand the commercial value of your data by scrutinising its quality and integrity.
Marketing and CRM
Campaign Design analysis and A/B testing. Getting campaign design right is paramount to being able to calculate uplift correctly.
Campaign Evaluation analysis. Depending on campaign design, calculating uplift correctly can be a tricky business involving target/control group and temporal adjustments.
Social Media analysis to understand user attitudes and patterns in behaviour from usage and engagement.
Our Descriptive Analytics Approach
We follow a five stage approach to delivering our descriptive analytics solutions, which encompasses the following phases:
1. Understand: Our client’s business goals and the full extent and scale of what they want to achieve. Understanding and identifying the key performance measures that the company wants to achieve, allows us to prioritise strategy and enables the business to bring key stakeholders on the journey.
2. Identify: The first phase of the data aggregation process is to identify the data assets and databases that we need to work on. Our experts work closely with our data operations specialists to ensure the correct governance procedures are in place to allow the secure and compliance identification of data.
3. Consolidate: This stage ensures that the data is collected and prepared effectively and is a critical step in ensuring the accuracy of the data prior to mining and analysing patterns and trends.
4. Assess: The data is mined to determine trends in/across the data sets. This may involve a variety of statistical analytics techniques including clustering, outlier detection, regression or classification, relative to the end goal and the data being assessed.
5. Transform: This phase summarises the key insights in a way that is easily understood and communicated to all parts of the business. We use a variety of data visualisation tools to present our findings back to our clients so they can make more effective decisions off the back of descriptive analytics.
Descriptive analytics techniques focus on the ‘as-is’ and look at present/past trends in the data to effectively identify patterns and inefficiencies.
By using descriptive analytics as part of an integrated data science and analytics strategy, our clients are able to develop a more well-rounded solution to putting data to work by implementing solutions including machine learning, segmentations and predictive modelling, that look beyond the present and into the future to incite change.