How AI driven data modeling can help your marketing
When we hear the term data modeling, we think of data scientists pouring over a humongous amount of data, working on high power computers and spending weeks or even months iterating and testing their models.
However, the game has changed with the emergence of AI-based data modeling tools. These AI tools are powered by machine learning which enables the tool to train itself based on historical data and create algorithms suited to specific use cases. Some of the benefits of AI-powered data models are:
- Enables users to build and deploy highly accurate machine learning models in a fraction of the time it takes using traditional data modeling process.
- Automatically tests hundreds of advanced algorithms to build the optimal model, delivering predictive insights to the business.
- Users can view the most important drivers of their business metrics, identify keywords within the freeform text, and understand the logic behind each decision the model makes.
For the AI tool to work, it has to have some historical data to run its machine learning on. This historical data set should be in a structured format. We recommend keeping each and every data field available, as the tool may highlight a field which has a high impact, which we might think is not relevant.
The AI driven data modelling approach can help marketers be creative and bold in what they can do with the data available to them:
- Multi-channel marketing attribution: Businesses want to know what marketing activities are giving them the best ROI. Data modelling tools can provide the answers to queries such as:
a) How different channels interact?
b) Which channel is truly driving sales?
c) How attribution has shifted between channels from time to time?
- Campaign Segmentation: By training itself on the past campaigns, AI can help identify & acquire prospects with attributes similar to the existing customers. This next-gen of look-alike modelling can work wonders for the business
- Predictive Scoring: AI can prioritize known prospects, leads, and accounts based on their likelihood to take action. The sales team can then focus more on nurturing the right contacts.