The Growth Mapping glossary: the 8 ideas behind the platform

Every concept inside the Growth Mapping framework traces back to a real body of research. This page defines each one in plain terms. It also shows why it matters for a business with fewer than 20 people — and how it maps to what the Hiilite platform actually does.

Use this as a reference. Cite it if it helps you.

TL;DR

Growth Mapping is built on eight intellectual foundations. Together they form a closed loop: sense where you are, seize the right move, transform your results, and repeat. The table below gives you the short version.

Code Name One line
DC Dynamic Capabilities The ability to sense change, seize the opportunity, and reconfigure what you do
GH Growth Hacking Rapid, data-driven experiments to find the highest-leverage growth actions
BX Business Experiments Controlled testing to know what actually caused a result
AI Agentic AI Software agents that take actions autonomously, with human oversight
MA Marketing Agility Short feedback cycles that let marketing adapt faster than the market moves
DL Deep Learning Neural-network pattern recognition applied to multi-source data
PA Predictive Analytics Using historical data and models to recommend what to do next
MS Marketing Strategy Setting goals, choosing where to compete, and aligning effort to outcomes

Dynamic Capabilities (DC)

Dynamic capabilities is a strategic management theory, developed by David Teece, Gary Pisano, and Amy Shuen, that describes a firm’s ability to do three things: sense changes in its environment, seize opportunities those changes create, and reconfigure its resources and processes to stay ahead. The theory holds that sustainable competitive advantage comes not from holding a fixed position but from the capacity to adapt faster than rivals.

For an SME, this matters because the market does not stand still. A firm that can detect a shift and act on it before competitors is the one that compounds growth over time.

In the platform: the Sense → Seize → Transform loop is a direct implementation of Teece’s microfoundations. Sense is the Diagnose agent reading live client data. Seize is the Advisor recommending and ranking the right plays. Transform is the measurement layer that closes the loop and feeds the next decision.

Source: Teece (2007), Strategic Management Journal, doi:10.1002/smj.640

Growth Hacking (GH)

Growth hacking is an approach to business growth, coined by Sean Ellis in 2010 and developed academically by René Bohnsack and Joost Liesner (2019), that uses rapid, low-cost experiments across the acquisition, activation, retention, and revenue funnel to find the highest-leverage paths to growth. It prioritizes testable, data-driven moves over large upfront marketing investments, and it treats growth as a function of the whole organization, not just a marketing department.

For an SME, this is the practical answer to the question: where do I spend limited time and money to get the most growth? Growth hacking gives you a method for finding that answer faster.

In the platform: the Plays catalog is the growth-hacking taxonomy in structured form. Each Play maps to a growth domain (Recruitment, Retention, Revenue), carries measurable inputs and outcomes, and runs against real client data rather than industry benchmarks.

Source: Bohnsack & Liesner (2019), Business Horizons, doi:10.1016/j.bushor.2019.09.001

Business Experiments (BX)

Business experiments are controlled tests run inside a real organization to determine whether a specific change causes a specific outcome. The concept, developed rigorously by Stefan Thomke at Harvard Business School, treats the company as a laboratory: form a hypothesis, run the test, measure the result, update your model. The discipline matters because correlation is not causation — most business decisions are made on one without the other.

For an SME, this provides something most small businesses never have: confidence that a marketing action actually worked, not just coincided with a good month.

In the platform: every Play is designed as a testable intervention. The Transform layer exists specifically to measure what moved, attribute the result to the action, and decide whether to repeat, adapt, or stop.

Source: Thomke, “Building a Culture of Experimentation” (HBR, 2020)

Agentic AI (AI)

Agentic AI refers to AI systems that can plan, take sequences of actions, use tools, and pursue goals with varying degrees of autonomy — going beyond single-turn question-and-answer toward sustained, goal-directed work. The framework for agentic systems has been developed by Anthropic (“Building Effective Agents,” 2024), Lilian Weng (“LLM-Powered Autonomous Agents,” 2023), and Andrew Ng’s work on agentic workflows at DeepLearning.AI. The key design choice is the autonomy model: how much the agent does on its own versus how much a human approves before an action is taken.

For an SME, agentic AI means the system does work — not just reporting. It runs plays, drafts content, analyzes data, and sequences tasks without requiring a full-time data or operations team.

In the platform: agents are the Workers who execute the Plays. The autonomy model is deliberate: recommend-and-approve. The platform proposes and explains; a human approves before anything outward-facing happens.

Source: Anthropic, “Building Effective Agents” (2024) · Weng, “LLM-Powered Autonomous Agents” (2023)

Marketing Agility (MA)

Marketing agility is an organization’s capacity to rapidly sense shifts in customer or competitive conditions and reconfigure its marketing strategy and execution in response. Kalaignanam, Tuli, Kushwaha, Lee, and Gal (2021), in the Journal of Marketing, define it as a dynamic capability with two components: sensing (detecting relevant change) and responding (reconfiguring resources and actions). Their research shows that marketing agility improves firm performance, particularly in volatile environments.

For an SME, agility is often the one structural advantage over larger competitors. The smaller firm can move in a week what a large firm debates for a quarter.

In the platform: the short Sense → Seize → Transform cycles are the operational form of marketing agility. Each loop iteration is a feedback cycle. The loop tightens as the platform accumulates data specific to that client.

Source: Kalaignanam et al. (2021), Journal of Marketing, doi:10.1177/0022242920952760

Deep Learning (DL)

Deep learning is a class of machine learning methods, based on artificial neural networks with many layers, that learn representations of data through hierarchical pattern detection. LeCun, Bengio, and Hinton — who shared the 2018 Turing Award for the work — established its theoretical and practical foundations in their landmark 2015 Nature review. Shrestha, Krishna, and von Krogh (2021) examined deep learning’s application to firm-level decision-making and strategy, finding it most valuable in contexts with large, complex, multi-source data.

For an SME, deep learning is the pattern-recognition layer that finds signals across disconnected data sources — signals a human analyst looking at a single dashboard would miss.

In the platform: deep learning underlies the reasoning layer. It detects anomalies and patterns across multi-source client data (financial, marketing, operational) to surface the signals the Advisor needs.

Source: LeCun, Bengio & Hinton (2015), Nature, doi:10.1038/nature14539

Predictive Analytics (PA)

Predictive analytics uses historical data, statistical models, and machine learning to forecast future outcomes and recommend actions. Thomas Davenport and Jeanne Harris, in their foundational work “Competing on Analytics” (HBR, 2006), argued that the firms that win are those that make better decisions faster using data. Chen, Chiang, and Storey (2012), in MIS Quarterly, framed business intelligence and analytics on a spectrum from descriptive (what happened) to predictive (what will happen) to prescriptive (what to do). Prescriptive analytics — recommendations, not just forecasts — is the most commercially valuable tier.

For an SME, predictive analytics does not require a data team or a data warehouse. It requires the right data joined together and the right model asking the right question.

In the platform: the Advisor builds interpretable recommendation rules bound to client financials. The goal is prescriptive output: not a chart, but a ranked list of plays with a projected impact on revenue, clients, or profitability.

Source: Davenport & Harris, “Competing on Analytics” (HBR, 2006) · Chen, Chiang & Storey (2012), MIS Quarterly, doi:10.2307/41703503

Marketing Strategy (MS)

Marketing strategy is the deliberate set of choices a firm makes about where to compete, how to create value for a defined customer, and how to sustain an advantage over time. Michael Porter’s “What Is Strategy?” (HBR, 1996) defines strategy as performing different activities from rivals, or performing similar activities in different ways, to create a unique and valuable position. Clayton Christensen’s Jobs-to-Be-Done (JTBD) theory, developed in Competing Against Luck, reframes strategy around the specific progress a customer is trying to make — the “job” they are hiring a product or service to do.

For an SME, this is the planning layer that turns goals into the right sequence of moves. Without it, growth activity is just noise.

In the platform: marketing strategy is the starting point. You declare the goal — revenue, clients, profitability — and the platform derives the right plays from it. Strategy sets the direction; the loop executes and measures against it.

Source: Porter, “What Is Strategy?” (HBR, 1996)

Frequently asked questions

What is Growth Mapping? Growth Mapping is a framework for driving business growth through a continuous Sense → Seize → Transform loop, bound to a firm’s real financial and marketing data. It is grounded in dynamic-capabilities theory (Teece), growth-hacking research (Bohnsack & Liesner), and predictive analytics (Davenport). The full framework is described on the Growth Mapping framework page.

How are these 8 philosophies related to each other? They form a stack. Dynamic Capabilities (DC) provides the strategic loop. Growth Hacking (GH) and Business Experiments (BX) supply the tactical methods. Marketing Agility (MA) and Marketing Strategy (MS) connect goals to execution. Predictive Analytics (PA) and Deep Learning (DL) supply the data intelligence. Agentic AI (AI) is what makes the loop run without a full team behind it.

What is the difference between predictive analytics and deep learning in this context? Predictive analytics produces the recommendation — a ranked output that tells you what to do next. Deep learning is part of the pattern-recognition layer that feeds that recommendation. In a small-business context, PA is the decision surface; DL is the signal-detection layer underneath it.

Who is the research behind Growth Mapping? Growth Mapping is the commercial implementation of William Walczak’s doctoral research at UBC-Okanagan, under the supervision of Dr. Eric Li. The theoretical spine is Teece’s dynamic-capabilities framework. The eight philosophies on this page form the full intellectual lineage. See the research hub for publications and citations.

Can an SME actually use academic research like this? That’s the point. The platform translates each of these research bodies into a concrete action: an experiment you run, a play you execute, a metric you track. The research tells you which actions are most likely to work and why. The platform does the work.

Go deeper

About the author

William Walczak is the founder and CEO of Hiilite Creative Group (2014) and the researcher behind Growth Mapping. He holds an MBA from UBC and an engineering degree from Simon Fraser University, and is a PhD candidate in Interdisciplinary Graduate Studies at UBC-Okanagan (supervisor Dr. Eric Li), with research interests in consumer behavior, machine learning, predictive analytics, and consumer experience. He was named Marketing Strategy CEO of the Year 2023 (BC) by CEO Monthly.

Peer-reviewed publication: Walczak, W., Li, E. P. H., & Nelson, S. (2024). “Logarithm: A Cinematic Exploration of Time.” Journal of Customer Behaviour.

hiilite.com/team/william-walczak · LinkedIn · Google Scholar

Read the framework

The eight philosophies above explain the intellectual foundation. The Growth Mapping framework shows how they combine into the system that runs inside the platform — and what it means for your business.