According to a recent study conducted by Retail Systems Research, most of the customer data on which retailers rely is inaccurate, incomplete and spread across multiple isolated storage centres, which means that these retailers are unable to make timely, data-driven decisions across all areas of their businesses. So much so that 54% of retail business leaders refer to the fact that they do not have a single view of their customers across channels as the biggest hindrance to more revenue-generating marketing campaigns, better customer interactions, and more efficient stock control practices. get the full Data Quality in Retail survey results by following the link
Risk of inaccurate insights
In the absence of trustworthy, fit-for-purpose data, there is the very real risk that the decision made by retailers to invest in technology to support big data, loyalty programmes, customer relationship management, e-commerce, and other data-driven business initiatives will deliver inaccurate insights for interconnected business process and decisions.
In short, poor data quality poses a massive threat to the effectiveness of a wide range of retail business functions.
Retail Systems Research noted that 34.69% of respondents highlighted the fact that poor data quality poses challenges when it comes to loyalty programmes, 24.49% experienced issues with cross-selling and up-selling and 22.45% cited poor data quality as a challenge with multichannel marketing campaigns, 20.41% with order fulfilment. What we can conclude from this is that poor data suggests that omni-channel marketing, loyalty programmes and stock management practices are not as effective as they could be, with the right, reliable data.
What are we talking about, when we talk about data quality?
Within the retail industry, inaccurate information, missing demographic information and missing contact information are the biggest problems affecting data quality. These problems are further exacerbated by inconsistent formatting, insufficient integration of multiple data sources, duplicate records, and insufficient linking of customers within the same household. Quality data is critical given the definite shift in many industries toward using data to drive the marketing effort rather than simply marketing for pure brand awareness purposes. This means that retailers need to understand the customer better so it is easier to position the correct product at the correct time to the correct people in order to maximise revenue.
How can retailers get better data about their customer in order to understand the customer?
Most have taken up loyalty programmes as a way to understand the buying patterns of the customer. The simple reality is that while a loyalty programme doesn’t guarantee loyalty, it allows retailers to link that card uniquely to purchasing pattern and behaviour, which can deliver insight into how customers purchase, how they interact with business, and what can what can be done to encourage them to spend when they may have otherwise gone to a competitor.
The loyalty programme has two major uses in a retail scenario: targetted direct marketing and stock control. For example, an individual might purchase blonde hair dye from retailer A, and swipe her retailer A loyalty card. She later receives an email from them offering her a range of products to keep her hair blonder for longer. This is where the data derived from the loyalty programme is used to cross-sell or even up-sell. The problem with data analytics is that it needs to be clever enough not only to analyse what the customer is going to buy, but also to suggest what else the customer might be interested in buying in the future. The second scenario occurs, by way of example, in the retailer B situation. Customers have to swipe their retailer B loyalty cards in order to buy from the store, and while direct marketing efforts do not reflect any of these buying patterns, the organisation is still able to utilise the loyalty card to deliver stock and purchasing insights.
Such a loyalty programme, which requires customers to provide data on registration, needs to be underpinned by effective data analytics. Retailers need to be able to access, integrate and improve on the vast array of pieces of data collected on a daily basis about customers. By implementing a data quality solution as the foundation of any technological infrastructure, retailers can pull timely, reliable business insights from customer data. The right data quality solutions will ensure that incomplete or unverified customer information that enters the organisation is standardised to a more complete format, and is made accurate and up to date. Additionally, disparate data sources are linked together to maintain the most accurate customer record, and the various business applications and platforms can draw on this information in order to help the retail business increase revenue and conversions by executing personalised omni-channel campaigns; decrease campaign costs by removing duplicate records and verifying customer contact details; increase customer retention and loyalty by delivering targetted offers based on accurate customer insights; and even go so far as to improve fraud detection by providing accurate record of buyer behaviour.