With the growth of in-store retail analytics, businesses can finally monitor retail performance in a range of new ways. Even more, they can make informed decisions around important metrics using the data.
What’s retail analytics?
It’s the process of providing analytical data on inventory levels, supply chain movement, consumer demand, and sales that are necessary for taking marketing decisions. Retail analytics provides in-depth customer and business insights with scope and need for improvement.
In thee recent years, retail sector has been undergoing drastic increase in the numbers of IoT devices that have been deployed in its operations. Such IoT devices provide greater efficiency in operations and facilitate customer service.
Retail analytics solutions are likely to offer more insights into the location of the user. Location-centric data analytics helps retailers to accumulate more data and offers geo-targeted push notifications to mobile devices. Moreover, location analytics helps in tracking the purchasing behaviour of consumers.
Emerging cross-platforms analytics and the role of big data
By generating data with great volume, variety and velocity, retail industry has converted into a perfect field for the big data application. Tracking customers’ digital footprint might seem a real challenge, yet, retail analytics solutions through cross-platform analytics have made that very easy. Thus, retail store owners got the chance to access data through retail analytics solutions and share it with operational and sales teams for better targeting. This can help them in convincing customers through alluring offers to make the purchase.
However, due to the immense big data use scope, there are significant hurdles that retailers have to deal with to truly harvest the fruits of big data:
1. Ensuring that the data collected is accurate. Whenever a big data project is on the implementation plan for a retailer, the ‘what data to be gathered’ is an important question. Closely tied to customer modes of payment is the amount of money they transact. Also, a customer may use multiple modes of payment for a single transaction. The data about products bought is hugely significant. The way data is qualified in a big data scenario is extremely important and an important hurdle, since the customer is generally unpredictable.
2. Collecting the data from disparate systems. Each system stores the data in its specific format. It is quite a challenge to gather data from these different systems that work in silos and unify that data into one single unit that is fit for analysis.
3. Ensuring data security and compliance. From hardening servers to carrying out repeated penetration tests to internal security audits, it’s of crucial importance to secure the data today. But with data breaches being constantly on the rise, the issue of data security is a hurdle that one must keep in mind for a successful big data implementation.
4. Ensuring timely adoption of implemented technology. The use of big data can be quite daunting to execute after buying into the great promises made by your vendors. If there is a data-gathering point, there is a device that either needs to be run by a human or it needs some sort of routine maintenance.
5. Drawing the right insights promptly. A big data tool can help you extract, transform and load the data and even crunch it to reveal patterns and trends. In retail, trends and patterns age out very soon. It’s risky to implement big data swiftly so that there is time to draw the insights needed and take the necessary steps to make use of that information.
6. Earning the customer trust to capture their data. It’s vital to secure the customer’s consent and to assure them that the data gathered is to be used in a safe and secure manner that will benefit no one else but the customer only.
Improve the way you do business with retail analytics
Nowadays, retail analytics software can bring your business to a totally new level for you can view the whole process and extract all of that information without having an effect on how or why the customer buys from you.
So, how exactly can retail analytics boost your operational efficiency? Below, you’ll find the list of its main benefits:
Reduced abandonments: when a customer spends too much time in a queue or they cannot find what they are looking for, you may lose that customer indefinitely.
Accurate conversion measurement: conversion rates are important because they show you how many of the people walking into your store are actually buying from you.
Improved staffing: with the increased efficiency and improved customer service, take advantage of the predictive capabilities to get the most of the data insights.
Impact of marketing activities: by getting clear customer counts that enable true and data based ROI conversions, you can track the movement of people in the store.
Improve stock management: predictive capabilities enable to ensure the stock availability throughout the store.
Improved decision making: Your ability to make short, medium and long term decisions improves substantially. You can base your decisions on data driven statistics, allowing you to predict how much stock you need, how many people you can anticipate on any given day.
The importance of retail analytics solutions is going to increase in the years to come. As the analytics technology becomes more popular, it will lead to greater benefits, both for the sellers as well as the consumers. Despite the enormous challenges, the opportunities open up the new facets of the data use. As the experts in the field claim, companies creating the perfect ‘omni-channel experience’ by using data analytics increased their shareholder's value to 8.5 times. They estimate that retailers can gain a 60% increase in their operating margins by using big data. For every above-mentioned challenge, the solution lies in careful, thoughtful big data implementation using mature, relevant tools, guided by the right mix of developers and data scientists. After all, this is really the next big technological revolution. If you are retailing anything today, big data analytics is the best way to get an insight into the customer’s mysterious mind.
