A Brief Guide To Data Management

Managing your customer and prospect data should always form the foundations of your business

Accurate data is essential. It means you’re able to truly understand how many customers you have, how often they spend, how you both interact and how they like to be communicated with. It means when things change for them, you’re always up to date.

Customer data management increases accuracy in most analysis projects such as Share of Wallet, Basket Analysis, Market Penetration, etc. Importantly, at Person level it ensures your customers are communicated to correctly. You could send a customer a discount offer because they’ve lapsed, but they’re actually spending on a different account. This is both damaging to profits and the perception of your business.

Knowing how many customers you truly have enables you to drive your business without wasting resources

It’s important to recognise that the definition of 'customer' can differ depending on your objective. Especially in the B2B world, you must be conscious of parent companies, brands, locations and departments in addition to people. Understanding the detail around the relationships within your data helps with targeting influencers, focusing on decision makers themselves or taking an account-based marketing approach.

Customer data management can be complicated, as while the aim is to build automated systems and processes for managing data, inevitably there will need to be steps that require human intervention.

Having clean data improves your business and it enables collaboration with other data sources

Good processes for cleaning and managing customer data opens many doors, enabling you to uncover new opportunities, reduce costs elsewhere in the business, increase the deliverability of your marketing communications, integrate easily with external data sources and better manage your brand perception.

Think of your data as an asset, you should do because it has value. If you own a building, it needs maintenance to keep it in good order and make sure it is fit for purpose, so you put the effort and resources into its upkeep. Your data is the same, if it isn't managed and treated properly, it becomes less fit for purpose and you can extract less value from it.