While huge data is something which might not relate to many small businesses (due to their size and limited resources), there is no reason why the principles of good DA can not be rolled out in a smaller sized business. Here are 5 ways your business can benefit from data analytics.
1 - Data analytics and client behaviour
Small businesses may think that the intimacy and personalisation that their little size enables them to bring to their consumer relationships can not be replicated by larger business, which this somehow provides a point of competitive distinction. Nevertheless exactly what we are starting to see is those larger corporations have the ability to reproduce a few of those characteristics in their relationships with consumers, by utilizing data analytics techniques to artificially develop a sense of intimacy and customisation.
Undoubtedly, the majority of the focus of data analytics tends to be on customer behaviour. What patterns are your customers showing and how can that understanding assistance you sell more to them, or to more of them? Anybody who's had a go at marketing on Facebook will have seen an example of this procedure in action, as you get to target your advertising to a particular user sector, as defined by the data that Facebook has recorded on them: geographical and demographic, areas of interest, online behaviours, etc
. For the majority of retail companies, point of sale data is going to be central to their data analytics workouts. An easy example might be identifying classifications of consumers (perhaps specified by frequency of store and typical spend per shop), and recognizing other qualities associated with those classifications: age, day or time of shop, suburb, kind of payment technique, etc. This kind of data can then produce much better targeted marketing techniques which can better target the ideal consumers with the right messages.
2 - Know where to draw the line
Simply because you can better target your consumers through data analytics, doesn't suggest you always should. US-based membership-only merchant Gilt Groupe took the data analytics process possibly too far, by sending their members 'we have actually got your size' emails.
A much better example of using the information well was where Gilt adjusted the frequency of e-mails to its members based on their age and engagement classifications, in a tradeoff in between seeking to increase sales from increased messaging and seeking to reduce unsubscribe rates.
3 - Customer grievances - a goldmine of actionable data
You have actually probably currently heard the expression that customer grievances supply a goldmine of useful info. Data analytics supplies a way of mining client sentiment by methodically analysing the material and categorising and motorists of customer feedback, excellent or bad. The objective here is to clarify the motorists of recurring issues come across by your customers, and determine options to pre-empt them.
One of the difficulties here though is that by definition, this is the sort of data that is not set out as numbers in neat rows and columns. Rather it will tend to be a pet dog's breakfast of bits of qualitative and sometimes anecdotal information, gathered in a variety of formats by various people across the business - therefore needs some attention prior to any analysis can be done with it.
4 - Rubbish in - rubbish out
Frequently most of the resources invested in data analytics end up focusing on cleaning up the data itself. You've probably heard of the maxim 'rubbish in rubbish out', which refers to the connection of the quality of the raw data and the quality of the analytic insights that will come from it.
An essential data preparation exercise might include taking a lot of customer emails with appreciation or complaints and compiling them into a spreadsheet from which repeating styles or patterns can be distilled. This need not be a lengthy process, as it can be outsourced using crowd-sourcing sites such as Freelancer.com or Odesk.com (or if you're a larger company with a lot of on-going volume, it can be automated with an online feedback system). However, if the data is not transcribed in a consistent manner, possibly since different team member have been involved, or field headings are uncertain, what you may end up with is inaccurate problem classifications, date fields missing, etc. The quality of the insights that can be obtained from this data will obviously be impaired.
5 - Prioritise actionable insights
While it is very important to remain open-minded and versatile when undertaking a data analytics task, it's likewise important to have some sort of method in place to direct you, and keep you focused on exactly what you are attempting 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 trick is to understand which questions deserve asking.
Just because your data is telling you that your female customers invest more per deal than your male clients, does this lead to any action you can take to improve your business? One or 2 truly pertinent and actionable insights are all you require to guarantee a significant return on your financial 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 much better decision making in your business. For a lot of retail companies, point of sale data is going to be main to their data analytics exercises. Data analytics supplies a way of mining consumer sentiment by systematically analysing the material and categorising and drivers of consumer feedback, bad or great. data analytics Typically many of the resources invested in data analytics end up focusing on cleaning up the data itself. Simply since your data is informing you that your female customers invest more per transaction than your male clients, does this lead to any action you can take to enhance your business?
