5 Ways Data Analytics Can Help Your BusinessData analytics is the analysis of raw data in an effort to extract useful insights which can lead to better choice making in your business. In a method, it's the process of joining the dots between various sets of apparently disparate data.
While huge data is something which may not relate to most small companies (due to their size and restricted resources), there is no reason why the concepts of excellent DA can not be presented in a smaller company. Here are 5 methods your business can take advantage of data analytics.
1 - Data analytics and client behaviour
Small companies might believe that the intimacy and personalisation that their small size allows them to bring to their customer relationships can not be reproduced by larger business, and that this in some way offers a point of competitive differentiation. Exactly what we are beginning to see is those larger corporations are able to replicate some of those characteristics in their relationships with consumers, by utilizing data analytics methods to artificially develop a sense of intimacy and customisation.
Most of the focus of data analytics tends to be on consumer behaviour. What patterns are your consumers displaying and how can that knowledge aid you sell more to them, or to more of them? Anybody who's attempted advertising 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 specified by the data that Facebook has recorded on them: group and geographic, areas of interest, online behaviours, and so on
. For many retail businesses, point of sale data is going to be main to their data analytics exercises. A simple example might be determining categories of shoppers (maybe defined by frequency of shop and typical spend per shop), and determining other characteristics related to those categories: age, day or time of store, suburban area, type of payment approach, and so on. This type of data can then generate much better targeted marketing techniques which can much better target the best shoppers with the ideal messages.
2 - Know where to draw the line
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 process 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 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 - Customer problems - a goldmine of actionable data
You've most likely already heard the saying that customer grievances provide a goldmine of helpful information. Data analytics supplies a way of mining client sentiment by systematically evaluating the content and categorising and motorists of customer feedback, bad or excellent. The objective here is to clarify the drivers of repeating issues experienced by your customers, and determine solutions to pre-empt them.
Among the obstacles here though is that by definition, this is the type of data that is not set out as numbers in neat rows and columns. Rather it will have the tendency to be a canine's breakfast of bits of often anecdotal and data analytics qualitative information, gathered in a range of formats by different individuals across business - and so needs some attention before any analysis can be made 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 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.
For instance, a crucial data preparation exercise may involve taking a bunch of consumer emails with praise or problems and compiling them into a spreadsheet from which repeating styles or trends 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 business 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 constant way, maybe since various staff members have actually been included, or field headings are unclear, what you might wind up with is inaccurate grievance classifications, date fields missing out on, etc. The quality of the insights that can be obtained from this data will of course be impaired.
5 - Prioritise actionable insights
While it is very important to stay flexible and open-minded when carrying out a data analytics task, it's likewise important to have some sort of technique in place to direct you, and keep you concentrated on exactly what you are trying to achieve. The reality is that there are a wide variety of databases within any business, and while they might well contain the answers to all sorts of questions, the trick is to know which concerns deserve asking.
All frequently, it's easy to get lost in the curiosities of the data patterns, and lose focus. Even if your data is informing you that your female consumers spend more per transaction than your male clients, does this result in any action you can require to enhance your business? If not, then move on. More data doesn't always result in much better choices. One or two actionable and truly essential insights are all you have 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 beneficial insights which can lead to much better choice making in your business. For the majority of 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 evaluating the material and categorising and motorists of consumer feedback, bad or excellent. 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 clients spend more per transaction than your male clients, does this lead to any action you can take to improve your business?