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.
Rarely do moments like this occur. Last week, while watching the evening news (yes, I still watch news), I was horrified by the continued coverage of the cruise ship disaster in Italy. But, while watching the coverage, I was wading through my mail and opened a direct mail piece (also a rare event) that I had just received. To my horror, I found an offer from American Express to sign up for the Costa Concordia cruise. Worse still, it offered to “immerse” me in a truly European experience. To make things even worse, notice the typo in the headline?
While marketers strive to achieve messaging relevance that would make you stop what you’re doing and take notice, this execution in particular was a case of bad timing and lack of foresight into the implications of marketing campaigns already in flight.
What lessons does this highlight for customer intelligence (CI)?
Agility. In our research, we find that direct mail is one of the top channels that CI professionals favor over other channels. Despite CI’s heavy use of direct mail, this faux pas no doubt occurred because of the cycle time between the cruise ship disaster and the direct mail drop.
CI Pros: Speed up CI processes to provide greater organizational value. Apply principles of agile development to CI, especially to channels that are not inherently real-time, such as direct mail in this case.
Analytics and creativity are seldom used in the same sentence. The natural instinct is to delineate the two as left-brain and right-brain pursuits. Analytics and creative teams speak different languages, use different tools, and find inspiration in different places.
Customer Intelligence (CI) professionals are usually closer to the world of analytics. They capture, manage, analyze, and apply heaps of customer data using advanced analytical tools and techniques. But in order for them to step out of a perceived geeky image, CI professionals should think about how to add a dash of creativity into their roles.
Analytics made its way to the creative world especially with various testing tools, but has enough creativity made its way into analytical projects? How can analysts and CI pros add some creativity?
Ask the same questions, differently. Arriving at the hypothesis or questions to pursue when analyzing data can be an output of a creative brainstorm. Framing the question to ask of the data is as important as the analysis itself.
Summarize data in creative ways. New types of data are pushing the limits of what traditional data mining and analytical tools can do. This requires creative ways of uncovering relationships between seemingly unrelated entities.
Make the data sing. Data visualization as both a data-mining tool as well as a presentation method is fast becoming popular to communicate complex trends and results into a digestible format, especially when the audience is not analytically inclined.
Relationship marketers love customer lifetime value (CLV) as a concept because it puts the customer at the core of the marketing investment decision and sneaks a peek into the future worth of the customer. But in reality, arriving at customer lifetime value is often a herculean task and the assortment of CLV approaches available doesn’t make the process any easier.
My latest research, titled “Navigating The Customer Lifetime Value Conundrum,” highlights key considerations for firms who plan to embark on the CLV journey. As a continuation of this research stream, I asked our Customer Intelligence community members what their experience with CLV was and a few interesting points emerged:
Inclusion of intangible value. At what point is it important to account for the intangible, non-transactional value that customers are generating especially through all the emerging channel interactions such as referrals, recommendations, likes, user-generated content, etc.?
Blurry definitions of "best" customers. Traditionally, resources are channeled toward your best customers with positive net present value (NPV). But often there is conflicting choice between investing in high-value, low-usage customers and low-value, high-usage customers. As a result, defining your "best" or "worst" customer/segment is not as obvious as a positive or negative NPV.
Diversity of CLV users. CLV is not just the domain of marketing or customer-focused teams, but it touches other stakeholders in the organizations. How do non-marketing stakeholders such as finance teams in your organization view this metric? Is CLV as important to non-marketing stakeholders as it is to marketing?
I was intrigued by the recent announcement that MasterCard and Brighter Planet were teaming up to mine carbon emission data based on corporate cardholder data. This announcement got me thinking about unlikely data partnerships across verticals to productize data and form mutually beneficial partnerships using data as the currency.
But what’s really interesting is that it elevates the conversation of customer intelligence beyond better campaigns and ROI to the use of customer data for sustainability efforts — a relatively uncommon use case for customer intelligence.
The concept of data sharing or data partnerships is not new — entire business models exist on making these services available to organizations for smarter targeting and remarketing. Retail data co-ops, online media audience aggregators, and data coalitions are just a few examples of these models. And MasterCard even sells its MasterCard Advisors solution to provide merchants with enhanced data and targeting capabilities.