Today, the amount of data companies collect can be overwhelming. This is especially true in marketing, where teams can get so bogged down with big data they may decide to shrug it off as futile—and in the age of AI, that’s a mistake.
Utilizing the right data is important. When properly implemented, data-based decisions can drive nearly every part of marketing, from content and messaging to focus areas and investments. Sifting through what is important and creating a signaling system for your sales team is where the magic lies—and this where most marketers are stalling. We have the data. Now what? What does it mean? And beyond that, how do we use it to make a difference?
To keep up with the competition, it’s imperative for every marketer to connect their data silos now in preparation for the next generation of decision making: the application of machine learning and artificial intelligence (AI). If you’re not keeping your data clean and structured, you may be swept away by the huge wave of AI set to revolutionize marketing as we know it. Here are three components to have in place that are necessary to be ready:
Data Collection Automation
Big data isn’t just about endless amounts of information, it’s also about different sources of information. In order to position your data so it’s ready to be used by next-gen machine learning, it’s important to look at how and where you get that data in the first place. Data that is collected automatically is generally better data. There are many relatively inexpensive, automated ways to collect information—just be sure that as you’re collecting the data, it stays relevant to your needs.
Utilize predictive analytics to narrow down the data variables that are relevant. Then, find better ways to collect and structure that data because not all data is created equal. By knowing what data is important, you can prioritize data collection and cleaning efforts. Being prepared is key to making machine learning worth the time and money.
Machine learning is the most powerful weapon in a marketer’s arsenal, as it takes the difficulty out of making important connections. Once your data is structured and interconnected, the machine will generate connections you could never have dreamed of, such as running thousands of correlations at once and boiling down big data into meaningful and useful pieces of information. You might think your ideal market and prospect are one thing, but machine learning could reveal that they’re actually something else. It may present new, ideal prospects or growing segments—that would otherwise be missed—that actually purchase at a higher rate than your current markets. Machine learning provides insights that can drive your product or service, allowing your business to gain new customers along the way.
Where to Start
Marketers that understand how to employ the smartest tools to get out of their black hole of big data are imperative—but what is the best way to begin implementation? Start with a few areas or markets that could create the greatest value and create ROI model. Then, use data that you’re already collecting or that you have on hand and can easily implement. Run a few tests, and, if they’re successful, apply the tools. Once you’re up and running, use machine learning to supercharge your marketing program and take it to the next level:
- Identify more granular, ideal customer profiles by predicting down-to-the-individual-level purchasing behavior. Use these profiles to create likely-to-purchase leads that your outbound sales teams can go after.
- Understand content that is most likely to reach the target audience and bring in “golden customers.”
- Prioritize inbound leads based on their likelihood to purchase before passing them off to your sales team.
- Enable customer success teams to predict when a customer is about to leave or is ready for an upsell.
- Implement a sales forecasting model to anticipate future sales performance figures.
Organizations that already employ big data initiatives are more efficient, can direct their marketing campaigns to yield results, are better able to target the most fruitful prospects, and, most importantly, will be positioned to take advantage of the power of machine learning.