The Key to Operationalizing Go-to-Market Around Customer Journey: Conversation Track Architecture

Making the shift to a strategic demand approach and going to market outside-in is a key step towards driving sustainable corporate growth, but how do you operationalize this transformation? How do you actually organize your go-to-market resources around customer journey? How do you do this in a scalable and efficient manner?

Step one is fully embracing a Demand Process approach by aligning all elements of people, process, content, technology, and data around customer journey. This is a foundational principle, which we won’t cover in detail here, but that you can read more about in this piece, A Framework for Sustainable Growth: ANNUITAS Demand Process™.

Step two is to anchor your go-to-market design. You need an efficient way to scale customer journey engagement.

You can’t expect to infinitely deliver personalized customer journeys to populations of one. Instead, you need to find an elegant way to address the maximum level of personalization for the maximum volume of customers with minimum resources. You need to build a model that you can automate and optimize.

The starting place is cluster analysis – i.e., looking at the stakeholders, their roles in the buying process, and their points of common interests as the buying cohort moves from pain points to solution seeking. Cluster analysis enables us to find patterns that transcend traditional (static) segmentation, such as titles or company size, and that identify common information interests and content needs – a more ethnographic approach to building segmentation insights.

Below are two examples from ANNUITAS client engagements as we have worked through this cluster analysis:

example 1



Building this model isn’t easy. While not an exhaustive list, we commonly see the same five challenges over and over again, especially when we try to scale demand marketing activities. They are:

  1. You can never create enough content to address the infinite iterations of personas and their buying stages. Finding key clusters and critical paths to focus on is an important guiding principle.
  2. Title-based segmentation too often has flaws. Instead of relying on role, it’s important to find common content-behavioral paths to focus on. Your flow must start with higher-level pain points and make its way to evaluating and enabling a solution. The result of this structure is a series of customer conversation flows, through ‘information request’ stages, that are focused on solving problems.
  3. Segmentation is not monolithic; rather, segmentation must be dynamic. Often segmentation must shift at different customer journey stages, and this can create opportunities to gain economies of scale — combining engagement content for multiple personas at lower-funnel stages, versus building a content marketing model that merely multiplies number of personas times number of buying stages – not efficient.
  4. Successful, dynamic segmentation is powered by the information we collect about prospective customers along their journey. Lead management, progressive profiling and data append models must work closely together with content models to ensure that what we learn along the way can improve the content served up in real time, across all channels.
  5. Customer journey happens across multiple engagement channels. The customer has one path, but he or she may jump from web to email to search, and so our model for continuing the dialogue with prospective customers must orchestrate multi-channel interactions as though they are one string of dialogue, not silos.

Ultimately, we all want to ensure our go-to-market is as efficient and effective as possible. In this case, the key to operationalizing go-to-market around customer journey is a Conversation Track Architecture.

A Conversation Track Architecture is the approach of finding a common set of paths – which may change in number and rationale by customer journey stage – but that create a scalable basis for orchestrating targeted interactions with prospective customers across engagement channels and across buying stages.

Conversation tracks define clusters of common customer critical paths. They translate customer targeting and segmentation into a viable, repeatable conversation thread by customer journey stage. See an example, below, from a B2B media company.

example 3

They enable us to elegantly orchestrate content marketing and sales enablement touchpoints across multiple engagement channels, therefore driving singular, connected conversations for key segments. And they make this approach scalable by defining a ‘minimum viable’ segmentation at each stage of the customer journey that personalizes dialogue to the maximum number of customer segments.

When defining a Strategic Demand Marketing program, conversation tracks serve as the core underpinning to all elements of the program, bringing together Demand Process elements:

  • Content Marketing Model + Sales Enablement Model: Using Conversation Track Architecture, we’re able to efficiently and scalably define and build content by customer stage — finding the right volume of content to maximize personalization without creating content for ‘segments of one’ or merely creating x content pieces by y customer journey stages by z personas – an inefficient and costly way to build content. This same architecture provides the logic for how content offers should be served by customer journey stage and by engagement channel.

ANNUITAS Demand Process Model 2021

  • Funnel Management Framework: Effective funnel management is not about arbitrary lead scoring or functional sales stages, such as proposal stage; rather, it is about continuously assessing the readiness and intent of a prospective customer by tying together persons, accounts and opportunities. Using Conversation Track Architecture, we’re able to tune funnel management to better determine customer intent based on content behavioral interactions. Conversation Track Architecture also helps better define critical segmentation questions asked via progressive profiling and implicit data captured via content behavioral interactions; thus, providing the data to effectively route prospective customers via conversation tracks.
  • Technology Systems + Data Architecture: Conversation Track Architecture helps better define the configuration and structure of the demand technology systems and data underpinning go-to-market programs. In fact, in effective Demand Process design, Conversation Track Architecture should precede demand technology architecture work. For example, a critical role of marketing automation is to continuously assess a prospective customer’s position in a conversation track and customer journey stage in order to orchestrate engagement — a different way to think about marketing automation for many sales and marketing leaders and a system charter that would not be imagined had Conversation Track Architecture work not been done before enterprise architecture. Also, Conversation Tracks help guide data field structure to ensure that demand data builds, supports and completes a 360-degree view of the prospective customer – a ‘data value chain’ approach to demand data flows.

Conversation Track Architecture is adaptable to a number of different go-to-market scenarios.

Three examples include:

Single Solution, Differentiated Buyers: In this situation, we have one solution journey, but we have multiple different types of personas that might engage as a buyer. In this scenario, the objective is less driving a buying cohort, and more enabling individual customers. Thus, the opportunity is to find differentiated content behavioral paths – enabling these educational journeys and funneling all personas eventually to a common outcome. The example below is for a software testing company.

example 4

Single Solution, Multiple Stakeholders: In this situation, we also have one solution journey, but we have multiple stakeholders to engage. The objective in this scenario is absolutely to drive a buying cohort – engaging differential stakeholders, based on their role. It is also to bring them along in parallel with each other. The example below is for a security software company.

example 5

Multiple Solutions, Repeat Buyers: In this situation, we have multiple Solution Areas of Interest (SAI) that we are trying to match to different customers. What you see on the left side is segmented personas with a focus on aligning to pain points upstream, but then finding out more about specific needs through content consumption and Progressive Profiling – leading to ‘matching’ the customer with an SAI on the right. There is also a sense with this model of being able to engage and re-engage multiple times – thus part of the model embraces the idea of an individual customer being able to become a Qualified Lead (QL) in multiple SAIs over time.



What does it take to make the shift to a strategic demand approach – building a go-to-market program operationalized around customer journey and anchored by Conversation Track Architecture?

Here are some additional blog posts, articles and white papers that provide further context:

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