6 Ways Retailers Reap the Rewards of Data Analytics


Becky Holton
Guest Contributor

Modern consumers leave all sorts of digital footprints both online and in-store, and unlocking insights from all that data allows you to deliver better shopping experiences more effectively and efficiently.

Retail business is getting increasingly competitive because it allows almost anyone to launch an online store within hours. Millions of digital retailers are already active on the Internet, but only a few top-performers know how to outpace competitors and grab a significant chunk of market share.

At the same time, traditional retailers still keep the majority of market share and successfully deal with the challenges of the Internet era. What is the secret to the success of both?

The answer lies in data analytics, including smart store retail analytics. If you are hoping to grow a stable retail business, you have to exploit the benefits of big data. Studies reveal that people, things, and organizations will be generating 463 exabytes of data daily by 2025, which is the equivalent of over 212 million DVDs.

This gives you more than enough opportunities to analyze consumer behavior and use those insights to improve professional performance. If you are still not sure about it all, keep reading this post to see six practical reasons why retailers should take advantage of predictive data analytics.

1 – Identify Buyer Personas

The first reason to use predictive analytics is simple but fundamental – it enables you to precisely identify a buyer persona.

By definition, a buyer persona is a semi-fictional representation of your ideal customer based on market research and real data about your existing customers. This basically means that you can use your existing business insights to figure out the following details about your buyers:

  • Demographic traits such as location, gender, and age
  • Marital or relationship status
  • Everyday interests, hobbies, and habits
  • Average income and level of education
  • Personal beliefs, mission, and values

Jake Gardner, an essay writing service professional at Rush My Essay, says data science can help you to stop creating generic products or services: “With all the consumer-related insights at your disposal, it will be easy to design a perfect offer for each customer separately.”

2 – Unleash Personalization

Speaking of perfect offers, it’s a goal you won’t be able to reach without data analytics. Namely, the concept is developed with one thing in mind and that is to ensure the highest level of personalization in retail.

Instead of bombarding potential customers with more or less irrelevant content, you can now take your business to new levels using tailored offers.

A well-known example comes from Amazon, the world’s largest e-commerce company. Whenever you visit its website, you will notice personalized product recommendations based on your previous interactions with the content.

An even better example comes from Netflix. One of the most popular media providers utilizes data analytics to design personalized content recommendations. Such a system allows Netflix to save over $1 billion per year in its efforts to capture – and keep – its audience’s attention.

The same principle applies to brick-and-mortar stores, but this time it is all about building relationships with shoppers in a real-world environment. Your sales reps need to remember regular customers, get to know their names, and recognize their needs and habits. That way, store visitors will feel acknowledged and appreciated.

3 – Enhance Inventory Management

A major issue among retail companies is the proper handling of their supply chains. After all, inventory management demands real-time reactions and no human being is able to constantly monitor changes without making substantial mistakes in the process.

This is where data analytics steps in to assist. Warehouse management software contributes to the retail industry in a few different ways:

  • Inventory management is getting more accurate. You can keep stocks to the optimal minimum without the fear of running out of popular items.
  • Data analytics successfully predicts seasonal peaks and low demand periods, thus allowing you to prepare for both in a timely manner.
  • It helps you to communicate with suppliers and get them ready for new orders.

4 – Enhance Customer Service

Another thing you can do with data analytics is to enhance your customer service operations. While the system itself cannot completely replace human agents, it can make customer service activities much faster and more efficient.

The idea is simple – customer service software analyzes information to come up with the right insights about each prospect individually. Your staff can instantly see the entire history of previous interactions with a given customer, which gives them many chances to improvise. A returning customer doesn’t have to explain his/her demands all over again, so you can speed up communication and probably cross-sell other items as well.

We cannot forget to mention chatbot technology, too. Nearly 50 percent of consumers are already open to buying items using a chatbot, and the figure is destined to grow bigger in the years to come. Smart bots are available 24/7, thus allowing you to maximize the availability of your digital business.

5 – Prevent Resource-Waste

With everything stated so far, it is clear that data analytics can help you to solve several critical issues. Namely, you can use it to reduce or perhaps even eliminate resource-waste. Instead of wandering around without a clear idea of what to do next, you can identify the right strategies to make your business much more productive.

What can you save using predictive analytics? There are three major benefits here:

  • Save time: Instead of spending hours every day doing manual work, let smart machines do the job on your behalf.
  • Save workforce: Now that the system itself can handle a lot of tasks, you and your team can focus on more creative and more important duties.
  • Save money: Predictive analytics also allows you to reduce staff and spend less money while completing the same amount of work.

6 – Predict Industry Trends

The last reason to use predictive analytics in retail is rather obvious – do it to identify forthcoming industry trends and prepare for shoppers’ new demands. Data analytics systems enable you to behave proactively and design fresh products or services based on consumers’ everyday comments, remarks and inquiries. It will make your business more competitive and help you to outperform less agile competitors in the long run.

Conclusion

Modern consumers leave all sorts of footprints online or in-store, which is something you have to exploit in order to boost the productivity of your retail business. In this article, we discussed six practical reasons why retailers should take advantage of predictive data analytics.

About the writer: Becky Holton is a journalist and a blogger at uk best essays. She is interested in education technologies, Edubirdie review and is always ready to support informative speaking at assignment writing service. Follow her on Twitter.

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