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:
- What do you know that our competitors don’t?
- How is your strategy different from theirs?
- How do the parts of your strategy fit together?
- What did you learn from implementing last year’s plan?
The last question is often the most difficult and I’ll help you answer that here, in this final article in the series.
This question is both fair and unfair. It’s fair to expect strategists to learn from experience and to use those lessons to improve their strategy. But it can be unfair, because it probes for the errors of last year’s plan. This makes it hard to answer effectively while also demonstrating your professionalism. I’ve seen strategists answer this question well and not so well. Here are three methods that work.
If this, then that
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.’
Read the article in full here.