For Retailers

Quicklizard enables retailers to automate their pricing strategies and move from manual pricing to a smart, fully automated digital pricing infrastructure. Our suite of pricing optimization and enrichment modules advances pricing excellence at scale, based on individualized business goals. 

Holistic pricing strategies take into account many factors, both internal and external. Internally, factors like revenue goals, marketing objectives, target audience, brand positioning, and product attributes are taken into account. Externally, pricing strategies are influenced by factors such as consumer demand, competitor pricing, and market and economic trends. 

Traditional mathematical methods cannot adequately depict the complexities of today’s competitive market. The need to react in real time and factor in multiple data sources and economical variables, such as regions, currencies, and regulatory requirements, poses a real challenge to implementing and maintaining effective pricing strategies. 

In a very competitive landscape, retailers have to use pricing strategies to attract customers. Dynamic pricing and optimization allow retailers to build more complex pricing strategies that truly align and serve the business goals. 

For Brands

Manufacturers and shoppers alike are looking for optimized value and are less loyal to a retailer or a sales channel. 

Ecommerce and other technological and consumer behavior advancements have made it easier than ever before for manufacturers to interact with their audience in a direct channel.

When brands open a D2C channel, they find themselves in the unique position of being the partner and competitor of their retailers (authorized and not). This is a delicate balancing act they must master, as they can’t significantly underprice or overprice their items compared to their retailers.

Once a a brand adopts and deploys pricing optimization software, they are poised for real growth through an additional growing channel.

On a business level, the brand needs a robust ecommerce website, a multi-region approach, and a sound pricing strategy that compliments its relationship with other retailer partners.

On an organizational level, brands are starting to realize they need a dedicated team to manage their D2C strategy. D2C requires its own team of specialists that can help differentiate the brand in a crowded ecosystem and across all direct and indirect channels.

Quicklizard Pricing Modules

Reactive Pricing

Trigger repricing of items based on a criteria such as low inventory levels, competitor price changes, and even unusual traffic patterns of specific products. 

The Quicklizard Reactive pricing module uses data science to calculate how a price change affects demand, while accounting for factors like seasonality, cannibalization, clustering, and competitor price changes.

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Inter-Product Relationship

The demand of a product sometimes depends on complement and substitute products and their prices. For example, generic and private brands often have similar items. These items are substitutes. The demand for the private brand would depend not only on its own pricing, but also on the price movements of the generic brand. Brands like Coke and Pepsi are examples of substitutes. Similarly, demand for complementary products, such as phones and phone covers, and tennis rackets and tennis balls, are also influenced by both product prices.

Multi-Channel Allocation

Multichannel inventory management is one of the top challenges in ecommerce. Both overstocking and being out of stock can cost the equivalent of nearly 12% of sales every year. Carrying costs, reverse logistics, and a negative customer experience impede future repeat sales. 

Fast-growing, high-volume retailers rely on multichannel inventory management solutions that sync inventory across channels and provide real-time visibility from a centralized hub that is the single source of truth. Quicklizard allows retailers to add profit into this equation, enabling retailers to allocate inventory not juston availability but also in a way that will maximize profit and customer experience.

Markdown

A markdown is a permanent price decrease on a product that is at the end of its lifecycle or seasonality. Markdowns are used to temporarily increase demand for low-demand products with a goal of selling through the remaining stock. The aim is to align to the sell-through curve. A common markdown period is the weeks leading up to Christmas where retailers attempt to sell through the seasonal stock. While many retailers treat markdowns as a last resort, a markdown strategy is an important part of the pricing continuum. Using markdowns strategically can help minimize inventory holding costs and maximize profits. An effective retail markdown strategy must be part of the overall product lifecycle strategy.

Product Branching

Product branching is the ability to monitor performance and set pricing variation on the basis of specific product attributes. Retailers can set specific pricing on the basis of attributes such as color or size, as determined by factors such as sales volume , inventory, consumer journey, or competitive data. Product Branching can yield an incremental profit of up to 10% by avoiding deep discounts on residual inventory.

Price & Promotion

Price promotions are a marketing technique used to offer a temporary reduced price for a short period of time to build customer loyalty and increase sales volumeThere are several types of price promotions. A popular one that is often used by retailers is a multi buy promotion such as ‘Buy 1 Get 1 Free’ (also known as 2 for 1). 

When using promotions, price is only one piece of the puzzle. Multiple promotional campaigns can result in discount stacking and can be difficult to manage alongside everyday prices. Quicklizard ensures that actual revenue and profit are attributed to the sold price, avoiding multiple discounts from being applied if not required.

LifeTime Value (LTV)

Lifetime Value (LTV) is an estimate of the average revenue that a customer generates throughout their lifespan as a customer. This customer worth is used to support many economic decisions including marketing budget and resource allocation, profitability, and forecasting.

Measuring the contribution of each of these product purchases on long-term profitability and user retention can be leveraged to make price changes that maximize the long-term value of your business.

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Long-Tail Pricing

Long tail products are difficult to manage. Limited demand and low sales make it very difficult to find the optimal price point; Our similar product module enables product grouping, making the long tail shorter. 

This module delivers individual price optimization recommendations using intelligent AI-clustering based on historical transaction data, click streams. product information, and additional available data.

Interbasket Relationships

Complementary products are products that are closely related to the main/leading product and very often can’t be consumed alone. Mobile phones are an example of a leading product with many complements; such as mobile covers, screen protectors, warranties etc. The demand for the leading product generates the demand in its complement. 

The halo effect involves setting the price of the main product at the optimum level so that the demand for the complementary product increases, thereby maximising the profits from both products together. 

The Halo effect module allows retailers to use the calculated contribution in pricing strategies, identifying products affected and creating pricing groups.

Simulation

Setting the right pricing strategy requires extensive testing, altering, and constant monitoring to ensure that the most profitable strategy is in place. Identifying the strategy that works serves as the pathway to higher revenue and profit margins. 

The QuickLizard simulation Module is an AI-driven process that enables pricing strategy testing with the ability to predict the effect on revenue and profit.

Inflation Protection

How can retailers protect their margins with cost prices increasing and stay competitive? Immediately raising prices in an inflationary market is not always the right strategy. Factors such as customer price perception, demand stability, cost of replenishment, and margin stability need to be taken into account when reacting to rising costs. 

Other than an immediate price raise, some retailers may choose stock up and wait for an advantageous moment to release stock at a high price. However, a strategy like this runs the risk of demand decreasing or multiple players with the same strategy causing a price war that drives prices down. It also misses potential seasonality or perishability for certain types of stock. 

Using the Quicklizard Inflation module we offer retailers the ability to factor inflation rates into their ongoing pricing model and thus adjust to fluctuations in demand, competitive pricing, and consumer behavior in real time.

Retail Data Lake

Data lakes are central data repositories used to store raw data. A data lake has no predefined schema, so it retains all original attributes of the data collected, Retailers of all sizes often find it difficult to combine assortment, market, and performance data. The Quicklizard retail data lake uses a very simple schema to make all data queryable, while supporting off-the-shelf reporting and custom report development.

Cross Border

For retailers and brands, selling into international markets is big opportunity. In the wake of the coronavirus crisis, retailers and brands must tap growth opportunities wherever they lie—and for many companies, that will mean looking outside of their domestic markets. 

Cross-border pricing poses unique challenges. Factoring in demand across multiple markets while accounting for local competition and dependencies is complicated. Fortunately, our cross-border module can handle all the heavy lifting.

Price Elasticity

For most goods, when price increases, quantity demanded decreases. But the size of this effect is bigger for some goods than others. 

The change in quantity demanded as a result of a change in price is called price elasticity of demand. It is defined as the ratio of the percentage change in quantity demanded to the percentage change in price of a particular good. The formula for the price elasticity of demand is:

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Where P is the price of the demanded good and Q is the quantity demanded. 

The Quicklizard elasticity module uses data science to calculate how a price change affects demand, while accounting for factors like seasonality, cannibalization, clustering, and competitor price changes.

Clearance

Clearance pricing and end-of-season inventory management are important challenges to solve. Sales rates depend on price, seasonal effects, and the remaining assortment of items available to consumers. There is little time to react to observed sales, and pricing errors result in either loss of potential revenue or excess inventory to be liquidated. Unlike markdowns, clearance pricing can be observed in retail following major holidays such as Christmas. The goal is to deplenish inventory. 

We offer big data and scientific models to predict demand while balancing profitability and inventory clearance.

Competitor Sensitivity

Understanding your competitive landscape goes beyond knowing competitors’ prices. It’s about identifying whose pricing are affecting your business. 

Price sensitivity is the degree to which the price of a product affects consumers’ purchasing behaviors. Competitor sensitivity indicates how demand changes when a competitor changes price. Using user behavior analytics and competitive data, the Quicklizard module rates the sensitivity level of each product against competitors to produce insights on who your true competition is.

Profit Optimization

Price optimization may be the most crucial factor in a business’ ability to increase revenue and stay ahead of the competition. Defining the right price sets the stage for everything, from sales and marketing to growth and profitability.

Find the best price for each product. Predictive and reinforcement learning.

Product Classification (Halo Effect)

Complementary products are products that are closely related to the main/leading product and very often can’t be consumed alone. Mobile phones are an example of a leading product with many complements; such as mobile covers, screen protectors, warranties etc. The demand for the leading product generates the demand in its complement. 

The halo effect involves setting the price of the main product at the optimum level so that the demand for the complementary product increases, thereby maximising the profits from both products together. 

The Halo effect module allows retailers to use the calculated contribution in pricing strategies, identifying products affected and creating pricing groups.

Seasonality

Seasonal fluctuations in demand can affect staffing, scheduling, and cash flow. These changes can often pose a real threat to businesses. Seasonality is handled differently across varying businesses and industries. . Some try to diversify product lines, while others hire temporary help, or simply close down during the slow season. A seasonal pricing strategy helps minimise loss of cash flow in low periods and maximise profitability in peak seasons. It smooths demand by enticing customers with low prices during the slow period, while maximizing revenues with higher prices when demand is strong.

Forecasting (Demand Estimation)

We facilitate the analysis of historical data (consumer sensitivity to price, historical demand, similar products etc) using machine learning in order to forecast future demand. This module offers retailers the ability to react to changes in real time and adjust inventory and pricing accordingly.

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Profit Attribution

Profit attribution calculates the profitability of each product based on the profitability of the entire basket of items. Grocery products are multi basket verticals and are typically categorised into multiple classes. Some categories such as KVi’s are sold at a net loss in order to drive traffic while relying on the rest of the basket items for profitability.

Predictive Pricing

In predictive pricing, artificial intelligence processes historical data about price optimization and sales dynamics and uses the established quantitative relationship to recommend optimal prices for retailers.

Cost Calculation

When optimizing for profit, cost is one of the key factors. However, calculating costs is not always straightforward. The Quicklizard dynamic costs module factors in rebates, returns, marketing costs and other direct and indirect costs into the cost equation.

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