While big data is something which might not relate to most small companies (due to their size and restricted resources), there is no reason that the principles of excellent DA can not be rolled out in a smaller sized company. Here are 5 ways your business can take advantage of data analytics.
1 - Data analytics and customer behaviour
Small companies may think that the intimacy and personalisation that their little size enables them to give their client relationships can not be reproduced by larger business, and that this in some way provides a point of competitive distinction. However exactly what we are starting to see is those bigger corporations are able to replicate a few of those attributes in their relationships with consumers, by using data analytics methods to artificially develop a sense of intimacy and customisation.
Indeed, most of the focus of data analytics has the tendency to be on consumer behaviour. What patterns are your customers showing and how can that understanding aid you offer more to them, or to more of them? Anybody who's attempted marketing on Facebook will have seen an example of this process in action, as you get to target your marketing to a specific user segment, as specified by the data that Facebook has actually recorded on them: geographical and demographic, areas of interest, online behaviours, etc
. For most retail companies, point of sale data is going to be central to their data analytics exercises. A simple example might be identifying categories of buyers (maybe specified by frequency of store and typical invest per store), and determining other qualities associated with those categories: age, day or time of shop, residential area, kind of payment method, etc. This kind of data can then produce much better targeted marketing methods which can better target the ideal consumers with the ideal messages.
2 - Know where to fix a limit
Simply because you can much better target your customers through data analytics, does not imply you constantly should. US-based membership-only seller Gilt Groupe took the data analytics procedure possibly too far, by sending their members 'we've got your size' e-mails.
A better example of using the details well was where Gilt adjusted the frequency of emails to its members based on their age and engagement classifications, in a tradeoff in between looking for to increase sales from increased messaging and seeking to reduce unsubscribe rates.
3 - Customer problems - a goldmine of actionable data
You've most likely already heard the adage that consumer complaints supply a goldmine of helpful details. Data analytics provides a method of mining customer belief by methodically evaluating the content and categorising and chauffeurs of customer feedback, bad or good. The goal here is to shed light on the motorists of recurring issues encountered by your clients, and identify options to pre-empt them.
One of the difficulties here though is that by definition, this is the kind of data that is not laid out as numbers in cool rows and columns. Rather it will tend to be a pet's breakfast of snippets of sometimes anecdotal and qualitative info, gathered in a variety of formats by various people throughout the business - and so requires some attention before any analysis can be made with it.
4 - Rubbish in - rubbish out
Typically the majority of the resources bought data analytics end up focusing on tidying up the data itself. You've most likely become aware 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 originate from it. Simply puts, the very best systems and the best analysts will have a hard time to produce anything meaningful, if the material they are dealing with is has not been collected in a constant and methodical way. Things first: you need to get the data into shape, which suggests cleaning it up.
An essential data preparation workout might involve taking a lot of consumer emails with praise or problems and assembling them into a spreadsheet from which repeating themes or patterns can be distilled. If the data is not transcribed in a consistent manner, perhaps since various staff members have been involved, or field headings are unclear, exactly what you might end up with is unreliable complaint categories, date fields missing, etc.
5 - Prioritise actionable insights
While it is very important to stay flexible and open-minded when undertaking a data analytics job, it's likewise crucial to have some sort of method in place to assist you, and keep you focused on what you are trying to accomplish. The reality is that there are a wide variety more info of databases within any business, and while they might well consist of the answers to all sorts of concerns, the technique is to understand which concerns are worth asking.
Just since your data is informing you that your female clients invest more per deal than your male consumers, does this lead to any action you can take to enhance your business? One or two actionable and truly essential insights are all you require to make sure a substantial 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 many retail services, point of sale data is going to be main to their data analytics workouts. Data analytics supplies a method of mining consumer sentiment by methodically analysing the content and categorising and chauffeurs of client feedback, bad or excellent. Typically many 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 clients, does this lead to any action you can take to enhance your business?
