Striking gold with AcuitySpark

Striking gold with AcuitySpark Leveraging customer merge processes to build a Golden Record

By Habib Ahmad, VP Data Analytics

“Retail is detail.”

Chadatip Chutrakul, CEO of Siam Piwat, Thailand

Thousands of customers walk in and out of your store or visit your website every single day. Do you know their likes and dislikes? What are their preferences in terms of products, colors, and styles? If you want to upsell or cross-sell products to them, do you know where to direct them? Are you aware of their buying power, their lifetime value, and whether or not they're likely to churn?

A clean, integrated customer dataset is a goldmine for retailers looking to improve marketing ROI and market share, and is key for retailers wanting to stay ahead of their competition. Retailers usually struggle to maintain what has popularly become known as a “Golden Record”. Studies have revealed that only 30 to 40 percent of customers are actually known to retailers unless a proper strategy is established and the necessary tools are put in place to maintain customer master data and convert unknown customers into known ones.

Even companies that have a strong CRM framework struggle to build a Golden Record because: 

Few attributes are typically available for unknown customers. This hinders the construction of a complete customer persona.



In eCommerce, the segment of unknown customers is large due to the availability of the guest checkout option. As a result, every transaction is treated as one made by a new customer.

The figure on the right illustrates this problem graphically.

Given the hefty benefits of knowing your customer, customer relationship and marketing departments devise methods to identify customers and track their activity and transactions over time so they can move as many people as possible from quadrants 1 and 2 to quadrants 3 and 4. These methods include giving out loyalty cards, offering discounts on social media registration, and encouraging people to make purchases via mobile apps.

Completeness of data 100%

Quadrant 3

Quadrant 4

Extensive information exists about the customer, but the customer is anonymous

The golden record: we have complete information about the customer and the customer is also known

Quadrant 1

Quadrant 2

We have only basic information about the customer and the customer is not known either

Data on the customer is incomplete, but the customer is known

0

1

0% Is the customer known?

While these methods have their merits, they disregard historical data and transactions, and only work for customers that buy into these programs. A customer merge process can overcome both of these issues and create a Golden Record using the following method: Search for and correct typos An NLP-based library like Python

Standardize “easy” fields These include phone number, city, state, and country. Existing

Identify swapped entries Use an algorithm that can identify

Filter and group Define filters and identify matches in cleaned data. Review manually in

Define master i.e. what you want your final entry to look like. e.g. if a name is entered as both M. Wilson and Margaret Wilson, which one do you want to keep?

Merge your data Once you’re satisfied with your filters and your defined master record, merge your data.

A properly executed customer merge process can yield a significant improvement in the percentage of customers that are known.

AcuitySpark, Visionet’s proprietary solution for retail analytics, has a prebuilt module for merging customers that has been developed using top-of-the-line practices, tools, and technologies. Our clients have been able to attain Golden Records and move up to 45% percent of existing customers from quadrants 1 and 2 to 3 and 4 with minimum effort.

Interested? Get in touch with Visionet today.