The emerging competitive innovators today are the ones that gain a very intimate knowledge of their customers gained through advanced analytics. The advent of ability to communicate in highly advanced manner via the internet with a virtually endless reach has completely changed our ideas of marketing and interacting with our customers. It is no longer about the big, indiscriminate broadcast to the masses. It has evolved into the big insight into an individual customer’s unique needs and behaviours. The Market of One!
The companies thriving and leading today outperform their competition by knowing who their buyers are and how to communicate directly to each consumer or decision-maker.
Gaining an insight into a customer’s propensity to buy or act is the core of customer analytics. Timely and accurate predictions of individual customers’ behavior help marketers to deliver the right message, at the right time, through the right channel. But to do this it takes a lot of data and sophisticated analytics. As we are fast learning more data is always better. This data is now blended with that which is generated by other sources and comes in structured, semi-structured, and unstructured form. The more information that we have, the clearer the patterns become that reveal what a consumer or decision-maker might do in a given scenario.
In other words, it’s a Big Data opportunity. That is the crux of the challenge for the analyst tasked with predicting consumer behavior. How to integrate massive amounts of diverse data from disparate sources, and discover the unknown relationships and linkages, in it. Traditional data mart or warehouse cannot provide these answers. They depend upon a data schema which in turn depends upon the data architects having accurate knowledge about the relationships in the data.
Predictive analysis works in exactly the opposite manner. It requires the analyst to first discover all the relationships within and between immense sets of diverse data, connect all the dots that suggest a customer’s propensity to buy or act. As a part of this process indirect linkages between data elements must be inferred, and different references to the same data reconciled. If this is not done predictive analysis is limited to what is already known and constrained by the existing assumptions.
As a precondition a far more flexible data model is required, where large sets of diverse data, coming from all sources, are easily loaded and integrated. In turn all the unknown relationships within and between all this data must be discovered and inferred analytically in order to provide the necessary insight. It is only then that we can begin to utilise predictive analysis that will provide the clearest picture of our customers, their experience, influence, sentiment, and habits that will enable us to truly deliver value through our products and services.
envdata is perfectly poised to assist you in deploying the necessary analytics as discussed above. We can supply everything from basic Big Data infrastructure either in your data centre or hosted as well as bringing the right analytics talent to address your needs. Please contact us for further details
Envdata Big Data Analytics involves:
- Big Data Analytics Strategy Definition
- Big Data Analytics Use-case Identification
- Big Data Analytics Models / Framework: Development and Enhancement
- Big Data Mining
- Machine learning
- Statistical analysis
- Pattern discovery
- Microsoft Hindsight
- Social Data analysis
- External Data analysis
Some examples of our solutions are:
- Data Migration
- Hadoop and NoSQL implementations
- Hierarchical Warm and Cold Archival
- Single View of Customer 360 deg
- Metadata Management
- Sensor Data Analytics
- Sentiment Analysis
- Enterprise Data and Information design and harmonisation
- Integration with external data sources
All our solutions are built with proven components of the Big Data ecosystem integrated with existing partner platforms, enabling a fast time-to-market and effective integration with and preservation of existing IT investments.