AI is a result of big data management. It helps to create algorithms and training tools for AI development. Basically, through various avenues available on the internet a large amount of raw data is collected from the sources then it is mined and patterns are identified with that we can use later on to design the artificial intelligence systems. The foundation of AI is to just act as per the algorithms and help to predict the possible future output. It can help us to accurately measure the result and analyze the different consequences of actions on the basis of input.
Data Analysis in marketing can be step by step explained as below:
1. Collection:
The data can be collected that for how much time the customers spend on the website, what is time period they are browsing on a particular section and what are the touch points. The marketing campaign reports on different social media: Instagram, Facebook, LinkedIn, and Twitter can be generated. The raw data includes shares, likes, conversions, comments, impressions, and clicks.
2. Measurement:
The measurement of data is a very crucial part to know the effectiveness that is Web traffic: Reach and frequency of the visitors on the site. The conversion rate is to be tracked by analyzing the traffic and the amount spends to know the lead generation cost.
3. Optimization
After measuring and knowing which audience can be tapped. Further, we can optimize the data by exactly targeting the customer’s behavior traits: where they are coming from, and what their purchase intent is, you’re better able to group them together and identify potentially unmet needs for distinct subsets of customers. This will help to increase the ROI of the firm and produce the quality of the leads.
4. Prediction
The most important part is planning strategy by studying past data properly. Many times the huge data might be able to draw some bigger patterns but marketer fails to understand. So a proper framework with parameters predicting a link between the input and leads should be established.