By Reddy Balaji C
Introduction
Introduction
In a globalized business environment, one of the major concerns for any business is to retain the existing customers, rather than attracting new ones. The cost of migrating to a new vendor for a customer is considerably low and much easier. It has become a challenge for the banks to enhance or tailor the offerings and to extend the banking product features or to unveil the untouched business areas. Customer spend analysis is a critical success factor for any business.
Challenges in Spend Analysis
Though spend analysis is a useful activity for any company, there are number of constraints in adopting it:
Spend Analysis solution helps banks / customers to visualize a consolidated view of the complete spending behavior spread across complex, multi-level accounts across several types of offerings. It encompasses the activities starting from data collection to intelligent business information derivation, which gives in-depth insight about the spend patterns. The analytics outcome is represented in highly dynamic and interactive dashboards.
Core Areas of Spend Analysis
These are the different analytical attributes that can be used for exploration. Further, analysis is performed instantly (in-memory analytics) and the reports are generated in various formats.
Since huge volume of data can arrive from different sources and its formats may vary depending on the sources, it's a challenge to follow the traditional BI approach and attain value out of the received data. To leverage huge and heterogeneous data scenarios, big data technologies like Hadoop can be adopted and analytics can be applied to derive knowledge out of the data. Since Hadoop can ingest different formats of data very easily in its file system, it becomes really simple to process this data in a distributed manner. Along with the static analytics on historic data, predictive analytics moves one step forward by calling out unique indicators for past events, which can be used to derive futuristic information.
Conclusion
Analysis of the available data and strategic decision making is imperative in large organizations. Spend analysis helps corporates to perform systematic data collection and extensive analysis on the data. Also, the predictive algorithms implemented will give futuristic projections, which will contribute in effective decision making.
Challenges in Spend Analysis
Though spend analysis is a useful activity for any company, there are number of constraints in adopting it:
- The required information is spread across various sources like general ledger, accounts payable, bank transactions etc. So, data consolidation and bringing it to a standard format is challenging
- Some important information like merchant information, product category may be incomplete or incorrect
Spend Analysis solution helps banks / customers to visualize a consolidated view of the complete spending behavior spread across complex, multi-level accounts across several types of offerings. It encompasses the activities starting from data collection to intelligent business information derivation, which gives in-depth insight about the spend patterns. The analytics outcome is represented in highly dynamic and interactive dashboards.
Core Areas of Spend Analysis
- Data integration
- Data cleansing and transformation
- Dashboard and advanced analysis
These are the different analytical attributes that can be used for exploration. Further, analysis is performed instantly (in-memory analytics) and the reports are generated in various formats.
- Source for the transactions
- Credit Card
- Debit Card
- Online transfers
- ATM cash withdrawals
- Cash payments
- Internal transaction books
- Date-time
- Region
- Merchant Category Code (MCC)
- Category of item
- Brand of the item
- Amount spent
- Data aggregations based on various dimensions/parameters
- Spend patterns based on the date/season
- Top least product/s categories sold in the given filter criteria
- Future trend pattern based on predictive analytics
Since huge volume of data can arrive from different sources and its formats may vary depending on the sources, it's a challenge to follow the traditional BI approach and attain value out of the received data. To leverage huge and heterogeneous data scenarios, big data technologies like Hadoop can be adopted and analytics can be applied to derive knowledge out of the data. Since Hadoop can ingest different formats of data very easily in its file system, it becomes really simple to process this data in a distributed manner. Along with the static analytics on historic data, predictive analytics moves one step forward by calling out unique indicators for past events, which can be used to derive futuristic information.
Conclusion
Analysis of the available data and strategic decision making is imperative in large organizations. Spend analysis helps corporates to perform systematic data collection and extensive analysis on the data. Also, the predictive algorithms implemented will give futuristic projections, which will contribute in effective decision making.
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