Presenting your brand strategy for review by your most senior colleagues is a big deal, both professionally and personally. Preparing good answers to the investable challenges is important to both your brand team and to your own career.

This series of four articles helps you answer the four most important of them. The four articles look at each of the following questions:

1. What do you know that our competitors don’t?
2. How is your strategy different from theirs?
3. How do the parts of your strategy fit together?
4. What did you learn from implementing last year’s plan?

The first approach to learning from last year’s strategy is easiest to understand if you have some scientific training, because it’s based on the deductive logic that often underpins those disciplines.

A deductive approach begins with the harsh reality that, when last year’s plan was written, its authors couldn’t possibly know everything about the market. And when we can’t know something, we fill the gaps with assumptions.

For example, we might assume that our innovative product will be most attractive to the most pioneering prescribers.

This is a perfectly reasonable assumption, but unless we have supporting data it is only an assumption, not a fact, and we would much rather our strategy was as fact-based as possible.

The best strategists turn their necessary assumptions into pairs of testable hypotheses, which is the essence of deductive logic. So, in our example of adopting pioneer prescribers, they might say:

H1 = If our assumption is correct, then pioneer prescribers will prescribe disproportionately more of our product

Or

H0 = If our assumption is incorrect, then pioneer prescribers will prescribe proportionately no more of our product than others.

They would then test this pair of ‘if this, then that’ hypotheses with the data.
Strategists blessed with good data and clever analytics can do this easily, but less fortunate strategists may have to rely on proxy data. For example, by assuming that pioneering prescribers are younger or are more likely to be found in big city teaching hospitals than in small, remote, rural hospitals.

While the analytical process of hypotheses testing is very case specific, the outcome is the same in all cases. The original assumption is tested and found to be correct, incorrect or, in some cases, qualified and nuanced. Either way, what was only an assumption has now been transmuted into a fact supported by data. Deduced new facts, eg, pioneer prescribers like us better only when their payers allow it, become valuable new inputs into the next planning cycle. Just as importantly, it gives you a credible and impressive answer to senior management’s challenges, eg, ‘This year’s strategy will focus more tightly that last year’s, concentrating on the innovative prescriber/enlightened payer contextual segment.’

The second approach to learning from last year’s strategy is the one that is easiest to understand if you have some humanities training, because it’s based on the inductive logic usually used by historians and political scientists.

In contrast to deduction, the inductive approach begins with observable facts but without any prior assumptions. It marshals together a lot of separate data sets, each of which looks at the market from a different perspective. It then draws sense from this multidimensional complexity between looking for relationships between two or more dimensions. Importantly, these data sets don’t have to be (indeed, are better if they are not) only quantitative. For example, quantitative data about prescribing rates, prescribing preferences and the demographics of primary care territories is no more valid than qualitative data about how prescribers react to propositions, how they consume information and the reasons they give for brand switching. Equally, qualitative data from external sources, such as commissioned market research, is no more valid than that from internal sources, such as sales term reports or informal field visits.

Inductive logic works by seeing what emerges from the interactions between different sets of data. For example, we might see that our prescribing volume is declining due to brand switching, rather than changing care protocols and that this is concentrated in primary care territories with the worst ratio of healthcare professionals (HCPs) to patients. On top of those relationships, we might overlay qualitative observations that prescribers cite overload as their reason for not reading clinical evidence and their preference for simple choices. This assembly of qualitative and quantitative data might then combine with analysis of a competitor’s simplistic, well-resourced messaging. From this confluence would arise the lesson, built on multiple sources, that there is an ‘under pressure’ prescriber segment that finds our own strong but complex messaging confusing.

‘It’s an accepted wisdom among management professors that the only sustainable competitive advantage is to learn better and faster than your competitors’

Again, fortunate strategists with lots of data and sophisticated analytics have an advantage here. Complementary forms of multivariate cluster analysis and smart content analysis of free-text, unstructured data make it much easier to see the lessons that emerge from the complex interactions between data sets. But although good tools make it easier, clever strategists can do almost as well, if more laboriously, with some spreadsheets and critical thinking. It’s here that colleagues with a humanities background become a cognitive asset.

While the deductive approach transmutes assumptions into facts, induction finds needles of insight in haystacks of data. The outputs of the inductive approach are less quantitively supported than deductive insights but they are often richer, more detailed and all the more valuable for that.

And, just as much as deductive logic, the inductive approach gives the strategist precious new material for the next iteration of the strategy. Like the deductive approach, it provides a powerful answer to the question about what you have learned over the last year. For example: ‘We’ve learned that our messaging needs to be simpler for the under pressure segment.’ For senior managers, this sort of answer both impresses with its insight and satisfies with its clarity, especially if complemented with answers from the deductive approach.

The third approach to answering the challenging question about what you have learned in the last year is based on abductive logic, the kind used by social scientists such as sociologists or social anthropologists.

The abductive approach sits somewhere between the if/then hypotheses testing of the deductive approach and the ‘emergence from complexity’ method of induction. It begins with questions that arise during the execution of the strategy to which we don’t know the answer. For example, why are certain prescriber categories less responsive than others? What is the value driver that makes some prescribers switch while others don’t? Why do prescribers in the same category respond differently to our value proposition? Then, the strategists trying to learn from their experience develop multiple competing theories that might answer the questions. For example, the differences in response by prescribers in the same category could have three possible explanations:

1. Differences in how fully our messages broke through the market’s noise
2. Some poorly understood heterogeneity of non-clinical needs within the prescriber category
3. A logistical, timing issue associated with where, when and how our marketing communications was executed.

‘Just as much as deductive logic, the inductive approach gives the strategist precious new material for the next iteration of the strategy’

This, and similar answers that the abductive approach provides, are often much more insightful, nuanced and commercially actionable that those provided by the other two approaches to learning. And even more so when compared to choosing neither deduction, induction nor abduction.

If the most senior person in the room asks you, ‘What did you learn from last year?’, then they are wise to do so. It’s an accepted wisdom among management professors that the only sustainable competitive advantage is to learn better and faster than your competitors. Typically, that’s what lies behind this question, which has caused many strategists to trip up, even after completing a strong presentation. The same trip hazard is created by the other three questions discussed in this series of articles – but you don’t have to land on your face. If you anticipate these questions and learn from the successful practices that I’ve described in these four articles, you can turn them into opportunities to improve your strategy and enhance your career prospects.

Professor Brian D Smith is a world-recognised authority on the evolution of the life sciences industry. He welcomes questions at brian.smith@pragmedic.com. This and earlier articles are available as video and podcast at www.pragmedic.com