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.Â
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.
Why leading retail teams embrace the use of data & AI solutions
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Make better decisions
Advanced forecasting improves decision-making from assortment planning to supply chain management
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Decrease time spent
Get accurate recommendations fast & automate tedious tasks
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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.Â
That is why we help both business and analytics leaders answer challenging questions, get to work and create instant value.
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.
Â
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
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.
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 à nd 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.
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.
Â
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.
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.
Â
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.
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.
Â
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.
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.
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.
Â
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
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.
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 à nd 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.
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.
Â
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.
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.
Â
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.
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.
Â
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.
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.
