The essential shape of the enterprise marketing landscape hasn’t changed much over the years. In last week’s Revisiting The Enterprise Marketing Software Landscape, I dissect technologies into the four basic categories of marketing management, brand management, relationship marketing, and interactive marketing. Consumers are rapidly changing behaviors, and marketing as a practice is evolving dramatically, but the technologies that marketers buy continue to come in essentially the same containers.
Notice, however, all of the decision management systems employed across the marketing landscape. From interaction management to online testing to recommendations to contact optimization, marketers are using automated systems to make an increasing number of customer-facing decisions. Viewed from the perspective of those decisions, the landscape of marketing technologies is shifting under our feet.
So is it time for a new take – say, customer decision management (CDM) – on marketing technology?
Why Do We Need Customer Decision Management?
Given that we’re already awash in marketing technology, do we need another three-letter acronym? Yes, because customers are:
- Expanding the number of engagements, each of which generate new data streams. Marketers know they must support the customer life cycle of customer interactions across products and services. The problem? As the set of objectives and subsequent stream of touch point data expand, CI pros face the daunting task of figuring out what’s relevant and responding in near-real time.
- Not building relationships on the backs of campaigns alone. Customers are, however, building relationships with companies across the variety of inbound, outbound, face-to-face, digital, direct, and social touch points. Yet building campaigns for each is nearly impossible. Doing so would require you to anticipate every opportunity at every point without “orphaning” interactions. Even if you could, how would you generalize learnings from one campaign to others?
- Unwilling to wait for the best response. Marketers have turned to marketing technology to improve process efficiency, often through rules-based automation. Systems are speeding up decisions on content, products, and offers. Yet because business rules are relatively rigid, those decisions often come at the expense of the customer’s needs at a given time.
What Is Customer Decision Management?
Allow me to hazard a definition of CDM, taking some liberties on James Taylor’s work on decision management systems:
Customer decision management applications are tools that tailor the content, products, offers, and next actions presented to individual customers based on analytical processes applied to that individual's profile and context and from the continuous learning accrued from other customers' interactions.
Many of these tools – from offer management to online testing tools – are familiar to and used daily by CI pros, though the concept provides a new perspective on the marketing technology landscape. Vendors range from the largest enterprise providers – such as IBM, Oracle, and Infor – to startups – like Conductrics and Featurespace.
What Are The Characteristics Of CDM Technologies?
CDM technologies should be:
- Decisive, not just supportive. Marketing has long employed decision support systems, from spreadsheets to calendars to sophisticated analytical systems. CDM tools must also be capable of enacting decisions, rather than being limited support decisions by marketing leadership.
- Goal driven and self-correcting. CDM should be capable of balancing multiple goals on any given interaction in the customer life cycle – from discovery to exploration to purchase to support to advocacy. CDM technologies should be capable of automatically learning and adjusting goals based on customer interactions.
- Inherently experimental and explicitly rules-driven. CDM systems must be structured as learning systems, testing the strength of each offer and response. Yet they must also operate within constraints, balancing predictive models with established business rules.
- Aware of the context and continuity of interactions. CDM technologies should be extensible various forms of contextual data, from preferences to social influence to intent. These tools should also treat each interaction as an opportunity to further the engagement, even when a customer successfully responds or converts.
What Are The Implications Of Customer Decision Management?
While many CDM technologies exist today, I’m interested in exploring the implications of their use. Particularly:
- Governance. Providing a CDM technology with more data and more domain, how should these tools be represented in privacy policies or accounted for in regulatory compliance, especially when decisions aren’t always made on the basis of explicit rules?
- People. How will CDM tools change hiring practices? How should CI pros incorporate non-deterministic and self-actuating tools into a formal CI practice?
- Process. How would CDM technologies change analytical practices, model development, segment definition, campaign design, asset development, and even strategy?
- Technology. What pull would CDM as a category exert on other systems, such as reporting/dashboards, business intelligence tools, asset management, analytical data marts, or campaign management tools?
- Applicability. In what industries will CDM be most applicable? How should CI pros assess the ROI of these tools? Build road maps incorporating CDM?
- Delivery. How is CDM packaged and sold into the marketing department as an enterprise category (rather than the current point-wise approach)? Are there inherent advantages to on-premise or SaaS based delivery?
What Are Some Good Resources?
If you do find this concept of customer decision management useful or interesting, please let me know in the comments or on Twitter (@brosnaro). I’ll also leave you with some related Forrester research.