While huge data is something which may not relate to a lot of small businesses (due to their size and restricted resources), there is no reason that the principles of great DA can not be presented in a smaller company. Here are 5 methods your business can benefit from data analytics.
1 - Data analytics and client behaviour
Small companies might believe that the intimacy and personalisation that their little size allows them to give their consumer relationships can not be reproduced by bigger business, which this in some way supplies a point of competitive differentiation. Nevertheless exactly what we are beginning to see is those bigger corporations are able to duplicate a few of those characteristics in their relationships with customers, using data analytics techniques to artificially create a sense of intimacy and customisation.
Many 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 procedure in action, as you get to target your marketing to a specific user section, as defined by the data that Facebook has captured on them: geographic and market, areas of interest, online behaviours, and so on
. For a lot of retail businesses, point of sale data is going to be central to their data analytics workouts.
2 - Know where to fix a limit
Just because you can much better target your customers through data analytics, does not imply you always should. In some cases ethical, reputational or useful issues might cause you to reconsider acting on the information you have actually discovered. For example US-based membership-only merchant Gilt Groupe took the data analytics process maybe too far, by sending their members 'we have actually got your size' emails. The campaign wound up backfiring, as the business received complaints from clients for whom the idea that their body size was recorded in a database somewhere was an intrusion of their personal privacy. Not only this, but numerous had actually since increased their size over the period of their subscription, and didn't appreciate being advised of it!
A better example of using the info well was where Gilt changed the frequency of e-mails to its members based upon their age and engagement classifications, in a tradeoff in between looking for to increase sales from increased messaging and looking for to minimise unsubscribe rates.
3 - Customer complaints - a goldmine of actionable data
You've most likely already heard the adage that customer problems offer a goldmine of beneficial info. Data analytics supplies a method of mining consumer belief by methodically categorising and analysing the content and drivers of consumer feedback, good or bad. The goal here is to shed light on the SR&ED consultants motorists of repeating issues encountered by your customers, and recognize services to pre-empt them.
One of the obstacles here though is that by definition, this is the type of data that is not set out as numbers in cool rows and columns. Rather it will have the tendency to be a pet's breakfast of snippets of qualitative and in some cases anecdotal information, collected in a range of formats by various people throughout the business - and so needs some attention before any analysis can be made with it.
4 - Rubbish in - rubbish out
Often 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 correlation of the quality of the raw data and the quality of the analytic insights that will come from it.
A crucial data preparation exercise may include taking a lot of customer emails with appreciation or problems and compiling them into a spreadsheet from which repeating trends or themes can be distilled. This requirement not be a time-consuming process, as it can be outsourced utilizing crowd-sourcing websites such as Freelancer.com or Odesk.com (or if you're a bigger business with a great deal of on-going volume, it can be automated with an online feedback system). If the data is not transcribed in a consistent way, possibly because various personnel members have been involved, or field headings are uncertain, exactly what you may end up with is incorrect problem categories, date fields missing, etc. The quality of the insights that can be obtained from this data will naturally be impaired.
5 - Prioritise actionable insights
While it is essential to stay open-minded and versatile when carrying out a data analytics project, it's also essential to have some sort of strategy in place to direct you, and keep you concentrated on what you are attempting to accomplish. The truth is that there are a multitude of databases within any business, and while they may well include the answers to all sorts of concerns, the technique is to know which questions are worth asking.
All frequently, it's simple to obtain lost in the curiosities of the data patterns, and lose focus. Just because your data is telling you that your female clients invest more per deal than your male consumers, does this lead to any action you can take to improve your business? If not, then proceed. More data does not always lead to better choices. A couple of actually pertinent and actionable insights are all you have to make sure a substantial 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 better decision making in your business. For many retail companies, point of sale data is going to be central to their data analytics workouts. Data analytics offers a method of mining consumer belief by methodically categorising and analysing the content and drivers of customer feedback, great or bad. Frequently many of the resources invested in data analytics end up focusing on cleaning up the data itself. Simply due to the fact that your data is informing you that your female consumers invest more per deal than your male customers, does this lead to any action you can take to improve your business?
