During a crisis it’s vital all data is ‘good’
With the economy starting to ramp up again as the lockdown eases, it’s time for those that have not yet done so to look at how they can improve their business practices to drive growth.
Improving business practices to provide clean and accurate customer data is particularly important, because having such data will help brands to survive and ideally thrive in these difficult times.
Good and bad data
To deliver clean customer data the mindset of marketers needs to change. Firstly, many need to step away from the traditional binary view of data as ‘good’ or ‘bad’. Just because customer data might be missing an element or be seen as old, doesn’t mean it’s ‘bad’ and should be discarded.
While the definition of what is ‘bad data’ is up to each organisation to define, 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?
Usually all the user defined ‘bad data’ needs is some cleaning that could help the brand to reengage and sell to a customer. Marketers, therefore, simply can’t afford to discard customer data in these challenging times.
What marketers have to realise is that without regular intervention customer data degrades at 2% each month and 25% over the course of a year.
This is exacerbated by more consumers providing contact data via their mobile devices. Inputting data on a small screen increases the risk of mistyping. In fact, approximately 20 per cent of addresses entered online contain errors such as spelling mistakes, wrong house numbers and inaccurate postcodes.
Issues with incorrect data, such as customer name, address, email or telephone number, can be easily fixed. Industry leading data cleansing, standardisation and verification services provide data quality in real time for new data capture and onboarding, as well as in batch for held databases and existing customer records. They help unlock the insight within ‘bad data’ and help make ‘bad data’ good. This is very important with studies revealing that it costs five times as much to acquire a new customer as retain an existing one.
It’s always best to collect accurate data at the customer onboarding stage, which requires the use of an address autocomplete service. Such a tool automatically recommends the correct version of the address as the customer completes an online contact form, in real time, solving the issue of mistakes caused by ‘fat finger syndrome’. It also saves up to 50% in data entry time and simplifies shopping cart checkout.
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. This is an important approach for brands to take with an increasing number of data breaches, along with criminals posing as legitimate consumers.
To ensure the customer is who they say they are brands must match a particular name to a specific physical address, telephone or email, ideally in real time, to deliver a standout user experience.
To do this requires 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. A further benefit is such a dataset can be used to verify the end user’s age to ensure they are legally entitled to the product or service offered.
It’s also important fill in any gaps and enrich customer data 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. It’s a form of AI that 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.
All data has value
It’s time to move away for considering data as ‘bad’ in today’s challenging economic environment. Instead, all data should be recognised as being valuable as it may hold the key to driving growth.
All it takes is a best practice approach with customer data quality to ensure the data is ‘good’ and value is maximised from it to drive growth and profitability.
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Barley Laing joined Melissa in 2014 during an exciting expansion phase of the California headquartered company.
As UK Managing Director with 15 years of data industry experience, his role is focused on meeting the data quality and compliance needs for organisations in the UK and worldwide.
The team that Barley heads at Melissa’s...