While big data is something which might 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 sized company. Here are 5 ways your business can gain from data analytics.
1 - Data analytics and client behaviour
Small businesses may think that the intimacy and personalisation that their little size allows them to bring to their consumer relationships can not be reproduced by bigger business, which this in some way provides a point of competitive distinction. Nevertheless what we are starting to see is those bigger corporations are able to replicate some of those attributes in their relationships with clients, by using data analytics strategies to synthetically produce a sense of intimacy and customisation.
Many of the focus of data analytics tends to be on client behaviour. What patterns are your clients showing and how can that understanding assistance you offer more to them, or to more of them? Anybody who's had a go at marketing on Facebook will have seen an example of this procedure in action, as you get to target your advertising to a particular user sector, as defined by the data that Facebook has recorded on them: market and geographic, locations of interest, online behaviours, etc
. For most retail organisations, point of sale data is going to be main to their data analytics exercises. A simple example might be recognizing categories of buyers (possibly defined by frequency of store and average spend per store), and determining other qualities related to those classifications: age, day or time of shop, suburb, kind of payment method, and so on. This type of data can then create better targeted marketing methods which can better target the right consumers with the right messages.
2 - Know where to fix a limit
Just because you can much better target your clients through data analytics, doesn't suggest you always should. Often ethical, practical or reputational issues might trigger you to reconsider 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' e-mails. The project ended up backfiring, as the business received complaints from clients for whom the idea that their body size was tape-recorded in a database somewhere was an intrusion of their personal privacy. Not just this, but numerous had given that increased their size over the period of their subscription, and didn't appreciate being reminded of it!
A better example of using the info well was where Gilt adjusted the frequency of e-mails to its members based on their age and engagement classifications, in a tradeoff in between seeking to increase sales from increased messaging and looking for to minimise unsubscribe rates.
3 - Client complaints - a goldmine of actionable data
You've most likely currently heard the expression that client problems provide a goldmine of beneficial information. Data analytics supplies a way of mining consumer sentiment by systematically analysing the material and categorising and drivers of client feedback, excellent or bad. The goal here is to shed light on the drivers of repeating problems 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 dog's breakfast of bits of in some cases anecdotal and qualitative details, collected in a range of formats by different people throughout the business - therefore requires some attention prior to any analysis can be done with it.
4 - Rubbish in - rubbish out
Frequently most of the resources purchased data analytics wind 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 come from it. Simply puts, the best systems and the best experts will struggle to produce anything significant, if the material they are working with is has actually not been gathered in a systematic and constant way. Things first: you require to get the data into shape, which means cleaning it up.
A key data preparation workout may involve taking a bunch of consumer emails with praise or problems and compiling them into a spreadsheet from which recurring patterns or styles can be distilled. This need not be a time-consuming procedure, 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 great deal of on-going volume, it can be automated data analytics with an online feedback system). However, if the data is not transcribed in a consistent way, possibly because different employee have actually been involved, or field headings are unclear, exactly what you might end up with is inaccurate grievance classifications, date fields missing, etc. The quality of the insights that can be gleaned from this data will obviously suffer.
5 - Prioritise actionable insights
While it's important to remain flexible and unbiased when undertaking a data analytics job, it's likewise crucial to have some sort of strategy in place to guide you, and keep you concentrated on exactly what you are aiming 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 understand which concerns are worth asking.
All too often, it's easy to obtain lost in the interests 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 consumers, does this cause any action you can require to improve your business? If not, then proceed. More data doesn't constantly result in much better choices. One or two actually relevant and actionable insights are all you have to guarantee a significant 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 better decision making in your business. For most retail services, point of sale data is going to be main to their data analytics exercises. Data analytics supplies a way of mining consumer sentiment by systematically evaluating the content and categorising and motorists of customer feedback, bad or great. Typically most of the resources invested in data analytics end up focusing on cleaning up the data itself. Simply due to the fact that your data is telling you that your female consumers spend more per deal than your male customers, does this lead to any action you can take to enhance your business?
