The Power of Retail Analytics: Leading to a Customer and Data-Centric Approach

The Power of Retail Analytics: Leading to a Customer and Data-Centric Approach

In an everchanging environment, retail businesses are in the frontline tackling new challenges and changing demands. New forms of digital competition, highly informed customers, and unprecedented product innovation are among the main reasons this sector needs to be evolving.

Regardless, the basic principle and paradigm remain: the player that manages to offer the right product, in the right place, at the right time, and for the right price, will win. So how can data help foresee all these variables?

Over the years, analytics has taken a highlighted position in every retailer’s strategy. One of the keys to achieving retail success is to be more data-centric, deriving actionable insights from data and making smarter decisions towards achieving better customer satisfaction, and sustainable growth and profitability. 

Although the raw material (data) and vision are fully integrated into the day-to-day operations, as the analytics mindset is already a commodity, the challenge arises: how to dive into massive amounts of data, growing each second, and unveil sharper insights?

Analytical capabilities can help retailers tackle the main pain points throughout their supply chains. The first step into a data-driven approach is to map and quantify all operational stages with data to thoroughly understand what happened and why: having a reliable database structure and a comprehensive set of reporting and business intelligence tools is essential. Only after it is possible to move towards more complex approaches, like simulation, forecasting, machine learning, artificial intelligence, and mathematical optimisation.

This new mindset can be applied in all stages of the supply chain, turning into a critical asset to monitor and improve the “cost-to-serve”: meaning the real cost to provide a product to a customer, from procurement to last-mile delivery, encompassing all operational stages.

Thus, we can divide the analytics role in retail into four domains:

Category Management and Merchandising

Analytics holds a key role in assortment optimisation. By having a comprehensive understanding of how the catalogue matches with client profiles, critical insights can be generated: what is the best assortment to tackle the endless number of products, limited shelf space, different store profiles, and supply chain complexity?

On the one hand, by identifying which attributes (e.g., brands, categories) are meaningful to customers, it is possible to estimate future demand for each product and forecast demand for potential new products, helping to manage the product lifecycle, constantly assuring the best profitability for that product category.

On the other hand, it is also possible to leverage cross-selling opportunities from better product placement.

With a better product mix and a strategic connection between customer needs and store portfolio, it is also possible to simplify the supply chain (less complexity) and empower procurement departments with deeper insights.

Additional to reshaping the product assortment methodology emerges the opportunity to capture greater value from pricing strategies. Identifying the “right” price remains a daunting task (pricing too high can reduce market share and compromise customer loyalty, pricing too low can erode margin). Additionally, this decision needs to be taken at the most granular and complex levels: across stores, segments, channels, and undertake brand image/strategic issues (like competitive intensity).

In parallel, we can add a second layer and rely on this information to create adjusted and dynamic pricing strategies and promotional initiatives.

Marketing

Clients are increasingly in control of how, when, and where they want to interact. Appealing, retaining, and enhancing customer values has become vital, leading to a paradigm shift: from a product-centric and intuitive approach to a client-data-driven framework.

Marketing analytics is at the core of many retailer’s strategies. By quickly combining all relevant customer data – from customer relationship management systems, loyalty cards, point of sale systems (POS), and social media data it is possible to build an engagement process which is critical to fully grasp the complete client profile and history across channels. What do they buy and how do they react to marketing initiatives?

With analytics, we can cluster clients and examine how past marketing stimulus has driven customer responses, allowing us to assess the best potential fit between client and campaigns. Advanced analytics are used to design better marketing campaigns, focusing on detailed messages/offers to clients, optimizing marketing spend and resources. This tailored model also leads to controlling the message - the when, how, and why it is displayed - endorsing a multichannel performance optimisation.

As to marketing modelling, it is essential to predict customer lifetime value to determine the most profitable customers over time and take the necessary measures to increase customer retention. Promoting this client engagement assessment (e.g., identifying clients with a propensity to spend more), predicting customer churn (predict clients more likely to attrite), along with a comprehensive campaign design model, is it possible to convert insights into smart decisions? Which campaign should be sent to each customer? What items should feature on the homepage to create sales? 

Shopper Intelligence & Store Operation

Both online and in-store, the mindset has changed due to new analytical capabilities and digital innovations. 

All movements, physical and digital, are mapped, stored, and processed with analytics for better decision-making by adding sensors to people, places, processes, and products. Several client interaction points can provide data: social media, e-commerce websites, payment cards, advanced POS systems, and foot traffic solutions. With all this information, it is not only possible to understand the past, but also predict where our client is going next and when. Nowadays, we can track client behaviour across channels, combining online and offline data, instead of storing and analysing this information in silos (e.g., monitor a shopper who researches in the digital store and then purchases the item in the physical store). Following the client journey, we can identify the behaviour inside the store: what are the most populated areas? Which path does the client take inside the store? All this information allows for the improvement of all processes related to store (website) design. Along with the right product assortment, we can build a data-driven framework to fully take advantage of all contact areas with our clients, maximising the added value of the client journey. 

Moreover, all this data can be integrated, allowing us to tackle another retail nightmare: inventory management. Predictive analytics helps to understand the right amount of stock available to avoid stockouts without creating a never-ending pile of slow-moving items and spoilage (overstock). Analytical models suggest which products to order and in what amount, focusing on eliminating wasted space, overhead costs and uncertainty, while reducing the number of purchases based on a hunch/relying exclusively on past orders. One critical area is dealing with fresh products, since manual orders and critical spoilage arises. It is possible to introduce perishable requirements to enhance inventory performance by having specific replenishment models and forecast methods.

Predictive analytics can help retailers stay ahead of customer preferences, and efficiently discover emerging sales trends while reducing inventory costs. This is done by ensuring that the right stock is at the right store, hence increasing sales instead of sunken costs.

Supply Chain Efficiency

Supply chain agility is critical as stores rely on it to ensure that the product can be delivered on time with maximum efficiency.

Managing inventory, reducing transportation costs, and increasing collaboration with suppliers can all be leveraged with analytics.

For example, regarding warehousing, mathematical, and simulation models are used to determine the best distribution of products throughout the warehouse and storage method (e.g., calculating how to reduce movement). Variability analysis is applied to understand productivity variations grasping the differences between the real and expected performance. Workforce development and capacity planning are key topics: how can we ensure the best team planning and a fair rewarding system?

Focusing on transport, it is critical to have a routing algorithm that fits the company’s needs and particularities. Having analytical algorithms to support fleet dimensioning optimises all that concerns rate management. Real-time visibility into transportation operations and costs is critical for a controlled and efficient supply chain.

From simulating operations to network design (defining the best place to open a new store or install a new warehouse), advanced analytics play a vital role in supply chain efficiency.

Again, it is all connected. Analytics can help retailers integrate with variables like the number of units per box/pack. How can we optimise this parameter and negotiate accordingly with suppliers across the supply chain, from warehousing to transports to store operations?

The future of analytics in retail encompasses two significant challenges: analytical capabilities and digital innovation. We are looking at a present and a future where: 

New models are tested every day: enlarging all the applications of predictive modelling, advanced analytics, and self-service analytics – making analytics a democratic process for all users.

Digital innovations make the data world bigger and better: cloud analytics, big data and hybrid architectures, real-time in memory.

Although computers and algorithms are powerful tools to tackle today’s retail challenges, it is critical to recognise the value of human insights. The real value of analytics resides in how technical skills help leverage operational knowledge.

A holistic deployment of analytics capabilities throughout the supply chain is vital in promoting synergies between data and experience by empowering a data-driven mindset, which is the best tool to face an unpredictable future.

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