A good growth marketing strategy uses data to guard against opinions, feelings, emotions and bias. With a combination of both quantitative and qualitative data growth teams can bring together the what and the why in order to make more informed decisions.
Data should inform all aspects of your growth marketing programme including:
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How your team is tracking towards growth goals and KPIs
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Prioritising high impact work when choosing what to work on
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Identifying your best customer acquisition channels
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What is and isn’t working at an individual experiment level
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Monitoring experiment cadence
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Reporting on a weekly/monthly/quarterly learnings
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Building a predictive growth model
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Gathering customer insights
Data-informed, not data-driven
Whilst we want to ensure our growth investments are backed by data, data is primarily a means to gaining insights into user behaviour and preferences, but it is not the only means. Marketers must recognise and guard against the biases that can lead us to interpret data poorly. Confirmation bias, for example, shows that as humans we have a tendency to focus on data that confirms our hypotheses whilst over-looking data that could oppose them.
Additionally unlike in consumer businesses, in B2B we often won’t have large enough sample sizes to run statistically significant AB tests, but that doesn’t mean we can’t run an effective growth experimentation programme and use data to identify positive signals that our activities are moving in the right direction.
You will never have perfect data so must get comfortable making bets without all the data. At Growth Method we prefer to be data-informed rather than data-driven.
Evidence guided
As a result of leading product and engineering teams at the likes of Microsoft and Google, Itamar Gilad believes there are 2 kinds of companies - opinion-based companies, and evidence-guided companies.
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Opinion-based: Rely on opinions, hide uncertainty, focus on output metrics, prioritise planning and execution, plans dictated by smart people making blind bets, a planning mindset.
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Evidence-guided: Rely on evidence, embrace uncertainty, focus on outcome metrics, prioritise hypotheses and idea evaluation, plans rooted in data, logic and evidence, an experimentation mindset.
You can read more about this here but the data over opinions mindset sits firmly within the evidence-guided company.
To summarise
A good underlying data system is required to achieve efficient/sustainable growth - so know where overspending and underspending, to remove waste. Trim a lot of the waste.