3 common pitfalls when implementing a data-driven marketing strategy

The most effective B2B marketing campaigns start with a nice clean dataset – for ABM especially, but in general if your database is in bad shape, you’ll create a lot of other problems for your campaign further down the line.

In this article, our Chief Data Officer Gina Goanta explores some of the common traps that marketers fall into when planning their strategies and how you can address them.

The most common data strategy mistakes

1. Poor definition of the best customer profile

All businesses have a dream customer who they’d like to partner with for whatever reason – could be their purchasing power, their values, the reputational boost of working with them etc. But is this customer actually buying from you? Focusing on who you want to sell to rather than who actually buys from you can set your whole strategy on course for failure, as you’ll end up focusing on an audience that isn’t necessarily a good fit.

Marketing and sales really need to align from the beginning to define the best customer, not just the dream customer. Building that ideal customer profile (ICP) requires great data analysis – who buys, how much, where upselling and cross-selling happens – supported by predictive intelligence models that help identify similar accounts to target.

2. The “once and done” approach to data strategy

Too many people see data strategy as a sprint, one good clean run and its done. The problem is of course that data ages pretty quickly – you need to be able to update buying signals, technographics and contact information on a regular basis, otherwise your database becomes completely irrelevant. It’s incredibly important to stay on top of account structures and hierarchies within multinational or multi-business companies so that you know who to reach out to and when.

Maintenance and ongoing enrichment are absolutely essential, although both are very hard to deliver in-house since they require many skillsets at different stages. An external data service can really help here since you can access the right specialisms as and when you need them.

3. Lack of alignment between budgets and stakeholders

A challenge that we often encounter is that data cleaning and maintenance is paid from a different budget than the original purchase of the dataset, making it very difficult to track exactly how costly a bad data strategy can be. It means that the team feeling the impact of the flawed data are very far removed from the team who requested it in the first place. Whoever is going to use the data must be involved from the beginning. 

How we can help with your data strategy

Working with an external data provider can help address all of these things. By working with us, you’ll be able to tap into the following:

  • Experience – we’re experts at building datasets for global technology companies like Oracle, SAP and Google. We know exactly the challenges that companies like yours are facing, and we know the questions to ask to avoid issues coming up later. We’re also experts in GDPR so you can be sure that your data will always be compliant.
  • Coverage – we have access to over 81 million contacts globally, covering all industries across 90+ countries. We use multiple third-party sources for firmographics, technographics and intent, and have a team of 10 researchers who generate our own first-party contact data, supported by proprietary AI.
  • Flexibility – we have all the skillsets you’ll need in-house – from data researchers to strategists to architects, CRM and AI experts. This means that whatever you need can be handled quickly, and we can accommodate shifts in verticals or geographies at short notice.

You can learn more about our Data Services here, or you can get in contact with us directly.