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How to classify and enrich customer data for B2C marketing part 1/3

Jan 2, 2018

Does your customer data give you a profit or a headache? The data and analytics scene is abuzz with expectations of high gains in ROMI. All marketers have heard of analytics-powered marketing strategies and face the considerable challenge of monetising their company’s data assets.


According to recent surveys, this potential seems to remain unrealised in many companies. For example, the EY & Forbes survey of 1500 global executives found that “many large enterprises throughout the world still struggle to achieve the promise of today’s analytics capabilities.”


We at Avaus wanted to ease your pain, with a series of practical blogs about the utilisation of customer data and about the tactics that can be employed for competitive advantage.


Data utilisation as part of data strategy


Customer data is a company’s most valuable asset; therefore it is essential to identify and unlock the opportunities that customer data and its utilisation offer. When marketing activities are designed to be analytics-driven, and their results are carefully measured, you will see how more productive they are.


A customer retention program can be planned, executed and monitored in a great variety of different ways. What data analytics allows you to do is focus on specific actions that will work best for your particular situation – from target group modelling and the drafting of marketing automation workflows to the creation of personalised content and the design of effective metrics to evaluate the impact of what you are doing. It makes a significant difference whether you base your conclusions on a longer or shorter timeframe: the more response data you have, the more finely you will be able to tune your marketing actions.


To succeed, marketers need a comprehensive data strategy aligned with their business goals. One aspect of the strategy must be to assess and classify customer data that is or should be available for utilisation.

Before you can utilise data, you need to enrich it. Before proceeding with data enrichment, a thorough assessment of all possible data sources and data quality must be carried out. Don’t forget that all activities relating to customer data must be GDPR compliant.


How to get started with data enrichment and utilisation


What follows is the Avaus approach to getting started with data classification and enrichment. It will help marketers understand what kind of data is available and how it can be utilised and enriched – and assist them in identifying the data’s inherent possibilities.


Classification and enrichment is visualised by splitting data into categories by source like this:



The lowest layers represent data that is usually readily available, and relatively simple to enrich –  for example, calculating a customer’s age from their date of birth or identifying the nearest store locations from address data. Higher layers such as preference and behavioural data require greater data management skills, more frequent data transfers, and more sophisticated integration from source systems involving web analytics tools, marketing automation and a DMP solution.


At the top level, profiling calls for a team of data scientists familiar with the very latest in advanced analytics methods using R, Python and other such tools. But for all layers, people are the most important success factor. A creative data analytics team will develop a sense of how best to enrich raw data to support business objectives.


This system of classification helps marketers to understand what data enrichment tactics they might employ. Each layer opens up a vast array of possibilities for more personalised customer experiences and better results from marketing activities such as customer engagement and retention.


In the next instalments of this blog, we’ll be looking at ways to leverage this classification framework for marketing purposes using specific data enrichment tactics – and demonstrate their effectiveness with concrete examples. Stay tuned!