6 Examples of Big Data In Retail (and How Successful Businesses Use It)

Among all industries, the retail sector is one of those that’s most affected by the impact of digital disruption.

That’s because, due to the rapid improvements in technology in recent years, small retailers like your business are dealing with a rapidly shifting business environment, as well as new competitive threats.

To adapt to the modern retail landscape, and retain a competitive edge against their rivals, retail businesses are turning to Big Data.

This involves gathering data from the most important sources in the retail sector, such as:

• Large-scale enterprise systems, such as Enterprise Resource Planning (ERP), Supply Chain Management (SCM), and Customer Relationship Management (CRM) software
• Major social media platforms
• Mobile devices
• Internet of Things
• Public data such as weather, traffic, and maps
• Retail transactions

Such data, when processed through advanced analytics tools and machine learning (ML) algorithms, can provide valuable in-depth insights about customer behaviours for retail businesses like yours.

And such insights can in turn help your retail SME predict market trends, make smarter decisions, and create better customer experiences that’ll drive repeat business and greater profits for your business.

But to achieve this, you need to learn how the major retailers make use of Big Data, and understand how they leverage the data collected from their business processes to their advantage.

Only then can you effectively apply the same strategy to your own business, and gain a competitive edge for your retail SME against your closest competitors.

To help you with this, we’ve compiled a collection of ways that businesses in the retail sector have applied the use of Big Data to optimise their critical business processes to their benefit.

It includes real-life case studies of well-known retail brands that have carried out these applications of Big Data to their own businesses, and the positive results they’ve seen from doing so.

We hope this will serve you as a resource that’ll show how you can also apply Big Data to give your retail SME the edge it needs to remain competitive, even against the great challenges it faces in the industry today.

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Knowing what your customers want, and adjusting your product offerings and prices accordingly to achieve enough sales for the greatest amount of profit, is the basis of all retail transactions.

In the past, retail businesses have acquired insights on their customers’ preferences and behaviours through direct interactions such as focus groups, and in-person surveys.

However, by leveraging on Big Data, retail businesses like yours can acquire a 360-degree view of your customers, from details such as their preferences, gender, location, social media presence etc.

This can be done by collecting and analysing customer data from a variety of sources, such as through online activity on e-commerce sites, as well as monitoring retail transactions through POS systems at brick-and-mortar locations.

With these insights, your marketing team will gain a better idea of what your target audience wants, and make it easier for them to put together sophisticated marketing strategies that cater to them.

Case Study: Zalora

Case Study: Zalora; Source: Marketing In Asia

Zalora, the e-Commerce fashion brand, has lead the way in utilising Big Data for its marketing purposes with its launch of Trender, an in-house insights platform built with the use of Tableau dashboards and analytics.

Designed as the latest iteration of Zalora’s ‘data first, then fashion’ marketing strategy, Trender was launched to help its partner brands navigate the impact of the COVID-19 pandemic by helping them make data-driven decisions about how they market and merchandise their products.

Combined with Tableau’s visual analytics, Trender allows any brand on the Zalora platform to make use of the analytics tools which Zalora uses itself, and reveals essential trends in the customer behaviour of their target market, such as which age groups and regions are shopping most.

This provide these brands with insights which helps them make decisions on marketing, merchandising, design and product development. It also offers a geolocation data layer which helps inform the real estate and e-commerce decisions of these brands, according to the location of their target market.

Through Trender, Zalora makes its data analytics capability available to its brands, allowing them to respond to fast-changing consumer demands; especially in the volatile circumstances brought about by the COVID-19 pandemic.

In the retail sector, understanding what the customer wants is key to promoting the right kind of products to a target market, and thus generate sales and profit.

Because of this, many independent retail businesses like yours have pointed to the personalization of customer experiences as a top driver of increased revenue in the near future.

In fact, research has revealed that 70% of consumers in Singapore expect a personalised digital customer experience; that is, one that treats them as individuals, and makes them feel like their preferences are understood and catered too.

But to achieve such a thorough understanding of the customer that you can personalise the buying experience on a one-to-one basis, you need to leverage on the insights that Big Data can provide your retail SME.

For example, retail businesses like yours can leverage Big Data analytics to anticipate what their customers are likely to be interested in purchasing based on their transaction histories. This empowers retailers to then tailor their marketing efforts to individual customers, personalising their customer experience.

One of many ways to do this is by training machine learning (ML) models on historical data collected by your retail business, and use them to power recommendation engines that generates personalised ads on your website to your potential customers.

Case Study: Sephora

Case Study : Sephora; Source: Tatler Asia

Having claimed the top spot in Sailthru’s Retail Personalization Index for three years in a row, Sephora is well known for getting customer personalization right.

For example, its mobile app is considered to be the best retail mobile app by far, with its in-app messaging tailored to user profiles which can include fine details about each customer, down to hair and skin type.

It’s also known for linking offers and other communications across multiple channels such as email, web and mobile devices, and driving both online and in-store purchases through them.

Sephora’s app also comes with a loyalty programme which integrates customer data across every channel, ensuring that personalised messages and offers are consistently sent to every customer according to their needs and interests.

Through all this, Sephora has demonstrated the power of customer personalization as a competitive differentiator, especially when powered by Big Data analytics.

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A perennial challenge for retail businesses like yours is the risk of shortages for products in high demand amongst your customers, or conversely the over-stocking of obsolete products that have become difficult to clear from your inventory.

To minimise the occurrences of both of these problems, retail businesses like yourself should make use of Big Data analytics to optimise your inventory.

Doing so would enable you to generate insights on customer habits, which then helps you understand which of your products and services are most in demand by your customer base and thus should be restocked on a regular basis.

It’ll also help you determine which of your offerings are falling in demand, preparing you to scale back on – and eventually phase out – the offering of those products and services.

Leveraging on Big Data can even help you improve on existing product offerings which are still in demand, but has potential for improvement.

Your sales department can use the insights gleaned from collected customer feedback to optimise your business’s product portfolios, and help them understand what features should be added, and which should be removed from your offerings.

Case Study: Kearea

Snapshot of Kearea E-Commerce Site; Source: Kearea

Launched as an e-commerce platform specialising in the sale of modern gadgets and devices in 2017, Kearea originally stocked items based on their internal estimations of what products would be popular in the market.

However, without processes in place to ensure the demand for their products in reality are in line with their estimates, the e-commerce platform found themselves stuck with excess inventory of ageing products.

Getting rid of these ageing stocks incurred losses for Kearea, and it became clear that a better way of handling their inventory had to be found.

Their first step was to gather data on their sales and stock movements, and with the assistance of third-party IT consultants, leverage on Big Data with a basket analysis of customer purchasing behaviours observed on their e-commerce platform.

With the insights gleaned from this analysis, Kearea became able to map associations between the different products their customers tended to purchase, and stock the appropriate ratio of products in their inventory based on these purchase estimates accordingly.

This paid dividends for Kearea, as they subsequently experienced a 50% improvement in business growth in the next 9 months.

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Retail is a seasonal industry, with certain types of products and services being more in demand at certain times of the year.

And the demand for a particular good or service can also vary widely depending on the demographics of your retail business’s target market, as well as between different target markets.

That’s why retailers are increasingly turning to Big Data, specifically trend forecasting algorithms, to help them forecast consumer trends and make key market predictions.

This can be done by monitoring your consumers’ demand for certain products in real-time, and using them to conduct predictive analysis of sales for your products and services amongst different time periods, and target market demographics.

With the insights you get from forecasting trends in product demand across time periods and demographics, you’ll get a better idea of how to direct your sales and marketing efforts, as well as your inventory management.

Case Study: Aimaizing

The "Aimazing" logo celebrates the amazing Artificial Intelligence (AI) technology; Source: Aimazing Facebook 

A Singaporean retail tech company, Aimazing offers a patented retail analytics solution which helps mall managers, as well as the retail businesses situated within these malls, to capture and analyse significant amounts of retail transaction data in real-time.

This is done by integrating the Aimaizing platform with the POS systems being used by the retail businesses in the malls.

This allows mall management to capture all transactional data happening within the mall seamlessly and accurate, and generate performance and benchmarking reports through the platform, as well as customise complex recommendation engines for multiple use cases.

All this data and reports are uploaded to the cloud, providing both mall management and tenants with direct and real-time access to insights that helps them make better-informed business decisions.

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Setting the right price on your products and services is key to ensuring the greatest possible number of sales, and thus maximization of profits for your retail business.

And to optimise price levels, retailers like your business can utilise price optimisation software that allows them to better understand what price levels yield the best results on specific products and services.

For example, your retail business can implement a dynamic pricing strategy that protects limited stocks of a product for which a surge in consumer demand has been forecasted for an upcoming peak season. effectively capitalises on an upcoming surge in demand for certain offerings.

Conversely, retailers like your business can also utilise the same dynamic pricing strategy to carry out ‘markdown optimisation’ – determining which products have low consumer demand, and lowering prices accordingly to clear remaining stocks from your inventory.

Leveraging on Big Data to optimise the price levels of your products and services enables your retail business to react to fluctuating consumer demand in real-time with dynamic pricing, instead of having to resort to guesswork when it comes to adjusting price levels for your products.

Case Study: Jet.com

Jet.com logo; Source: Wikipedia

A relatively new player in the e-commerce sector, Jet.com has already stood out for embracing Big Data and artificial intelligence (AI) to implement dynamics pricing for the products on their platform.

Through the use of Big Data analytics, the online retail website is able to review their pricing data to pinpoint an optimal price point for their products, and automatically change prices in real-time to maximise their sales.

Beyond the implementation of dynamic pricing, Jet.com is also known for leveraging on Big Data to help their consumers get the best deals on items in their shopping cart.

They achieve this by having their real-time pricing algorithm analyse the items in each shopper’s carts, and taking into account factors such as where they are shopping from, what prices other retailers are charging for these same items, and which sellers offer the best prices not just on the item itself but also the shipping fees.

This algorithm even suggests other ‘smart items’ that can be added to each shopper’s cart, that will help individual consumers save even more money by making their purchases more efficient budget-wise.

The COVID-19 pandemic brought e-commerce to the forefront as a retail channel, and this in turn has greatly increased the importance of omnichannel retail to businesses like yours.

Because of this, omnichannel is emerging as the business function which has the potential to benefit the most from optimisation through Big Data analytics.

By utilising Big Data analytics to collect data from website analytics, heatmap studies, clickstream data and other sources, retail businesses like yours will be equipped with the customer insights to:

• Boost customer loyalty by delivering relevant offers through any channel at the right time
• Provide seamless shopping experiences in both brick-to-mortar and online retail channels
• Improve customer satisfaction by crafting offers based on the individual customer's needs
• Leverage AI to build smarter, more effective advertisements and campaigns that resonate with customers through every retail channel.

This boosts the resulting sales across each of your business's retail channels, maximising profits for your business.

Case Study: 1800-FLOWERS.COM, Inc.

Snapshot of 1800-FLOWERS.COM E-Commerce Site; Source: 1800-FLOWERS.COM, Inc.

A floral and gift retailer that has embraced new technologies throughout its history to better serve their customers, 1800-FLOWERS.COM, Inc. has doubled the size of its business in the past 3 years.

This was possible thanks to the retailer adopting SAS Viya hosted on Microsoft Azure, to strengthen its analytical capabilities and enhances its customer experience.

It feeds data generated by its retail processes through Snowflake - a cloud-based data warehouse - and uses AI solutions to generate useful customer insights from the raw data.

These insights power digital marketing activities, such as calculating the next-best offer across multiple marketing channels.

This enables 1800-FLOWERS.COM, Inc. to personalise marketing activities no matter which retail channel a customer uses to interact with the retailer.

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As we’ve just seen, there are many ways that retail businesses like yours can leverage Big Data to maintain an edge against your closest competitors.

But what kind of solution would give you the analytics capabilities you’d need to leverage Big Data for your retail business in the first place?

To that question, we recommend the implementation of an ERP software as the answer.

For one thing, because an ERP software integrates multiple retail sales channels – brick-and-mortar stores as well as e-commerce storefronts – into a unified platform, it simplifies the data collection process by capturing, storing and managing data from all of these channels in real-time.

It can also retrieve real-time data from your supply chain, which can then be used to predict consumer demand for your products, and ensure that your supply chain remains in place to fulfill demand.

However, collection of data is one thing. To properly leverage Big Data, solutions which can process and analyse such large volumes of data to analyse usage patterns, demand peaks and consumption fluctuations are also necessary.

And an ERP software alone does not bring such capabilities to the table.

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However, you can integrate your ERP software with a Big Data analytics solution through an API to help your small retail business achieve business intelligence at the scale of the large retailers.

For example, we recommend that you integrate Microsoft Dynamics 365 Business Central with Microsoft Power BI, to give your retail business the ERP software and BI platform it needs to leverage Big Data.

That’s because Power BI comes with dashboards that help you:

• Visualize each of your outlet’s sales performance with a 360-degree view
• Generate value insights based on product turn-over and customer behaviours
• Make informed business decisions as you plan for the future.

With Business Central integrated with Power BI, your retail business has all the tools it needs to collect data generated across all of your online and offline retail channels, analyse them to generate insights, make them accessible to you and your people through easy-to-understand dashboards, and thus inform business decisions that provide your retail business with an edge in every aspect of its business operations.

Keeping your retail business profitable in an increasingly competitive industry requires that you utilise Big Data analytics to the fullest to gain an edge.

With the insights into your customers that you can generate with Big Data, you'll be better equipped to target your marketing efforts to the audience you want, and personalise their customer experience to get repeat business from them.

Big Data can also help your retail business forecast upcoming trends when it comes to demand for specific products, and optimise your inventory accordingly to minimise stock-outs or excess inventory.

It can also enable your retail business to optimise prices of your products, as well as their landing pages on your e-Commerce website, to maximise sales and profits. 

But to carry out any of the above, you need an ERP software that can collect and unify all of the data generated by your retail business, integrated with a BI solution that provides the Big Data analytics to generate insights from this raw data.

We hope that this list of applications and examples of Big Data usage in the retail industry has helped in giving you an idea of how you can apply these same strategies to your own retail business.

At AFON IT, we always keep the industry-specific challenges that your business are dealing with in mind, and have experience in recommending the right solution for your particular needs.

If you’d like to know how we can help your retail business overcome its industry-specific challenges with the right solution, do schedule a free consultation with us today!