Lack of coordination between marketing and sales is a problem which plagues many companies. It’s a strange thing, given that their objectives are identical: generating and converting leads.
And that’s the crucial point; if there’s any (professional) friction between your sales and marketing teams, the cause is usually purely operational.
For example, sales and marketing might each have their own criteria for what makes a Sales Qualified Lead. In reality, they may both be right, or at least partially right, but simply coming at it from different perspectives.
Another cause of friction can be much more basic: vital intelligence isn’t making it from marketing to sales. So what seemed like an obviously qualified lead to the former doesn’t appear so to the latter. This can cause frustration at both ends – sales will be frustrated at being handed what they believe is an unqualified lead, while marketing will fume at sales dumping a hard-earned, qualified lead.
Or vice-versa: certain leads may be failing to convert, but sales aren’t communicating who and why, so marketing keep fielding them the same type of faulty leads.
Or maybe they’re just not communicating, period. B2B lead generation is high-pressured stuff, and both sales and marketing may simply be struggling to fulfill their own respective targets irrespective of each other.
Artificial Intelligence can help streamline cooperation between marketing and sales
There are no quick-fixes to a systemic problem like this. But incorporating Artificial Intelligence (AI) into your B2B sales and marketing stacks could hold the key.
Specifically, AI-powered predictive analytics can potentially put marketing and sales on the same page – if applied correctly.
No, simply running a machine-learning algorithm won’t make them communicate better – though it may at least give them a common playbook to work from. But a “semantic model” – combining machine learning with human input – can provide an ideal platform for coordination.
We’ve talked before about how the expertise and experience of your sales and marketing reps should inform your Ideal Customer Profile. Certainly, it will make your predictive model more accurate. But the process of doing so can also help the two teams exchange perspectives and align their approaches.
Predictive analytics as a basis for cooperation
First of all, running a machine-learning predictive model will provide an all-important objective, data-driven road-map to start with. (It goes without saying that a prerequisite to all of this is ensuring a high level of data quality.)
At this point, marketing and sales can (and indeed should) have their input.
This process itself presents an ideal opportunity to facilitate an open exchange of ideas and feedback between departments. Valuable professional experience can be thrown into the mix – including nuances and personal relationships machine-based models can’t account for. Marketing and sales can also use the opportunity to identify key pipeline bottlenecks more generally, and establish more effective modes of communication.
Marketing and sales – we’re all on the same side
The end product will be a model that both sides understand and appreciate. Marketing will know what types of leads to target or to avoid, and sales can be confident that if a lead makes it to them, it’s sales qualified. Equally, your SDRs will be directed to only the best prospective leads.
That will mean greater efficiency and productivity, and higher conversion rates.
To find out more about how AI-powered predictive analytics can help you build a more reliable pipeline and generate impressive ROI, download the free case study below:
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