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:
ExactTarget today announced plans to acquire two companies: Pardot and iGoDigital. The acquisitions signal that ExactTarget, only recently public, intends to use its cash reserves to grow aggressively against the competition in revenue, market segments, and features. So what does it mean?
Are the two acquisitions related?
No, the dual acquisitions are a quirk of timing, allowing ExactTarget to drive the marketing technology conversation in advance of Connections, its user conference in Indianapolis next week. I’ll separate my comments to better address each.
Still, I’ll risk a theme for these two acquisitions: Marketing automation without predictive analytics is blind, but analytics without automation is empty.
Why did ExactTarget make the acquisitions now?
The acquisition is unlikely to make a big impact in the short term. Recommendations are a small part of the marketing software mix for retailers. ExactTarget can cross-sell online recommendations into its significant B2C base, but in the end, ExactTarget is acquiring the firm for a longer-term move.
Eighteen months ago, when I started down the path of what would become our body of Personal Identity Management (PIDM) research, there were only a few customer intelligence professionals who gave much credence to the picture we were painting. What a difference a year makes. Today, privacy, data governance, consumer empowerment, and understanding "the creepy factor" are core to the conversations I have with CI pros in both marketer and vendor organizations.
At the center of those conversations is often the question, "Who are the players in tomorrow's consumer data ecosystem?" We've just published a report, Making Sense of a Fractured Consumer Data Ecosystem, that reviews the strengths and weaknesses of four existing vendor categories plus three emergent business models. These include:
Consumer data giants: Companies, like Acxiom, Epsilon, Experian, and Infogroup, that have an opportunity to become consumer-friendly data managers but are at greatest regulatory risk
Reputation management providers: Companies, like Intelius and Reputation.com, that could help consumers manage data access but need to focus on their B2C business models to do so
Online services giants: Companies, like Google, MSN, and Yahoo, that already have access to highly personal data but serve too many masters
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.
We've spent a lot of time in the past year looking at how the customer intelligence services landscape is changing. For one thing, it's a heck of a lot more chaotic: everyone from management consultants to systems integrators to KPO vendors is putting a stake in the ground of CI services. We've also seen a dramatic shift in the way some digital & direct agencies and database MSPs are thinking about their most strategic client relationships. This change has been so noticeable that, a few months ago, we actually published research that defines a new business model: The Customer Engagement Agency (CEA).
It's no surprise that clients and vendors alike are excited about this model. These agencies help elevate customer intelligence within the client organization. They bring attention and focus to the importance of customer knowledge, and they work hard at infusing that knowledge throughout every customer touchpoint. They measure customer value, not just marketing campaigns. And they help clients use CI to answer questions about everything from product development to logistics and resource management.
But, this is an emerging market — the players are evolving from very different backgrounds; they offer substantially different "value-added" capabilities; and many of them have proprietary methods and models that make it hard to compare apples to apples.
That's why we've just kicked off a Customer Engagement Agency WaveTM that will publish in the fall. If you're intrigued with the idea of working with a CEA, I encourage you to:
SaaS vendors must collect customer insights for innovation and compliance.
As of the end of last year, about 30% of companies from our Forrsights Software Survey, Q4 2011, were using some software-as-a-service (SaaS) solution; that number will grow to 45% by the end of 2012 and 60% by the end of 2013. The public cloud market for SaaS is the biggest and fastest-growing of all of the cloud markets ($33 billion in 2012, growing to $78 billion by the end of 2015).
However, most of this growth is based on the cannibalization of the on-premises software market; software companies need to build their cloud strategy or risk getting stuck in the much slower-growing traditional application market and falling behind the competition. This is no easy task, however. Implementing a cloud strategy involves a lot of changes for a software company in terms of products, processes, and people.
A successful SaaS strategy requires an open architecture (note: multitenancy is not a prerequisite for a SaaS solution from a definition point of view but is highly recommended for vendors for better scale) and a flexible business model that includes the appropriate sales incentive structure that will bring the momentum to the street. For the purposes of this post, I’d like to highlight the challenge that software vendors need to solve for sustainable growth in the SaaS market: maintaining and increasing customer insights.
Before the clouds, webs, and distributed networks people had to create their own spaghetti of logic inside a single building using machinery that looked like props from Doctor Who. Spurred by the need to crack the ‘Enigma’ naval communication codes during the Second World War Alan Turing developed an electromechanical device called the Bombe which played a major part in defusing the war. 2012 is the 100 year anniversary of the birth of Turing and he is rightly considered to be the father of computer science and Artificial Intelligence. Turing had both a wonderful and terrible time of it and his life story is well worth a wiki.
The British genius didn’t just advance computer science using valves and wires. He is almost as famous for his thought experiments concerning how we may build machines and computers that can engage in intelligent discourse with humans. Could machine responses fool us into thinking that they were sourced from a human? To answer this question Turing developed a methodology to test the validity of the machine generated responses, fans of Science Fiction are likely to recognize this as the inspiration behind the ‘Voight-Kampff’ test administered by Deckard in Ridley Scott’s ‘Blade Runner.’
I’ve been on the CI team for a few years in a supporting role and, more recently, working behind the scenes with Suresh Vittal to drive our research around loyalty. I’m excited to announce that, going forward, I will be the analyst leading our coverage of the technologies, services, and analytics that support customer loyalty.
My first report in this new role will provide best practices on building a world-class loyalty program. Then keep an eye out for an analysis of existing and emerging loyalty program features. Future research will dive into topics that include reward design, revenue models for loyalty programs, the future of loyalty, and more Wave evaluations of the loyalty vendor ecosystem.
I am looking forward to getting to know many of you better and following the evolution of this exciting space. Whether you have insights to share, questions to ask, or loyalty technology and services that you want to tell me about, I want to hear from you! Please engage with me via our Inquiry and/or Briefing teams, or track me down at Forrester’s upcoming Customer Intelligence Forum (April 18-19 in Los Angeles).
Last week, I ran into an infographic on Ad Age about The People of Walmart. It compares the demographics of Walmart, Kmart, Kohl’s, and Target shoppers: for example, age, sex, income, and region of the customers. It shows that more women than men shop at Walmart, and that their audience is quite equally spread across age as well as income. Recently, Forrester conducted a survey where we gained insights on customers of retailers like Walmart. We found that while it’s great to examine the demographics of shoppers, it’s much more powerful (and actionable) to look at other insights about these retailers’ customer base, like marketing preferences, spend levels, and brand consideration.
Below you'll find some of the results from this Forrester study. You'll see that the average US online adult who shops at Walmart spent about $848 on average in the past year, but that only about half are likely to recommend the retail giant to a friend or family member. When these results are compared to other retailers, and by demographic, you create real insights.
I’d love to hear from you: How do you target your customers? Are there any behavioral and attitudinal variables that have been very helpful in defining your target segments?
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.