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 much better decision making in your business. In a way, it's the procedure of joining the dots between various sets of apparently disparate data.

While huge data is something which might not relate to most small companies (due to their size and minimal resources), there is no reason the principles of excellent DA can not be presented in a smaller business. Here are 5 methods your business can benefit from data analytics.

1 - Data analytics and consumer behaviour

Small companies may believe that the intimacy and personalisation that their little size enables them to bring to their consumer relationships can not be duplicated by bigger business, which this in some way provides a point of competitive distinction. Nevertheless what we are beginning to see is those bigger corporations have the ability to duplicate a few of those qualities in their relationships with customers, by using data analytics strategies to synthetically create a sense of intimacy and customisation.

Many 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 procedure in action, as you get to target your advertising to a particular user section, as specified by the data that Facebook has recorded on them: market and geographic, locations of interest, online behaviours, etc

. For the majority of retail companies, point of sale data is going to be main to their data analytics exercises.

2 - Know where to fix a limit

Just because you can much better target your clients through data analytics, does not imply you constantly should. In some cases ethical, useful or reputational issues may trigger you to reevaluate acting upon the information you have actually uncovered. For instance US-based membership-only retailer Gilt Groupe took the data analytics procedure perhaps too far, by sending their members 'we have actually got your size' emails. The project ended up backfiring, as the business received problems from customers for whom the idea that their body size was taped in a database somewhere was an intrusion of their privacy. Not just this, but lots of had considering that increased their size over the period of their subscription, and didn't appreciate being advised of it!

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 seeking to increase sales from increased messaging and looking for to minimise unsubscribe rates.

3 - Consumer complaints - a goldmine of actionable data

You have actually probably already heard the expression that client problems provide a goldmine of beneficial information. Data analytics offers a method of mining customer belief by systematically categorising and analysing the material and chauffeurs of consumer feedback, bad or excellent. The objective here is to shed light on the motorists of recurring issues come across by your customers, and recognize solutions to pre-empt them.

One of the obstacles here though is that by definition, this is the kind of data that is not set out as numbers in cool rows and columns. Rather it will tend to be a pet dog's breakfast of bits of qualitative and sometimes anecdotal information, collected in a range of formats by different people throughout business - and so needs some attention prior to any analysis can be done 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 have actually 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.

A key data preparation exercise might include taking a lot of consumer emails with praise or grievances and assembling them into a spreadsheet from which repeating trends or styles can be distilled. If the data is not transcribed in a constant way, maybe because various personnel members have actually been included, or field headings are unclear, what you may end up with is incorrect problem classifications, date fields missing, etc.

5 - Prioritise actionable insights

While it is very important to stay open-minded and flexible when undertaking a data analytics job, it's likewise crucial to have some sort of method in place to guide you, and keep you focused on what you 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 know which concerns deserve asking.

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? One or two truly important and actionable insights are all you need to guarantee a considerable return on your investment in any data analytics activity.


Data analytics is the analysis of raw data in an effort to extract helpful insights which can lead to much better decision making in your business. For most retail businesses, point of sale data is going to be central to their data analytics exercises. Data analytics provides a way of mining client sentiment by systematically categorising and evaluating the material and motorists of customer feedback, good or bad. Often most of the resources invested in data analytics end up focusing on cleaning up the data itself. data analytics Just because your data is telling you that your female customers 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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