5 Ways Data Analytics Can Assist Your BusinessData analytics is the analysis of raw data in an effort to extract beneficial insights which can lead to much better decision making in your business. In a way, it's the procedure of signing up with the dots between different sets of obviously disparate data.
While huge data is something which may not be relevant to most small businesses (due to their size and limited resources), there is no reason why the concepts of great DA can not be rolled out in a smaller business. Here are 5 methods your business can take advantage of data analytics.
1 - Data analytics and client behaviour
Small businesses might think that the intimacy and personalisation that their little size enables them to bring to their consumer relationships can not be reproduced by larger business, and that this in some way supplies a point of competitive distinction. However what we are beginning to see is those bigger corporations have the ability to duplicate some of those characteristics in their relationships with consumers, by utilizing data analytics techniques to synthetically produce a sense of intimacy and customisation.
Most of the focus of data analytics tends to be on client behaviour. Anyone who's had a go at marketing on Facebook will have seen an example of this process in action, as you get to target your marketing to a specific user sector, as defined by the data that Facebook has caught on them: demographic and geographical, areas of interest, online behaviours, and so on
. For most retail services, point of sale data is going to be central to their data analytics workouts.
2 - Know where to draw the line
Even if you can much better target your customers through data analytics, does not imply you constantly should. Sometimes ethical, useful or reputational issues may trigger you to reevaluate acting on the details you've revealed. For instance US-based membership-only retailer Gilt Groupe took the data analytics procedure perhaps too far, by sending their members 'we have actually got your size' emails. The campaign ended up backfiring, as the company got grievances from consumers for whom the thought that their body size was taped in a database somewhere was an intrusion of their privacy. Not just this, but lots of had given that increased their size over the period of their subscription, and didn't appreciate being advised of it!
A better example of using the details well was where Gilt adjusted the frequency of e-mails to its members based on their age and engagement classifications, in a tradeoff between looking for to increase sales from increased messaging and seeking to reduce unsubscribe rates.
3 - Consumer grievances - a goldmine of actionable data
You have actually most likely already heard the saying that customer grievances provide a goldmine of beneficial information. Data analytics supplies a way of mining consumer sentiment by systematically categorising and analysing the material and motorists of customer feedback, excellent or bad. The goal here is to clarify the chauffeurs of repeating problems come across by your customers, and determine solutions to pre-empt them.
One of the obstacles here though is that by definition, this is the kind of data big data analytics that is not set out as numbers in neat rows and columns. Rather it will have the tendency to be a dog's breakfast of snippets of sometimes anecdotal and qualitative info, collected in a variety of formats by various people throughout business - and so needs some attention prior to any analysis can be done with it.
4 - Rubbish in - rubbish out
Typically most of the resources invested in data analytics end up focusing on cleaning up the data itself. You have actually most likely heard of the maxim 'rubbish in rubbish out', which refers to the correlation of the quality of the raw data and the quality of the analytic insights that will come from it.
A crucial data preparation workout might involve taking a lot of consumer emails with praise or grievances and assembling them into a spreadsheet from which recurring patterns or themes can be distilled. If the data is not transcribed in a consistent way, possibly because different personnel members have actually been involved, or field headings are unclear, exactly what you might end up with is unreliable complaint categories, date fields missing, and so on.
5 - Prioritise actionable insights
While it is necessary to remain unbiased and versatile when undertaking a data analytics project, it's also essential to have some sort of technique in place to assist you, and keep you concentrated on what you are trying to accomplish. The truth is that there are a plethora of databases within any business, and while they may well contain the answers to all sorts of concerns, the technique is to understand which concerns are worth asking.
Just because your data is telling you that your female customers spend more per transaction than your male clients, does this lead to any action you can take to enhance your business? One or 2 actually significant and actionable insights are all you need to ensure a significant return on your investment in any data analytics activity.
Data analytics is the analysis of raw data in an effort to extract useful insights which can lead to better decision making in your business. For most retail businesses, point of sale data is going to be central to their data analytics exercises. Data analytics provides a way of mining customer sentiment by methodically categorising and analysing the content and drivers of customer feedback, bad or good. Often most of the resources invested in data analytics end up focusing on cleaning up the data itself. Just because your data is telling you that your female customers spend more per transaction than your male customers, does this lead to any action you can take to improve your business?