While huge data is something which might not be relevant to most small companies (due to their size and restricted resources), there is no reason why the concepts of great DA can not be rolled out in a smaller business. Here are 5 ways your business can benefit from data analytics.
1 - Data analytics and consumer behaviour
Small businesses may believe that the intimacy and personalisation that their little size enables them to give their client relationships can not be replicated by larger business, which this somehow supplies a point of competitive distinction. However what we are beginning to see is those bigger corporations are able to replicate a few of those qualities in their relationships with customers, using data analytics methods to artificially develop a sense of intimacy and customisation.
Most of the focus of data analytics tends to be on client behaviour. Anybody who's had a go at advertising on Facebook will have seen an example of this process in action, as you get to target your advertising to a specific user segment, as defined by the data that Facebook has recorded on them: geographic and market, locations of interest, online behaviours, etc
. For many retail organisations, 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 clients through data analytics, doesn't imply you constantly should. In some cases ethical, practical or reputational concerns may cause you to reevaluate acting upon the information you've uncovered. US-based membership-only seller Gilt Groupe took the data analytics procedure possibly too far, by sending their members 'we've got your size' emails. The campaign ended up backfiring, as the business got problems from customers for whom the thought that their body size was tape-recorded in a database someplace was an invasion of their privacy. Not just this, however lots of had actually since increased their size over the period of their membership, and didn't appreciate being reminded of it!
A much better example of using the information well was where Gilt changed the frequency of emails to its members based on their age and engagement categories, in a tradeoff 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 have actually most likely already heard the saying that client problems supply a goldmine of useful info. Data analytics offers a method of mining consumer sentiment by methodically evaluating the material and categorising and drivers of customer feedback, bad or great. The goal here is to clarify the motorists of repeating issues experienced by your consumers, and recognize solutions 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 canine's breakfast of snippets of often anecdotal and qualitative information, collected in a range of formats by different people across the business - therefore needs some attention before any analysis can be finished 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.
An essential data preparation workout might involve taking a bunch of consumer emails with praise or grievances and assembling them into a spreadsheet from which recurring themes or patterns can be distilled. This need not be a lengthy process, as it can be outsourced utilizing crowd-sourcing sites such as Freelancer.com or Odesk.com (or if you're a larger company with a great deal of on-going volume, it can be automated with an online feedback system). Nevertheless, if the data is not transcribed in a consistent manner, possibly because different employee have actually been included, or field headings are unclear, what you might wind up with is inaccurate complaint classifications, date fields missing out on, and so on. The quality of the insights that can be gleaned from this data will naturally suffer.
5 - Prioritise actionable insights
While it is necessary to stay flexible and open-minded when carrying out a data analytics task, it's likewise important to have some sort of strategy in place to direct you, and keep you concentrated on exactly what you are aiming to attain. The reality is data analytics that there are a wide range of databases within any business, and while they may well include the answers to all sorts of concerns, the technique is to understand which concerns deserve asking.
Just since your data is telling you that your female clients spend more per transaction than your male clients, does this lead to any action you can take to enhance your business? One or 2 actionable and really relevant insights are all you need to guarantee a considerable return on your financial investment in any data analytics activity.
Data analytics is the analysis of raw data in an effort to extract beneficial insights which can lead to much better choice making in your business. For the majority of retail organisations, point of sale data is going to be central to their data analytics workouts. Data analytics provides a way of mining customer sentiment by methodically analysing the content and categorising and chauffeurs of client feedback, bad or great. Typically 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 consumers, does this lead to any action you can take to enhance your business?
