Today, Oracle announced that it will acquire Eloqua, a marketing automation firm. Oracle positions the deal as a comprehensive customer experience cloud that enables business to create an integrated, end-to-end process of marketing, sales, service, and support. I look forward to insight from my colleague Lori Wizdo on what the Oracle-Eloqua deal means for a marketing and sales alignment.
I think the deal has larger ramifications for the future of all customer relationship marketers and marketing vendors. Here’s my take on the deal:
I am a measurement geek. I get great joy from analyzing data, measuring customer behavior, and assessing marketing campaigns. It’s something that I’ve done for years, across different organizations, agencies, and service providers. And that’s why I’m so interested in attribution measurement — it’s a data geek’s dream. Connecting data and unveiling the dimensions of customer behavior and interaction paths is fascinating; it provides an enormous amount of information about customers and prospects, their preferences, and how they want to interact with a brand.
However, attribution measurement requires more than just analyzing data. It requires coordination of lots of moving parts. Data needs to be connected; technologies need to communicate with each other; resources need to work together; and organizations need to agree on metrics and analysis standards. It’s not an easy task. And speaking from experience, attribution measurement is a long, complex journey.
My most recent report, “Are You Ready For Cross-Channel Attribution?,” enables organizations to score their attribution maturity across six core component areas: strategy, organization and resources, technology, data, analytics, and optimization. Once an organization tallies up its scores in each area, it can determine where it falls on the attribution maturity spectrum: novice, intermediate, proficient, or expert. This framework allows organizations to pinpoint their attribution strengths and determine attribution gaps. Also, the framework provides organizations with specific recommendations on what attribution-related tasks they need to focus on to become experts.
When we set out to evaluate the new breed of firm that we call "customer engagement agencies," we sent our initial screener to an incredibly long list of firms -- over sixty, in fact! -- ranging from MSPs to digital agencies to management consultancies. We felt that we needed to cast a wide net if we wanted to understand the range of approaches vendors take to customer engagement: how they use data and analytics, the channels they enable with customer intelligence, and how they service their most strategically engaged clients. As the responses rolled in, a hypothesis began to take shape in my mind: The emerging customer engagement agency model hails from two mature markets -- digital/direct agencies and database MSPs -- and, depending on provenance, these evolving agencies take one of two primary approaches to customer engagement.
Turns out, I was on the right track, though the reality is not quite so black and white.
In our final evaluation of 13 vendors in The Forrester Wave: Customer Engagement Agencies, Q4 2012, we did find different strengths and weaknesses depending on legacy business model, but ultimately EVERY firm still has a long road ahead of evolving its people and processes to support CEA clients. We also found, though, that each CEA we evaluated is working hard to connect the dots between strategy, analytics and execution in order to optimize customer experience and profitability. And that can only be a good thing for the marketers and CI leaders who are visionary enough to hire them.
Google recently announced, on Tuesday, plans to offer its Attribution Modeling Tool through Google Analytics via a public white list. The Attribution Modeling Tool was previously offered through the Google Analytics Premium product at an additional cost. The move to make its Attribution Modeling Tool available through Google Analytics for free indicates that Google is aggressively looking to extend its current analytics and measurement capabilities. Specifically, Google’s Attribution Modeling Tool allows users to:
Work with data they’re already tracking in Google Analytics. That means no additional setup or work for your IT department, marketing, or analytics groups. Flip the switch and you’re on. You can input and view values across channels, including affiliates, display ads, paid and organic search, and email.
Customize the attribution model. Google Attribution Modeling Tool provides either last-click or rules-based attribution models to their users. Google allows the user to have control of their attribution model, allowing the user to compare various models to each other, including the contributed value of channels, campaigns, and various other dimensions.
Access the Attribution Modeling Tool for FREE. We all love free things. All users have to do is sign up for the tool and the tool is available through the Google Analytics product. If you want more information about the tool, Google is hosting a webinar, which will give an overview of the capabilities.
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.
Over the past year-and-a-half, I’ve seen a surge of loyalty programs in the marketplace. And it’s not just existing programs expanding into emerging channels or revamping their reward mix. Industries that typically shied away from loyalty programs, like utilities, media and insurance, are jumping on the bandwagon. But although marketers understand that value of identifying, retaining, and improving relationship with their best customers, their execution usually doesn’t lead to lasting loyalty. Loyalty programs largely revolve around financial incentives that drive spikes in short-term behavior but don’t necessarily establish deeper or long-term customer relationships.
To add to that challenge, consumers see declining value in the programs that exist in the marketplace, and if marketers want to develop better relationships with their best consumers, their programs need more differentiation. And that’s where customer intelligence comes in. Loyalty programs generate a lot of customer data that often goes unused. Customer intelligence helps marketers create customer insights that improve their strategy and programs through targeting and segmentation, and customized offers. To assist marketers in applying customer intelligence and evolving their customer loyalty strategies, I’m excited to introduce Forrester’s Customer Loyalty Playbook.
The Customer Loyalty Playbook lays out the path to help you establish the right framework and mature your practices around executing loyalty programs that drive long-term customer engagement and incremental value. It contains twelve reports, focusing on four key phases:
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.
However, one of the questions that we haven't answered yet is how product strategists get their firms to organize for open innovation. Our hypothesis is that this is more of a cultural shift than a straightforward change in organizational structure. We are kicking off some research on this important topic now, and in the spirit of being "open," I'm asking you to either post your comments in reply to this blog post or reach out to directly to my colleague email@example.com to schedule an interview so we can discuss how you are organizing for open innovation at your firm.
In return for your participation, we'll send you a copy of the report (if you're not already a client), and perhaps even feature your organization as an example — depending on your willingness to be included, of course. So please chime in and tell us about your best (or worst) experiences in trying to make your product innovation more open. We look forward to hearing your thoughts.