Clean data needed for loyalty insights


If you want loyalty get a dog“If you want loyalty get a dog!” – Grant Fairley

The “loyalty card” has become as essential tool for brands, and many consumers now have wallets filled to the brim with different cards that provide different rewards based on various consumer behaviours.

However, the very fact that customers have so many different loyalty cards highlights a single, vitally important fact – the idea of brand loyalty is a myth. Customers may prefer one brand over another, however, given the right circumstances, such as price sensitivity, they will happily purchase a competing branded product.

Loyalty cards and rewards programs have become part and parcel of the consumer purchasing experience today, offering discounts, cash back or other benefits for continued patronage. However, the concept of the loyalty program as a mechanism for keeping customers loyal is a misconception.

The true value of a loyalty program lies not in retaining customers out of loyalty, but in the wealth of information and insight that can be derived as a result of these programs, which enables data driven pricing models, and more effective product and marketing targeting.

Unlocking this value requires that data be clean and of high quality – this is essential in accessing the veritable gold mine of information provided willingly by customers when they sign up as part of a rewards program.

Aside from all of the data gathered when a customer signs up, such as all of their demographic details, the fact that customers’ swipe their card every time they shop has the potential to offer valuable insight. For example, a grocery store can monitor the age of the customer, their buying habits, products or brands that they prefer and so on. With this information, the grocer is able to – for example – send specials to the customer of only the brands they prefer. This will provide the customer with a feeling that the grocer ‘cares’ by giving the customer only information that they want and none of the brands that they never purchase.

However, delivering accurate insight into customer behaviour requires that customer data is effectively managed and quality assured. It is essential to ensure accurate information is obtained and maintained right from the data capture stage, with certain information fields being validated to ensure that they are correct and complete.

Data quality processes can also enrich basic client data with additional value, for example by adding geospatial coordinates where we have a valid address. These additional fields create a broader, more accurate picture of the customer that can only enhance analytics.

Finally, data quality can uncover hidden relationships between data sets – identifying households and other related parties that can give a broader understanding of how groups of customers are interacting with you.

On-going data cleansing and maintenance is also essential to ensure details are kept up to date and correct, ensuring that insight continues to be driven using the most accurate information. This can in turn provide cost saving benefits, such as correct postal addresses which will reduce the amount of returned mail.

Once the information is ‘clean’ and accurate, the benefit derived is the intelligence gained through analysing this information, allowing retailers to identify buying behaviour and preferences. Data analytics is critical to creating competitive edge and boosting sales.

Customer loyalty data is another source of big data, which along with information from areas such as social networking sites, can be useful in generating and reinforcing customer loyalty.

Big data can be harnessed to enrich existing customer relationship management (CRM) and marketing data to deliver greater insight into customer preferences and attitudes. However, without data quality assurance, retailers run the risk of achieving exactly the opposite – damaging relationships and undermining customer loyalty as a result of poor insight.

The more information retailers are able to gather about their customers, the more effectively they are able to target products and marketing initiatives. This in turn enables retailers to drive increased customer spend as a result of better customer understanding. Analytics is where the true value of any loyalty programme lies.

Big data enables retailers to combine external and internal data sources around customers for even more effective insight, including who they are and how they shop and behave. Combined with social media, this can give powerful insight into how customers interact and how this affects purchasing behaviour. However, the quality of insights is tied directly to the quality of data. Ensuring quality data will in turn ensure successful direct marketing campaigns, retaining customers for the future and understanding and tapping into buying behaviour.

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