5 Ways Data Analytics Can Help Your Business

Data analytics is the analysis of raw data in an effort to extract helpful insights which can lead to better choice making in your business. In a way, it's the process of signing up with the dots in between various sets of apparently diverse data.

While huge data is something which may not pertain to a lot of small businesses (due to their size and limited resources), there is no reason that the principles of good 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 customer behaviour

Small companies may believe that the intimacy and personalisation that their little size allows them to bring to their consumer relationships can not be duplicated by bigger business, which this somehow supplies a point of competitive distinction. However what we are beginning to see is those bigger corporations are able to reproduce a few of those qualities in their relationships with customers, using data analytics methods to artificially develop a sense of intimacy and customisation.

Indeed, the majority of the focus of data analytics has the tendency to be on customer behaviour. What patterns are your customers showing 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 advertising to a particular user section, as defined by the data that Facebook has actually caught on them: geographic and market, 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 basic example might be recognizing classifications of shoppers (maybe defined by frequency of store and typical spend per store), and identifying other attributes associated with those classifications: age, day or time of shop, residential area, kind of payment method, and so on. This type of data can then create better targeted marketing methods which can better target the ideal consumers with the ideal messages.

2 - Know where to draw the line

Just due to the fact that you can better target your consumers through data analytics, doesn't suggest you always should. US-based membership-only seller Gilt Groupe took the data analytics process possibly too far, by sending their members 'we have actually got your size' emails.

A much better example of using the information 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 minimise unsubscribe rates.

3 - Consumer complaints - a goldmine of actionable data

You have actually probably already heard the expression that customer grievances supply a goldmine of useful info. Data analytics supplies a way of mining client sentiment by methodically evaluating the material and categorising and drivers of client feedback, bad or great. The goal here is to shed light on the chauffeurs of repeating problems experienced by your consumers, and identify services to pre-empt them.

Among the challenges 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 have the tendency to be a dog's breakfast of bits of in some cases anecdotal and qualitative details, collected in a variety of formats by various individuals across business - therefore requires some attention prior to any analysis can be made with it.

4 - Rubbish in - rubbish out

Frequently many 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.

A crucial data preparation exercise might include taking a bunch of customer e-mails with appreciation or complaints and compiling them into a spreadsheet from which repeating trends or styles can be distilled. This requirement not be a time-consuming procedure, as it can be contracted out using crowd-sourcing websites such as Freelancer.com or Odesk.com (or if you're a bigger business with a lot of on-going volume, it can be automated with an online feedback system). If the data is not big data analytics transcribed in a consistent way, perhaps because various staff members have been involved, or field headings are unclear, exactly what you might end up with is unreliable grievance categories, date fields missing out on, and so on. The quality of the insights that can be gleaned from this data will of course suffer.

5 - Prioritise actionable insights

While it is essential to stay open-minded and flexible 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 focused on what you are attempting to achieve. The truth is that there are a wide variety of databases within any business, and while they may well contain the answers to all sorts of concerns, the trick is to understand which concerns deserve asking.

Just because your data is telling 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 2 actually pertinent and actionable 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 helpful 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 main to their data analytics workouts. Data analytics supplies a way of mining customer sentiment by methodically categorising and analysing the content and drivers of customer feedback, good or bad. Frequently most of the resources invested in data analytics end up focusing on cleaning up the data itself. Simply since 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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