Marketers – what your number one priority should be in 2014

By Kathy Salter, @Marketsoft

What are your marketing priorities for 2014? Behavioural targeting? Refining your content marketing? Developing your social channels?

While  each of these are increasing in importance for marketers, I’m going to go out on a limb here and say that you could be wasting your time if you don’t have one fundamental thing sorted out first. Your data quality.

Really? Data quality? While it’s hardly the hottest new trend, data hygiene is still a major challenge for many marketers.  In the Marketsoft 2013 Data Challenges survey, it was revealed that only11% of marketers surveyed felt that they were ‘doing fine’ when it comes to data quality. That leaves around 9 out of 10 still struggling.

The problem today is that customer data changes so rapidly and the accuracy of data is constantly under assault. In the B2C world, people update their social status and preferences on a daily basis, and in B2B, people are now changing jobs on average every 26 months. Faced with the deluge of data that is part and parcel of the big data environment, organisations need to find the capability to structure and stitch that data together so that it is accurate and meaningful.

But is data quality really such a big deal? The statistics show that most organisations put up with the impact of dirty data, but surprisingly few are on their way to fixing the problem.

They should, when data underpins so much of what marketers do. If the quality of that data isn’t up to scratch, it’s going to be pretty tough making the rest of it work properly.

Here are the top 5 reasons why marketers need to make data quality their number one priority in 2014.

 

1. Good data-driven decisions needs good data quality

Organisations today can no longer rely on gut feel to solve their marketing problems. Instead, they are turning to their data to find the optimal ways of understanding and connecting with audiences.

Naturally, if you are starting with poor quality or incomplete data, it will be unreliable in how it supports your decision-making and the outcomes of your decisions will be likely to reflect that.

As the saying goes, ‘garbage in, garbage out’.

 

2. Data quality increases revenue by increasing audience size

There is a compelling business case for improving data quality. It improves ROI, first by lifting revenue. And while revenue certainly matters to business leaders, the connection with data quality is still so often missed.  When incorrect, missing and duplicate data impact your ability to communicate with your customers, of course it affects the revenue you can generate from them.

In the November 2013 DemandMetric survey report ‘Sales & Marketing Data Quality’, the research team found organisations that are experiencing revenue growth to be around three times more likely to have clean data than organisations with flat or declining revenue growth.

Further research indicates that, for every 1% of data quality improvement, marketing can generate 5-6% of incremental revenue.

Correcting and enhancing your data will help you build audiences as the number of people you can contact via their various channels increases. In a campaign environment, you will see better contact rates and more responses.

 

3. Improving data quality reduces costs

As well as driving revenue to improve ROI, data quality management will bring marketing costs down by reducing waste.

How? Every customer data set contains records that shouldn’t be there.

Data redundancy is an increasing factor in today’s big data environment. There could be records that include incorrect data, or have vital pieces of information missing. There might be obsolete contacts that are no longer in that role or company, records that are missing fields such as phone number or email address, or records with inaccurate information, such as phone numbers or incorrectly formatted emails.

Attempts to communicate with these customer records inflate campaign costs and when data sets include duplicate and non-contactable records, this will lead to inaccurate market sizing and results estimates at planning stage.

Further  segmentation can help identify and remove chronic non-responders as well as increasing relevance through targeting and enabling marketing resources to be focused on the most profitable customer types.

 

4. Customer centricity and a positive customer experience depend on data quality

So, improving data quality impacts ROI, increasing revenue and decreasing waste. Compelling enough, but perhaps the most meaningful reasons for companies to clean up their acts lie in the essential importance of data quality to customer centricity.

In this customer-centric age, marketers rely on insights with foundations in good quality data to understand customers and provide the products, services and offers they want, when they want them, and via the right channels.

Offering a more personalised service will delight your customers and keep them coming back for more. But tread cautiously. If your data is not accurate, it will slowly tarnish your credibility with customers and they won’t take long to turn to your competition. Small errors can be highly visible when it comes to personal information, and today’s customers expect you to capture their details accurately and use them right.

While, for some, data quality is perceived to be focused on traditional data points, the ability to serve the customer’s needs in a customer-centric manner demands the ability to listen. What happens if you hear the wrong thing or interpret something incorrectly? There is an explicit link between quality, accuracy and interpretation.

 

5. The quality of your customer data is directly related to your ability to manage organisational risk

Data quality is also what allows you to ensure that correct customer preferences are followed, especially where a holistic view of the customer across many channels is needed.

Nowhere is the adherence to customer preferences across channels more important than in the realm of data privacy. Under the new Australian Privacy Principles in force from March 2014, individuals’ data must be accurate, complete and up-to-date.

Accuracy of information is there in black and white in the new National Privacy Principles, and with fines of up to $1.7m for non-compliance, clean customer data becomes an imperative.

Data needs to be accurate to allow the suppression of records that need to be removed from marketing data, whether these are email unsubscribes, deceased customer records or customers who have already been declined for product offers such as insurance.

 

How to start a sustainable approach to data quality management

Good customer data quality is the foundation of modern marketing, bringing customers and organisations closer together and driving ROI in thriving businesses.

As well as recognising the importance of data quality, it is critical that organisations address data quality management in a sustainable way.

To ensure long-term success, data quality needs to become embedded in corporate culture, with solid senior management support, policies, standards and metrics, moving beyond reactive and ad-hoc data cleansing initiatives undertaken in isolated pockets of the business.

Data quality is more than just a technical process – it is an organisational culture defined by a framework of process, technology and people. A database cleanse can’t correct an incorrect data selection, nor can an audit highlight married customers with a new surname. Embedding data quality as a cultural habit is the most important element of success.

The starting point will always be to understand the extent of the problem and the improvements that can be achieved to build a business case, and the ideal way of achieving this is with a data audit. After that, the planning can start.

For more on data quality, watch a video of Joel Nicholson, Managing Director at Marketsoft, here, speaking as part of Data Pass, ADMA’s new industry training program.

Click here to contact us and request your free data audit to start your journey towards sustainable data quality and the benefits it can bring to your organisation.