Advanced Analytics & AI in Retail & E-Commerce

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

Why emerging retailers are thriving

Emerging players have been demonstrating that a decade of digital transformation and drastic shift in customer behaviour provides a broad array of opportunities.

In each subsector, be it fashion, DIY, furniture or other, new companies are thriving because they have understood that we're entering a radically different era where new recipes decide over profit or loss.

Those recipes entail a seismic shift in how decisions within a retail organization are made.

This concerns both ‘big’ & ‘small’ decisions, meaning decisions of a strategic nature or a lesser-impactful tactical nature.

We are talking about smaller, tactical decisions such as how to adjust the price of a single item in a catalogue of 5000 items, every single day. Or deciding when to replenish items or redistribute these items across the store network, to avoid out-of-stock situations.

And we are talking about high-impact, strategic decisions. Decisions such as whether one should even have a given item on sale from a category management point of view. Or whether a strategic shift in pricing is required to lure more customers to a given online or offline sales channel.

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

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. They transform a retailer in a data-driven organization that can thrive in both an online and offline environment.

Such requires that a lot of data, generated at scale in an omnichannel organization, needs to be brought together. To fuel algorithms in providing insights and predictions (analytics) for people making decisions. And, where possible, they should even fuel the automation of such decision-making.

  • 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?
Let's answer your questions

How we help retail organizations

Discover the key value domains where Advanced Analytics & AI are transforming the retail industry. In these key value domains, we've listed the business processes where data & algorithms can drive revenue or reduce costs. Where data & algorithms can assist people in making better decisions or automate such decision-making, setting the organization on a sustainable path for growth.

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 an increased importance of price perception making the setting of the initial price 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 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. Moreover, it can have a tremendous impact on any subsequent decisions related to the procurement of items and managing of inventory.

+ Learn More about how Advanced Analytics & AI can transform category management in retail

+ Blog - Long Read: Smart Algorithms in Retail: More Bounce to the Ounce by Investing in Operations!

In search of the optimal price point

By bringing data together and applying data science techniques such as machine learning algorithms, AI-driven tools can suggest the optimal price point for each individual item.

It enables retailers to move away from intuitive or less state-of-the-art price setting methods. Having an easy-to-use dashboard that predicts what price to set for every single item significantly simplifies the job of a category manager. It ensures that the weekly recurring task of updating the lengthy list of prices is done swiftly ànd consistently.

Moreover, it provides that same category manager with more time to perform more in depth strategic analysis of its overall pricing strategy.

+ Learn More about how Advanced Analytics & AI can transform category management in retail

+ Blog - Long Read: Smart Algorithms in Retail: More Bounce to the Ounce by Investing in Operations!

+ Client Case: How an online retailer can monitor competitor pricing to increase its margins

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.

+ Learn More about how Advanced Analytics & AI can transform category management in retail

Define a markdown strategy that doesn't leave money on the table

The strategy of a discounting or markdown process 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 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 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

+ Whitepaper: Why Is It So Hard to Get End-of-Season Markdown Pricing in Retail Right? Here Are Three Essentials for Better Results

+ Whitepaper: We Created a Unique Markdown Tool for the 2020 Summer Sales Season. Here Is How It Can Make All the Difference.

+ Whitepaper: We Created a Unique 2020 Summer Sales Markdown Tool. Here Is How It Works

+ Client Case: How SCHOENEN TORFS achieved staggering results by embracing advanced analytics for end-of-season discounts or markdown pricing.

+ Client Case: How an AI-Driven Markdown Tool Enabled a Fashion Retailer to Optimize Its Sales Strategy

+ Learn More about how Advanced Analytics & AI can transform category management in retail

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

For many retail organisations, 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.

+ Learn More about how Advanced Analytics & AI can transform inventory management in retail

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.

+ Learn More about how Advanced Analytics & AI can transform inventory management in retail

+ Blog - Long Read: Smart Algorithms in Retail: More Bounce to the Ounce by Investing in Operations!

+ Client Case: How an online beer retailer benefits from AI-driven replenishment

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.

+ Learn More about how Advanced Analytics & AI can transform inventory management in retail

+ Client Case: How a predictive algorithm enables an omnichannel retailer to timely redistribute items

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.

+ Long Read: How to update your current marketing environment into an effective analytics-driven marketing environment that secures results

+ Client Case: How Analytics-Driven Marketing Enables Beerwulf to Disrupt the Craft Beer Market

Extracting actionable insights from your customer data

The task of following up on changing customer behaviour and spotting opportunities for growth is now more challenging than ever. Customer behaviour 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 behaviour, such as a customers likelihood for cross-selling in new product categories, enables them to increase the personalization and effectiveness of marketing efforts and customer journeys.

+ Long Read: How to update your current marketing environment into an effective analytics-driven marketing environment that secures results

+ Client Case: How Analytics-Driven Marketing Enables Beerwulf to Disrupt the Craft Beer Market

+ Whitepaper: How to Significantly Increase Sales Conversion Rates by Using Customer Data & Cross-Sell Predictions

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 an increased importance of price perception making the setting of the initial price 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 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. Moreover, it can have a tremendous impact on any subsequent decisions related to the procurement of items and managing of inventory.

+ Learn More about how Advanced Analytics & AI can transform category management in retail

+ Blog - Long Read: Smart Algorithms in Retail: More Bounce to the Ounce by Investing in Operations!

In search of the optimal price point

By bringing data together and applying data science techniques such as machine learning algorithms, AI-driven tools can suggest the optimal price point for each individual item.

It enables retailers to move away from intuitive or less state-of-the-art price setting methods. Having an easy-to-use dashboard that predicts what price to set for every single item significantly simplifies the job of a category manager. It ensures that the weekly recurring task of updating the lengthy list of prices is done swiftly ànd consistently.

Moreover, it provides that same category manager with more time to perform more in depth strategic analysis of its overall pricing strategy.

+ Learn More about how Advanced Analytics & AI can transform category management in retail

+ Blog - Long Read: Smart Algorithms in Retail: More Bounce to the Ounce by Investing in Operations!

+ Client Case: How an online retailer can monitor competitor pricing to increase its margins

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.

+ Learn More about how Advanced Analytics & AI can transform category management in retail

Define a markdown strategy that doesn't leave money on the table

The strategy of a discounting or markdown process 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 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 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

+ Whitepaper: Why Is It So Hard to Get End-of-Season Markdown Pricing in Retail Right? Here Are Three Essentials for Better Results

+ Whitepaper: We Created a Unique Markdown Tool for the 2020 Summer Sales Season. Here Is How It Can Make All the Difference.

+ Whitepaper: We Created a Unique 2020 Summer Sales Markdown Tool. Here Is How It Works

+ Client Case: How SCHOENEN TORFS achieved staggering results by embracing advanced analytics for end-of-season discounts or markdown pricing.

+ Client Case: How an AI-Driven Markdown Tool Enabled a Fashion Retailer to Optimize Its Sales Strategy

+ Learn More about how Advanced Analytics & AI can transform category management in retail

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

For many retail organisations, 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.

+ Learn More about how Advanced Analytics & AI can transform inventory management in retail

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.

+ Learn More about how Advanced Analytics & AI can transform inventory management in retail

+ Blog - Long Read: Smart Algorithms in Retail: More Bounce to the Ounce by Investing in Operations!

+ Client Case: How an online beer retailer benefits from AI-driven replenishment

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.

+ Learn More about how Advanced Analytics & AI can transform inventory management in retail

+ Client Case: How a predictive algorithm enables an omnichannel retailer to timely redistribute items

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.

+ Long Read: How to update your current marketing environment into an effective analytics-driven marketing environment that secures results

+ Client Case: How Analytics-Driven Marketing Enables Beerwulf to Disrupt the Craft Beer Market

Extracting actionable insights from your customer data

The task of following up on changing customer behaviour and spotting opportunities for growth is now more challenging than ever. Customer behaviour 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 behaviour, such as a customers likelihood for cross-selling in new product categories, enables them to increase the personalization and effectiveness of marketing efforts and customer journeys.

+ Long Read: How to update your current marketing environment into an effective analytics-driven marketing environment that secures results

+ Client Case: How Analytics-Driven Marketing Enables Beerwulf to Disrupt the Craft Beer Market

+ Whitepaper: How to Significantly Increase Sales Conversion Rates by Using Customer Data & Cross-Sell Predictions

Our Client Stories

Torfs

Pricing & Promotions: How Schoenen Torfs embraced advanced analytics for end-of-season discounts or markdown pricing and achieved staggering results

Here is how Crunch Analytics demonstrated the impact of replacing a rule-of-thumb pricing process by an analytics-driven markdown process. One that is enabled by automated data retrieval, pricing algorithms, and a set of easy-to-use dashboards indicating actions or showing results. One that lead to a double-digit increase in sales season revenue.

Download Client Case: How SCHOENEN TORFS embraced advanced analytics for end-of-season discounts or markdown pricing and achieved staggering results

The process of deciding on end-of-season discounts or 'markdown pricing' is often still rooted in tradition, leaving money on the table. By working with pricing algorithms and a set of other tools, we proved the business case and tremendous value of embracing advanced analytics for markdown pricing. Find more details in the client case!

Beerwulf

Pricing & Promotions: Assisting Beerwulf in monitoring the pricing & assortment choices of other online market actors

Setting the appropriate base price of an item, or adjusting at a later time, requires plenty of information & insights. Our custom-made monitoring tool helped the teams at Beerwulf to make better pricing decisions, by providing insights in pricing and assortment choices of other market actors.

Download Client Case: Pricing & Promotions: How we enabled BEERWULF to monitor the online pricing & assortment choices of other online market actors

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

Bent

Inventory Management: How advanced analytics helps BENT Shoes to prevent out-of-stock & overstock situations

Finding that optimal spot between overstock & out-of-stock situations, has become ever more important for retailers. As they find themselves between thin margins and clients that can purchase at a different online store within the click of a button, BENT wanted a solution that can predict an ideal range of stock to safeguard.

Download Client Case: Inventory Management: How BENT uses advanced analytics to prevent out-of-stock & overstock situations

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

Beerwulf

Marketing & Communications: How Analytics-Driven Marketing Enables Beerwulf to Disrupt the Craft Beer Market

We provided the fast-growing e-commerce retailer BEERWULF with the appropriate toolset & infrastructure that enables its marketing team to retain customers and increases customer lifetime value.

By bringing customer data together and introducing advanced analytics, the Beerwulf team was able to improve customer targeting and personalization which resulted in both higher engagement and conversion rates.

Download: Marketing & Communications: How Analytics-Driven Marketing Enables BEERWULF to Disrupt the Craft Beer Market

Bent

Marketing & Communications: How BENT introduced more data-driven marketing through a B2C-specific CRM tool, insights & automation.

With retail customers requiring ever more tailored & personal communication, BENT shoes was faced with the challenge of picking an appropriate B2C-specific CRM tool, fuelling it with customer data insights & introducing marketing automation. Today, insights from online and offline customer interaction, allow the Marketing Team to convert more new customers and retain existing ones.

Download Client Case: Marketing & Communications: How BENT introduced more data-driven marketing through a B2C-specific CRM tool, insights & automation.

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!