This year’s Polish SAS Forum conference gathered more than 900 enthusiasts of the use of analytics for the generation of innovation. The business session in the Customer Intelligence domain was focused on the methods of meeting the challenge of maximization of revenue while keeping the customers satisfied. Market leaders highlighted the key role of Data Scientists and shared their experience of how to become an Analytics Driven Organization.
Customer Intelligence is a team effort
In the existing or evolving business model, persons responsible for value generation are continuously following market trends, competitors’ activities and internal performance reports. As a domain experts, they define what they want to extract from data and how to monetize this knowledge. However, they may not always have time or the competences needed to perform data mining or an in‑depth analysis. This need is being addressed by Data Scientists, who enhance traditional data sources with Big Data, macroeconomic variables and competitive intelligence information to climb to the next level of analytical maturity.
Fig1: Added value from the integration of SAS and Big Data Hadoop – Adam Bartos, SAS Big Data Competency Center Director
How to become an Analytical Driven Organization
A chance must be given to the Business, who in addition to its current operations, should have enough time and a purpose to innovative use of the potential hidden in data. Firstly, the Business must define what exactly they want to draw out from the data, and only then Data Scientists may start their work. The process should start from defining the scope of necessary data, and then adequate analytical methods must be used to understand the reasons of the current status and how to proactively forecast the future.
An organization should support Data Scientists in knocking down the walls that block access to data needed, providing analytical tools with proven algorithms, and accelerating the operationalization of what has been discovered through the collaborative effort of Business Departments and Data Scientists. This applies particularly to the implementation of decision-making rules that capture the relevant behavioral patterns, or to analytical models for online evaluation of e.g. customer price elasticity in the dynamic offering process in touch points.
Fig. 2: A slide from the presentation entitled: “Relevance drives results!” – Rene van der Laan, SAS Customer Intelligence Global Practice Director
Operational excellence of an analytical process
It is a form of Innovation Scouting for efficient estimation of business idea potential and results of Data Scientists’ analytical work in order to commercialize them. The operational excellence is reflected in two dimensions in the organizational culture. Firstly, in financing the different hypotheses tests with the awareness of risk that return on investment may take longer than a quarter or a year. Secondly, in a faster operationalization of confirmed theses to achieve a competitive advantage. An interesting analogy between the progress in analytics maturity and popcorn preparation was described by the Head of Product Marketing, Vienna Insurance Group.
Fig. 3: A slide from the presentation entitled: “Future with your clients. Customer intelligence in Česká Podnikatelská Pojišťovna” – Igor Rolínek, Head of Product Marketing CPP, Vienna Insurance Group.
During the Polish SAS Forum conference, Customer Intelligence practitioners shared their experience of how they use SAS tools to maximize both the company’s revenues and customer satisfaction. At an Analytics Driven Organization, decisions on implementation of changes must be based on deep understanding of the customer, then on predicting their behavior and operationalization of discovered rules to capture patterns in real time. The common goal is to drive Customer Engagement by analysis-based prediction of the Next Best Action to ensure a consistent Customer Experience across the Customer Journey.
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