I’m excited to announce that our new research on how firms use customer analytics was just published today. The new research reveals some interesting findings:
Customer analytics serves the customer lifecycle , but measurement is restricted to marketing activities. While customer analytics continues to drive acquisition and retention goals, firms continue to measure success of customer analytics using easy-to-track marketing metrics as opposed to deeper profitability or engagement measures.
Finding the right analytics talent remains challenging . It’s not the just the data. It’s not the just technology that hinders analytics success. It’s the analytical skills required to use the data in creative ways, ask the right questions of the data, and use technology as a key enabler to advance sophistication in analytics. We’ve talked about how customer intelligence (CI) professionals need a new breed of marketing scientist to elevate the consumption of customer analytics.
CI professionals are keen to use predictive analytics in customer-focused applications, Forty percent of respondents to our Global Customer Analytics Adoption Survey tell us that they have been using predictive analytics for less than three years, while more than 70% of respondents have been using descriptive analytics and BI-type reporting for more than 10 years. CI professionals have not yet fully leveraged the strengths of predictive analytics customer applications.
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.
You don’t need to be a scientist to boost your business with applied mathematics
On 22/9/09 SPSS Inc. announced a new certification process to confirm an individual’s expertise with some of their statistical solutions. “Look at this”, I thought “sophisticated software still requires experts to unfold the value they can provide”. Being a physicist by background, I like it how applied mathematics can improve business. However, not everyone sees beauty in algorithms or is interested in statistics.
The IT mega vendor acquires the predictive analytics specialist SPSS
On July 28th IBM announced the plan to acquire SPSS, a leading provider of predictive analytics solutions. The acquisition, which is subject to shareholder and regulatory approval, is expected to close later this year and will position IBM as a leading vendor of Business Intelligence in the market.