CRM systems are often seen as self-running solutions that will automatically deliver better customer relations. However, it’s important to remember what drives it – data – the key to the success of your CRM operations. Unfortunately many CRM systems are riddled with erroneous data, resulting in duplications of customer records, spelling mistakes in customer information and other errors that can undermine even the best-planned marketing strategies. Data quality problems are prevalent across organisations and addressing them is vital for improving the outcome of customer interactions, delivering a better customer experience and more efficient marketing campaigns.
With this in mind we’ve compiled three top tips to help organisations achieve a working environment that puts data quality at the heart of its CRM processes.
#1 – Build your case and find your champions
Often the people suffering with poor data quality aren’t the management team and the key decision makers within the business. This often leads to lack of awareness about the importance of data quality for improving marketing and CRM operations. So for data quality to get some attention in the boardroom, its importance and benefits need to be communicated clearly to the key decision makers. Finding the right advocates to help you build the case for data quality at a board level will not only help attract investment, but will also secure long-term support for data quality improvements across the business.
It’s also important that leaders from the line-of-business (LOB) as well as IT work together to maximise the impact of the improvements in this area. For instance, business users could focus on managing the project goals, while IT could concentrate its efforts on the steps to achieve those goals and manage the execution.
#2 – A flexible plan cannot break
The initial implementation of a data quality solution will be seen as a project, however, a good planning method is needed that will work more smoothly with your CRM efforts. The following are four factors that you should think about when making a data quality plan for your business.
Discover what the quality of your data is; taking into consideration the context, accuracy, completeness and consistency of it.
Develop the kind of data quality services will be needed to support your CRM operations. Thinking about guidelines for new data – what forms, fields and information will be needed as well as the nature of the data (what and where).
Deploy your data operations, deciding who will be responsible and how often the data will be cleansed.
Manage a framework of measures, metrics and KPIs for tracking data quality operations over time. This should include both inbound data and the monitoring of existing data.
It’s also important to remember when building your data quality architecture to try to match it to your business to ensure that goals and expectations align. For instance, it’s important for your CRM system to have access to financial, ERP, HR, supply chain and logistics systems in order to gather all the information necessary for a clear customer view.
#3 – Future data quality processes
Data quality shouldn’t be seen as a project with an end. Customers are not frozen in time; they’re dynamic – changing jobs, addresses and their telephone numbers. Data that was good can and will go bad – periodic health checks are needed to ensure the accuracy of your CRM records.
Budget spending can also affect data quality, and research has shown that investment in CRM is likely to be prompted by a business’s desire for improved sales, increased customer satisfaction, increased revenue and cost reduction amongst others. If you can figure out what your organisational priorities are for CRM, you can align your data quality goals and ensure the benefits can be seen in relation to those goals.
Ensuring you can quantify your data quality improvements and share them with management is key to safeguarding sustained commitment to your data quality operation. These reports can also help with pre-empting data quality problem areas, allowing you to proactively address them. Whether that poor data can be tied to a specific member of staff or data source and from there take the necessary steps to ensure better quality data moving forward.