Retail & E-Commerce

We help leaders in retail & E-commerce confidently move teams forward, when applying data science, statistics, machine learning, and AI.

Leading retailers have understood that over a decade of digital transformation, coupled with drastic shifts in customer behavior, provides vast opportunities. 

Today, traditional and generative AI technologies amplify these opportunities, ushering in a new era where success is determined by the ability of retail teams to embrace these new technologies in their day-to-day activities. Learn how we help our clients do just that.

Have a look at our cases

Why leading retail teams embrace the use of data & AI solutions

  • Make better decisions

    Advanced forecasting improves decision-making from assortment planning to supply chain management

  • Decrease time spent

    Get accurate recommendations fast & automate tedious tasks
     

AI-driven decision-making. A seismic shift in how decisions within a retail organization are made.

Leading retailers have understood the need for an overhaul of the fundamentals of a retail organization.

They have understood that those new recipes for success require data & algorithms as a key ingredient. 

That is why we help both business and analytics leaders answer challenging questions, get to work and create instant value.

  • What data & AI use cases deliver value fast?
  • What IT & data infrastructure do we require?
  • Should we build or buy a solution?
  • What should our internal team look like?
  • How do I ensure long-term adoption in our teams?
Let's answer your questions

How we assist retail & e-commerce teams

Discover the key value domains where data & AI are transforming the retail industry. Learn about the business processes where algorithms and automation can make a substantial difference, driving more revenue or reducing costs.

A markdown optimization solution that doesn't leave money on the table

The strategy of setting end-of-season discounts or price markdown is straightforward. It is about finding the optimal balance between selling at the highest possible margin while maintaining a solid rotation of items. The goal is not to sell out at once. The strategy aims to clear out the inventory gradually, ensuring all stock is gone by the end of a given period.
 

Yet, the current process applied by many retailers leaves money on the table. The process contains a lot of manual and repetitive tasks and leads to suboptimal results. Teams hardly have the time to focus on high-value-added tasks. Little time is left to evaluate, for instance, additional data elements such as weather impact, footfall data, and product characteristics.

Such results in some parts of the collection or assortment marked down too much. A more reserved markdown would not significantly decrease rotation and lead to a higher margin. While on the other hand, some parts of the collection or assortment are marked down too little - a higher markdown would increase rotation, decrease overstock and lead to higher overall revenue.

By bringing data together and applying algorithms, AI-driven analytics is able to quantify what impact can be expected from a price markdown decision. Using these insights, a team can identify actions to be taken, quickly evaluate if these actions are adding or destroying value, and swiftly take action. Moreover, it allows a retailer to determine what the exact markdown should be for a given product at a given point in time to achieve the most optimal result

Learn more about markdown optimization at Torfs Schoenen

Setting the initial price of an item

E-Commerce has changed the playing field for every retailer. Customers have more information on pricing or item alternatives available at their fingertips. Such creates increased importance of price perception making the setting of the initial or base price of an item ever more crucial.

The challenge category managers face is weighing each factor that contributes to setting a given price correctly. To do so, they are often confronted with the tedious task of bringing all useful data together and interpreting the data correctly, trying to figure out what drives the perception of the value of a given item.

Especially for retailers with thousands of different items, such seems a daunting task. AI-driven analytics can significantly improve the accuracy of such a pricing decision with straightforward predictions. Moreover, it can have a tremendous impact on any subsequent decisions related to the procurement of items and managing of inventory.

The move towards more dynamic pricing

In the current, omnichannel environment, it no longer makes sense to set prices just once per season. With business processes going digital, retailers are provided with the opportunity to change prices in (near) real-time based on supply and demand indicators. The difficulty of having to change those prices in stores is no longer an issue, as even in fashion digital price tags are now being used widely.

Data & algorithms can be used as a tool to respond towards a competitor move, towards inventory turnover, or sudden spikes in demand. With a set of business rules that act as boundaries, dynamic pricing provides a retailer more strategic options and liberty to get the most out of every sale.

Have a look at our client cases

Get the distribution of items across all sales channels at the start of the season right

For many retail organizations, getting the initial distribution of items right is crucial. The cost of redistribution is just too high and lead times in replenishment are often too long.

Moreover, as retailers make the push towards omnichannel retail, the challenge becomes even more difficult. Teams are confronted with a broader variety in offline ànd online (webshop, marketplace) sales channels, and thus inventory to distribute.

With the use of AI-driven analytics, 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, on what channel, and at what cost.

Have a look at our client cases

When items threaten to pile up, let Advanced Analytics and AI guide the redistribution of inventory

AI-driven tooling can redefine the cumbersome process of periodically analyzing inventory levels of each store, both online and offline, for every single product.
 

It allows retail teams to make timely decisions and push the appropriate instructions through the supply chain, ensuring that no sales are lost or stock remains unsold.

Such tooling is driven by a custom-made redistribution algorithm and presents its insights in an easy-to-use dashboard.The person managing inventory can query the tool to see whether inventory shifts are needed, what the impact on revenue could be and what expected redistribution costs the shift entails. The tool can even take into account additional parameters such as the timing of planned intra-store transport.

Have a look at our client cases

Do not miss another sale, by applying advanced analytics & AI to timely replenish items.

Avoiding out-of-stock situations has become essential in omnichannel retail. Due to increased competition both online & offline, a sale is lost in the blink of an eye.
 

Traditional supply-chain planning was designed around a fixed, rule-based approach with regard to the replenishment of items.

Today, large amounts of available data and a lot of computational power are able to generate highly-accurate predictions on future demand. Predictions that assist the procurement or inventory manager when making decisions, and allow him/her to respond more dynamically.

How is it done? Company-owned data is brought together with supplier data (batch size, lead times) and publicly available data such as online competitor data, public holidays or local weather. Those sizeable datasets allow a machine-learning algorithm to produce demand forecasts, indicating what is likely to sell and what pace.

The procurement manager is provided with a dashboard, that visually suggests the required number of items of each product that need to be purchased, taking into account actual stock levels, actual open orders and lead times. The procurement manager merely needs to accept or adjust and push a final decision to the ERP system.

Have a look at our client cases

Using data to build smart customer segments

In today’s digital world, brands continually bombard consumers with marketing messages. Research shows that the average consumer is exposed to about 5.000 ads every single day.

This has resulted in most digital native customers are ignoring content that is not relevant to them. If you want to grasp the attention of your customers, you better make sure that your message is hyper-personalized.
 

The main challenge facing marketers is finding a solution to the task of achieving personalized marketing at scale in a cost-effective way.

The sheer volume of customers, all with different behaviours and interests, can be overwhelming and is often holding back marketers to experiment with tailored marketing messages.

To overcome this main challenge, it is crucial to unlock the value hidden in customer data. AI-driven segmentation can help marketers to identify subsets of customers with similar behaviours and interests. This will enable them to drastically increase customer engagement by creating tailored content on a manageable scale.

Have a look at our client cases

Extracting actionable insights from your customer data

The task of following up on changing customer behavior and spotting opportunities for growth is now more challenging than ever. Customer behavior is continuously evolving, contributing to an ever more competitive and dynamic retail landscape.

To keep track, retailers started to collect significant amounts of customer data. However, that sheer volume of data often leaves business analysts stunned.

Unifying data across channels and extracting actionable insights out of the many customer interactions and data points can be a very challenging task.

Advanced analytics and AI can help retailers make smarter decisions by identifying trends in customer spending habits.

Understanding and predicting future customer behavior, such as a customer's likelihood for cross-selling in new product categories, enables them to increase the personalization and effectiveness of marketing efforts and customer journeys.

Have a look at our client cases

A markdown optimization solution that doesn't leave money on the table

The strategy of setting end-of-season discounts or price markdown is straightforward. It is about finding the optimal balance between selling at the highest possible margin while maintaining a solid rotation of items. The goal is not to sell out at once. The strategy aims to clear out the inventory gradually, ensuring all stock is gone by the end of a given period.
 

Yet, the current process applied by many retailers leaves money on the table. The process contains a lot of manual and repetitive tasks and leads to suboptimal results. Teams hardly have the time to focus on high-value-added tasks. Little time is left to evaluate, for instance, additional data elements such as weather impact, footfall data, and product characteristics.

Such results in some parts of the collection or assortment marked down too much. A more reserved markdown would not significantly decrease rotation and lead to a higher margin. While on the other hand, some parts of the collection or assortment are marked down too little - a higher markdown would increase rotation, decrease overstock and lead to higher overall revenue.

By bringing data together and applying algorithms, AI-driven analytics is able to quantify what impact can be expected from a price markdown decision. Using these insights, a team can identify actions to be taken, quickly evaluate if these actions are adding or destroying value, and swiftly take action. Moreover, it allows a retailer to determine what the exact markdown should be for a given product at a given point in time to achieve the most optimal result

Learn more about markdown optimization at Torfs Schoenen

Setting the initial price of an item

E-Commerce has changed the playing field for every retailer. Customers have more information on pricing or item alternatives available at their fingertips. Such creates increased importance of price perception making the setting of the initial or base price of an item ever more crucial.

The challenge category managers face is weighing each factor that contributes to setting a given price correctly. To do so, they are often confronted with the tedious task of bringing all useful data together and interpreting the data correctly, trying to figure out what drives the perception of the value of a given item.

Especially for retailers with thousands of different items, such seems a daunting task. AI-driven analytics can significantly improve the accuracy of such a pricing decision with straightforward predictions. Moreover, it can have a tremendous impact on any subsequent decisions related to the procurement of items and managing of inventory.

The move towards more dynamic pricing

In the current, omnichannel environment, it no longer makes sense to set prices just once per season. With business processes going digital, retailers are provided with the opportunity to change prices in (near) real-time based on supply and demand indicators. The difficulty of having to change those prices in stores is no longer an issue, as even in fashion digital price tags are now being used widely.

Data & algorithms can be used as a tool to respond towards a competitor move, towards inventory turnover, or sudden spikes in demand. With a set of business rules that act as boundaries, dynamic pricing provides a retailer more strategic options and liberty to get the most out of every sale.

Have a look at our client cases

Get the distribution of items across all sales channels at the start of the season right

For many retail organizations, getting the initial distribution of items right is crucial. The cost of redistribution is just too high and lead times in replenishment are often too long.

Moreover, as retailers make the push towards omnichannel retail, the challenge becomes even more difficult. Teams are confronted with a broader variety in offline ànd online (webshop, marketplace) sales channels, and thus inventory to distribute.

With the use of AI-driven analytics, 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, on what channel, and at what cost.

Have a look at our client cases

When items threaten to pile up, let Advanced Analytics and AI guide the redistribution of inventory

AI-driven tooling can redefine the cumbersome process of periodically analyzing inventory levels of each store, both online and offline, for every single product.
 

It allows retail teams to make timely decisions and push the appropriate instructions through the supply chain, ensuring that no sales are lost or stock remains unsold.

Such tooling is driven by a custom-made redistribution algorithm and presents its insights in an easy-to-use dashboard.The person managing inventory can query the tool to see whether inventory shifts are needed, what the impact on revenue could be and what expected redistribution costs the shift entails. The tool can even take into account additional parameters such as the timing of planned intra-store transport.

Have a look at our client cases

Do not miss another sale, by applying advanced analytics & AI to timely replenish items.

Avoiding out-of-stock situations has become essential in omnichannel retail. Due to increased competition both online & offline, a sale is lost in the blink of an eye.
 

Traditional supply-chain planning was designed around a fixed, rule-based approach with regard to the replenishment of items.

Today, large amounts of available data and a lot of computational power are able to generate highly-accurate predictions on future demand. Predictions that assist the procurement or inventory manager when making decisions, and allow him/her to respond more dynamically.

How is it done? Company-owned data is brought together with supplier data (batch size, lead times) and publicly available data such as online competitor data, public holidays or local weather. Those sizeable datasets allow a machine-learning algorithm to produce demand forecasts, indicating what is likely to sell and what pace.

The procurement manager is provided with a dashboard, that visually suggests the required number of items of each product that need to be purchased, taking into account actual stock levels, actual open orders and lead times. The procurement manager merely needs to accept or adjust and push a final decision to the ERP system.

Have a look at our client cases

Using data to build smart customer segments

In today’s digital world, brands continually bombard consumers with marketing messages. Research shows that the average consumer is exposed to about 5.000 ads every single day.

This has resulted in most digital native customers are ignoring content that is not relevant to them. If you want to grasp the attention of your customers, you better make sure that your message is hyper-personalized.
 

The main challenge facing marketers is finding a solution to the task of achieving personalized marketing at scale in a cost-effective way.

The sheer volume of customers, all with different behaviours and interests, can be overwhelming and is often holding back marketers to experiment with tailored marketing messages.

To overcome this main challenge, it is crucial to unlock the value hidden in customer data. AI-driven segmentation can help marketers to identify subsets of customers with similar behaviours and interests. This will enable them to drastically increase customer engagement by creating tailored content on a manageable scale.

Have a look at our client cases

Extracting actionable insights from your customer data

The task of following up on changing customer behavior and spotting opportunities for growth is now more challenging than ever. Customer behavior is continuously evolving, contributing to an ever more competitive and dynamic retail landscape.

To keep track, retailers started to collect significant amounts of customer data. However, that sheer volume of data often leaves business analysts stunned.

Unifying data across channels and extracting actionable insights out of the many customer interactions and data points can be a very challenging task.

Advanced analytics and AI can help retailers make smarter decisions by identifying trends in customer spending habits.

Understanding and predicting future customer behavior, such as a customer's likelihood for cross-selling in new product categories, enables them to increase the personalization and effectiveness of marketing efforts and customer journeys.

Have a look at our client cases

Have a look at our cases

Clean Case Image Hornbach

Optimizing stock levels in DIY retail. How Hornbach was able to reduce capital expenditure by introducing advanced demand forecasting

How can you refine your inventory management and reduce capital expenditure with AI and data-driven insights? 

Discover how Hornbach, a leading European DIY retailer, revolutionized its supply chain by implementing advanced demand forecasting techniques. This strategic move enabled Hornbach to optimize stock levels across its stores, significantly improving cost efficiency and customer satisfaction

Read more
Clean Case Image Beerwulf AI Price

Improving your bottom line in e-commerce: How Beerwulf boosts margins & revenue with AI-driven price optimization

Finding that optimal price point for each product in your catalog across markets is a daunting task. With the help of AI & data-driven insights, Beerwulf was able to bring scattered data together and create a unified view on pricing. Here is how.

Read more
Case Image No Text Torfs

Get more value out of end-of-season markdowns in fashion retail: How Torfs uses the Crunch Markdown Assistant to boost revenue & margins

How can you optimize markdown pricing and enhance retail efficiency? Discover how Torfs, a leading Belgian shoe retailer, significantly improved its profit margins and streamlined operations by implementing the Crunch AI-driven Markdown Assistant. This strategic initiative leveraged advanced analytics to revolutionize Torfs' end-of-season discounts, boosting revenue by up to 30% and margins by up to 8%.

Read more
Case Image Clean Beerwulf Price

Transforming e-commerce operations efficiency: How Beerwulf was able to scale operations using advanced demand forecasting & automation

How can you scale the challenging process of creating themed packs that contain various—perishable—types of beer, especially with regard to stock replenishment? Learn how Beerwulf leveraged AI and data-driven insights to obtain more adequate sales forecasts and significantly decrease out-of-stock and overstock situations.

Read more

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