I’m sure most of those reading this will agree that having accurate customer data helps to drive business success. Not only in delivering highly targeted customer communications, but in engendering wider confidence in the integrity of the data, so that it can be successfully analysed to aid future growth.
Collecting customer data in the mobile age
A big issue impacting on the accuracy of customer data is the increased reliance by brands on customer provided data in the mobile age. Data collection is commonly taking place on small mobile screens, where ‘fat fingers’ make mistakes more likely when completing online contact forms.
Data entry errors can also take place within call centres, leading to inaccurate data being collected. In fact, it’s estimated that 20% of addresses entered into systems contain errors such as spelling mistakes, wrong house numbers, and inaccurate postcodes. It’s these mistakes that also increase the likelihood of duplicate customer records – causing confusion.
Incorrect data has cost, reputational and planning issues
There’s significant time and cost implications in correcting faulty data, with on average 2% of a company’s revenue lost on modifying such data. It’s time and money that, for example, could be better spent on marketing and improving customer engagement.
Moreover, any errors in the data not only has cost implications for the business – for example return and re-shipping costs due to inaccurately addressed orders — but in reputation, as a disappointed shopper can ultimately impact on customer retention.
Decision making can be hindered using inaccurate data, which impacts on decisions made around future planning, such as investment in new product development. This, in turn, negatively impacts on the long-term viability of any organisation. It’s currently a huge issue with 81% of large companies stating they experience significant problems linked to delivering meaningful business intelligence due to poor quality data.
To prevent these issues the answer lies in capturing correct customer data across all touchpoints.
Best practice collecting customer data – autocomplete and data verification
A good place to start for those serious about improving the quality of their customer data is to leverage autocomplete functionality. Such tools are able to automatically provide a correct version of the address as the customer types in their details, allowing them to choose a valid address that’s easily recognised. Along with preventing mistakes in address data, these tools can cut data entry keystrokes by up to 70%, thus speeding the checkout process and improving the customer experience.
It’s also vital to not only implement checks for data accuracy at the customer onboarding stage, but take verification to another level to protect against fraud. We live in an age where a growing number of data breaches has seen an increase in the number of criminals posing as legitimate consumers. To avoid possible fraud brands must match a name to an accurate physical address, email address and phone number and include cross checks of captured data against trusted reference data, such as electoral roll, credit agency and utility company, at the touchpoint in real time. It’s the only way organisations can be certain that their customers are who they say they are, and prevent fraud.
By using autocomplete technology and adhering to a best practice approach with ID verification to stop fraud, brands will have huge confidence that the customer data held in their database is accurate. As well as using this data to deliver highly targeted customer communications they will be safe in the knowledge that they can use this data effectively for planning and strategy purposes, to accurately inform the future focus of their organisation and help them supercharge their growth in 2020.
It’s time for businesses to put procedures in place to ensure they have clean, verified data so they can start to trust their customer data once again.