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How can data quality help marketers overcome familiar barriers?

FOUR COMMON MARKETING CHALLENGES FROM A DATA QUALITY PERSPECTIVE

 

For marketers, the emergence of data as a vital tool in the way they target customers and execute their strategy has been one of the most important paradigm shifts in recent memory. The emergence of vast quantities of digital customer data has provided further opportunity for marketers to work even more effectively and intelligently.

For leaders in this sector, data quality initiatives are no burden - on the contrary, working with the most accurate information possible enables marketers to successfully execute on key priorities such as cross-channel engagement, personalisation and customer acquisition. This means that with the right investment of time and resources, data quality initiatives can deliver a substantial uplift in results and help marketing departments to overcome some of the most common barriers to success that they face.

Here are four common problems that marketers face today, with accompanying insight into how a well-developed data strategy can help to overcome these challenges and drive better outcomes:

Four marketing challenges from a data quality view

1. Targeting

For as useful as good-quality data can be, the opposite is true of poor or inaccurate data. Companies with a lax approach to information quality often find their databases quickly becoming overrun with incorrect data, which harms their ability to identify the right audiences and makes accurate targeting much more difficult. At a basic level if you can’t physically reach customers due to incorrect details then response rates will drop and resources can be wasted through campaigns that are never delivered. Further issues then can arise if marketers are using the inaccurate data to analyse their customer base and make important targeting decisions.

To overcome this issue, companies need to confront the scale of the problem and develop a realistic appraisal of where the data gathering and cleansing process is breaking down, before working alongside representatives from across the organisation to support a broad-based improvement programme.

A good way to get this off the ground is by communicating how accurate data can help everyone to achieve their day-to-day and longer-term objectives. At the same time, spending money on the right data quality tools to help capture data correctly and clean the existing database can help automate and accelerate the process.

In doing so, marketers can ensure their data is kept clean and up to date over time, allowing their communications to reach their intended destination and maximising customer engagement, cost reduction and return on investment.

 

2. Regulation

As businesses handle an ever-growing amount of sensitive customer information, government policy around data protection is growing more intense, creating an increasing variety of compliance issues.

This is not an area in which companies can afford to make a mistake, as a slip-up can lead to financial losses and fines not to mention substantial reputational damage.

As such, a data quality strategy that combines the right technology with people and process is essential for planning and implementing better ongoing data governance policies. For example, it will make it easier to regularly monitor customer channel preference, which no longer wish to be contacted, and instances where details may out of date or improperly kept.

Steps like these will help marketers to avoid regulatory breaches, prevent inappropriate mailings and ensure their hard-earned brand reputations are not needlessly harmed.

 

3. Efficiency

It remains a fact that marketers are often heavily scrutinised in terms of their spending, meaning they need to be able to justify all of their outlays and costs, as well as the return on investment that they can deliver.

It is therefore vital to demonstrate how good data is good business. Luckily this can often be demonstrated in tangible ways via a direct impact on response rates, a drop in goneaways after a mailing or an increase in overall email deliverability when an email database is regularly cleaned.

By doing so, marketers have clear evidence to show how their data quality initiatives are leading to happier customers, reduced costs and increased returns, thus making the case for further data accuracy investment that will drive additional improvements later down the line, for example a Single Customer View.

 

4. Insight

It is incredibly difficult for marketers to maximise their client retention rates without the deep customer insight that good data can provide.

Achieving this firstly requires a basic foundation of data quality which ensures they can be confident that the information they hold about customers is fully up to date and accurate. It’s possible to then enhance that data by overlaying additional demographic insight such as Mosaic codes that can provide a detailed picture into different customer types and lifestyles.  All this helps to define a clear picture of client needs and makes it easier to target them with tailored communications, and improving impact without increasing costs.

This will ultimately create a virtuous cycle, with a rise in customer engagement leading to a corresponding uplift in retention and acquisition. Marketers will also earn the implicit trust of their customers, making it easier to upsell them on products they need and will be excited by.

 

Conclusion

Data-driven marketing can be the key to unlocking the full potential of your communications strategy and gaining a key competitive advantage over less agile and responsive rivals. To read more about these four challenges take a look at our whitepaper ‘Leverage data to make your marketing fly’.

What is encouraging is the growing trend towards installing a Chief Data Officer at board level who is responsible for defining how the organisation as a whole manages data to deliver strategic benefits. This will undoubtedly ensure that marketers have better access to the technology and processes that will support their own priorities.

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