This past Christmas in the UK saw more and more retailers profiting from an in-store pickup model, where products are sold online and collected in-store by the consumer. In fact, these companies represent a large proportion of retailers that had a successful holiday quarter. 

This “Click and Collect” phenomenon has increasingly grown in popularity amongst both consumers and retailers as more customers turn to the convenience and expedite of in-store shopping during the timely holiday season. When faced with a lack of a brick-and-mortar option, some online retailers are partnering with physical stores to accept and hold consumer online purchases, while providing customers the convenience of collecting their online purchases locally. 

As major retailers search for ways to encourage their customers to shop online, respond to the looming threat of eCommerce giants like Amazon and Alibabe promote in-store shopping. Two of the UK’s biggest retailers, John Lewis and Next, reckon that Click-and-Collect services could be the key to profitable holiday sales figures. Both these retail giants have high volume e-commerce sites as well as many bricks and mortar stores.

The Obstacles to Click-and-Collect Services

Despite its numerous benefits, eCommerce brands first starting out with the Click-and-Collect model often encounter disparities and inaccuracies in their customer data that can ultimately determine the success of such services.

Truth be told, retails have their work cut out for them when it comes to how to manage customer records. In-store customer profiles are often incomplete, inaccurate, or riff with errors. More importantly, they’re almost never linked to eCommerce, loyalty, or CRM profiles that provide a complete view of interactions. 

To complicate matters further, individuals typically have multiple phone numbers, such as home, office, and mobile, multiple emails, or physical mailing addresses like office and home locales. Consolidating these millions of rows and mismatched data points into something useable and useful can seem like a downright impossibility, especially without the proper tools.

How Businesses Can Profit from Click-and-Collect Service Offerings

All these reasons can make successfully matching disparate customer data a nightmare for Marketing and IT departments in the effort to establish a Single Customer View. In order to do this safely, accurately, and timely, employees need customer record unification software that can fulfill multiple requirements, such as:

  • Accurate matching of customer records without being thrown off by data that is different or missing, while minimizing false positives and mismatches.
  • Sophisticated fuzzy matching to allow for keying mistakes and inconsistencies between data input by sales representatives in-store and in call centers, and customers online.
  • Recognizing data that should be ignored – for example, the in-store purchase records where the rep keyed in the address of the store because the system demanded an address and they didn’t have time to ask for the customer’s address, or the customer didn’t want to provide it.
  • Address verification using postal address files to ensure that when the customer does request delivery, the delivery address is valid – and even when they don’t request delivery, to assist the matching process by standardizing the address.
  • The ability to match records in real-time, whether in-store or on the website, off-line, record by record as orders are fed though for fulfillment and,  as a batch process, typically overnight as data from branches is fed through. The important point to note here is that the retailer needs to be able to use the same matching engine in all three matching modes, to ensure that inconsistencies in matching results don’t compromise the effectiveness of the processing.
  • Effective grading of matches so that batch and off-line matching can be fully automated without missing lots of good matches or mismatching records. With effective grading of matching records, the business can choose to flag matches that aren’t good enough for automatic processing so they can be reviewed by users later.
  • Recognition of garbage data, particularly data collected from the web site, to avoid it entering the marketing database and compromising its effectiveness.
  • Accept multiple types of data, such as different schemas or different file formats, so employees can easily unite data across multiple systems without needing to know standardization or normalization practices beforehand.

With a wide range of data quality solutions on the market, finding a data unification solution that can check all of these boxes can seem daunting. That’s where 360Science comes in. If any of these challenges sound familiar, contact us for a personalized look into how you can achieve a Single Customer View.