5 Ways Data Analytics Can Help Your Business

Data analytics is the analysis of raw data in an effort to extract beneficial insights which can lead to better decision making in your business. In a manner, it's the process of signing up with the dots in between different sets of apparently diverse data. In addition to its cousin, Big Data, it's recently become very much of a buzzword, specifically in the marketing world. While it assures terrific things, for the majority of small companies it can typically stay something magical and misinterpreted.

While huge data is something which may not be relevant to most small companies (due to their size and restricted resources), there is no reason the principles of good DA can not be presented in a smaller business. Here are 5 methods your business can take advantage of data analytics.

1 - Data analytics and customer behaviour

Small companies may 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 somehow supplies a point of competitive distinction. Exactly what we are beginning to see is those larger corporations are able to reproduce some of those qualities in their relationships with customers, by utilizing data analytics methods to artificially develop a sense of intimacy and customisation.

Certainly, most of the focus of data analytics has the tendency to be on client behaviour. What patterns are your customers displaying and how can that knowledge help you offer more to them, or to more of them? Anyone who's attempted advertising on Facebook will have seen an example of this procedure in action, as you get to target your marketing to a specific user sector, as defined by the data that Facebook has actually caught on them: geographic and market, areas of interest, online behaviours, etc

. For a lot of retail companies, point of sale data is going to be main to their data analytics exercises. A basic example might be determining categories of consumers (perhaps specified by frequency of shop and typical spend per store), and determining other characteristics connected with those categories: age, day or time of store, suburb, kind of payment method, and so on. This type of data can then generate much better targeted marketing strategies which can much better target the right buyers with the best 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 info well was where Gilt changed the frequency of emails to its members based upon their age and engagement categories, in a tradeoff in between seeking to increase sales from increased messaging and looking for to minimise unsubscribe rates.

3 - Client problems - a goldmine of actionable data

You've most likely already heard the expression that customer problems provide a goldmine of beneficial information. Data analytics supplies a way of mining client sentiment by systematically categorising and analysing the material and motorists of client feedback, excellent or bad. The goal here is to shed light on the motorists of repeating issues experienced by your consumers, and identify options to pre-empt them.

One of the difficulties here though is that by definition, this is the sort 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 qualitative and sometimes anecdotal details, collected in a variety 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 the majority of the resources invested in data analytics wind up focusing on tidying up the data itself. You've probably 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 experts will struggle to produce anything meaningful, if the product they are working with is has actually not been collected in a consistent and methodical method. Things initially: you need to get the data into shape, which indicates cleaning it up.

A crucial data preparation workout may involve taking a bunch of client e-mails with appreciation or complaints and assembling them into a spreadsheet from which repeating themes or trends can be distilled. If the data is not transcribed in a constant manner, perhaps since various staff members have been involved, or field headings are uncertain, exactly what you might end up with is inaccurate complaint categories, date fields missing out on, and so on.

5 - Prioritise actionable insights

While it is necessary to remain flexible and unbiased when undertaking a data analytics project, it's also essential to have some sort of method in place to assist you, and keep you focused on what you SR&ED consultants 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 questions, the trick is to understand which concerns deserve 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 cause any action you can take to improve your business? If not, then move on. More data doesn't always result in better decisions. A couple of actionable and actually relevant insights are all you need to ensure a considerable 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 most retail businesses, point of sale data is going to be central to their data analytics exercises. Data analytics offers a way of mining client sentiment by methodically analysing the content and categorising and chauffeurs of client feedback, bad or great. Frequently most of the resources invested in data analytics end up focusing on cleaning up the data itself. Just since your data is telling you that your female clients spend more per transaction than your male customers, does this lead to any action you can take to improve your business?

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