You wouldn’t expect to arrive at your destination without following directions. So why would anyone invest in B2B digital marketing without a roadmap? The reason so many fall short when it comes to data-driven marketing is they lack direction. And social media engagement, website usage, email marketing data are the digital breadcrumbs you use to find your way. In B2B customer journey mapping, your first order of business is making sure you’re accurately tracking how people consume your data. If you’re using a service like Shopify, that means you need to track sessions across subdomains in the same account in order to measure the path visitors take between your website and your Shopify subdomain as one session. It also means you should be using Google Tag Manager to run Google Analytics, Google Search Console and Hotjar in your domain and subdomain. That way, you can track the path tp purchase, regardless of how it transpires. If you want to make sure you’re getting the absolute most out of the data you are collecting, you’re in the right place.  Thanks to my friend John Wall, a partner at Trust Insights, I’m going to break down what data-driven marketing is, how machine-learning may or may not influence the path to purchase, and how you can use data to create a B2B customer journey that drives sales.  Or you can listen to my interview at the top of this post and hear it straight from him. A quick lesson on data-driven marketing What is data-driven marketing? Data-driven marketing is a method that uses data from customer interactions to: Enhance the customer experience, which basically means making it easier for people to get what they need from your website. Online, ease-of-use drives adoption.Optimize marketing performance, which is focusing on how quick your site loads on desktop and mobile. Google penalizes sites that load slowly, so it’s important to make sure that’s not getting in your way.And increasing revenue, which is about targeting the right customer, in the right place, at the right time. This is sometimes called a target audience, which is a kind of tacky expression, since it plays off a hunting metaphor. SimilarWeb and Talkwalker are tools you can use to find customer-rich environments. “Data can show which programs are the most successful...and what terms are trending at what times so that you can time your content and your advertising to take advantage of it,” says John Wall. It is worth noting, though, that the amount of data you have at your disposal can vary.  With small B2B companies, you may not have enough traffic to collect a lot of data. Larger scale companies and most B2C brands are using machine learning to find insights, but that requires a big enough data set to be statistically relevant. Small companies can accelerate the acquisition of user data by buying traffic through paid media. And we’re only just beginning to integrate data from the internet of things, which are devices with GPS tracking that are tethered to the web. In his best-selling book AI Superpowers, former Google China President Kai Fu-Lee writes, “Smart Finance’s deep learning algorithms don't just work too obvious metrics, like how much money is in your WeChat wallet. Instead, it derives predictive power   from data points that would seem irrelevant to a human loan officer. For instance, it considers the speed at which you typed your date of birth, how much battery power is left on your phone, and thousands of other parameters.” As the amount of data points we have to work with grow exponentially,