Data analytics: the analysis of raw data to extract useful insights which can lead to better decision making.
It’s the process of joining the dots between different sets of data within your business. Why is it then, that for the majority of businesses it can often remain something mystical and misunderstood?
Here are 5 reasons your business can benefit from data analytics.
1. DАTА АNАLУTIСЅ АND СUЅTОMЕR BЕHАVIОUR
Anyone who’s had a go at Facebook advertising will have seen a examples of this process in action. You get to target your advertising to very specific user segments based on geography, demographic, areas of interest, online behaviours, etc. It’s quite unnerving that they can be so specific about so many of their users.
For most retail businesses, point of sale data is central to their data analytics exercises. A simple example might be identifying categories of shoppers based on frequency of visits, average spend, etc and then identifying other characteristics associated with those categories, like age, day or time of shop or where they live. This last one is a favourite of nearly every zoo I’ve ever visited. They always ask for your post code!
Once you have a reasonable set of data telling you more about certain groups of customers, you can better target marketing strategies that send the right message to the right people.
2. ANY DATA IS BETTER THAN NO DATA
The point of data analytics is to support strategic decisions. That may be as simple as choosing the frequency of email campaigns or as complicated as forecasting 24 month revenue targets. Some decisions benefit from additional insights from data analytics; others need much more than that.
Suffice to say, without data you are losing out on the value that is lying dormant in your business.
3. CUSTOMER COMPLAINTS – A GOLDMINE ОF ACTIONABLE DATA
Categorising and analysing the content and drivers of customer feedback, good or bad, provides a way of mining customer sentiment. The objective here is to shed light on the drivers of recurring problems encountered by your customers, and identify solutions to preempt them.
One of the challenges though is that by definition this is the kind of data that is not normally laid out as numbers in neat rows and columns. Rather, it will tend to be a dog’s breakfast of words with snippets of qualitative and sometimes anecdotal information, collected in a variety of formats by different people across the business. It will require attention before any analysis can be done with it.
4. STARTING SMALL HAS AN IMPACT
Often, most of the resources invested in data analytics end up focusing on cleaning the data itself. You’ve probably heard the old adage, ‘garbage in, garbage out’ when referring to the correlation of the quality of raw data and the quality of the analytical insights that will come from it. In other words, the best analytical tools will be unlikely to find anything of valuable meaning if the material they are working with has not been gathered in a methodical and consistent way. First up, you need to get the data into shape before you do anything else.
The solution is to start with small well understood data sets and grow your efforts from there.
5. PRIORITISE ACTIONABLE INSIGHTS
More data doesn’t always lead to better decisions. But any data will lead to better decision support than no data at all. One or two really pertinent and actionable insights are all you need to ensure a significant return on your investment in any data analytics activity.
It’s easy to get lost in the curiosities of the data patterns and lose focus.