Top 4 Retail Analytics Trends Shaping the Retail Industry in 2019
The Business Challenge
To succeed in today’s complex business scenario, FMCG brands must stay well equipped to manage the overwhelming amounts of data by leveraging big data and analytics. These datasets are set to multiply in the coming years owing to the rise in digitization and hence, capturing and analyzing a wide variety of data generated from disparate data sources is just one part of the challenge. Furthermore, the time-sensitive decisions on campaigns, promotions, and other aspects of sales rely on a retailer’s ability to make sense of these vast datasets. Factors such as these make it even more essential to leverage customized big data and analytics solutions to harness the true potential of the gigantic troves of data that are available in today’s data-centric era.
Gain a holistic view of your datasets with our customized big data and analytics solutions. Request for a FREE Demo right away!
The client, an FMCG brand had deployed an analytical processing tool that turned out to be inefficient in processing the huge volumes of campaign data and other information retrieved from several other data sources. Most reporting tools used today are time and effort-intensive and fail to serve the purpose when it comes to processing huge volumes of data. Due to this, the client had to invest heavily in IT infrastructure and licensing fees for analyzing and visualizing the datasets. Also, the inability of key decision-makers and stakeholders to access reports on-the-go further resulted in misalignments across business units. They approached Quantzig to leverage its expertise in big data and analytics to deploy a dashboard to transform the massive volumes of data into valuable insights, accessible on a real-time basis.
Solution Offered and Value Delivered
The big data and analytics experts at Quantzig helped the client to deploy a robust platform that leveraged big data and analytics to address the key challenges. As a part of this engagement, we devised a big data analytics dashboard that offered an accurate, comprehensive view of campaign performance on the mobile applications and in comparison with other active campaigns. The big data analytics solution also helped them harmonize multiple format datasets gathered from disparate sources. This empowered the client to gain a uniform view of all siloed datasets for enhanced decision-making. Moreover, the ability to gauge customer behavior and analyze sales volume enabled the client to develop targeted campaigns to suit the demands of specific customer segments.
Trends in Big Data Analytics
The rapidly-growing, competitive retail landscape has increased the need for customer analysis across different business segments, especially e-commerce. This is the reason why the demand for in-store analytics has gone off the roof. Today conventional retail businesses have started implementing in-store analytics aggressively to retain their customers. Due to this factor, in-store analytics is now n integral part of conventional physical stores.
At Quantzig, we understand the importance of in-store analytics in retail business. And to help companies in the retail sector to excel in the competitive landscape and stay ahead of the curve, our team of experts has curated a detailed guide to in-store analytics. This guide can assist companies to improve profitability and retain their customers significantly.
Are you finding it difficult to track all the cart and basket values in your store? In-store analytics may hold the key to success. Get in touch with our experts to know how our analytics solutions can help your retail business.
What is in-store analytics?
In-store analytics is the process of collection of customer data and applying them to operations of the retail store. This can offer actionable insights into their business on both macro and micro levels. Is it sounding too complex? But it is not, provided you have proper retail analytics solutions in place. A proper analytics dashboard offers data-driven insights that make all data easily understandable and quickly applicable. In-store analytics solutions provide pre-built reports that help retail business in identifying key metrics responsible for retail performance. Furthermore, this helps in decision making and devising an overall business strategy that is based on data-driven facts and not on assumptions.
Want to know more about why in-store analytics is important for retail business? Download our free supplement here:
What is in-store analytics important for retailers?
#1: Facilitates better pricing decisions
In-store analytics has the potential to analyze price sensitivity efficiently. It can offer better visibility to retailers into the factors responsible for fluctuations in price. Also, it helps in identifying the points that have a major impact on the volume of sales. Furthermore, this can help in generating additional revenue for the retail business and safeguard them from any future price fluctuations.
#2: Track conversion rates
In-store analytics plays a crucial role in helping retail businesses to make informed decisions by keeping track of factors leading to conversions which is the heart of retail analytics. Also, these insights assist in monitoring retail performance, understanding consumer behavior, and enhancing the shopping environment. Furthermore, advanced analysis combined with data obtained from all these sources enables retailers to improve marketing operations, drive sales and boost the productivity of retail staff.
#3: Understand customer behavior
In-store analytics helps in understanding the behavior of customers. This can further help businesses to send targeted offers and content depending on the response of a customer towards the brand. Marketing operations become more efficient if marketing is done through a targeted approach. This has high chances of inducing sales and thereby, converting a prospective buyer to a loyal customer.
Our retail analytics guide is here to help you improve sales and achieve higher profits. Download the free supplement now.
Benefits of In-Store Analytics
Benefit #1: Improves the shopping experience
Today customers are well informed, and they research a lot before making any purchase. A retail business if incorporate in-store analytics to their marketing operations, can identify buying patterns of customers and their buying preferences. Consequently, this can help in enhancing the marketing strategy and improving the shopping experience of customers.
Benefit #2: Enhances customer support service
If you are into retail business and want to improve your customer service, the most important way to do that is gaining insights into customer’s response towards your brand. Conducting surveys help but it is not the proper solution because sometimes the customer’s response is not genuine. Therefore, tracking and analyzing customer data can help more in improving customer service and in-store analytics makes this very easy.
Our in-store analytics solutions can help you see how the customer flow translates into conversion. Request a FREE demo to gain better insights.
Benefit #3: Prevents theft and burglary
For retail stores, shoplifting is one of the most regular problems that they face. In-store analytics can come to the rescue here. By leveraging in-store analytics, retail stores can identify the spots that are highly vulnerable to shoplifting. Also, the data from the in-store analytics dashboard can help retail stores to strategize their marketing operations and take key decisions in improving security at the store. Consequently, this can help in dealing with theft and burglary.
Benefit #4: Optimizes store performance through real-time dashboards
In-store analytics dashboards offer specific insights to examine the product placement in the store and the optimal floor layout. Also, it helps in identifying frequented and non-frequented areas in the store. The in-store analytics dashboard also offers actionable insights into conversion by section and traffic by section. Furthermore, it helps in identifying profit maximizing patterns and paths.
Want to know more about the benefits of in-store analytics and how it can enhance your retail store performance? Request for more info below.
Get More Info Form
The retail industry has grown steadily over the past few years owing to the popularity of online shopping. But with the rapid growth, the retail businesses face serious challenges such as improving customer experiences, maintaining an online presence, keeping pace with consumer demands, staying ahead of rivals in the competitive landscape, and integrating technologies to drive marketing and profit. Even top retail businesses face hurdles such as staying up-to-date with social media trends.
If customers are not served with a seamless experience that can be one of the most critical factors behind the unsuccessful retail business. Therefore, companies in the retail sector must analyze retail market trends and leverage retail data analysis to improve their pricing strategies and sales and demand forecasting methods. This is where retail analytics comes into the picture. Retail analytics solutions can help companies in devising the most profitable sales strategies and optimizing sales force for creating maximum business value.
At Quantzig, we understand the impact that retail analytics can have on your business. And to help companies excel in the fiercely competitive environment, our team of experts has highlighted five key ways in which retail analytics solutions can help businesses in developing successful cross-channel and multi-channel retail strategies and obtain actionable insights into customer demands and preferences. But before putting down the benefits of retail analytics solutions, let’s understand a bit more about retail analytics first.
What is Retail Analytics?
Retail analytics is the process that provides detailed insights into inventory, sales, customers buying pattern, and other crucial aspects that retail companies need to watch in the decision making process. It takes into account several granular fields to create a broad picture of the health of a retail business.
Furthermore, retail analysis goes beyond superficial data analysis by utilizing techniques like data discovery and data mining and sanitizes datasets to provide actionable insights that can be applied in the short term.
Moreover, retail companies can use retail analytics to create better snapshots of their target demographics. By harnessing retail data analysis, retailers can easily identify their potential customers according to diverse categories such as buying patterns, age, preferences, location, and more.
Benefits of Retail Analytics
Benefit #1: Enhances store performance
Retail analytics can help retailers understand and analyze in-store customer behaviors. Additionally, retail analytics help retail businesses in tracing their dwelling times and shopping patterns. Furthermore, this unveils immense opportunities for all kinds of retail operations, right from individual stores to shopping malls. Also, by analyzing retail sales trends, retailers can analyze the most appealing design techniques, the best staffing options, and the most effective selling tactics.
Benefit #2: Improves business decision making
Retail analytics become a source that can help in optimizing AI models. These models enable retailers to feed the knowledge to any transactional application including POS, e-commerce, CRM, workforce management, buying and planning and prevention of loss. Additionally, retail analytics help retail companies to dive deeper into the customer data to make informed decisions. Furthermore, by utilizing retail analytics solutions, retailers can make smarter pricing decisions through alerting and price monitoring. This enables them to sell the right product at the right price. Thus, retail analytics offers easy-to-understand retail business insights at the most granular level possible which in turn help retail professionals to gain the best outcome and improved ROI from their retail business.
Benefit #3: Optimizes customer experience
The recent research data shows that 65% of marketers have found that improving their data analysis plays a very crucial role in delivering a better customer experience. Retail analytics can be used to improve the shopping experience by solving customer problems in real time. Also, it can enable companies to avoid out-of-stock situations. Furthermore, this can ensure the availability of enough merchandise to meet the customers’ demand. Moreover, by analyzing the purchasing behavior of the customers, retailers can fulfill their needs by offering the products in demand at the most appropriate time.
Benefit #4: Provides better insights into customer behavior
This is one of the most essential benefits of leveraging retail analytics that they offer actionable and tangible insights into the behavior of customers. It is quite easy to apprehend that managing any aspect of business becomes a lot easier if one knows to estimate the return on investment. From analyzing the social responses to gauging the impact of a campaign on the store’s conversion rates to a product, retail analytics offer the most accurate picture to retailers of what works for them and what doesn’t. This leads to better sales effectiveness and improved ROI.
REQUEST FREE DEMO
Today, big data analytics is one of the most crucial processes for any business, big or small. For data scientists, it acts as a pair of glasses that helps them see the actual reality of a business’ performance, beyond scattered numbers in graphs. A proper, solid, and reliable analysis allows you to make fact-based and rational decisions, but if mistaken, advanced data analytics can lead you astray and you might end suffering a huge loss. So, it can be safely presumed that it is not enough to have good quality data unless you use the datasets efficiently. However, there are many hurdles that businesses might encounter along the way. While implementing a new strategy to strengthen your business with advanced data analytics, mistakes can prevent you from realizing its complete potential. So, in this article, we have summed up some of the common blunders businesses should avoid while developing a big data analytics strategy.
Mistakes to Avoid in Big Data Analytics
Being rigid in processes and products
If you are rigid with your process and product, you are committing a big mistake. You should begin your project in a way that is both strategic in vision and agile in execution. Therefore, you need to pick technologies that are open and expandable. For example, you must avoid vendor lock-in by using open source tools. For obtaining optimum results from advanced data analytics, it is important to foster a culture that fosters failing fast and learning from mistakes. You must avoid letting egos drive your project and understand that if your team experiments on ten things, eight of them might not work. You should get people on board in your data project team who can thrive in this sort of DevOps style of work.
Neglecting security and governance at the beginning
Today, security and governance are extremely important, as privacy is one of the major concerns in almost every industry. Businesses still tend to start big data analytics projects as pilots, with just a handful of people working on them, and without security and governance baked in. This is a huge mistake when it comes to big data analytics. You must get compliance, governance, and security conversations started on the very first day of the project. You must carefully choose the right governance strategies, as well as the right technology of governance.
Relying on the same KPI’s always
As things are constantly changing and your business is subjected to constant dynamics, so you must learn to adapt to the ever-changing environment. This is how you can prosper. So, try not to hold on to the old performance indicators that are used to measure your success in the past. You need to use newer and more suitable tools to make advanced data analytics tools reflect the current performance of your business and identify what really drives your business forward.
Being a leader in offering big data analytics services, Quantzig helps businesses to manage, store, and integrate huge datasets. Also, we help businesses to gain predictive insights that facilitate proactive business decisions and pre-emptive planning. Additionally, Quantzig promises to deliver best-in-class frameworks for multi-dimensional data aggregation and utilizes visualization-based data discovery tools for insight generation.
Want to leverage Quantzig’s analytics solution and BI visualization services for strategic decision making?
Artificial intelligence and machine learning have been making headlines for topics like driverless cars, predictive texts, personal assistant, and gaming. Although not much discussed, machine learning has been bringing about landmark changes in the area of industrial manufacturing. The manufacturing industry is always on the lookout to improve productivity and efficiency. They want to go beyond the gains achieved by automation and build on top of that. Machine learning’s core technologies do align seamlessly with complex manufacturing problems. Numerous algorithms are being developed which seek to learn continually and bring about optimizations in the manufacturing plant. So how will machine learning revolutionize the manufacturing industry?
Increased production capacity and decreased material consumption
Smart manufacturing systems make use of machine data including production capacity, daily production, and machine load. Such data is essential to perform a predictive data analysis, which uses machine learning to know the ideal production capacity and decrease the rate of material consumption.
Today, more and more companies are integrating their machine learning databases, apps, and algorithms into the cloud platform. Data generated from industrial equipment can be fed to predictive algorithms, which will then be assessed by remote experts. Such data is essential to generate predictive models to alert the local technician to perform a precautionary check and prevent any sudden machine downtime.
Improved supply chain management
The modern supply chain generates a massive amount of data. It is humanly impossible to analyze such data, so experts turn towards AI and machine learning to organize it into useful bits and pieces. AI and machine learning make it easier for companies to adapt to dynamic market conditions. As a result, many vendors have been providing cloud-based supply chain management solutions, which collect and analyze data from different sources, including weather forecasts, historical data, news feed, and social media. Such data can be processed to enhance performance in all areas of supply chain including transportation, warehousing, packaging, customer feedback, and production.
Focus on customers
The customer is the king. It comes as no surprise that companies are aligning their business processes to address the needs of the customer. AI and machine learning can deliver exactly what people demand; thus, allowing for a high level of customization. Such technologies allow companies to build smart manufacturing processes that can seamlessly adapt to changing customer demands. Additionally, companies can collect data from smart homes to understand the latest consumer trends and design products accordingly.
Players across the manufacturing industry are pouring big chunks of money into machine-learning based quality assurance. Till today, companies rely on sensors and computers to remove low-quality products from their assembly line. Machine learning will provide a seamless quality control over the entire manufacturing process. Manufacturers can easily identify defects with efficiency and accuracy, and thus optimize its production and maintain customer satisfaction.
To know more about the use of machine learning in the industrial manufacturing sector and artificial intelligence:
Retail stores have progressed a long way in terms of efficiency. Planogram and store layouts are not merely a means of depicting aesthetical ability. The layout plays a pivotal role in stimulating parts of the consumers’ brain to purchase goods and service. It’s all part of the experience. Using soothing scent, playing background music to set the mood, using colored lights for appropriate luminance, and appealing visual cues influence customers to reach out for their wallets. Although customers may have their pre-determined shopping list, they end up impulse buying more often than not. It’s amazing when you think that billions of dollars are spent on advertising products in the real world, and yet when consumers step to the store, decisions are made instantly when they see products on the store. Shopper marketing focuses on driving such behavior. Today, increasing shopper marketing ROI is not only limited to brick and mortar stores but also the e-commerce sector. So how can shopper marketing help boost sales?
Sell to the actual shopper
There is a fine line between a shopper and a consumer. Nevertheless, the line does exist. In a microscopic view, consumers use the products; whereas, shoppers buy. Shoppers buy the product for their children, husband, wife, or friends. They buy based on what they think others might need. So it is essential to understand what really drives such shoppers to buy from a particular brand or a product. It is also necessary to know what stops them from buying a certain brand and how they perceive the pricing.
Know your brand
Every brand spends millions of dollars on market research to know how customers perceive their brand. However, it is essential to have the same level of understanding as it influences shopper choice. Not every brand is created equal; some shoppers are driven by price, some by brand image, others by trust and quality. One tactic that always works is when brands drive up the benefit but bring the costs down. People may have their favorite brand of beer, and when the brand is not available, they may choose not to buy beer at all.
Retail stores are always facing the pressure to control their costs as they operate in a very tight margin. One way to drive the efficiency is by collaborating with retail partners. Understanding their workings can help react to customer behavior and economy changes and bring in new opportunities. For instance, Kraft and Wal-Mart partnered together to create simple mealtime ideas banner, which offered shoppers affordable and delicious meal solutions. The program used advertising mediums such as POP materials, sampling, emails, print ads, and in-store screens to increase visibility, which increased shopper marketing ROI.
Understanding shopper segments
No two shoppers are identical. They differ in terms of demographics, behavior, psychographics, and geography. Retailers should understand customer segments better to tailor their offering towards a particular segment. For instance, the age factor decides what kind of chocolates customers prefer. Segmentation should not be limited only to demographics, since shoppers with frugal financial mindset may not opt for premium products even if they fall in the same age category.
Customer behavior is always evolving, and so is the retail landscape. Retailers should constantly evaluate their shipper marketing performance to address changes in the environment. It prepares them for any unforeseen changes in the customer behavior or the economy.
To know more about shopper marketing ROI, retail industry, and retail analytics:
LONDON: Quantzig, a global analytics services provider, has recently completed their latest supply chain analytics solution for a retail firm. The global retail industry is witnessing an unprecedented growth with the rise in the global GDP, growing population, and increased consumer spending. With the recent economic recovery, retail stores are finding ways to offer convenience and authenticity to cater to the growing demands of the customer segments.
“The supply chain analytics solution offered by Quantzig helped the retail firm improve the forecasting capability to anticipate customer needs and build a reliable supply chain.” says an industry expert from Quantzig.
Request your FREE demo today!
The supply chain analytics solution offered by Quantzig helped the client to assess and identify the top performing suppliers based on the key performance indicators. Furthermore, they were also able to make on-time deliveries, accurate shipments, and process payments on time. Also, the client was able to understand the current state of their supply chain and recognize the risks associated with the supply in terms of purchasing overlaps and prices.
Additional Benefits of the Supply Chain Analytics Solution
- Help retail stores to fine-tune their existing supply chain
- Leverage predictive analytics and machine learning to maintain optimal inventory level
- Identify and anticipate changing customer needs to optimize the supply chain accordingly
- To know more, request a free proposal
To know more about how our supply chain analytics solution helped the retail client
The use of AI and machine learning in the retail industry is growing at an exponential rate. Retailers have been able to see the results delivered by artificial intelligence systems instantly. AI is expected to become pervasive across customer journeys, merchandising, marketing, and supply networks as it can provide detailed insights to optimize the retail operations. Big data and machine learning have been successfully used by several retailers to achieve substantial increase in their operating margins. Such technologies can enable retailers to deliver personalized experiences to the customer in order to increase loyalty and spending. There are various use cases for AI in retail industry that can change the way this sector operates.
Uses of AI in retail industry
Sales and CRM applications
In 2010, Japan’s SoftBank telecom developed a humanoid robot, Pepper, which can interact with customers and perceive human emotions. The robot was used as a customer service greeter and representative in over 140 stores. The company later reported up to 70% increase in footfall in multiple stores. Additionally, an American company developed AI-powered sales-assistant software, Conversica, which identifies and converses with internet leads to enhance sales. The customized sales assistant software is also used for cross-selling and re-engaging existing leads.
Product recommendation tools are adding significant revenues to e-commerce businesses. IBM Watson is one of the most advanced AI systems that exhibits order management and customer engagement capabilities. IBM Watson uses personality insights taking into account users personal information, browsing history, past transaction data, and other dynamic data including weather, location, time, and items in cart to develop its recommendation engine. By calculating respective personality profile, IBM Watson can accurately suggest brands and products users are most likely to buy. For instance, North Face has used IBM Watson’s cognitive computing technology to suggest jackets for the customers based on variables like gender and location.
Long after automation revolutionized the manufacturing industry, AI is set to be next wave of change in this sector. AI can help companies keep inventories lean and reduce the cost. For instance, GE’s Brilliant Manufacturing software enables manufacturers to predict, adapt, and react more effectively by incorporating SCADA, MES, and analytics. It empowers decision makers with deep operational intelligence and real-time visibility to reduce unplanned downtime and inventory.
Logistics and delivery
Domino’s Robotic Unit (DRU) has developed a prototype delivery robot that can keep food and drinks at an appropriate temperature. Its sensor also helps the device to navigate the best travel path for delivery. Alongside DRU, Amazon’s Prime Air is expected to be the future of delivery systems. Such drones can deliver parcels up to five pounds in weight in less than 30 minutes. The autonomous delivery of goods can significantly improve the performance of the retail industry and increase customer satisfaction.
With a view to reign in the retail industry, Amazon introduced its brick-and-mortar store, Amazon Go which enables check-out free technology that allows customers to shop and leave. Their check-out free shopping experience uses the same kind of AI technology used in autonomous cars including computer vision, sensor fusion, and deep learning. It automatically detects when the products are taken from or returned to shelf keeping track of it in a virtual cart. When customers are done with their shopping, they simply check out of the store, and Amazon will deduct the amount from their Amazon account. With regards to payment services, AI is also showing its potential in payments fraud. Payment fraud is a matter of concern in the e-commerce space, where fraudsters are using stolen accounts to make purchases. AI technologies can study thousands of purchase patterns to differentiate between payment made by the genuine user and a fraudster.
To know more about uses of AI in retail industry, machine learning, and automation:
The level of understanding a small mom and pop store has about its customers is very impressive. They know exactly what their customers want to buy, in what quantity, and at what time. But how to achieve the same level of understanding when scaling to a retail giant like Walmart? This is where data analytics comes to the rescue. Data analytics can help retailers understand customer behavior by analyzing massive data sets regarding their information and transaction history. Big data allows retailers to focus on improving customer experience in terms of providing them with what they want at the right time. Retail analytics also enables retailers to introduce dynamic pricing and customize promotions and discounts to each customer. Emerging technologies such as RFID tags, beacons, QR codes, and NFC technology enable retailers to effectively guide the customers in their journey from the store to making a purchase. Retail analytics offers endless possibilities to retailers in terms of improving their operational efficiency.
Types of Retail Analytics
Descriptive analytics collects data from transactional history, promotional success, and inventory changes to give a detailed summary of such business activities. The retailers have used this type of analytics for a long time. For instance, retailers use measurement metrics such as response rates, cost per lead, and conversion rates to ascertain the success of their direct mail campaigns. However, with the advent of big data, descriptive analytics has started encompassing multiple data points including social media data, time on page, link clicks, conversions, and engagement. Descriptive analytics is an elementary form of retail analytics, which provides a visual summary of past performances.
Diagnostic analytics is an augmented form of descriptive analytics, which compares the relationship between two variables and outcomes to discover ongoing trends. Descriptive analytics provides information on the lines of what happened in the past whereas diagnostic analytics gives insights into the ‘why’ aspect of the outcome. For instance, diagnostic analytics compares the data set of two different promotional campaigns to ascertain why one campaign succeeded while the other failed. By establishing a correlation between multiple variables, a retailer can determine what factors can be changed to achieve the desired result.