Modern Retail

Big Data Versus Dirty Data

Big Data vs Dirty Data

With so many aspects of daily life now conducted online, the flow of information about people – their habits, preferences and influences – is unceasing and vast. For the retail industry, the big data deluge creates both a huge opportunity and a sizable challenge. The opportunity is to understand customers as never before and to cater to them accordingly: a level of personalisation that the local newsagent or corner shop may have with its regulars, but which brands struggle to replicate at scale in the ecommerce world. Of course, this is all dependent on one fundamental principle – data.

Data plays a crucial role in the success of a retailer influencing improved decision-making, customer satisfaction, acquisition and of course, increased profitability. Without it, retailers face a huge number of pitfalls, such as the inability to fulfil an order on time, or at all. Orders that arrive late, or never make it to the consumer, inevitably lead to frustrated customers who are unlikely to buy from that retailer again. In a world where customer feedback is becoming ever more accessible to the masses the potential for negative reviews to have knock on brand issues to the wider public are significantly increased.

Dun& Bradstreet have estimated that the cost of bad data could be as much as 15 to 20% of corporate operating revenues – a huge amount of wastage for something that’s seemingly simple to rectify. The spread of data and the increasing use being made of it has led also to an increase of interest and action from legislators. It is no longer just a business advantage to collect and maintain good quality data – it is increasingly becoming a legal requirement.

With data sourced by hurried retail associates at the point of sale, call centre operatives, or online where customers provide their own data and an ever-increasing number of customer touch points dirty data can filter through a business at an alarming rate.

For the best data quality, it is far more cost effective to prevent data issues than it is to resolve them. PCA Predict’s range of tools avert the problems of inaccurate data by validating it at the source. For example, when the user starts typing their address in an online checkout, the technology works in the background to automatically display and verify the results. The latest enhancement to PCA Predict’s address validation tool also includes Fuzzy matching so users can auto-complete an address even if misspellings or typos are entered – an unavoidable foible when typing on mobiles with small screens. Even the smallest error – the transposed letters in a street address or an inaccurate email address can lead to revenue loss, process inefficiency and failure to comply with industry regulations.


And it’s not just about data quality. A usability study by Etre and Luke Wroblewski found that real-time data validation caused a 22% increase in ecommerce success rates and 31% increased satisfaction rating. Instant feedback turns data collection into a two-way conversation enabling customers to get answers and fix mistakes as they complete forms instead of after the fact, which in turn helps improve conversion.

Although inaccurate data is often the result of the customer, it is up to the retailer to ensure they are collecting data that is accurate and up to date, as they will be the ones to bear the brunt of the customer’s disappointment when their order disappears.

While big data might be the topic on everyone’s lips, nothing is more frustrating and potentially damaging to a brand than dirty or neglected data. By making a few small amendments to the way customer and prospect data is managed and updated, retail companies stand to realise huge savings and increased revenue opportunities.

For more information about PCA Predict’s range of ecommerce tools please visit the website.


Modern Retail

Modern Retail

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