By Barley Laing, the UK Managing Director at Melissa
In the retail sector the transformative power of artificial intelligence (AI) promises to, and in many cases already is, driving real-time business success. From enhancing data-driven insight and productivity, to improving the customer experience.
With Black Friday looming, AI can potentially play a vital role in driving sales during one of the most important periods in the retail calendar.
However, in the rush to adopt AI, many retailers haven’t taken into consideration how having poor quality data on customers could lead to gibberish, or worse, biased and inaccurate outcomes. This is what is commonly called AI induced ‘hallucinations’, which leads to poor results.
For example, poor personalisation could be delivered by AI having access to incorrect customer data, such as an incorrect name or address, for example, which would have a negative impact on sales and the customer experience.
Data decay is an issue
Data decay is a significant factor impacting the effective implementation of AI.
Data decays fast when customer contact data lacks regular intervention, degrading at 25 per cent a year as people move home, die and get divorced.
Additionally, 20 per cent of addresses entered online have errors; these include spelling mistakes, wrong house numbers, and incorrect postcodes.
To avoid the scourge of inaccurate contact data it’s important to have verification processes in place at the point of data capture, and when cleaning held data in batch. This typically involves simple and cost-effective changes to the data quality process.
Use an address autocomplete or lookup service
A valuable piece of technology to use at the customer onboarding stage is an address autocomplete or lookup service. These deliver accurate address data in real-time when onboarding new customers by providing a properly formatted, correct address when they commence inputting theirs.
Geocoding
With the correct address you can go further to ensure correct delivery and improve the customer experience via geocoding, which helps to provide a consistently accurate delivery service.
It works by taking a verified postal address and enriching it by appending rooftop latitude and longitude location coordinates.
This highly accurate location information speeds up the delivery process, reduces shipping costs, and prevents expensive, in monetary and customer experience terms, ‘return to sender’ scenarios.
Deduplicate data
Many customer databases have duplicate rates of 10 to 30 per cent. It’s a significant issue and frequently occurs when two departments merge their data and errors in contact data collection take place at different touchpoints.
Not only does duplication have the potential to confuse an AI application, but it adds cost in terms of time and money, particularly with printed communications, and it negatively impacts on the sender’s reputation.
Using an advanced fuzzy matching tool to deduplicate data can help reduce AI induced hallucinations.
By using such a service, it’s possible to merge and purge the most challenging records and create a ‘single user record’ which delivers an optimum single customer view (SCV) that AI can make learnings from.
Additionally, organising contact data in this way will maximise efficiency and reduce costs, because multiple outreach efforts will not be made to the same person. An added benefit is that the potential for fraud is decreased because a unified record will be established for each customer.
Undertake data suppression
Data suppression, or data cleansing, that highlights people who have moved or are no longer at the address on file, is a vital element of the data cleaning process, and consequently in supporting efforts with AI.
Along with removing incorrect addresses, these services can include deceased flagging to stop the distribution of mail and other communications to those who have passed away, which can cause distress to their friends and relatives.
By employing suppression strategies retailers can save money, protect their reputations, avoid fraud and aid their AI efforts.
AI has the ability to give your retail business a competitive edge, but this is dependent on the quality of data fed into the AI models. Inaccurate data leads to AI ‘hallucinations’ with unreliable predictions and hence bad outcomes.
Therefore, before Black Friday maximise the success of your AI efforts by applying best practice data quality procedures – it can help to boost your sales and improve the customer experience at this vital revenue generating time of year.
Utilise data cleaning
Today, there is greater focus on delivering data quality in real-time to support AI and wider business efficiencies. It’s possible for retailers to do this with no coding, integration, or training.
With technology, such as that provided by Melissa, retailers can cleanse and correct names, addresses, email addresses, and telephone numbers worldwide. They can also match records in real-time, ensuring no duplication, and undertake data profiling to help source issues for further action.
This is possible using a single, intuitive interface which provides the opportunity for data standardisation, validation, and enrichment, resulting in high-quality contact information across multiple databases.
This can take place with held data in batch and as new data is being collected, and can also be accessed via cloud API or on-premise, if required.
Published 04/10/2024
