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Brands are currently in an era of disruption with changing consumer behaviours, the constant evolution of new technology and the varying data sets available to them. Digital transformation has meant the customer has become the focal point for brands and the challenge for marketers is how to use data in a smart and sensitive way to provide a personalised experience across all touch-points, online and offline.

With the introduction of data privacy laws the ethics of how brands use their marketing data is a constant challenge with more transparency required in the ever changing marketing landscape. Today, the biggest challenge for many brands is understanding and vetting the quality of data they have available to them.

Poor quality data in > poor quality data out

Emarketer reported 50% of executives believe poor data has impacted the business bottom line. Poor data and no trust in data is a regular complaint from marketers but there has never really been a push to get their data in a good place. It’s not seen as the “sexy thing” to focus on and it’s not as easy to implement as the next “new shiny toy” in marketing. Poor data quality has a significant impact on the business leading to inaccurate targeting, poor campaign performance, poor customer experience and negative ROI.

For brands today, achieving a competitive advantage requires them to get their data in order so that it can be trusted and produce tangible, positive results for the business. The quality of data available impacts the insights that can be generated and the results it delivers, which makes it surprising how far down the food chain data integrity is for brands.

Data should be seen as a brand’s competitive advantage

Using data to answer key business questions

The goal for any brand is to grow revenue and brand awareness and this cannot be done without a high level of data integrity across different parts of the business, not only focusing on marketing data.

What every business wants is a complete end-to-end data set, which means collating all data sets in one place. This allows the brand to understand the customer better from marketing campaign data to sales data and CRM data. Richer the data quality the better the insights that enable robust analysis to be completed to answer key business questions.

This cannot be done without focusing on 3 key areas: ARC (see below). Brands need to ensure they maintain a high level of data quality and transparency is pivotal in this process to understand where the different data sources are coming from.

ARC Explained

Accuracy of data: Needs a consistent form of accuracy across all data platforms that will help provide rich actionable insights.

Relevancy of data: Having access to the right data sets for analysis to be completed will help with answering key business questions efficiently.

Consistency of data: Having a common taxonomy across all data platforms is critical to the data collection process. This will help with any analysis that will be done knowing that the data is consistently structured in the right way.

ARC is critically important cross-functionally, but especially helpful to allow marketing teams to access trustworthy and reliable data to prove the impact that their activities are having on the business.

For the brands who can successfully navigate the quality and application of their data, they will not only deliver better customer experiences, but improved campaign performance, better informed targeting and make marketing budgets work harder. The important accompaniment to the marketing team here is analytics, in the world of big data it thrives on high quality data. This should galvanise marketers knowing they can trust the data to get insights to completely understand the consumer and plan the best possible marketing campaigns with data at the heart of it. This will finally allow marketers to prove ROI, the accountability of marketing and the impact it has on the business.