Technology continues to push the boundaries of what’s possible for marketers. While most marketing executives understand that software can drive growth, not everyone is familiar with the underlying technologies that power the latest tools.
That’s okay because your job is to put software to work, not put it together. But a basic understanding of how some of these tools work can allow you to make better decisions about where to invest your budget. With that in mind, let’s take a look at one Technology that is typically accompanied by a lot of hype but little clarity: Deep learning.
Modern marketing teams are concentrated on full-funnel engagements and want to track every interaction prospects have with their company through the website, content, events, and elsewhere. This data can potentially give them a holistic view of the factors and touchpoints that move prospects down the funnel to the sales team.
Unfortunately, today's marketing teams can suffer from something that only a decade ago would've been considered an enviable problem: too much information. This leads to a number of often contradicting models for gauging the return on investment of different strategies and a general lack of understanding about where and when to engage.
Enter Deep learning. It's a particularly powerful form of machine learning based on neural nets (mathematic operators that simulate the structure of the human brain) and is extremely valuable when you have vast amounts of reasonably structured data. It can help marketers understand client interactions on a granular level and predict how those interactions will lead to particular outcomes.
Moreover, because deep learning can consider thousands of potential decision paths at once, the predictions are significantly more powerful than what can be done via traditional statistical methods. To get a better idea of how this is possible, let’s look at two use cases: lead prioritization and sales alignment.
1. Is this company or person a good fit for our business? 2. What do lead actions tell us about interest?
It’s far from an exact science.
Consider two potential leads. Both are founders of startups, have C-level titles, and sell to similar industries. However, the non-founder executive teams have significantly different experiences and abilities.
Using traditional methods, you'd probably score these leads similarly, and your marketing would reach both. However, the ROI might be significantly different. With deep learning tools, a program can analyze different types of background information for each and interpret those factors in an interconnected way to tell you whether an individual matches your product rather than your persona.
If you’re following an account-based marketing strategy, your focus is on a smaller number of top-tier accounts and the key stakeholders at each. Your goals aren’t necessarily the same as those of a traditional inbound marketer; they’re probably more tied to sales organization and supporting the processes that impact revenue.
Deep learning can tell ABM marketers which buyers associated with an account are most important to target, where other likely influencers sit in the account, and how likely an account is to convert. It can even help you align your resources around the success criteria that are most important to your organization.
Modern marketers are ahead of the curve in implementing artificial intelligence-powered tools, as just over half already rely on at least one to get customer insights or make workflows more efficient. Whether you outsource a deep learning solution to a vendor or bring that expertise in-house (if you already have a dedicated data science team), here’s what marketers need to do to make deep learning work:
With any type of deep learning, the most important thing is understanding what outcome you’re trying to optimize for and then building an effective training set that helps you get there.