Advanced Analytics & AI in Retail

How retailers can walk the path of sustainable growth by embracing advanced analytics, driven by data and algorithms.

Traditional retail is in stormy waters.

The first waves of a digital transformation have put the entire retail value chain under pressure. There is not a single manager in procurement, inventory management, category management or marketing & personalization that is not confronted with shrinking margins and considerable profitability issues.

Yet, Emerging players are thriving by using substantial amounts of data to create a new competitive advantage. They embrace data science, machine learning and artificial intelligence to create tools that improve human decision-making and enable the automation of such decision-making.

Learn more about the shift towards data-driven decision-making in retail

Data-driven retailers embrace advanced analytics and artificial intelligence to make better decisions.

The growth of research in the domains of data science, machine learning and AI has made new tools available that can help turn data into business insights. Tools that enable a retailer to halt the loss of revenue and indicate where costs can be reduced

Insights that help data-driven retailers to better understand what is happening and why it happened. Insights that permit a retailer to predict what will happen, anticipate and prescribe actions, applying ever more advanced analytics.

This process paves the way for increased automation, as a computer system learns from the actions taken by a human decision-maker. Applying advanced analytics & artificial intelligence is paving the way for the creation of highly-efficient data-driven organisations and a sustainable path for growth.

  • What can I do with Advanced Analytics & AI?
  • How do I get started?
  • How to properly implement your Advanced Analytics & AI project?
  • Do I need an in-house Data Scientist?
  • How do you define the budget?
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How we help retail organizations

Discover how Advanced Analytics & AI can help your retail organization make better decisions, automate decision-making and provide a sustainable path for growth.

Get a better view on facts, as you prepare negotiations

Decision-makers in procurement tasked with purchasing items at the best price, at the most convenient moment, can no longer afford the luxury of making decisions based on inferior insights or mere gut feeling. Fact-based preparation of negotiations with vendors based on verified events and new insights has become essential.

Tools making use of machine learning algorithms can be applied to classify and categorize data, enabling the use of advanced analytics that describes, explains, predicts or prescribes what purchasing decisions should be taken.

+ Learn More about improving your price negotiation tactics with Advanced Analytics & AI

Find insights that can improve how items are procured

By bringing together data from a variety of sources, procurement managers find themselves ever more equipped with insights that can improve how they procure items.

Finding price drivers, spend outliers, hidden costs or many more. Enabling early warning systems for price deviations between purchase price and ‘should price’ or creating dynamic vendor scorecards in terms of pricing. By applying advanced analytics and AI, a decision-maker can significantly increase his or her negotiating power and ensure better purchasing decisions are made.

+ Learn More about improving your price negotiation tactics with Advanced Analytics & AI

Read More about how we helped our Online Retail Client

Access more data to enable predictions

The rise of e-commerce and digitization of other retail business processes significantly multiplied the ability to gather relevant data, enabling much better planning.

The demand for ever-lower prices, vast assortment and swift delivery, accompanied by an appetite for convenience and frictionless ordering & returning of goods, prompts a data-driven procurement strategy.

+ Learn More about how to use Advanced Analytics & AI to predict order quantities

The use of demand forecasting in retail

The use of advanced analytics and AI enables retailers to walk that fine line between over- and underordering. By deploying machine learning algorithms and other data science techniques, patterns can be found that permit a better prediction of materials required or forecast of sales.

It allows a retailer to develop an overall strategy that focuses on accuracy where needed or allow for more flexibility were such can be sustained.

+ Learn More about how to use Advanced Analytics & AI to predict order quantities

Learn more about our Product Distribution solution for our client

Putting a wealth of data to work

Use the substantial amount of data that is generated in the procurement department to spearhead the transformation towards more evidence-based decision-making driven by data and algorithms.

Improved insights on the vendor-retailer relationship can increase transparency, eradicate bias and even increase the aforementioned trust between parties.

How? Advanced Analytics and AI can be used to measure the individual performance of a single vendor. Such can entail measuring real-time performance, providing info on quality, lead times, compliance to payment terms or various types of contract compliance. When performed with multiple vendors, retail organisations can develop new insights through benchmarking of individual results against clusters of similar vendors

Learn More about how to use Advanced Analytics & AI to improve vendor relationships

Growing categories in a fundamentally changing retail landscape

Ever since the dawn of category management, retailers have learned the importance of growing a category, not merely brands. The importance of teaming up with manufacturers. With the global decline of the power of brands, the latter has become even more crucial.

With a growing amount of sources to buy from, customers have shifted tactics. Shoppers more and more pick different sales channels for different categories, in which they hope to find a product that helps them get a specific job done.

Retailers therefore need to take necessary actions to recreate vibrant and dynamic categories able to lure people in, for instance by creating individualised store assortments.

Increased data-driven category management is essential to empower category managers to excel at their task. As internal and third-party data is brought together to enable advanced analytics, machine learning algorithms can plough through large sets of data and help discover insights unheard-of.

+ Learn More about how to use Advanced Analytics & AI can hook customers to your categories of choice

Improve your understanding of the brand - shopper relationship

The use of advanced analytics can help unravel the dynamics between shopper and brand on the level of a single item. By joining loyalty card data, online & offline purchase data, competitor insights, financial data and various other sets of data, retailers can perform, for instance, improved substitution analysis, determining the crucial and non-crucial items for a category.

Advanced analytics can be used to rebuild trust between manufacturers/vendors and retail organisations. Both can thrive from the exchange of data to create appropriate items that enable a retailer to get the most out of managing a category.

+ Learn More about how to use Advanced Analytics & AI can hook customers to your categories of choice

In search of the optimal price point

Customers are looking for more personalised offers. Digital processes such as the act of buying in an online store enable a retailer to gather data on customer behaviour. Those insights provide the intel on how to differentiated product and service offers.

The advent of advanced analytics and AI, driven by data science techniques such as machine learning algorithms, enable retailers to move away from intuitive or less state-of-the-art price setting methods. The technology can influence several aspects of the retailers pricing strategy.

Setting the initial price of an item

Given an increased importance of price perception and greater price transparency, setting the initial price has become ever more crucial. The challenge category managers face is weighing each factor correctly as well interpreting the data correctly to find out what drives the perception of value.

Especially for retailers with thousands of different items, such seems a daunting task. Advanced analytics can significantly improve the accuracy of such pricing decisions and subsequent decisions related to the procurement of items and managing inventory.

The move towards more dynamic pricing

With business processes going digital, retailers are provided with the opportunity to change prices in (near) real-time based on supply and demand indicators. For retail, dynamic pricing driven by data & algorithms can be used as a tool to respond towards a competitor move, towards inventory turnover or sudden spikes in demand.

Finding out what promotions are creating an impact

Finding the optimal mix between pricing and promotion in a given category can be a tough row to hoe. Certainly, as retail seems to have found itself in a permanent state of ‘discounting’ due to a changing - more digital - retail environment.

Nonetheless, retailers continue to lose profit as they remain hesitant to scale back the volume of such promotions. Often, the reason is both simple and troublesome. It is not clear which ones are working.

Applying advanced analytics & AI enables a retailer to sift through the data and find patterns that indicate which promotions are truly effective. It can also help determine whether the same strategy can be run for a different item with similar traits, tweak parameters and test scenarios to determine their impact.

Hyper-personalisation of discounts

Applying advanced analytics and AI enables hyper-personalisation of discounts that focuses on persuading the right individual and the correct item with a specific discount. Data stemming from the aforementioned variety in sales channels permits a retailer to understand at what moment a promotion has the most impact on a given customer.

Levering data to test and better prepare promotion calendars, as well as using insights for customized discounts at the level of an individual shopper or shopping cart, will prove to be the most rewarding when retailers use promotions to drive growth.

Making improved inventory management decisions and reach optimal inventory levels

Each executive understands the impact of the significant amount of working capital that is tied up in stock, each season again. Yet, with the stellar increase in the number of sales channels, managing own inventory levels as well as assessing the impact of increased competition has become utterly challenging.

By applying advanced analytics and AI retailers can optimize their spending behaviour with regard to stock, as the organization is presented with better insights with regard to those uncertainties.

Getting the distribution of items over various shops right, the first time

For certain retail organisations, getting the initial distribution of items right is crucial as the cost of redistribution is just too high or lead times in replenishment are too long.

With the use of advanced analytics & AI, a retailer can obtain insights into what is likely to sell where, and in what quantities. By applying data-science and machine-learning techniques, retailers can simulate sales and predict what inventory levels should be maintained, at what location and at what cost.

Avoiding out-of-stock situations has become essential because of increased competition. Traditional supply-chain planning was designed around a fixed, rule-based approach with regard to the replenishment of items. Future-proof planning builds on readily-made available data and the application of Advanced Analytics and AI.

A variety of influencing parameters can be brought together to allow a machine-learning algorithm to produce demand forecasts with regard to specific items. It can lever company-owned data in a combination with supplier data (batch size, lead times) and publicly available data such as online competitor data, public holidays or local weather.

Retailers can harness advanced analytics & artificial intelligence to automate the cumbersome process of periodically analyzing the inventory level of each store, both online and offline, for each product. It allows them to make timely decisions and push the appropriate instructions through the supply chain.

Driven by a custom-made redistribution algorithm and dashboard, the person managing inventory can query a tool to see whether inventory shifts are needed, their impact on possible revenue and their expected costs. This redistribution tool can furthermore take into account additional parameters such as the timing of planned intra-store transport

Get a better view on facts, as you prepare negotiations

Decision-makers in procurement tasked with purchasing items at the best price, at the most convenient moment, can no longer afford the luxury of making decisions based on inferior insights or mere gut feeling. Fact-based preparation of negotiations with vendors based on verified events and new insights has become essential.

Tools making use of machine learning algorithms can be applied to classify and categorize data, enabling the use of advanced analytics that describes, explains, predicts or prescribes what purchasing decisions should be taken.

+ Learn More about improving your price negotiation tactics with Advanced Analytics & AI

Find insights that can improve how items are procured

By bringing together data from a variety of sources, procurement managers find themselves ever more equipped with insights that can improve how they procure items.

Finding price drivers, spend outliers, hidden costs or many more. Enabling early warning systems for price deviations between purchase price and ‘should price’ or creating dynamic vendor scorecards in terms of pricing. By applying advanced analytics and AI, a decision-maker can significantly increase his or her negotiating power and ensure better purchasing decisions are made.

+ Learn More about improving your price negotiation tactics with Advanced Analytics & AI

Read More about how we helped our Online Retail Client

Access more data to enable predictions

The rise of e-commerce and digitization of other retail business processes significantly multiplied the ability to gather relevant data, enabling much better planning.

The demand for ever-lower prices, vast assortment and swift delivery, accompanied by an appetite for convenience and frictionless ordering & returning of goods, prompts a data-driven procurement strategy.

+ Learn More about how to use Advanced Analytics & AI to predict order quantities

The use of demand forecasting in retail

The use of advanced analytics and AI enables retailers to walk that fine line between over- and underordering. By deploying machine learning algorithms and other data science techniques, patterns can be found that permit a better prediction of materials required or forecast of sales.

It allows a retailer to develop an overall strategy that focuses on accuracy where needed or allow for more flexibility were such can be sustained.

+ Learn More about how to use Advanced Analytics & AI to predict order quantities

Learn more about our Product Distribution solution for our client

Putting a wealth of data to work

Use the substantial amount of data that is generated in the procurement department to spearhead the transformation towards more evidence-based decision-making driven by data and algorithms.

Improved insights on the vendor-retailer relationship can increase transparency, eradicate bias and even increase the aforementioned trust between parties.

How? Advanced Analytics and AI can be used to measure the individual performance of a single vendor. Such can entail measuring real-time performance, providing info on quality, lead times, compliance to payment terms or various types of contract compliance. When performed with multiple vendors, retail organisations can develop new insights through benchmarking of individual results against clusters of similar vendors

Learn More about how to use Advanced Analytics & AI to improve vendor relationships

Growing categories in a fundamentally changing retail landscape

Ever since the dawn of category management, retailers have learned the importance of growing a category, not merely brands. The importance of teaming up with manufacturers. With the global decline of the power of brands, the latter has become even more crucial.

With a growing amount of sources to buy from, customers have shifted tactics. Shoppers more and more pick different sales channels for different categories, in which they hope to find a product that helps them get a specific job done.

Retailers therefore need to take necessary actions to recreate vibrant and dynamic categories able to lure people in, for instance by creating individualised store assortments.

Increased data-driven category management is essential to empower category managers to excel at their task. As internal and third-party data is brought together to enable advanced analytics, machine learning algorithms can plough through large sets of data and help discover insights unheard-of.

+ Learn More about how to use Advanced Analytics & AI can hook customers to your categories of choice

Improve your understanding of the brand - shopper relationship

The use of advanced analytics can help unravel the dynamics between shopper and brand on the level of a single item. By joining loyalty card data, online & offline purchase data, competitor insights, financial data and various other sets of data, retailers can perform, for instance, improved substitution analysis, determining the crucial and non-crucial items for a category.

Advanced analytics can be used to rebuild trust between manufacturers/vendors and retail organisations. Both can thrive from the exchange of data to create appropriate items that enable a retailer to get the most out of managing a category.

+ Learn More about how to use Advanced Analytics & AI can hook customers to your categories of choice

In search of the optimal price point

Customers are looking for more personalised offers. Digital processes such as the act of buying in an online store enable a retailer to gather data on customer behaviour. Those insights provide the intel on how to differentiated product and service offers.

The advent of advanced analytics and AI, driven by data science techniques such as machine learning algorithms, enable retailers to move away from intuitive or less state-of-the-art price setting methods. The technology can influence several aspects of the retailers pricing strategy.

Setting the initial price of an item

Given an increased importance of price perception and greater price transparency, setting the initial price has become ever more crucial. The challenge category managers face is weighing each factor correctly as well interpreting the data correctly to find out what drives the perception of value.

Especially for retailers with thousands of different items, such seems a daunting task. Advanced analytics can significantly improve the accuracy of such pricing decisions and subsequent decisions related to the procurement of items and managing inventory.

The move towards more dynamic pricing

With business processes going digital, retailers are provided with the opportunity to change prices in (near) real-time based on supply and demand indicators. For retail, dynamic pricing driven by data & algorithms can be used as a tool to respond towards a competitor move, towards inventory turnover or sudden spikes in demand.

Finding out what promotions are creating an impact

Finding the optimal mix between pricing and promotion in a given category can be a tough row to hoe. Certainly, as retail seems to have found itself in a permanent state of ‘discounting’ due to a changing - more digital - retail environment.

Nonetheless, retailers continue to lose profit as they remain hesitant to scale back the volume of such promotions. Often, the reason is both simple and troublesome. It is not clear which ones are working.

Applying advanced analytics & AI enables a retailer to sift through the data and find patterns that indicate which promotions are truly effective. It can also help determine whether the same strategy can be run for a different item with similar traits, tweak parameters and test scenarios to determine their impact.

Hyper-personalisation of discounts

Applying advanced analytics and AI enables hyper-personalisation of discounts that focuses on persuading the right individual and the correct item with a specific discount. Data stemming from the aforementioned variety in sales channels permits a retailer to understand at what moment a promotion has the most impact on a given customer.

Levering data to test and better prepare promotion calendars, as well as using insights for customized discounts at the level of an individual shopper or shopping cart, will prove to be the most rewarding when retailers use promotions to drive growth.

Making improved inventory management decisions and reach optimal inventory levels

Each executive understands the impact of the significant amount of working capital that is tied up in stock, each season again. Yet, with the stellar increase in the number of sales channels, managing own inventory levels as well as assessing the impact of increased competition has become utterly challenging.

By applying advanced analytics and AI retailers can optimize their spending behaviour with regard to stock, as the organization is presented with better insights with regard to those uncertainties.

Getting the distribution of items over various shops right, the first time

For certain retail organisations, getting the initial distribution of items right is crucial as the cost of redistribution is just too high or lead times in replenishment are too long.

With the use of advanced analytics & AI, a retailer can obtain insights into what is likely to sell where, and in what quantities. By applying data-science and machine-learning techniques, retailers can simulate sales and predict what inventory levels should be maintained, at what location and at what cost.

Avoiding out-of-stock situations has become essential because of increased competition. Traditional supply-chain planning was designed around a fixed, rule-based approach with regard to the replenishment of items. Future-proof planning builds on readily-made available data and the application of Advanced Analytics and AI.

A variety of influencing parameters can be brought together to allow a machine-learning algorithm to produce demand forecasts with regard to specific items. It can lever company-owned data in a combination with supplier data (batch size, lead times) and publicly available data such as online competitor data, public holidays or local weather.

Retailers can harness advanced analytics & artificial intelligence to automate the cumbersome process of periodically analyzing the inventory level of each store, both online and offline, for each product. It allows them to make timely decisions and push the appropriate instructions through the supply chain.

Driven by a custom-made redistribution algorithm and dashboard, the person managing inventory can query a tool to see whether inventory shifts are needed, their impact on possible revenue and their expected costs. This redistribution tool can furthermore take into account additional parameters such as the timing of planned intra-store transport

Our client stories

Distribution

A smarter way to handle inventory management

How a custom-made algorithm reduced a client’s time on managing inventory levels.

Download Client Case: Smart Inventory Management

How a custom-made algorithm reduced a client’s time on managing inventory levels.

Online Retailer

Get a grip on the pricing and assortment strategy of your competitors

How a competition-monitoring tool helped raise a client’s margins by providing insights in competitor pricing and assortment.

Download Client Case: Competition Monitoring Tool

An essential component of running a retail business is making decisions on assortment and price. Discover how we helped our client achieve its goal.

Customer Relationship Management

How a Belgian Retailer turns visitors into customers with marketing automation using a B2C CRM tool

Nurture visitors with hyper-personalised communication towards their next purchase.

Download Client Case: B2C CRM for Retail

Leading retailers all over the world are upgrading their customer experience through data- and AI-driven marketing. How does a Belgian retailer compete with these online, heavily personalized retail giants?

We'll make sure you stay one step ahead!