In the first two articles in this series of three, I approached the question of data requirements from a strategic point of view. I looked at how a robust data plan can both reflect, support and even direct the overall business model. Data acquisition for new and growing businesses represents first the search for the ideal customer, then the profile of an actual customer or prospect.
This time we are going to turn that on its head. We will move from data as a guide to strategy to data as a tactical asset to be used for execution.
For larger and more mature businesses the focus changes. If you are a large and mature business you will have done a lot of things right! Marketing and therefore data strategy will be firmly in place, reflecting experience in the field. As we move from strategy to execution the key question is: can the data you have acquired through various methods support the marketing programs you are deploying, not only today but in the future?
Mature companies’ data and how to leverage it
Let’s look at the requirements of four crucial B2B stratagems that will enable you to stress-test your data set. These are not in any order of priority and the part any of them play in your plans is up to you. There is also a degree of overlap between the programs in both function and requirement. But it’s likely that all of them will form some part of your overall plan.
Before we start looking at these matches in more detail, let’s have a quick refresh of the core elements of a B2B data set. The basics are universal, even if they differ slightly from sector to sector. Additional data will likely be needed for the different programs, as we will see. You may well have to look at a number of different suppliers to get all these covered, especially when looking at international coverage.
So how do these rank for different marketing applications, and what additional data do you need? Back to the programs:
Demand generation
Effective demand generation messages have to be personal, timely and relevant. It is crucial therefore to establish preferences around communication preferences (email, phone, etc.) and to keep that data accurate and recent. You must have enough information at the enterprise, site and contact level to build segments and personas. Is the contact an influencer, decision maker, stakeholder, or potential hurdle? This will likely require effective links between the various components of your marketing stack and your sales CRM tool.
Account-based marketing
All the components above are needed – but priority has to be given to accounts based on the models you have created, potentially linked to intent data gathered from third parties. Intent data comes in a variety of forms and disguises,and you have to be careful in your choice. This is not so much a question of picking a good supplier over a bad one, as ensuring that you know what you are buying.
Intent data comes in three basic bundles:
- Activity discerned from the web and other sources and social listening, that indicates an organisation is ramping up interest in a certain challenge or solution (cybersecurity, ERP, e-commerce and so on.)
- Predictive models based on current installs and their lifecycle
- Actual engagement through ads and calls made by the data provider that get closer to a true lead.
Obviously, each of these has a sweet spot at different stages of the funnel, and you will likely want to combine all three at some point.
Programmatic advertising
Depending on your corporate view of permission marketing and consent, and the new GDPR restrictions, it is likely that programmatic ads will play an increasing role in your marketing mix. Make sure that you can easily exploit the right advertising opportunities at the right price. This is perhaps not so much about what data you have as how it’s organised.
Inbound marketing
As you focus on turning an initial connection to a true engagement, you will need to put a premium on data standardisation and accuracy across the marketing stack. This leads directly to enabling you to track the buyer’s journey and ultimately to ensuring a good customer experience. There is a tendency now to spend a lot of money and resources on marketing platforms while neglecting the data basics. Data must be accurate, it must be standardised, it must be consistent across tools, it must be ‘matchable’. Processes must be in place, data governance must be clear, and sales, marketers and technical teams aligned.
Just before an election, an Australian prime minister finished his campaign with the rallying cry: “Life wasn’t meant to be easy!” Obviously, he lost. Data isn’t easy. But with effort we can all get it right!
Have an opinion on this article? Please join in the discussion: the GMA is a community of data driven marketers and YOUR opinion counts.
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