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Three box solution coined by Professor Govindarajan is a proven methodology for business model innovation. The basic premise of the model: It is well-understood that creating a new business and optimizing an already existing one are two fundamentally different management challenges. The real problem for leaders is doing both, simultaneously. How do you meet the performance requirements of the current business—one that is still thriving—while dramatically reinventing it? How do you foresee a change in your current model before a crisis forces you to abandon it?
The model expands the leader’s innovation toolkit with a proven and straightforward method for allocating the organization’s energy, time, and resources—in a balanced measure—across the three boxes:
Box 1: The present — Strengthen the core
Box 2: The past — Let go of the practices that fuel the core business but fail the new one
Box 3: The future — Invent a new business model
The “three box” framework makes leading innovation easier because it gives leaders a simple vocabulary and set of tools for managing and measuring the different sets of behaviors and activities across all levels of the organization.
What if we try to use the model for strategic planning for B2B Tech / IT marketing? In simple terms, we need to decide
Let’s explore the boxes one and two with some examples. With the advancement of analytics tools, at least for the digital channels, it’s easy to determine which media are not working well for our new lead generation activities. I am not only talking about the lowest common denominator like Cost per Lead (CPL). We need to also take into account the qualitative value of the leads generated by those channels. Do those leads respond well to nurturing? What about the conversion ratio of Marketing Qualified Leads (MQL) to Sales Qualified Lead (SQL)?
For some of our clients, we’ve effectively implemented a 3V model of lead quality scorecard: volume (number of leads generated, measured by CPL), velocity (movement through nurturing tracks, measured by lead score over time), and value (contribution to revenue pipeline).
Based on this multi-dimensional evaluation, we can ascertain the non-so-effective channels. Now we have to form some hypothesis based on certain assumptions. For the non-effective channels, is there any mismatch between value proposition and expected outcome? For example – LinkedIn’s Direct Sponsored Updates or DSU is an excellent channel for raising awareness, but not so great for generating leads. Is there any content – channel mismatch? e.g., dumping a whitepaper in Facebook?
In each case, we can classify the assumptions in three categories – Certain, Educated Guess, and Wild Guess.
Now we need to decide the action points. What do we need to implement? Create a series of thought leadership and brand authority content for LinkedIn DSU? What’s the value we are going to get out of it? In other words, how attractive is that idea versus the difficulty (regarding effort, budget, and capability) to execute such task? In many cases, when marketers place the assumptions and attractiveness matrix side by side, the discussion becomes more objective and fact-driven.
For example, if you are trying to get your website rank higher for a specific keyword, it’s better to exercise the simulation and decide whether to spend the effort to optimize for search ranking or to invest in SEM or paid search.
All the points above are for optimizing current marketing activities. What about the future? We need to brainstorm in two parallel tracks here: the probable challenges the brand might face in the near or far future and the problems the marketing department will undergo. Brand challenges are specific to your company, so let’s talk about some common marketing challenges soon. Again, the hypothesis needs to be placed in the assumptions and attractiveness matrix, as we did for Box 1 (optimizing current operation). We need to present the questions like the following:
What will happen if our prospect database shrinks drastically due to GDPR compliance?
What’s going to be the effect of AI in B2B tech marketing?
From the matrix with the questions, hypotheses, and corresponding action items, we can pick and choose the topics for Box 3 (the future box) and set aside some resources to experiment on within a controlled environment.
Do you think the 3 Box solution is useful for marketing planning in your organization? What are the possible challenges you can predict to implement the framework?
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