5 Ways Data Analytics Can Help Your Business

Data Analytics is an analysis of raw data in an effort to gain valuable information that can contribute to improved decision-making in your business. In a way, it’s the process of linking dots between different sets of seemingly disparate data. Together with its twin, Big Data, it has lately become a buzzword, particularly in the marketing field. Although it offers amazing stuff, it may still stay enigmatic and confused for the majority of small businesses. Also, read about Cloud Data Warehouse

Although big data might not be applicable to most small companies (due to their scale and restricted resources), there is no justification that the ideals of good DA can not be applied in a smaller organization. Here are five forms the company will profit from data analytics.

1 — Data analytics and customer behavior

Small businesses that assume that the proximity and personalization that their small scale allows them to carry into their customer relationships can not be replicated by larger businesses and that this somehow gives rise to a strategic advantage. Nevertheless, what we are beginning to find is that big corporations are able to replicate some of these features of their relationship with customers, utilizing data processing methods to deliberately establish a feeling of trust and customization.

Indeed, much of the focus of data processing appears to be on customer behavior. What trends are your customers seeing, and how does the information help you sell more or more to them? Anyone who has been interested in Facebook ads may have seen an indication of this mechanism with practice when you direct your advertisement to a particular user segment, when identified by the data that Facebook has gathered on them: geographic and demographic, places of interest, online behavior, etc.

For most stores, point of sale details should be essential to their data analytics activities. A basic illustration could be the identifying categories of shoppers (may be identified by the number of sales and average transactions per shop) and the identification of certain characteristics correlated with such groups: age, day or time of purchase, suburbs, a form of payment method, etc. That type of data will then create more tailored content strategies that will help attract the right shoppers with the right messages.

2 — Know where to draw the line

Only because you can help reach your customers through data analytics, that doesn’t imply you should always do it. Ethical, realistic, or reputational considerations can often force you to rethink acting on the information you have uncovered. For example, US-based membership-only retailer Gilt Group took the data analytics process perhaps too far by sending their members ‘we’ve got your size’ emails. The campaign ended in backfire when the business received complaints from customers who felt that their body size had been recorded in a database somewhere was a violation of their privacy. Not only this, but many have since increased their size over the period of their membership, and have not appreciated being reminded of it!

A great illustration of leveraging the information well was where Gilt adjusted the frequency of emails to its members depending on their age and interaction ranges, in a trade-off between trying to boost revenue through increased messaging and attempting to reduce unsubscribe rates.

3 — Customer complaints — a goldmine of actionable data

You’ve already heard the adage that the customer’s complaints provide a gold mine with useful information. Data analytics is a means to mine customer sentiment by methodically categorizing and analyzing the nature and sources of customer feedback, whether positive or poor. The objective here is to shed light on the drivers of recurring issues faced by the customers and to find strategies to discourage them from happening. Also, read about Data Warehousing Solutions

However, one of the challenges here is that, by definition, this is the type of data that is not represented as numbers in neat rows and columns. Instead, it would continue to be a dog’s breakfast with bits of qualitative and often subjective information collected across a number of ways from different people around the business-and that takes considerable attention until some analysis can be performed.

4 — Rubbish in — rubbish out

Often, much of the resources invested in data analytics end up focusing on cleaning up the data itself. You’ve also heard of the term ‘rubbish in rubbish out,’ which relates to the relation between the quality of the raw data and the quality of the empirical insights that emerge from it. In other words, the best systems and best analysts will struggle to produce anything meaningful if the material they are working with has not been compiled in a methodical and consistent manner. First things first: you need to form the data, which means you need to clean it up.

For example, a key data preparation exercise can include taking a ton of customer emails with encouragement or complaints and organizing them into a spreadsheet from which recurring themes or trends may be distilled. It does not need to be a time-consuming operation, because it can be outsourced through crowd-sourcing websites such as Freelancer.com or Odesk.com (or if you are a larger organization with a lot of on-going production, it can be streamlined through an online feedback system). However, whether the data is not transcribed in a consistent manner, maybe because various staff members have been involved or the field headings are not explicit, what you can end up with are inaccurate complaint categories, missed date areas, etc. For example, the quality of the information that can be gleaned from such data would be impaired.

5 — Prioritise actionable insights

Although it is vital to be flexible and open-minded when undertaking a data analytics project, it is often important to have some form of strategy in place to direct you and keep you centered on what you are seeking to accomplish. The truth is that there is a multitude of databases in every business, and while they may well provide answers to all kinds of queries, the challenge is to decide which queries are worth asking.

Very often, it’s possible to get lost in the curiosities of data patterns and lose focus. Only because your data shows you that your female customers spend more on a sale than your male customers do, does this contribute to any action you might take to boost your business? If not, move on. Further data will not necessarily translate to better decisions. One or two genuinely important and actionable insights are what you need to make a good return on your investment in any data analytics activity.

Addend Analytics is a young and rapidly growing Data Analytics consultancy. We specialize in the application of Business Intelligence and Data Science tools to solve business challenges. We have a team of technical and functional experts who ensure that we understand the client's needs and deliver the right-fit, cost-effective solutions. Our team not only designs and implements these solutions but also trains all stakeholders and hand-holds for faster adaption. To know more about Power Bi Online Training. visit us today.