Benefits of Data Analytics for Your Business | Advantages

1. Consumer Behavior and Data Analytics

Small businesses might think that the trust and customization that their scale allows them to bring to their client relationships is unmatched by larger companies, and that this kind of provides a point of differentiation and competitive advantage.

What we are beginning to observe, though, is that those larger corporations are able to mimic some of those qualities in their interactions with customers by using data analytics techniques to fictitiously produce an air of intimacy and customization.

In fact, customer behavior is often the main focus of data analytics. What trends are your customers showing, and how can you use that information to increase the amount you sell to them or to more of them?

Anyone who has tried Facebook advertising will have seen an example of this process in action, as you can target your ads to a particular user segment based on the information Facebook has collected about them, including their location, demographics, areas of interest, online behaviors, etc.

Point of sale data will be at the heart of most retail businesses’ data analytics efforts. An easy example would be to group shoppers into categories (perhaps based on frequency of purchases and average spend per purchase) and then group the attributes that belong to those groups: age, day or time of purchase, suburb, type of payment method, etc.

The right messages can then be better targeted at the right customers using this type of data to create better targeted marketing strategies.

2. Understand Where the Line is.

Data analytics can help you target your customers more effectively, but that doesn’t mean you should. Sometimes you may want to think twice before acting on the information you’ve learned due to ethical, practical, or reputational concerns.

By sending their members emails that said, “We’ve got your size,” the US-based membership-only retailer Gilt Groupe may have taken the data analytics process too far. The campaign ultimately backfired because the business received complaints from clients who felt violated by the idea that their body size was recorded in a database.

In addition to this, many members had grown larger since they had joined, and they didn’t like being reminded of it.

A better illustration of how to effectively use the data was when Gilt changed the frequency of emails sent to its members based on their age and engagement levels, striking a balance between trying to boost sales through more messaging and trying to reduce unsubscribe rates.

3. An Untapped Source of Useful Information, Customer Complaints

The proverbial “gold mine of useful information” refers to customer complaints, which you have probably already heard. By methodically classifying and analyzing the content and factors behind customer feedback, whether positive or negative, data analytics offers a way to mine customer sentiment.

Here, it’s important to identify the causes of recurring issues that your customers experience so that you can find ways to prevent them. However, one of the difficulties is that by definition, this type of data is not organized as a set of numbers in orderly rows and columns.

Instead, it will typically consist of fragments of qualitative and occasionally anecdotal information that have been gathered in a variety of formats by various people across the business. As a result, it needs some attention before any analysis can be performed on it.

4. Waste Goes in, Waste Goes Out.

The majority of the time and money spent on data analytics usually go towards cleaning up the data. The adage “Waste goes in, waste goes out” refers to the relationship between the calibre of the raw data and the calibre of the analytical insights that will be derived from it.

In other words, if the data they are working with has not been gathered in a methodical and consistent manner, even the best systems and analysts will struggle to produce anything meaningful. First things first: you need to clean up the data and get it in shape.

An important data preparation exercise, for instance, might entail compiling a number of customer emails that contain compliments or criticism into a spreadsheet so that recurring themes or trends can be identified.

This doesn’t have to take a lot of time because it can be automated with an online feedback system or outsourced using websites for crowdsourcing like Freelancer.com or Odesk.com if you’re a larger company with a lot of ongoing volume.

You might end up with inaccurate complaint categories, missing date fields, etc. if the data is not transcribed consistently, perhaps as a result of involvement from multiple staff members or unclear field headings. Of course, this will lower the caliber of the insights that can be drawn from the data.

5. Prioritize actionable insights

When working on a data analytics project, it’s crucial to be adaptable and open-minded, but it’s also crucial to have some sort of plan in place to direct you and keep you focused on your goals.

There are a lot of databases in every company, and while they might have all the answers to your questions, the trick is knowing which ones are important to ask. Too often, it’s simple to lose concentration by becoming fascinated by the data patterns.

Is there anything you can do to improve your business just because your data indicates that your female customers spend more per transaction than your male customers? If not, carry on.

One of the Benefits of Data Analytics is Better decisions making capabilities. You only need one or two really important and useful insights to guarantee a sizable return on your investment in any data analytics activity.