Decisive Analytical Systems: Leveraging Novel Data Model for Better Customer Conversion Rate
Machine Learning (ML) is at the crux of a host of innovations that are already set to enhance our daily lives. The phenomenal escalation in data and computing capabilities has ensured the possibility of this exponential progress. With massive data consumption, Machine Learning assists enterprises in simplifying product marketing and offers accurate sales forecasts and offers better customer segmentation with the recommendation of the right product. The technology promises to provide businesses with an edge over any rivals in the field. However, while most business expectations are focused in the areas of customer conversation, engagement and recommendations, it is imperative to optimize internal processes that involve operational and business decisions as well. Simultaneously, security and data privacy have been stated as the topmost inhibitions companies may have whilst sharing sensitive data with the ML systems. Established in 2010, Decisive Analytical Systems spearheads the aforementioned challenges with their novel product Plumb5. Diving Deeper into the Offering
Essentially, the data model is an integrated platform that proffers marketing, sales and support functions onto a unified platform in order to maintain a single customer identity stack to gather intelligence and serve the next actions in real-time.
Engineered with a complete customer-centric approach, the innovative model compiles data into a holistic customer-centric stack, which feeds off the learning algorithm with complete data-sets. Customers utilize a plethora of platforms as interaction points which can prove to be an arduous task for businesses to convert the overwhelming data into personalized campaigns. Plumb5 uses a hybrid approach to achieve this. It maintains an archive of the customer’s information amalgamated via web engagement data, SMS responses, app behavioral data and the likes through proprietary scripts, tags and SDKs.
In essence, it tags customer data across the broad range of data sources containing customer information and maintains a meta-data repository to unify data around a single customer. This permits running real-time scoring models to arrive at states, triggering contextual engagement instantly based on these states. The data amassed is continually updated with the help of web services or batch processing routines. Each file is segregated and analyzed through real-time models to attain the required outputs. The results are then plugged back to the engagement scripts to trigger instant dynamic personalization campaigns. The Co-founder and CEO of Decisive Analytical Systems, Vijay Chander goes on to reveal the key role that the customer-centric stacks play in executing real-time learning.
It assures customer-specific sorting for collaborative filtering. This is used for achieving real-time decisions, which can be integrated with the engagement engine to serve automated machine based engagement. Apart from reducing costs, it translates into optimized expenditure on each customer obtained by leveraging the inbuilt attribution model to target customers at effective touch-points.
Eliminating Security Risks
The company perfectly understands every business’s apprehension towards the input of data into ML systems and bracing themselves to potential data loss or a malicious data breach. The data model is specifically designed with the learning or mining models inbuilt within the platform, as it needs to perform run-time computations for real-time pattern matching and decision processes. In conjunction with this, upon the exclusive request of the clients wish to use external ML libraries, encrypted data without the customer’s unique information is served to computational models and the resultant outputs are collected back into Plumb5. “Privacy concerns do not arise as the encrypted data does not contain unique user information,” affirms Vijay. Helping us understand the workings better, Vijay goes on to explain that particular information about a customer may be served as a unique token number clubbed with the behaviour, interaction and preference parameters. The requesting library ingests the data for processing and the resulting outputs are collected back to Plumb5 for further engagement.
With a proven track record of successfully serving a gamut of industry verticals, the company worked with a renowned retail business with Plumb5’s Personalized Automation. With a promise of higher return on investment, all active channels resulted in 70 percent growth in revenue, 66 percent rise growth in transactions and 60 percent escalation in customer conversions.
Carving a niche for themselves in the machine learning domain, Decisive Analytical Systems is continually striving towards developing a supervised learning environment. The enterprise data management would potentially be reduced to a lesser number of hours of configuration. The supervised learning environment would be capable of extracting all possible patterns across each data relationship and make decisions based on high probability to serve engagement or customer communication. The user can monitor, supervise and manage creative strategies for customer segments. Enthralled by their progress in this sector, the company continues to diligently work towards maintaining a strong foothold in this domain.
Eliminating Security Risks
The company perfectly understands every business’s apprehension towards the input of data into ML systems and bracing themselves to potential data loss or a malicious data breach. The data model is specifically designed with the learning or mining models inbuilt within the platform, as it needs to perform run-time computations for real-time pattern matching and decision processes. In conjunction with this, upon the exclusive request of the clients wish to use external ML libraries, encrypted data without the customer’s unique information is served to computational models and the resultant outputs are collected back into Plumb5. “Privacy concerns do not arise as the encrypted data does not contain unique user information,” affirms Vijay. Helping us understand the workings better, Vijay goes on to explain that particular information about a customer may be served as a unique token number clubbed with the behaviour, interaction and preference parameters. The requesting library ingests the data for processing and the resulting outputs are collected back to Plumb5 for further engagement.
With a proven track record of successfully serving a gamut of industry verticals, the company worked with a renowned retail business with Plumb5’s Personalized Automation. With a promise of higher return on investment, all active channels resulted in 70 percent growth in revenue, 66 percent rise growth in transactions and 60 percent escalation in customer conversions.
Carving a niche for themselves in the machine learning domain, Decisive Analytical Systems is continually striving towards developing a supervised learning environment
Carving a niche for themselves in the machine learning domain, Decisive Analytical Systems is continually striving towards developing a supervised learning environment. The enterprise data management would potentially be reduced to a lesser number of hours of configuration. The supervised learning environment would be capable of extracting all possible patterns across each data relationship and make decisions based on high probability to serve engagement or customer communication. The user can monitor, supervise and manage creative strategies for customer segments. Enthralled by their progress in this sector, the company continues to diligently work towards maintaining a strong foothold in this domain.
