How Retailers are Leveraging Big Data for Marketing

Analytics

Today’s customers expect retailers to provide a highly-personalized shopping experience across various digital touchpoints — smartphones, social media platforms, consumer channels, and various other applications. To achieve this level of personalization, retailers are leveraging marketing analytics to better understand their customers, their habits, buying behaviours, and needs.

According to Data Never Sleeps 6.0, about 1.7MB of data is being generated every second for every person on the Earth. While the data consumed globally was forecast to be 79 zettabytes in 2021, it is projected to grow exponentially to 180 by 2025.

Retailers can’t use traditional forms of data processing to analyze and evaluate such massive volumes of data. They need to incorporate the latest advanced technologies to gain accurate insights into their business processes.

Better business decisions are only possible when large amounts of data are analyzed. But, where does this data come from, and what exactly is this big data?

What is Big Data?

We’ll let these numbers astonish you.

Google processes about 63,000 search queries per second.

5.6 billion search queries per day. 2 Trillion searches per year on average.

It was not possible to collect or segment such a huge volume of data, let alone analyse it until two decades ago.

Enter Big Data analytics, and the world of retail, healthcare, banking, manufacturing and more dramatically. It is powering business growth by allowing companies to turn objective data into value and strategies rather than working on mere subjective information.

Big Data refers to deriving insights, trends, patterns and relationships from massive amounts of structured and unstructured datasets — in terms of volume, complexity, integrity, and variety — generated by the digital ecosystem. Since each customer interacts with your business via various customer touchpoints and channels, they leave massive amounts of information.

The collected data is mined, scrutinized, and analyzed using big data analytics in real-time to derive meaningful business and customer-centric actionable insights. Big data means the massive volume of dynamic data that is too complex for traditional applications or analytics to process.

How Retailers are Adopting Big Data Analytics Effectively?

As consumers are adopting a variety of technologies and undertaking multi-channel shopping, data is collected from various sources. For example, consumers might begin their product research using the company website, purchase the product using a mobile application, and pick up the product from their physical store. This way, large amounts of data are directed at the retailer from various sources constantly.

Marketers can now plan their merchandise and inventory management, distribution, marketing, sales, service, and returns using big data analytics. Moreover, using big data analytics, retailers can create customized customer experiences based on customers’ shopping history, buying behavior, market trends.

Big Data analytics also helps businesses undertake advanced predictive and prescriptive analysis, which allows them to manage their inventory, merchandising, and procurement strategies. Statistics reveal that the global big data analytics market could reach a whopping $68.09 billion by 2025.

Five Challenges Big Data Analytics Could Face In The Future

While big data can benefit retailers, it can also bring about several big challenges. Some of the big data challenges are:

1. Data Privacy

Data privacy is one of the significant challenges retail marketers face. As companies are mining data from various sources, mainly social media information, data privacy and security management will play a crucial role in big data analytics.

To adhere to privacy policies, retailers have to seek permission from consumers for data collection and processing. Consumers should determine how their identifiable information is accessed and used. While data privacy should not become the reason retailers don’t leverage the power of data mining and analysis, it should be in tune with privacy compliance policies.

2. Data Collection

If big data is expected to bring value to the business, the data should be valuable, clean, well-organized, up-to-date, and understandable.

Collecting data filled with mistakes, duplications, errors, or incomplete values doesn’t help the desired outcome. Retailers should employ robust validation procedures that don’t forgo data integrity.

Retailers also have a limited understanding of the right ways of handling large amounts of datasets. As big data is collected constantly from various sources, it comes in many unstructured bits of videos, text files, documents, and audio sources. Data validation and integration become vital for retail data analysis.

3. Data Analytics Support

Retail data analysis needs people with data science talent and experience. Big data, as such, cannot create any valuable insight for you unless that data is processed, analyzed, and put to practical use.

To solve business challenges, retailers must actively seek and engage in core big data analysis activities such as data mining, visualization, structured data analysis, and predictive modelling. For this, they require big data analytics skilled people.

To handle the data science talent bottleneck, retailers should constantly fill the service gap that can hinder project progress, increase costs, deadlines, and efforts.

4. Advancements in Technology

Harnessing the power of technology is at the foundation of big data analytics. Big data analytics doesn’t work in isolation; you can reap immense benefits if it works alongside other latest technologies like machine learning and AI. These technologies have a huge effect on the outcome of the data analysis.

5. The Need for Information Foundation

It is essential to have a foundation of scalable, integrated, and secure information to counter this infrastructural challenge.

Cross-departmental data from internal sources should also be accessible by systems and people and brought under a single umbrella standard. The data has to be clean and available to those who need it.

The next challenge would be storage. Big data is a repository of large and complex datasets that needs a scalable and secure storage warehouse.

How Big Data Analytics is Transforming the Retail Market?

Retailers have always received information from their customers to gain insights into their business and marketplace and have used these insights to devise data-backed strategies. However, retailers did not have the type of data they needed and the tools to analyze the constant data flow.

With big data gaining greater traction in the last decade, retailers have access to customer information they weren’t privy to so far. Although there are many use cases of big data marketing analytics, some of the common applications are:

1. Personalized Recommendations

Service personalization and customized recommendations help retailers stand out from the competition. Using big data marketing analysis, retailers can keep track of their customer interactions at every step of the sales funnel.

Retailers can provide tailor-made communication and recommendations using historic shopping information and buying behaviour.

2. Enhanced Customer Experience

Businesses are always on the lookout for strategies that can enhance customer experiences. Using big data, retailers can anticipate the future demands of customers and develop strategies to offer seamless experiences.

3. Insight and foresight into customer behaviour

Data analytical models help retailers gain insights and foresight into customer behaviour. Descriptive analytical models collect past behaviour data while predictive models forecast future outcomes.

4. Optimizing price management

For a small business to access such data volumes to accurately determine whether a slight change in the price impacts sales or profit is a challenge. Yet, determining the correct pricing that can affect the bottom line is vital, and it needs large amounts of data. This is where big data makes a difference.

Data analytics can help retailers come up with optimal pricing strategies. It also allows retailers to set dynamic pricing based on real-time data acquired from customers.

5. Enhanced Customer Loyalty

A customized shopping experience can enhance customer loyalty and brand recognition. It also increases brand engagement and identity.

6. Better ROI

Retailers can use market analytics to identify ways to improve their ROI. Predictive analysis, in particular, can accurately determine the success of marketing campaigns.

7. Demand and Trend forecasting

By using predictive analysis and historical and current data modelling, retailers can build trend and demand, forecasting models. One major problem with forecasting is that these products are in demand and subject to boom and bust cycles.

With big data trend prediction, retailers can ensure inventory management, product stocking, market segmentation, and customized advertising.

8. Inventory Management

Running on the close heels of demand forecasting is inventory management. Improving inventory management helps increase operational efficiency, quick order fulfilment, warehouse management, calculating lead times, and safety stock to prevent stock-outs.

9. Strategic Decision Making

By data mining and analyzing large datasets, retailers can take a data-driven approach to strategic planning to effectively improve their bottom line.

10. Understanding omnichannel Shopping behaviour

Marketers, these days, are turning to omnichannel marketing analysis to build a seamless customer experience across channels, connected devices, and platforms. Big data analytics is driving the change towards a more streamlined customer experience.

11. Fraud Detection and Prevention

Fraud detection and prevention doesn’t pertain to a credit card or identity frauds committed by customers. It encompasses fraudulent actions and transactions by company personnel as well. While there are many methods in which companies try to prevent fraudulent transactions, big data analytics is probably the most effective tool that can prevent ongoing and potential fraud.

Using big data, retailers can also enjoy real-time fraud detection — which means the program can stop or block any ongoing transaction that seems potentially suspicious.

12. Deeper Understanding of Customers

Big data analytics offers businesses a deeper and clearer view and understanding of the customer psyche. While data can be insightful, mere numbers can’t show the entire picture to retailers. With big data analytics, businesses now have access to soft signals and sentiments such as social media mentions. And, now they are in a better position to combine hard data with soft data effectively.

With big data analytics, retailers can now pinpoint the exact step in the sales journey that a customer lost interest, or impact of an advertising campaign. Big data analytics will help retailers understand cart abandonment rates, improve engagement and streamline customer experience across channels.

The Future of Big Data in Retail Market Analytics

The future of retailing will be controlled by retailers who are willing to employ advanced analytics and technologies to deliver exceptional customer experience across various channels.

Big data-powered informed decisions will be helpful to retailers. Automation will grow consistently in shaping the customer experience. Retailers who train their AI-powered tools using big data analytics and machine learning models will be able to secure a robust future in retailing.

Big data has the power to alter how retailers have been conducting their business and improve their efficiency across every department and customer touchpoint.

There is information abundance — and you need to analyze this critical information to gain valuable insight into customer behaviour, business process and operations, and your place in the marketplace.