However, despite this investment, many CRM initiatives will fall far short of expectations. The reality is that CRM will not magically give organisations a single view of their customers, which is the heart of the problem. Data quality plays a major role in the success of CRM – without quality data it is impossible to achieve that elusive ‘single customer view’. Without a single view of the customer, the insights delivered by the CRM solution, no matter how sophisticated, will be fundamentally flawed. Addressing customer data quality issues is thus critical to leveraging value from CRM investments.
CRM systems rely on customer data in order to provide insights that will help organisations to maintain and retain customers. One of the biggest data quality issues for CRM is data duplication – the same contact or lead may be captured multiple times due to small, seemingly insignificant details such as their city of residence being listed as Cape Town, Capetown and Kaapstad, for example. Human error is the most common cause of this problem. As a result, data duplication will always be an issue. If it can be addressed quickly, it will not significantly affect the performance of CRM. However, studies have shown that should data duplication levels rise above just 2% of data, reporting becomes unacceptably inaccurate, leading to flawed insights and actions.
Furthermore, problems with existing data and constantly changing data can add yet another layer of complexity. Identifying related customers is also challenging with manual processes. When errors creep in and begin to affect the quality of reporting and insight, users will stop trusting the system and stop using it, resulting in a wasted investment. In order to ensure value is leveraged from CRM investments, it is vital to maintain the credibility of the system.
Automated data quality maintenance systems, which should form part of any CRM investment, will assist with correcting and standardising data, as well as consolidating multiple and duplicate records about a single person.
Local knowledge is important. For example, South African client records may be captured in English or Afrikaans, while multinationals may have to cater for emerging markets across Africa, China, India and South America. A true multinational data quality solution must identify consumer households and individuals across records irrespective of the language used to capture each record.
Without a focus on data quality, an organisation’s CRM efforts will not be successful. Learn more about managing the data complexities and risks inherent in your CRM implementation and plan to ‘Create a Single Customer View’ with this whitepaper.
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