Any big data or analytics conversation would be remiss without the mention of "data scientists." Much has been written about data scientists– who they are, who they should be, and where to find them. My colleague James Kobielus wrote an interesting series of blog posts about the skills required to become a data scientist.
From a customer intelligence (CI) perspective, we outlined four segments of CI professionals — marketing practitioners, marketing technologists, marketing scientists, and customer strategists. Of these, marketing scientists typically orchestrate the customer and marketing analytics function. They manage the reporting, analysis, and predictive modeling processes using marketing and customer data.
In a CI context, we find that the role of the marketing scientist has evolved from being a pure data analyst drowning in data analysis to that of an analytics translator — someone who is equally comfortable with building advanced predictive models and also adept at embedding the output of the models into customer-facing processes. What type of marketing scientist does your analytics team have?
We recently published a report on why "Customer Intelligence Needs A New Breed Of Marketing Scientist" (accessible to Forrester clients). In the report, we highlight ways to develop analytics translators across the staffing cycle — starting from attracting the right talent, nurturing the relevant skills, training with new skills, and incenting them based on business impact.
Does your firm use customer analytics to optimize relationship marketing efforts? Does your firm use analytical techniques to understand and predict customer behavior? If so, we want to hear from you.
We are launching our first Customer Analytics Adoption Survey for customer analytics users. With this survey, we want to understand how you use and apply customer analytics in your organization. In particular, we’re fielding questions to understand the goals and challenges with using customer analytics, the descriptive and predictive analytics techniques and models you use, the business impact of customer analytics, the customer metrics you track, and how you prioritize customer analytics initiatives across the customer life cycle. We encourage you to participate in this survey, as this information will help you benchmark your customer analytics adoption against peers and assess future opportunities.