Let’s make it a walkover for you to understand what I mean through this scenario. A startup insurance company had to put its shutter down. It was a wind up call for his business. The clientele was contracting; revenue was frequently rolling down; sale of policies was very steep and the profitability became a dream. But the entrepreneur decided to bounce back. Rather than building castle in the air, he chose SAP-based predictive analysis.
What’s SAP predictive analysis?
As foresaid, the prediction stands for foreseeing. In conjunction with SAP or System Applications and Products, it determines the analysis of large data sets to draft future outcomes and customers’ behaviours. SAP predictive analysis derives sense by combing through crude data sets. Unseen opportunities, better customers and uncover hidden risks are tapped to make the predictions.
By employing this analytics, that entrepreneur decided to kick-start a marketing campaign. But he couldn’t hit the bull’s eye until collecting valuable customers’ data. So, the very first thing he did was data mining. It identifies the process of scraping data from various resources. Let’s check how he tuned that stone to catch on meaningful data:
- Data Sources: For deriving sense, the want of resource pops up in the mind. But it was a no big deal since digital landscape has it in abundance. He dug out valuable contact details from Google.
- Type of data mined: While scraping customers’ data, his mind had the blueprint of what he required. Not just any but meaning should be there in the data to make out sense. And digital landscape has efficiency to provide real-time data. So, he funneled out preferences, click streams of the customers (that are on his competitor’s site), types of devices that stream the leads, apps installed, popular social network usage, preferred browser and search patterns. Finally, he had the arsenal to explode the sales.
- Analysis of medium: By analyzing the data collected from various resources, he conceived some exclusive future plans. These plans had the traces of customers’ want, behavior and preferences. Hot selling policies were before him. He was no more a greek to customers’ requirements. Even, the digital analytics derived the list of their intentions. What more he wanted for analysis (nothing)!
- Drilling the business objects: By SAP (Systems, Applications and Products) business objects, like devices, data sources and media, & predictive analysis, he built up business intelligence. He comprehended and examined the data gathered to unearth customers’ buying patterns.
- Data structuring: Finally, data sets were created to target specific groups of people, which included the key influencers and socials groups to cater his offering.
Which predictive strategies did he derive through analysis?
- Finalized the streams of leads (interested customers) from where data was incoming.
- Used predictive models, such as social media analytics, affinity modeling, classification and recommendation to offer to the customers.
- Prepare a list of target audience to dispatch offers. Segregated it as per its requirements so that the idea to draft ads, newsletters and emails, personalized direct mails that consisted of tailored policies to generate leads.
- Forwarded the offers to the large target groups and audience with whom he built up connections to share prospective insurance policies on social media. And later, those groups would follow the same fashion to claim discount on the offerings.
- Eventually, its fat revenue as return of investment (ROI) that stated the success of predictive analysis.
- Later, he would be able to further the spinning of revenue by looking in to business problem, assess data, feature engineering, modeling, operationalization, business outcome.