How to understand and engage customers in physical spaces?

Today's customers expect brands to provide them with a highly personalised shopping experience across multiple digital touchpoints: smartphones, social media platforms, consumer channels and in physical spaces.

To achieve this level of personalisation, retailers are relying on data and leveraging marketing analytics to better understand their customers, their habits, shopping behaviours and needs.

The main problem with this is that small retailers cannot use traditional forms of data processing to analyse and evaluate such massive volumes of data. They need to incorporate more advanced technologies to obtain accurate information about their business processes.

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

What is Big Data and how can it help retailers?

Big Data refers to deriving information, trends and patterns from massive amounts of structured and unstructured data sets, in terms of volume, complexity, completeness and variety, usually generated by the digital ecosystem.

As each customer interacts with the business through multiple touch points and channels, they leave a wealth of information behind.

The collected data is extracted and analysed using real-time big data analytics to obtain actionable insights focused on the customer and meaningful business.

In short, big data means the massive analysis of dynamic data that is too complex for traditional applications to process.

How are retailers effectively adopting Big Data Analytics to better understand their customers?

As consumers adopt different technologies and shop across multiple channels, data is collected from a variety of sources.

For example, consumers may begin their product research using the company's website, purchase the product using a mobile app, and pick up the product from your physical shop.

In this way, large amounts of data are constantly being directed to marketers from various sources.

Marketers can now plan the management, distribution, marketing, sales, service and returns of merchandise and inventory using big data analytics. In addition, by using big data analytics, retailers can create personalised customer experiences based on customer purchase history, shopping behaviour and market trends.

Big Data analytics also helps companies perform advanced predictive and prescriptive analytics, enabling them to manage their inventory, merchandising and procurement strategies.

Decision support in retail

Physical shops often face pressing business and strategic questions related to:

• Real-time occupancy: Has a threshold been exceeded?
• Customer behaviour: How are customers behaving and what is their customer journey?
• Shop appeal: What is the impact of my marketing campaigns?
• Forecasting: How much foot traffic is my shop expected to receive?
• Sales optimisation: How can I size my sales force to optimise sales?
• Omni-channel experience: Can I connect digitally with my offline visitors?

Ultimately, through KPIs and information on real-time occupancy, foot traffic or customer behaviour, we obtain valuable analytics with which to address new challenges to increase sales in physical spaces.

Through data we capture the customer journey and behaviours and help retailers communicate personally and in real-time, greatly improving the shopping experience.

How is real-time data analytics transforming the retail market?

Retailers have always collected data from their customers to gain insight into their business and market and have used this knowledge to design data-driven marketing and sales strategies.

However, physical shops did not have the kind of data they needed or the tools to analyse the constant flow of data.

With big data gaining more traction in the last decade, retailers have access to customer information that they were previously unaware of. While there are many use cases for big data marketing analytics, some of the common applications would be:

• Personalised recommendations. Service personalisation and personalised recommendations help retailers stand out from the competition. Retailers can now provide personalised communications and recommendations using historical purchase information and shopping behaviour.
• Improved customer experience. Companies are always looking for strategies that can improve customer experiences. By using big data, retailers can anticipate future customer demands and develop strategies to deliver seamless experiences.
• Understanding and forecasting customer behaviour. Data analytics models help retailers gain insight and forecast customer behaviour.
• Optimising price management. Data analytics can help retailers devise optimal pricing strategies. It also allows retailers to set dynamic prices based on real-time data acquired from customers.
• Improved customer loyalty. A personalised shopping experience can improve customer loyalty and brand recognition. It also increases engagement and brand identity.
• Improved return on investment. Retailers can use market analysis to identify ways to improve their ROI.
• Demand and trend forecasting. With Big Data trend forecasting, retailers can ensure inventory management, product stocking, market segmentation and personalised advertising.
• Strategic decision making. By mining data and analysing large data sets, retailers can take a data-driven approach to strategic planning to effectively improve their bottom line.
• Understanding omni-channel shopping behaviour. Marketers these days are turning to omnichannel marketing analytics to create a seamless customer experience across channels, connected devices and platforms. Big data analytics is driving the shift towards a more optimised customer experience.

The future of Big Data in retail analytics

The future of retail will be driven by retailers who are willing to employ advanced analytics and technologies to deliver an exceptional customer experience across multiple channels.

Informed decision making based on big data will be critical for retailers.

Automation will steadily grow to shape the customer experience.

Retailers that leverage their AI-powered tools using big data analytics and machine learning models will be able to secure a strong future in traditional retail.

Simply put, big data has the power to alter the way retailers have been conducting business and improve efficiency across all departments and customer touch points.

Among other benefits, Ladorian's solutions bring to market by providing useful KPIs and information on real-time occupancy, foot traffic and customer behaviour, empowering your decision making with analytics and helping you create omni-channel customer experiences and effectively engage your customers with your brand.

Are you ready to boost your company's communication and capture the attention of your consumers through our offline recommendation engine?

Contact our sales team and request a free demo.