"Let's just say I'm not lost when it comes to data . . . but I could be more found . . ." – (eBusiness team member at a top 50 US bank)
Digital teams are surrounded by data and metrics — from KPIs to customer analytics. Yet I often hear from clients who wish they were just a little more comfortable knowing what the data is really saying, or which metrics are most important.
We just published a brand new report on The Mobile Banking Metrics That Matter which outlines how mobile strategists at banks can put the right metrics in place and work with their analytics teams to get data outputs that guide them toward smart business decisions.
Writing this report got me thinking about which books, blogs, and articles I’ve found most useful when it comes to really getting data and metrics. Here are five I think might help you too:
The Tiger That Isn’t. Probably my personal favorite book about stats and measurement. Written for a mainstream audience, the book works as a guide to thinking through what a given stat or data point really means — and when to trust or doubt such data. It’s also a great read, full of interesting nuggets and statistical oddities (like how the vast majority of people have an above-average number of legs). The book’s thesis is that people who consume data should be skeptical but not cynical about statistics. From there, it helps the reader more easily contemplate and act on the data and metrics they encounter.
The deluge of customer data shows no signs of abating. The perpetually-connected customer leaves data footprints in every interaction with a brand. This presents tremendous opportunities for customer insights professionals and analytics practitioners tasked with analyzing this data, to not only get smarter about customers but ensure that the insights get appropriately used at the point of customer interaction.
When we asked customer analytics users about the challenges and drivers of customer analytics adoption, we found that data integration and data quality continue to inhibit better adoption of customer analytics while users still want to use analytics to improve the data-driven focus of the organization and drive satisfaction and customer retention.
Forrester’s Customer Analytics Playbook guides customer insights professionals, marketing scientists and customer analytics practitioners into this new reality of customer data and helps discover analytics opportunities, plan for greater sophistication, take steps towards building a customer analytics capability and continually monitor progress of analytics initiatives. It will include 12 chapters (and an executive overview) that cover different aspects of customer analytics.
Customer Intelligence (CI) professionals invest in data-mining, predictive analytics and modeling tools and technologies to make sense of the deluge of data. In the past, they've had to adapt horizontally-focused analytics and modeling solutions to a customer intelligence and marketing context. Today, however, they can consider a gamut of customer analytics and marketing-focused analytics providers that have not only analytics production expertise but also domain and role-focused expertise.
We just published our first evaluation focusing on the customer analytics category here: The Forrester Wave™: Customer Analytics Solutions Q4 2012 . After screening more than 20 providers for analytics products specifically catering to customer analytics applications, we identified and scored products from six of the most significant providers: Angoss Software, FICO, IBM, KXEN, Pitney Bowes, and SAS. Our evaluation approach consisted of a 70-criteria evaluation; reference calls and online surveys of 60 companies; executive briefings; and product demonstrations. The core criteria included key dimensions such as core functionality (data management, modeling, usability); analytics production; analytics consumption; analytics activation and customer analytics applications. The evaluation also included the strength of the current product and corporate strategies in the customer analytics market as well as the future vision for this category.
We found that four competencies define the current customer analytics market:
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.
Companies adopt advanced analytics tools and techniques to convert data into intelligence and drive key customer-facing business decisions. We see that customer intelligence (CI) professionals involved in customer analytics broadly perform three activities:
Generate analytics: Create and produce analytical insights using analytical tools and technologies.
Apply analytics: Choose the appropriate analytical methodology for the business problem and apply it to the context of the customer lifecycle.
Activate analytics: Use analytical output and insights to optimize customer experiences and to drive customer growth, share of wallet, retention, and lifetime value.
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.
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.
In today’s fast-paced global economy, examples of how empowered customers and citizens use social technology to influence everything from brands to governments are all around us. The Arab Spring clearly shows the ability of technology to empower people. In this new digital age, marketing teams must react at the speed of the market: Product development life cycles that used to last many years are compressed into months or weeks; customer service expectations have moved from same-day response to instant response; public relations snafus must be handled in minutes rather than days; marketing campaigns are adjusted in real time based on instant feedback from social media. In this new era, mastering customer data becomes the key to success and, in my opinion, represents the biggest opportunity for IT to impact business results since the dawn of the Internet.
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?