Working from home and worried about the future of your business? You are not alone in a very challenging economic environment.

Nevertheless, forward-thinking businesses have an opportunity to use the time and space outside their busy office wisely. They have the chance to think about how they can improve their business practices so they can come back stronger and ensure they hit the ground running once the crisis passes.

One of the most important elements marketers need to consider is their customer data. Having access to clean and accurate customer data holds the key to helping organisations survive and ideally thrive in these troubling times.

Good and bad data

The issue is the mindset of many marketers. Too many view their customer data in a binary way – ‘good’ or ‘bad’. Just because data might be missing an element or be seen as old, doesn’t mean it’s ‘bad’ and should be discarded. Also, in today’s big data world it’s difficult to classify data in this way, especially when the driving force behind delivering improved marketing and business success is derived from effectively analysing and cleverly using customer data.

With no easy definition of what ‘bad data’ is, it’s up to each organisation to define it. But the question marketers need to ask themselves is should a whole record be purged because it’s missing a phone number, or the address is wrong, for example? After all, usually all the user defined ‘bad data’ needs is some cleaning that could help you reengage and sell to a customer, which could help your business survive in the current economic climate. Therefore, can brands really afford to discard customer data in these challenging times? The answer is no.

Clean data

The fact is without regular intervention, customer data degrades at two per cent each month and 25 per cent over the course of a year – a major issue for brands. Additionally, in an age when customers are increasingly providing contact data via their mobile devices, mistyping contact details on a small screen has become a problem. In fact, approximately 20 per cent of addresses entered online contain errors such as spelling mistakes, wrong house numbers, and inaccurate postcodes.

The good news is data that is simply incorrect, such as customer name, address, email or telephone number, is an issue that can be easily fixed. Industry leading data cleansing, standardisation and verification services can provide data quality in real time for new data capture and onboarding, as well as in batch for held databases and existing customer records. It’s the best way to help unlock the insight within ‘bad data’ and help make ‘bad data’ good. This is important with studies revealing that it costs five times as much to acquire a new customer as retain an existing one.

Address autocomplete

The customer onboarding stage is the best place to put procedures in place to ensure you are collecting accurate data. This requires the use of an address autocomplete service. It’s able to automatically recommend the correct version of the address, in real time, as the customer completes an online contact form, promoting the selection of the one that’s not only accurate but easily recognised. It solves the issue of mistakes caused by ‘fat finger syndrome’. It also reduces the number of keystrokes required when typing an address by up to 70 per cent, accelerating the checkout process and lowering shopping cart abandonment.

ID verification an important focus

Another way to ensure ‘good data’ when onboarding is to take customer checks and verification to another level to protect against fraud. With data breaches increasing, along with the number of criminals posing as legitimate consumers, it’s more important than ever that brands match a particular name to a specific physical address, telephone or email. They must do this to ensure the customer is who they say they are; and undertake these checks in real time to deliver a standout user experience. This requires sourcing and having access to a global dataset of billions of records containing data from trusted country specific reference sources, such as credit agency, government agency, utility company and international watchlist data. Furthermore, such a dataset can also be used to verify the end user’s age to ensure they are legally entitled to the product or service offered.

Also, it’s important to take the opportunity to enrich customer data and fill in any gaps as part of the ID verification process. This helps to deliver a 360-degree single customer view (SCV) – something that can aid future marketing and sales efforts.

Artificial intelligence (AI)

AI in the form of machine learning semantic technology should be considered to deliver ‘good data’ and in-depth intelligence on existing customers. Semantic technology, or semtech, associates words with meanings and recognises the relationships between them. It works by delivering powerful real time connections between customer records – combining the missing pieces of customer data to support an informed decision about whether to provide a particular product or service to a customer. Machine reasoning does this by filling any gaps in information left by the customer during the onboarding process or via other communications. This AI not only improves data quality, it delivers the information that empowers organisations to make informed decisions around the products and services it offers to customers.

In summary

In today’s challenging economic environment customer data should never be considered ‘bad’. All data should be  perceived as valuable as it may hold the key to surviving the current crisis. All it takes is for far-sighted marketing professionals, and others, to recognise and deliver best practice with data quality. This will make their data ‘good’ and ensure they maximise value from it to drive growth and profitability.

Barley Laing

Barley Laing


Barley Laing, UK Managing Director at Melissa.