Among the most perplexing challenges facing demand gen marketers — and B2B marketing and sales more generally — is the difficulty seeing your customers through a never-ending mass of data, which only keeps growing.
According to a recent industry study, most existing B2B marketing technologies do not adequately address this problem. Rather than making sense of all that big data and creating a clear picture of marketers’ audiences, most B2B companies’ existing sales and marketing stacks simply complicate things further, by adding poorly-integrated silos containing incomplete and often conflicting data.
So, instead of automating the boring, data-heavy aspects of their work to focus more on the creative side of marketing, B2B marketers are spending more time than ever pouring over data, data and more data. And, as the Marketing Technology Industry Council survey revealed, they’re mostly doing so to little effect.
But it doesn’t have to be this way. Advances in B2B martech such as Artificial Intelligence and predictive scoring have opened up a unique opportunity for marketers to finally harness their data to its full potential, and use it to identify, target and engage their audiences.
So, what should an effective audience-centric platform look like?
Single Source of Truth
The most fundamental aspect of the data dilemma facing B2B marketers today is that they are using multiple data sources — many if not all of which are not fully integrated, and contain conflicting information.
This can include first-party data from their CRM, Marketing Automation Platform and event forms; and third-party and unstructured data from sources as varied as data vendors, social media and Google searches.
It’s time to move on from this rudimentary, frankly rather backward way of processing data. Marketing organizations need a single source of truth — a way to blend, cross-check and verify multiple data points to the highest possible level of accuracy, ready to deploy across all sales and marketing channels.
Individual and Company Data
Put aside account-based marketing for a moment. The simple fact is that modern B2B marketers and salespeople are both expected to have a holistic picture of both the leads and accounts they’re dealing with. In this age of big data-driven business, having sales and marketing speaking different languages (accounts vs. leads) isn’t just an inconvenience — it has the potential to seriously hamstring your business.
Modern B2B marketing stacks must provide comprehensive, detailed data on both tiers: the individual and company level.
And it’s not enough to dump the relevant lead and account lists in your CRM or Marketing Automation Platform either; you need to tie the two together by accurately matching leads to accounts, to ensure full alignment both within and between sales and marketing.
In-Built Intelligence and Scoring
Batch-and-blast marketing, if it ever worked, is a thing of the past. Today’s B2B audiences expect you to speak to them and their needs directly.
Businesses need actionable intelligence to know who the right people are, how to reach them, what messaging they are receptive to, and when to act.
AI-powered tools such as predictive scoring enable marketers to do just that, by helping to prioritize and segment leads according to their propensity to buy.
But the applications of AI extend beyond basic lead/account scoring. Advanced machine learning offers new and exciting ways of processing and refining intelligence on your audiences, by monitoring and learning from your sales cycles — both those that close, and those that don’t.
At the start of our list, we mentioned the problem of multiple, siloed data sources. While that is indeed a problem, the solution is not to attempt to reinvent the wheel by drawing data from a single homegrown database. Rather, B2B marketing organizations should seek a solution which effectively aggregates and cross-checks multiple data sources, to provide the widest available market possible.
Marketers need to have a 360-degree view of every lead and account — ranging from basic insights like job title, age and company name, to more granular, personal insights for each contact, such as job function, industry and technologies they’re already familiar with. To do that, you need to draw upon a truly vast range of data sources — but the information must all be processed together, on a single platform.
Real-Time, at Scale
Of course, none of the above will matter in the long-term if the underlying data is constantly deteriorating. An effective audience-centric martech platform must constantly refresh itself, so marketing and sales alike can rest assured that the data they’re working with is fully aligned and accurate.
Image credit: Pixabay | CC0 Public Domain
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