Processing all the customer data and creating actionable customer analytics is a very complex task. It requires coordinated efforts by skilled and specialized professionals working across multiple departments of a company; particularly customer experience, IT and marketing. However, the division of task varies from company to company depending on the scale of customer intelligence operations present in the organization.
In order to get to the bottom of who should ideally manage customer intelligence, we need to first understand what steps customer intelligence operations essentially encompass. It includes –
- The storage wherein the huge chunk of customer information extracted from various social media platforms, customer feedback, and other sources are stored together. This information can be structured or unstructured.
- The next step is processing the customer data to segregate the relevant from the irrelevant.
- The third step is analyzing. Once the processing is done, the relevant information is scrutinized for actionable insights to formulate further business strategies.
Among these activities, storage and processing go hand in hand. The storage and processing can only be as good as the other. It’s all well and good having an amazing processing system but if your data storage isn’t up to par and you keep having to get a hard disk data recovery service to retrieve your data then it’s not going to be very productive. Plus, the servers capable of processing the customer data require technical expertise to handle it. Hence, many customer intelligence professionals work in the IT department. Some companies may have their analysts working in business functions that use customer data to gain insights and make decisions. Since customer intelligence is still very much in its evolution stage, many companies may not actually have a separate group that consists of a team of professionals designated to solely overseeing its processing and analyzing.
Let us see some of the typical customer intelligence organization –
Customer analytics experts in business functions like marketing –
Customer intelligence experts working in marketing departments have an upper hand when it comes to modeling plans and policies more in sync with customer preferences. This is because the key to attracting customers and building customer loyalty lies with them. The customer data can be analyzed for insights that can be used to achieve more personalization in customer services. Marketing campaigns with a sharper focus on customer needs designed through analytical insights would reap far better results than those designed vaguely, without the highly reliable analytical data support to rely on. Since marketing functions revolve around the business, they generally don’t have customer intelligence experts handling the processing. That majorly remains the prerogative of the IT department.
Customer analytics experts in IT functions –
Since customer intelligence is very much a technology-driven process, in many companies, the IT department is the one where the core function is located. Technology is the backbone of customer intelligence. Customer intelligence professionals working in the IT functions know exactly the kind of technological infrastructure that is required to process customer data. The analysts can make recommendations for high-tech upgrade, weighing the scalability and the flexibility of the customer data with them. The needs and expectations of the analysts and the technology must match at all levels, so as to ensure that efficiency in processing is always maintained. They have to be mindful of the costs, reliability, packaging and ergonomics along with the pre-requisite technical knowledge.
Customer intelligence professionals working in different functions, having an expertise in their distinct capabilities, need to maintain a coordinated working pattern and a lucid communication channel to process and analyze the customer data for actionable insights.
Customer analytics experts in a separate group-
Some companies understand that understanding of the customer, creating actionable insights and utilizing them towards business goals is an interdisciplinary activity. A separate group of experts who understand data, technology as well business are most suited not only to bring together all the customer data, but also to transmit the customer intelligence to appropriate teams within an organization. This leads to better results and more success. Companies with a separate function generally had a higher ROI than those who did not.
Centralization of customer intelligence operations helps in multiple ways-
1) The experts can offer unbiased opinions strictly on the basis of customer intelligence, without intervention of vested interests of other company functions; whose judgment might be clouded in favor of its department’s well-being or constrictive due to a department-specific limited scope of understanding.
2) The customer intelligence professionals, together with their freedom, get a sense of assurance of job security and scope for progress, which is otherwise limited to other business functions due to the hierarchical structure of the businesses.
3) It helps in providing a singular view of customers, which is of absolute necessity for delivering better customer experiences. It saves the confusion that often arises with disorganized and multiple versions of customer information present in different departments.
4) It squashes the chance of being limited by traditional thinking. This is because it gives much space and independence for innovation and technological evolution outside the realm of typical business bounds.
5) It builds high levels of trust between the data scientists, who present insights on customer data, and the functional managers.
Having a separate centralized group of customer analytics professionals along with analysts in specific business units would establish a more cohesive, stable and healthier system of analytics communication. The analysts should be able to put in place the recommendations put forth by the central group, whose main task would be processing rigorous data and arriving at business intelligence through its data science capabilities.
What is your take on this?