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How can Companies Scale-up to next level of Customer Data Analytics?
Drive a shift from 'Data as an IT-Asset' to 'Data as a Key-Asset for a Decision making' culture. Effective big data initiatives require cultural changes within the organization and a concerted shift towards a datadriven behavior. To drive successful big data programs, companies should strive towards full executive sponsorship for analytics initiatives, develop and promote a company-wide analytics strategy, and embed analytics into core business processes. In essence, banks need to graduate towards a model where analytics is a company-wide priority and an integral element of decision-making across the organization.
"A comprehensive and flexible system for achieving, sustaining and maximising Business Sucess. Big Data is uniquely driven by close understanding of Customer needs, disciplined use of facts, data, and statistical analysis, and diligent attention to managing, improving, and reinventing business processes."
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Transformation across Culture, Capabilities and Technology is Critical for the Success of Big Data Initiatives
In order to graduate to higher levels of maturity in customer data analytics, Companies will need to build the right organizational culture and back it up with the right skill sets and technological components, consolidate Silo's, digitize Offline Data.
Establish a Strong Data Management Framework for Structured as well as Unstructured Data
The quality, accuracy, and depth of customer data determine the value of customer insights. Consequently, Enterprises will need to establish robust data management frameworks to formalize the collection, storage and use of structured as well as unstructured data. Additionally, Enterprises must graduate to more advanced analytics techniques such as predictive and prescriptive analytics that enable more precise modeling of customer behavior. These in turn will drive increased cross-selling opportunities, pricing optimization and targeted offerings.
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Analyse as-is Data use in Organization, Run POC's, Discovery Use-Case's, Set up Business related Process Algorithms.
The use of customer data invariably raises privacy issues8. By uncovering hidden connections between seemingly unrelated pieces of data, big data analytics could potentially reveal sensitive personal information. Companies are cautious in their use of big data due to privacy issues. Further, outsourcing of data analysis activities or distribution of customer data across departments for the generation of richer insights also amplifies security
risks. For instance, a recent security breach at a leading UK-based bank exposed databases of thousands of customer files. Although this bank launched an urgent investigation, files containing highly sensitive information — such as customers’ earnings, savings, mortgages, and insurance policies — ended up in the wrong hands10. Such incidents reinforce concerns about data privacy and discourage customers from sharing personal information in exchange for customized offers.
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From ‘next best offer’ to cross-selling and up-selling, the insights gleaned from big data analytics allows marketing professionals to make more accurate decisions. Big data analytics allows banks to target specific micro customer segments by combining various data points such as past buying behavior, demographics, sentiment analysis from social media along with CRM data. This helps improve customer engagement, experience and loyalty, ultimately leading to increased sales and profitability.
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Customer data typically resides in silos across lines of business or is distributed
across systems focused on specific functions such as CRM, portfolio management and loan servicing. As such, banks lack a seamless 360-degree view of the customer. Further, many companies have inflexible legacy systems that impede data integration and prevent them from generating a single view of the customer.
Move Up the Analytics Maturity Curve with Four Sequential Controlled Steps
Big data initiatives are typically time and resource-intensive. To pave the way for a smooth implementation, we recommend afour-step approach that begins with an assessment of existing analytics capabilities (We can help you for the Assesment), Industry Target and is followed by the launch of pilot projects with variety of selectes use-case's and delivering measurable results, which are subsequently expanded into full-scale organization-wide programs.
Stage I - Asses Big Data Analytics capabilities
Stage II - Check Industry Targets
Stage III- Run 2 Discovery POC's
Stage IV - Expand Big Data Initatives accross organization
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