Big Data now is a unique garage-crafted supercharger that propelled business leaders of today to their enviable status.
Big Data of tomorrow is the ‘table stakes’ basic engine of commerce that is an indispensable part of any surviving business out there.
Jack Phillips, CEO of the International Institute of Analytics, draws this conclusion on the use of Big Data in the near future:
“If you are not paying attention through expenditure and [acquisition of] talent you will be behind."
This Statista graph demonstrates how worldwide revenues from the big data and business analytics market are forecasted to soar from 168.8 billion in 2018 to a whopping 274.3 billion in 2022 [a colossal 63% growth in just 4 years].
What is Big Data?
Google receives 3.8 million searches per minute.
YouTube gets 4.5 million video views in that same one minute.
188 million emails are sent per minute.
This volume of data was unfathomable to collect, segment, and analyze just a couple of decades ago. No computer would be able to process this much information.
With the dawn of the digital era and cloud computing, big data analytics is turning into an advanced tool helping companies grow their business based on objective data, as opposed to subjective knowledge.
What is Big Data anyway?
Big Data is a discipline of studies related to collection, storage, processing, aggregation, and analysis of the extremely large sets of structured and unstructured data to find trends, associations, and patterns, which can be used to enhance the performance of the analyzed unit.
How It Works
Big Data is currently being stored and processed with the help of frameworks like Cassandra, Spark, and Hadoop.
As an example, a Hadoop distributed file system [HDFS] is used by Hadoop to store big data. This system breaks up a huge file with big data into smaller batches, which are stored in different machines. Moreover, for extra security, copies of files are created and stored in separate nodes so as to create double protection of your data.
Further, the MapReduce method is used to process these files, breaking down a huge task into digestible chunks and processing them simultaneously on various machines. This way, a few computers work on processing one Big Data task at the same time. The technique is known as parallel processing.
How Can Retailers Adopt Big Data More Efficiently?
If you are still struggling to understand why big data analytics is important, let’s check out one example.
A staggering 35% of Amazon revenues comes from its recommendation engine.
More. Than. 1/3. Of. Amazon. Revenues. Come. From. Big. Data.
Well, one may argue, it’s not Big Data per se, as Amazon's recommendation engine gets a huge boost from its AI algorithm.
True. But it’s still based on big data, right?
The question is: How can big data analytics in retail provide optimal leverage?
As with any technology in its early stages of genesis, big data is being adopted by leaders of the most discerning of companies, who can see the value of this as-yet unpolished diamond of future commerce and retail.
- Invest in the talent that is qualified to utilize current frameworks and develop more advanced solutions and algorithms for big data processing.
- Consider purchasing algorithms as opposed to spending time on creating your own. Algorithm exchange may be a thing of the future, but there are pioneer solutions on the market in 2020:
3. Ensure the process of big data collection, storing, sorting, analysis, experimentation is distributed appropriately among software engineers, data engineers, data scientists, and research scientists, depending upon the stage, for increased efficiencies.
As with any innovative hi-tech breakthrough, Big Data will yield the best results for early adopters & technology pioneers.
Ultimately, in less than a decade, there will be no niche unaffected by this consequential development in technological evolution.
Meanwhile, big data analytics in the retail sector are bringing billions to the bottom line of industry leaders as we speak.
How Do Retailers Benefit From Big Data Analytics?
Companies like Amazon, eBay, Wal-Mart, Costco, and Tesco manage to harness the power of big data across pretty much every process in a company.
Big masses of data, when amplified by real-time analytics, make retail big data applicable across all company departments, optimizing major missions and smaller everyday tasks.
As an example, Wal-Mart, the #1 retailer in the world with 11.5K+ stores in 27 countries, created a Data Café in its Arkansas HQ. The mission of this state-of-the-art facility is to convert raw Big Data into Actionable Data in minutes. All departments are encouraged to send their emerging issues to the Data Café and receive answers and issue fixes in hours – or maybe even minutes.
Interestingly, on top of the massive sets of information flowing from its transactional data from POS, mobile applications, social media, website analytics, the company also tracks weather conditions, gas prices, local events, and telecom data. Each of these parameters could bring added value when it comes to the interpretation of results.
Wal-Mart Big Data best practices include use cases like:
- Store checkout manning optimization based on predictive analysis;
- Optimizing merchandise presentation on store shelves depending on customer preferences;
- Improving personalized shopping experience while using a mobile app.
8 Ways Big Data Analytics Transforms Retail Industry Right Now
Big Data-Driven Detection And Prevention of Fraud
Whether it's credit card fraud, return fraud or identity fraud by a customer, or internal infringements and fraudulent actions by company personnel, big data is the best tool for detection and prevention.
Real-time fraud detection can help a store to block a potentially fraudulent transaction during a purchase in a physical store, based on a myriad of data points, like customer's weblog, current GEO location, purchase history, and even social media feed.
Analyze Data Sets With 360-Degree View Of The Customer
There are facets of business, like customer service and marketing, where having a 360-degree client view is vital for enabling big data to bring actionable results.
Whenever hard data needs to be complemented with soft data, (like social media mentions and sentiments), a business can benefit only from a comprehensive understanding of the client’s needs and motives.
As an illustration, big data allows businesses to be alerted to a step in a customer's journey that manifests a drop in interest or outright dissatisfaction with a certain item.
It takes further drilling into a 360 Degree Customer View to understand the reasons behind the downward curve and stop the growth of the churn rate or bring shopping cart abandonment stats back to norm.
Streamline Back-Office Operations
Merchandising, inventory control, price book management, loyalty program management, logistics, and workforce management are all part of the back-office hierarchy of departments that can be optimized with big data.
The use of big data in retail back-office functions can be illustrated by the case of Costco managing risk in addressing a potential listeria contamination. The company’s loyalty system managed to urgently contact individual clients to recall the potentially contaminated stone fruit – all thanks to Big Data.
Buy Now, Pay Later (BNPL)
Buy Now Pay Later phenomenon emerged on the spur of the recession-related credit-phobia, and is widely loved by customers and retailers alike.
Specifically, the brick and mortar stores who incorporated the service into their payment system find that it helps them stay relevant and provide a competitive edge in the battle for a client. That’s the battle that physical stores have been steadily losing to ecommerce.
Apps like Afterpay, Splitit, Laybuy, Klarna garnered affection on both sides of the retail equation. Clients love paying in installments or at a later stage. Retailers thrive on this model too, with 30% growth of conversions on purchases made with layaway apps.
Needless to reiterate, BNPL model is a commodity made possible due to data analytics for retail businesses.
Applying Market Basket Analysis (MBA)
Market Basket Analysis is one of the most powerful analytical methods enabling big data analytics in the retail industry.
It allows sellers to understand and anticipate the demand for a specific product based on the purchase of another product. Simply put, Market Basket Analysis allows data engineers to deduce product association: the probability of a product B to be purchased if preceded by a purchase of product A.
If there is bread in your basket, how likely is that that there will be milk in your basket too? If you purchased a smartphone, how likely are you to purchase a case for it too?
Have you ever been recommended a discounted item at a store from a cashier at the check-out? This is because you may have fit the profile of a client who is likely to buy that item based on big data.
And yes, Market Basket Analysis helps Amazon's recommendation engine to sell users more items through “Frequently Bought Together” and “Customers Who Bought This Also Bought” sections.
Pricing & Revenue Management in Retail
While revenue management used to be a thing exclusively employed by the hotel and airline industry, big data precipitated the penetration of revenue management principles into consumer goods retailing.
The pricing optimization software systems for on-premise deployment as well as Software as a Service (SaaS) solutions are expanding rapidly to keep up with increasing demand. As the use cases for pricing optimization engines like Competera and Prisync grow, awareness of powerful in-house and SaaS pricing solutions will spread and they will be recognized soon by the industry at large.
Competera, based on big data and reinforced by analytics and machine learning modules, states that all of its clients see a minimum of revenue increase of 7%.
Predicting Trends As The Way To Be a Trend-setter
Emerging trends are an obvious revenue-making opportunity for retail and ecommerce.
By monitoring sets of carefully selected keywords in chosen industries, big data techniques are used to spot the trends and items in early popular demand at the very beginning of their evolution.
Enhancing Customer Experience
Big Data is all about perfecting customer experience in retail.
With so many retail brands competing to sell the same item to a client, it is only in the best interest of retail giants to create the best customer experience with their brand.
Starting from never running out of SKU to avoid upsetting a client, to employing sophisticated algorithms for sentiment analysis in social media, brands work ‘round the clock to create the best customer experience on the market.
Examples Of Big Data Employment In Retail
Not every international retail chain can afford processing centers quite as grand as Data Café by Wal-Mart, but one thing is for sure: all major retail players appreciate the potential and keep investing in the development of big data and analytics.
Below are a couple of big data use cases in retail just to give you a real taste of how analytics based on masses of information can impact the industry.
Target, started a big data-driven replenishment system in a newly-opened store in Perth Amboy, NJ, showcasing an impressive result of 40% improvement in out-of-stock items management.
Amazon, the king of the ecommerce jungle, has perfected and condensed the art of price optimization to one minute.
OK, maybe 10 minutes.
This is the average time for the price of a product on Amazon to change. With 200 million users and 1.5 billion items on sale, the volume of data may seem unimaginable. Yet, it’s perfectly digestible for Amazon servers, as this pricing feature helped the company boost its profits by a jaw-dropping 25%.
Home Depot VP of integrated media and marketing, Dave Abbott, states that the company utilizes big data to ensure item availability – be it a swimming suit for a tropical climate or a snow shovel for states with colder seasons.
Mr. Abbott observes:
“Retailers are learning to balance the art and science of merchandising, and data’s a big part of that. Data can help companies anticipate customers’ wants and needs based on frequency, seasonality, weather patterns, perishability, basic historical replenishment trends, and more.”
Technologies Useful In Analytics
Major frameworks used for big data storage, processing, and analysis are:
When it comes to technologies that facilitate the integration of Big Data in business, the following major methods are worth a mention:
- Predictive analytics
- Data visualization
- Data Integration
- Stream Analytics
- Data processing
- Data Quality
- NoSQL Databases
The Future Of Big Data In Retailing
One thing is certain: the future of big data in retail is bright.
The future of retail without big data, on the other hand, is not too sparkly.
Big Data experts mention a few emerging global trends that are building up in popularity and are predicted to evolve into future constants:
- Machine learning
- Artificial Intelligence (AI)
- Augmented analytics
- In-memory computing
- Augmented Reality (AR) & Virtual Reality (VR)
- Multi-cloud and Hybrid solutions
- Continuous Intelligence (CI)
- Further refining of the role of Chief Data Officers (CDO)
- Data Operations
Keeping Customer Data Secure
With the recent introduction of the GDPR regulations in the EU, the USA may follow suit at some point in the bid to keep clients personal data private – or at least provide a greater level of security.
At this time, many international companies had to react and adjust their privacy guidelines to the EU data protection legislation due to international presence.
Big Data Analytics Solutions
Big Data Analytics software can still be referred to as an emerging market, as the technology is relatively new.
On the other end of the spectrum, the dividends that businesses manage to gain through early adoption of the concept prompt, this horizon is going to get extremely competitive in no time.
We found these two reputable resources that carry nearly exhaustive information and comparative overviews of Big Data Analytics Software and platforms:
Importance of big data in retail: Zoolatech radar
As a Software & Mobile development company with extensive expertise in the retail industry, Zoolatech keeps Digital Data’s vigorous genesis on the radar.
We are well-versed on the newcomers and veterans in the domain of Big Data analytics software. This way we can provide our clients with custom solutions that integrate the latest in this up-and-coming technology.
Keep an eye on the Zoolatech blog for more informative pieces on the evolution of analytics and its application in the retail segment.