Collecting and processing large volumes of data to create bespoke marketing solutions
We live in a world where data has become one of the single most valuable resources on our planet. Using data in the right way can be hugely valuable, and in the realms of marketing can provide companies with insight, strategies, and solutions to more challenging tasks and problems.
Our in-house team specialises in collecting and processing large volumes of data – be that numbers, text, images, audio or other formats – and leveraging that data to provide bespoke solutions to our client’s needs.
Project & market scoping
Starting with a clear understanding of the problem and anticipated output solution, as well as the challenges to overcome and potential limitations. This will include understanding the resources available in terms of a working dataset (or building one from scratch) and how it can be augmented to achieve a desired output.
Data mining & pre-processing
Automating the collection of data through in-house tools, third party platforms and client databases.
Data before pre-processing is often messy and patchy – it needs to be processed and normalised before being put into action.
Data visualisation & insights
Exploratory analysis and investigations on data, to discover patterns, remove anomalies and check hypotheses.
This is done through the use of summary statistics and graphical representations.
Using both supervised and unsupervised machine learning models to generate a desired output, prediction or insight. This is centred around classification, clustering, regression and dimensionality reduction.
For example, classifying image alt text to optimise images (classification), grouping customers based upon their booking habits to create custom marketing plans (clustering) or forecasting seasonal sales (regression).
Constructing high volume data capacity that can scale to meet your organisation’s marketing and customer support needs.
Dashboards & reporting
Putting insights into action and using bespoke dashboards and reporting software to monitor performance over time. This is to check our model is working as expected with real word data (think under/over fitting) and having the desired effect.