1. Creating Personalized Content
The amount of data capture points for businesses has led to an overabundance of data that is often neglected and not put to use. However, when machine learning is brought into the mix, incomprehensibly vast datasets can be digested and converted into actionable insights.
For example, if a lead falls into segment “J”, have them be presented with experience “J” as a result. In an ideal world, every individual piece of content would be developed for an individual target—this is a step towards that reality.
Bookmark.com has taken the machine learning approach to build individually tailored websites based on the profiles of a business’ clients. The end result is not only an entirely unique website, but also one that is specifically built around the triggers that the customers are most likely to take action on.
An advanced machine learning algorithm that you’ll find commonly used for personalization is collaborative filtering. Amazon uses it on an item-to-item basis to recommend similar products to customers that have been marked by the system as having an affinity towards them.
Overall, machine learning insights can be used to deliver hyper-personalized content that resonates with the intended audience on a level much deeper than through a traditional content strategy. Simply A/B testing content can lead to higher engagement—combining it with personalization can be a game changer.
2. Visualizing data
The requirement being able to read the data generated by machine learning models prevents it from being used by those who would benefit most from it.
By combining data visualizations with machine learning, complex findings can be refined into easily accessible formats. This powerful capability can even allow access to actionable insights in real time, when they are most ripe to be deployed. Even companies with continuous data streams can rapidly execute and measure in order to optimize processes.
To get an idea of what machine learning data visualizations can look like, check out this post by R2D3.
Visual insights can apply to cases on a very minute scale such as a specific time or individual, or they can be applied to broader situations where more general findings are crucial. In any case, the end result is a more thorough understanding that is no longer kept within the confines of data teams. It can extend to your front-line sales teams or be used by your marketers to better equip their campaigns. No longer will a company only have a vague strategy outlined by management to strive for. Data visualizations that can keep every department in tune with the objective reality that the data is showing will profoundly change how we think about our roles.
3. Predictive Scoring
Predictive scoring makes cold outreach warm. Rather than treating every lead with equal priority, machine learning algorithms can weigh filters from sentiment on a topic, mutual connections, indicated interests, social media keyword scraping, and more, to show you who you should be reaching out to. Hurdles to creating an effective model mainly reside in data quality and robustness. Even the most state of the art models are at the whim of the quality of data being fed to them. Once in place however, a predictive scoring model can yield efficiencies across the board.
Basix Concepts takes predictive scoring a step further by not just stopping at identifying the best leads—it dials and emails them automatically for you. Coupled with a sales system that captures data, Basix becomes not only your tool, but your partner in outreach.
Does this sound like something that you think your team would be suitable for? Reach out to us today and we’ll schedule a demo for you!
Part 2 coming soon…