By Elliot Hogg, Senior Solutions Consultant, Validity
Effectively communicating with customers and prospects is crucial in building, retaining, and converting relationships. In a climate of disruption to both personal and professional lives, retaining a laser focus on communication is at the heart of business success.
With lockdowns persisting (for now!) and the importance of planning for future success as restrictions lift, where should businesses be prioritising in a climate of change? What can they do to mitigate against customer churn and maintain good client relationships? A data-driven solution to this customer relationship dilemma is one option that businesses have at their disposal. Any data-driven ethos is reliant upon a strong foundation, a high level of data quality.
Data quality offers a unique set of challenges to organisations and perhaps the most pertinent is that all areas of an organisation are responsible. Whether it’s sales, marketing, service or finance, all departments generate data. It’s therefore critical that data governance teams are made up of company-wide stakeholders, meaning decisions can be made with the input of all those that the burden of data quality impacts.
This cross-functional approach will ensure that the business is complying with all relevant regulations and industry standards, minimising the possibility of blind spots that can lead to business risks. A cross-functional approach to data quality can become very complex, very quickly. In practice, it needn’t be. There are a number of steps organisations can take to ensure simplicity spreads whilst promoting better data quality and integrity. Critical to this is removing friction from data input and updating processes for end-users, whilst also simplifying process to ensure best data practices are followed.
Despite this, to ensure processes promote high productivity and data quality, they still need to be adopted by end users. Educating end users on why processes are critical to follow (and the consequences of not following them) will encourage shared responsibility in the goal of achieving high quality data. The improvement will only optimise resulting customer communication.
Companies that do follow such a data-driven strategy to gather insights on customers see clear benefit. According to research by Forrester, they were collectively targeted to earn $1.8 trillion by 2021. Getting data quality right isn’t a nice to have, it’s essential for any business looking to succeed.
The reason being that only with the right data can a business target and communicate effectively with prospects, customers and the market, in a way that’s relevant to them. Good data is at the heart of a great customer experience.
For organisations pursuing the same goals, there are many options when looking to improve data quality, therefore promoting improved customer communications. One such tool is Validity’s DemandTools, which manages duplicates, standardisation, and record reassignments, alongside Validity’s DupeBlocker, which prevents duplicates at the source.
Of particular value is the ability to automate jobs within DemandTools. Ensuring that whilst a once-over of an organisation’s data can achieve good, the real value is in the ongoing effectiveness and impact that high quality data can have for an organisation.
Despite the importance of ongoing maintenance, Validity and Demand Metric’s State of CRM Data Management 2020 report found that 63% of the companies surveyed were using manual processes for CRM data maintenance. This reflecting that for the majority, there is still a great opportunity cost attached to the status quo.
Despite this, businesses should be wary of leaving data maintenance processes entirely up to technology. It is still necessary to promote stakeholder and shared responsibility, to help improve other critical factors such as adoption and productivity.
This is a concern which is commonly ignored. The DMA’s latest Marketer Email Tracker report found that 30% of organisations are not currently providing ongoing training for their teams, concluding that nearly a third of businesses have a large area of opportunity for improvement in their data quality and resulting customer communications.
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Key Steps
There are five key steps that cross-functional data governance teams should take into account when evaluating a data quality solution for the business.
- Profiling
When it comes to profiling, the data team can look for accuracy, whether the data is complete, and any inconsistencies in the data that may question whether the data is stored in the right location and if it is up to date.
- Standardisation
Profiling is likely to have identified any areas that require standardisation, which is a process that allows the data to flow through the company and into the analytics in a logical sequence, where it can inform critical business decisions.
- Duplication
Without consistent management and prevention, duplicates will find their way into every CRM. Governance teams can help the business to define the parameters for what it considers to be duplicate records, and seek a flexible third-party tool that can be customised to merge duplicates according to these parameters.
- Verification and enrichment
As data is constantly changing, businesses should entrust external sources to verify their customer data. Following a verification process, the customer data can be enriched with other key data points so the business has a better understanding of the customer needs in order to improve service and drive revenue.
- Monitoring
Once the previous four steps are taken, monitoring is the final and the easiest step in a successful data-driven CRM strategy. Tools that automate the process of cleansing data can also be enlisted – providing dashboards and alerts that can help the business to track data anomalies.
If a business is truly invested in achieving a high level of data quality, insight from a cross-functional data governance team will ensure that the cause becomes a shared responsibility. This team will decipher the value and seek out the best technology available to automate the challenges of data quality and educate end-users to promote high adoption. With all areas of the business committed, benefits of more effective communication, and the prospect of increased revenue will follow.
