Businesses collect more information on their customers than ever before, but clients often tell us it is difficult to know whether they are using data in the best way. It’s not surprising – collecting more data means more complexity and choice, and it is often difficult to see the impact directly. So how do companies ensure that more data leads to better results?
The short answer is that businesses are increasingly investing in their own data strategy to meet the challenge of generating maximum value from their data. In this article we offer some top tips on how to design a data strategy to gain the most insight possible.
Start by making a data map
A useful way for businesses to start a data strategy is to make a data map, setting out how data supports the business model. Doing this early will help businesses see the bigger picture and the full range of investment options, while navigating some of the finer details on how data can yield better performance.
Every business will have a different data map, but here’s a high-level overview.
This data map has five key elements:
- Business aims: What is the business trying to achieve? Why does it want to invest in data?
- Levers: What levers can be pulled to achieve these aims? How can decisions on pricing, product range and customer experience influence outcomes that are consistent with the business aims? How might data-based insights inform which of these levers to pull, and when?
- Capabilities and processes: What are the processes linking data insights to business levers? Are the business’s internal capabilities fit for purpose?
- Analysis: Does the business generate the best insights from data? Does it perform the right analyses?
- Datasets: What datasets are collected? Is data collected on individual customers or at a more aggregated level? Can the business link together data sources to unlock new analyses?
Building your data strategy
When the data map is complete, businesses can use it to build their data strategy in practice. Our preferred approach is for businesses to think from the top of the map downwards, starting on business aims and following the logic through to finish on what datasets they need to collect.
Here are our tips on how businesses can follow this process to build out their data strategy.
The natural starting point is for businesses to focus on their high-level business aims from investing in data. What do they ultimately want to achieve, and why? It’s important that businesses are as clear and specific about their aims as possible, since they should steer the business’s thinking through the remaining elements of the data map.
Next businesses should determine how data fits into the business, and where it is most useful. Businesses can think “top-down” into what levers influence outcomes related to their specific business aims, and then further into how decisions on which levers to pull could be informed by data. To do this well, businesses should be creative and consider whether data could provide new ways to generate value, for example, asking themselves “if I had all the data in the world, what would I do with it?”. In many ways, all planning should relate back to how data fits with the business’s strategic advantage and distinctive capabilities. Businesses should use this to prioritise what areas of the business to focus their data strategy on.
Businesses should take a step back and make sure that they have the right capabilities, so that any changes to data collection and analysis will deliver real value in practice. Having the right people and techniques is important to translate data into insight, which feeds into decision-making on the customer proposition. But businesses also need slick internal processes that efficiently connect data insights to decision-makers, since the value from data is ultimately realised when insights inform business decisions.
It’s worth noting that ‘efficiency’ looks different to different businesses – a food-to-go retailer responding to hourly shifts in demand needs data insight reaching the pricing and promotions decision-maker very quickly. In contrast, a large supermarket might design their offer according to changes in behaviour across weeks or months, and these businesses can afford a longer lead time from insight to decision.
Together, a business’s plan, capabilities and data audit will inform what specific changes to data collection should be made and when. Businesses could also consider ways to improve the characteristics of datasets that they already collect – like how granular their data is (store level or customer level?), whether datasets can be linked together, or whether data on the same customers is collected across time. But it’s important to keep in mind that datasets will only be valuable if they are aligned to actual business decisions, so businesses should be sure there is a case for investing in datasets that are linked to specific levers before they start this phase. A useful approach is for businesses to set up hypotheses they want to test and then target data that helps them to answer these. This will streamline the collection process.
Finally, to effectively implement the strategy, businesses should ensure that responsibility clearly lies with teams. And they should find ways to monitor whether the strategy is generating real value, iterating further as appropriate.