Data-driven customer understanding is not a new concept but, until now, it has been very much thought of as a tool for tier 1 and tier 2 retailers. Is this because big data is too big for SMEs, or have smaller enterprises just not yet found the right use cases for business intelligence?
Certainly, the principle of analysing shopper behaviour and adapting to those trends is not something that is restricted by size – so perhaps SMEs are put off by the term ‘big’. In fact, with fewer resources than their larger counterparts, it’s almost more imperative that growing companies use business intelligence to streamline their product range, enhance customer service and increase staff productivity.
However, the concept of big data in an SME environment can be somewhat overwhelming, so it’s best to start simple. One of the first things that enterprises experimenting with data should do is invest in a business intelligence solution, which is capable of automating information analysis.
By harnessing the power of technology, SMEs can immediately get a handle on what metrics provide valuable information, calculate trends with minimal time commitment from their already busy workforce, and then share that information in a consistent manner.
Finding the right business intelligence tools to analyse key business data is only the first step, though.
In organisations of any size – not just SMEs – there is a lot of data being generated and measured, but this doesn’t always lead to actions. Ultimately, the data retailers have on their customer is of limited use, if that data is not then turned into actionable change.
The beauty of small business is that, in many ways, they are better equipped to respond to big data insights. Many larger corporations are constrained by siloed working, legacy systems and entrenched mindsets; SMEs on the other hand are smaller and more open minded, and therefore can react much more quickly to what customers are doing.
To make the most of this data in a small business environment, owners and key decision makers need to adopt a three step process:
- Put a strategy in place
Before making any kind of investment, SMEs need to understand exactly what data they have access to, what business intelligence platforms are available to analyse this information, and what metrics they can generate as a result. By carrying out an audit like this up-front, they can then create a strategy for how these insights will be use – and also identify whether any publicly available information, such as the weather, should be incorporated to enhance their customer understanding.
- Create a measurement and reporting plan
To an extent, the mechanical act of reporting will be covered by the software chosen to manage data analytics. However, decisions need to be made over who is responsible for managing reports, who they should be shared with, and what information should be shown to each member of the team. While the key to leveraging big data is transparency, it’s easy to overwhelm people with too many metrics. Focus on sharing the information that will enable each member of staff to do their job better.
- Prioritise outcomes
Especially in the initial stages, big data analysis is going to generate a lot of points for change. Rather than being blinded by possibilities, SMEs should look to focus on which insights are critical, and which can wait until later on. Prioritisation not only focuses the mind and makes it easier to embrace change, it delivers business enhancements with the biggest impact on customer results first, so enterprises should start to see tangible improvements relatively quickly.
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