We've been talking about Adaptive Intelligence (AI) for a while now. As a refresher, AI is is the real-time, multidirectional sharing of data to derive contextually appropriate, authoritative knowledge that helps maximize business value.
Increasingly in inquiries, workshops, FLB sessions, and advisories, we hear from our customer insights (CI) clients that developing the capabilities required for adaptive intelligence would actually help them solve a lot of other problems, too. For example:
A systematic data innovation approach encourages knowledge sharing throughout the organization, reduces data acquisition redundancies, and brings energy and creativity to the CI practice.
A good handle on data origin kickstarts your marketing organization's big data process by providing a well-audited foundation to build upon.
Better data governance and data controls improve your privacy and security practices by ensuring cross-functional adoption of the same set of standards and processes.
Better data structure puts more data in the hands of analysts and decision-makers, in the moment and within the systems of need (eg, campaign management tools, content management systems, customer service portals, and more).
More data interoperability enables channel-agnostic customer recognition, and the ability to ingest novel forms of data -- like preference, wearables data, and many more -- that can vastly improve your ability to deliver great customer experiences.
As digital marketers, we know the importance of tracking, measuring, understanding, and meeting customers’ expectations at their preferred interaction points. We have convinced our budget masters of digital intelligence’s importance to the business as a whole and our spend on digital measurement and marketing technologies continues to increase—exciting vendors and enticing new ones to continually improve products.
But despite this increased investment in technologies, the same stubborn problem remains: different teams are working with siloed data sets while failing to understand and delight the customer across a variety of digital touch points. Why? Because while technology has provided the pieces for digital marketing, these pieces have not come together to deliver completed suites. Achieving this suite goal requires more than just an investment in technology; it requires a considerable effort and a strategy supported by executives that:
Recognizes the multi-channel digital customer experiences firms wish to project using customer insights
Realigns teams and processes to for better cross functional cooperation
Builds skills set and focuses more investment in staff and partnership
Consumers don’t trust your ads. In fact, fewer than one out of four US online consumers trust offline ads, and the numbers are even worse for digital. It’s time for a new approach to marketing, based on deep customer insights derived from a contextual, self-perpetuating, interaction cycle. Each interaction with your brand teaches you what a customer is trying to accomplish at that moment. You must build a mechanism that allows you to act on that insight, deliver utility in the moment of need, and propel the customer to the next best interaction. We call this mechanism a contextual marketing engine, and our latest research – The Power Of Customer Context – shows you how to build it, and why you need to start now.
We unveiled this new research last week at Forrester’s Forum for Marketing Leaders to an on-site audience of more than 900, and we'll do it again in a few weeks across the pond at our London Forum. What are the key takeaways?
Consumers interact with brands across various mediums — their smartphones, computers, call center, in-store — just to name a few. Their interactions with brands — from viewing a TV ad to re-tweeting a promotion — are creating such a rich trove of data that marketers are left wondering: How do I glean relevant insights to optimize my marketing and media? Which channels should I optimize? How can I meet my growth goals while expanding into new, unfamiliar markets and channels? These questions keep marketers up at night; they are looking for best-in-class examples of measurement and analytics success. Well, look no further.
Our latest report, “Extract Business Value From Your Mix Model,” co-authored by Jim Nail, showcases USAA’s successful approach in leveraging marketing mix modeling to measure channel performance with greater precision and to identify and optimize future strategic investments, including marketing investments, product development, pricing structures, and organizational support. The report highlights USAA’s measurement and optimization challenges, how it used marketing mix to help refine metrics and identify the right levers to optimize business strategy and investments, and how it used the results to guide significant customer-driven changes at USAA. The case study is a good blueprint for firms that want to create a marketing mix model — and how to do it successfully!
We are thoroughly impressed by the analytics and marketing team at USAA; every decision made at the organization is driven by data and insights. Further, USAA is committed to using insights — including insights from its marketing mix model — to improve the overall customer experience.
In 2014, customer loyalty is a bit of an anomaly. Customers are empowered, informed, and have myriad options to choose from. They don’t really need to be loyal. But for companies doing business in the Age of the Customer, earning customer loyalty is more important than ever before. Satisfied loyal customers are the only reliable source of growth.
So, what do loyalty strategies look like today? For most companies I talk to their loyalty program is their strategy. While narrow, this approach doesn’t completely miss the mark. After all, the premise of a loyalty program is to create a mutually beneficial exchange for rewarding customers and collecting customer insight. And, for many marketers I talk to in retail, hospitality, and other industries, their most valuable customers are active participants in their loyalty program. The issue is that programs today are better positioned to “lock-in” customers rather than leverage member insights to drive personalization and improve the way they serve their customers. The traditional means of “driving” loyalty with points and discounts are no longer sufficient. It’s time for companies to evolve their approach to loyalty programs and strategies with a focus on relationships, advocacy, and engagement.
Customer analytics takes center stage in the age of the customer for firms trying to understand and predict customer behavior. From descriptive to predictive methods, customer insights (CI) professionals can apply a wide array of analytics methods to behavioral customer data. CI professionals have a lot to consider when deciding on the right portfolio of methods to drive customer understanding – what dependencies exist between analytics methods, what investment levels are required, where to get help and what business value do these methods drive.
To make it easier, we identified 15 key customer analytics methods that help firms win, serve and retain their customers. In our latest report, “TechRadar™: Customer Analytics Methods, Q1 2014” (subscription required), we evaluate each of these methods in detail taking into consideration their current adoption as well future potential. These methods, ranging from behavioral customer segmentation, lifetime value analysis, next-best offer analysis to recommendation analysis, allow firms to analyze customer data and use the analytical insight to drive acquisition, retention, cross-sell/upsell, loyalty, personalization and contextual marketing.
Our analysis shows that:
Methods that drive contextual insights are in early stages. Emerging methods such as sentiment analysis, location analysis, and device usage analysis are in early stages of development, but they have the potential to provide valuable context around behavior and other customer analytics methods.
This morning, as I was writing this blog post, I got an email from one of my colleagues, saying "Is it weird that since Google bought Nest, I no longer want one?" Her sentiment isn't that unusual because, as it turns out, plenty of people feel like Google + Nest = HAL. (It's hard to miss the resemblance)
My colleague Frank Gillett just published a post outlining a collection of ten key thoughts about the acquisition. As the privacy-identity-personal data wonk advising Forrester's marketing strategy clients, I thought I'd drill down on some of the more salient points for those issues.
As 2013 comes to a close, it's clear to me that much of the rhetoric about privacy's death was not only premature but downright wrong. Just in this past week, there have been several events that point to how very alive and critically important the topic of privacy is:
The US Senate Committee on Commerce, Science, and Transportation released a report (in advance of a public hearing) about the practices of the data brokerage industry, and how they impact consumers. The report claims that "data brokers operate behind a veil of secrecy, subject to limited statutory consumer protections." This certainly portends the possibility of new legislation being introduced by the committee in 2014.
US District Court Judge Richard Leon ruled that the bulk collection of millions of Americans' call records likely violates the Fourth Amendment of the Constitution. While conflating surveillance with marketing privacy is a dangerous thing, I suspect that this ruling will draw further attention to the volume, scale, and methods of data collection, irrespective of who's doing the collecting.
Adobe Cesareans Cross-Channel From The Email Market
Image Source: Ronald Grant Archive
Over the summer, we were all treated to an abundance of headlines proclaiming that Adobe, Oracle, and Salesforce were engaging in a marketing cloud war. Yet the relevant acquisitions — Neolane, Eloqua, and ExactTarget, respectively — only engaged in border skirmishes, since each focused on the distinct, yet adjacent, markets of campaign management, B2B marketing automation, and email marketing. Indeed, each of the strategic acquirers either already had partnership agreements in place or agreed to partner on the heels of the acquisitions.
We recently wrapped up our second evaluation of loyalty program service providers. From a potential pool of over 30 loyalty providers, we selected eight leading vendors that offer soup-to-nuts loyalty strategy, technology, and program management services. What has changed since our last evaluation? Notably, we found an increased focus on building programs that go beyond transactional rewards and loyalty technology. Wraparound program management services are still a key component of their solutions, but every vendor included in this Forrester Wave evaluation offers a productized platform that can be configured to meet client requirements.
In our final evaluation of eight vendors in“The Forrester Wave™: Loyalty Program Service Providers, Q4 2013,” we found a relatively competitive field of providers. Each firm has strengths and weaknesses in its current offering, but the leaders differentiate themselves through their forward-thinking company strategies. From a road map and development perspective, further increasing customer engagement capabilities, continually improving technology, and investing in more sophisticated loyalty analytics are major focus areas.
I want to extend my sincere thanks to each vendor in the report — Aimia, Brierley+Partners, Connexions Loyalty, Epsilon, Kobie Marketing, Maritz Loyalty Marketing, Olson 1to1, and Tibco Loyalty Lab — for committing to and participating in the often grueling Forrester Wave evaluation process. In addition, thank you to my CI colleagues Samantha Ngo, Carl Doty, and Shar VanBoskirk for supporting and editing this research.