Discover How Imprice Can Help You Achieve Your Business Goals
We will make a detailed demonstration of Imprice and use cases:
Discover How Imprice Can Help You Achieve Your Business Goals
We will make a detailed demonstration of Imprice and use cases:
Customer Success Stories
Please click on a link below to read a case study.
➜ Hypermarkets increase gross margin by 6,6% with AI and KVI pricing
➜ Leading eBook retailer hits +8% growth in revenue
➜ Supermarket chain turns to data-driven pricing to add value to consumers and increase profits
➜ Hypermarket chain automates pricing by leveraging artificial intelligence to stay on top of competition
➜ Artificial intelligence calculated optimal prices for e-commerce, increased gross profit by 20.1%, and reignited demand
➜ Leading optical retailer increased sales by 32%
➜ Esteemed online beauty store increased gross profit by 30%
➜ Hypermarkets increase gross margin by 6,6% with AI and KVI pricing
➜ Leading eBook retailer hits +8% growth in revenue
➜ Supermarket chain turns to data-driven pricing to add value to consumers and increase profits
➜ Hypermarket chain automates pricing by leveraging artificial intelligence to stay on top of competition
➜ Artificial intelligence calculated optimal prices for e-commerce, increased gross profit by 20.1%, and reignited demand
➜ Leading optical retailer increased sales by 32%
➜ Esteemed online beauty store increased gross profit by 30%
AI-Driven Price Optimization
➜ Maximize customer traffic
➜ Improve the price and assortment perception of your stores
➜ Increase your stores' gross profit
with the ready-made pricing methodology and AI-driven pricing algorithms!
➜ Improve the price and assortment perception of your stores
➜ Increase your stores' gross profit
with the ready-made pricing methodology and AI-driven pricing algorithms!
The algorithms:
● Find all KVIs in your assortment: the items which generate customer traffic and upsells.
● Offer the optimal prices for these items, compared to your competitors.
● Find underpriced products in your assortment, and raise their prices to the optimal level.
● Find overpriced products in your assortment, and reduce their prices to the optimal level.
● Find all KVIs in your assortment: the items which generate customer traffic and upsells.
● Offer the optimal prices for these items, compared to your competitors.
● Find underpriced products in your assortment, and raise their prices to the optimal level.
● Find overpriced products in your assortment, and reduce their prices to the optimal level.
Result:
● Optimal KVIs' prices entice shoppers; your stores' traffic increases.
● Your retail company gets growth in sales of both KVIs and high-margin products that are bought along with KVIs.
● Your stores look like good dealing places compared to competitors, without over reducing prices.
● Your company's gross profit grows (study success stories HERE)
● Optimal KVIs' prices entice shoppers; your stores' traffic increases.
● Your retail company gets growth in sales of both KVIs and high-margin products that are bought along with KVIs.
● Your stores look like good dealing places compared to competitors, without over reducing prices.
● Your company's gross profit grows (study success stories HERE)
How it works:
AI-driven algorithms identify for each item:
✓ its role in your stores' sales,
✓ its impact on the sales of other products,
✓ its sales sensitivity to competitors' prices.
Artificial intelligence automatically divides the assortment into segments:
● products that generate traffic,
● goods that drive sales of other goods,
● goods that affect the price and assortment perception of the stores,
● profit generating goods,
● substitute goods,
● complementary goods,
● and others.
In particular, you get a complete list of your soft KVIs and hard KVIs.
More about Step 1
✓ its role in your stores' sales,
✓ its impact on the sales of other products,
✓ its sales sensitivity to competitors' prices.
Artificial intelligence automatically divides the assortment into segments:
● products that generate traffic,
● goods that drive sales of other goods,
● goods that affect the price and assortment perception of the stores,
● profit generating goods,
● substitute goods,
● complementary goods,
● and others.
In particular, you get a complete list of your soft KVIs and hard KVIs.
More about Step 1
Step 1. Analyzing the assortment.
➜ For your soft KVI and hard KVI, you launch competitive pricing. KVIs' prices automatically follow the prices of your competitors and remain at the position you specified.
➜ For your Foreground and Long Tail segments, you launch automatic AI-driven price optimization. Imprice machine learning algorithms calculate a set of prices that maximizes your profit with current demand. If your goal is to increase market share, you set another optimization criterion for algorithms, such as revenue growth or sales in pieces growth.
➜ For substitute products, the algorithms form a unified price vector that maximizes total profit across substitutes and prevents sales cannibalization.
More about Step 2
➜ For your Foreground and Long Tail segments, you launch automatic AI-driven price optimization. Imprice machine learning algorithms calculate a set of prices that maximizes your profit with current demand. If your goal is to increase market share, you set another optimization criterion for algorithms, such as revenue growth or sales in pieces growth.
➜ For substitute products, the algorithms form a unified price vector that maximizes total profit across substitutes and prevents sales cannibalization.
More about Step 2
Step 2. Increasing profits. Setting up the optimal pricing strategy for each product's role.
The artificial intelligence module will help you further increase sales of:
✓ Soft KVIs,
✓ infrequently sold products, whose prices shoppers tend to compare to competitors' offerings before purchase.
More about Step 3
✓ Soft KVIs,
✓ infrequently sold products, whose prices shoppers tend to compare to competitors' offerings before purchase.
More about Step 3
Step 3. Further increase in profits. The optimal price positions of Soft KVIs and products with high competition.
The artificial intelligence module will identify for each product:
● which competitors .............. actually have an impact on your sales, by analyzing the history of your sales and competitors' prices changes,
● and which competitors' prices are better to exclude from your price calculations.
Your stores in each region will stop missing out on sales and profits, when you consider all true competitors in this region in your pricing rules, and exclude "false" competitors.
We would strongly recommend applying this module to KVI products.
More about Step 4
● which competitors .............. actually have an impact on your sales, by analyzing the history of your sales and competitors' prices changes,
● and which competitors' prices are better to exclude from your price calculations.
Your stores in each region will stop missing out on sales and profits, when you consider all true competitors in this region in your pricing rules, and exclude "false" competitors.
We would strongly recommend applying this module to KVI products.
More about Step 4
Step 4. Further increase in profits. Revealing of the true competitors.
Examples of customer success stories
More information about the steps of improving pricing with AI
1
Step 1. Analyzing the assortment. Clustering
AI-driven algorithms identify what role each product plays in the buyer's basket, including how the product's price affects sales of other products, and what impact competitors' price has on its sales. The algorithm automatically segments the assortment range into clusters, with each cluster corresponding to a specific product role. This process is called clustering.
Let's delve into the most important clusters in more detail.
Let's delve into the most important clusters in more detail.
Clusters by roles. Hard and Soft KVIs
KVI (key value items) are key clusters:
✓ Demand for these items is highly sensitive to price.
✓ Customers compare KVIs' prices with those of competitors and choose the store with the best price of KVI, unless this way creates critical inconvenience.
✓ If some KVIs are missing from the store's assortment, customers perceive the store as a "place where the products I need are often unavailable". So they will come to such a store last.
✓ If KVIs' ............ prices are higher than the optimal level, customers perceive the store as "too expensive; they overprice the products here."
✓ Demand for these items is highly sensitive to price.
✓ Customers compare KVIs' prices with those of competitors and choose the store with the best price of KVI, unless this way creates critical inconvenience.
✓ If some KVIs are missing from the store's assortment, customers perceive the store as a "place where the products I need are often unavailable". So they will come to such a store last.
✓ If KVIs' ............ prices are higher than the optimal level, customers perceive the store as "too expensive; they overprice the products here."
1 - Hard KVIs.
These are highly price-sensitive items; only the first or second minimum market price is efficient for them.
In some niches (such as the pharmaceutical sector), a case of forced negative markup on Hard KVI is common. Retailers recover it by sales of more marginable items.
These are highly price-sensitive items; only the first or second minimum market price is efficient for them.
In some niches (such as the pharmaceutical sector), a case of forced negative markup on Hard KVI is common. Retailers recover it by sales of more marginable items.
AI algorithms divide KVIs into two groups:
2 - Soft KVIs.
Such items affect the price and assortment perception of the store ("they have excellent prices here / it's expensive here", "this store has a great assortment / the store never has half of what I need").
However, Soft KVI are less price-sensitive, and the optimal price for them will be the third, fourth, or even fifth market price.
If one sets the price lower, the store will simply lose margin without gaining additional traffic and sales.
Such items affect the price and assortment perception of the store ("they have excellent prices here / it's expensive here", "this store has a great assortment / the store never has half of what I need").
However, Soft KVI are less price-sensitive, and the optimal price for them will be the third, fourth, or even fifth market price.
If one sets the price lower, the store will simply lose margin without gaining additional traffic and sales.
We would suggest studying:
Four errors in working with the KVI list that cause the store to lose profits and customers:
✘ Error 1. Part of the retailer's "KVI list" are not actually KVIs.
✘ Error 2. The company considers some of its KVI to be "ordinary goods".
✘ Error 3. The retailer sets the "first minimum price" for all of its KVIs.
✘ Error 4. The retailer sets an unattractive price for KVI with extremely high price sensitivity.
An article using examples from mature retailers shows how profits increase when these errors are eliminated — READ NOW.
Four errors in working with the KVI list that cause the store to lose profits and customers:
✘ Error 1. Part of the retailer's "KVI list" are not actually KVIs.
✘ Error 2. The company considers some of its KVI to be "ordinary goods".
✘ Error 3. The retailer sets the "first minimum price" for all of its KVIs.
✘ Error 4. The retailer sets an unattractive price for KVI with extremely high price sensitivity.
An article using examples from mature retailers shows how profits increase when these errors are eliminated — READ NOW.
AI algorithms sort ....... Soft KVIs by the recommended pricing position relative to competitors.
As a result, you receive a complete list of KVI broken down into subgroups.
Each subgroup corresponds to an individual pricing strategy.
As a result, you receive a complete list of KVI broken down into subgroups.
Each subgroup corresponds to an individual pricing strategy.
Examples of factors that Imprice algorithms take into account when identifying KVIs.
● Penetration of the product into cash receipts.
●Stable product pairings …..…… that occur in cash receipts.
● Sales volume of the product.
● Inventory turnover.
● Whether changes in the price of this product in your store and changes in prices of competitors affect its sales.
● Penetration of the product into cash receipts.
●Stable product pairings …..…… that occur in cash receipts.
● Sales volume of the product.
● Inventory turnover.
● Whether changes in the price of this product in your store and changes in prices of competitors affect its sales.
The complete list of KVI, with specifying of the optimal price position relative to competitors, empowers you to:
a – Efficiently manage the store's assortment perception.
According to our statistics, companies usually don't know at least 20% of their KVIs. Such SKUs can often be out-of-stock, creating an impression of a "poor assortment".
With the complete list of KVIs, your stores are protected from such errors.
According to our statistics, companies usually don't know at least 20% of their KVIs. Such SKUs can often be out-of-stock, creating an impression of a "poor assortment".
With the complete list of KVIs, your stores are protected from such errors.
c – Significantly increase sales.
Algorithms identify "hidden KVIs" and Hard KVIs of the store, and reduce their prices to optimal ones.
The stores' traffic grows, sales of KVIs themselves and products that are purchased together with KVIs increase.
Algorithms identify "hidden KVIs" and Hard KVIs of the store, and reduce their prices to optimal ones.
The stores' traffic grows, sales of KVIs themselves and products that are purchased together with KVIs increase.
b – Efficiently manage price perception.
According to our statistics, retailers commonly don't know at least 20% of their KVIs. The price of such SKUs is often overpriced relative to optimal, creating an impression of an "expensive store".
With the complete list of KVI, you are protected from such errors.
According to our statistics, retailers commonly don't know at least 20% of their KVIs. The price of such SKUs is often overpriced relative to optimal, creating an impression of an "expensive store".
With the complete list of KVI, you are protected from such errors.
d – Increase gross profit.
Algorithms reveal "false KVIs" and Soft KVIs with a lowered price. They increase the price of such SKUs to the optimal levels.
The sales in pieces remains the same, but the marginality increases.
The additional traffic boost leads to an increase in gross profit.
Customers come because of KVIs with an attractive price, so also the sales of high-margin goods purchased together with KVIs increases. Gross profit grows.
Algorithms reveal "false KVIs" and Soft KVIs with a lowered price. They increase the price of such SKUs to the optimal levels.
The sales in pieces remains the same, but the marginality increases.
The additional traffic boost leads to an increase in gross profit.
Customers come because of KVIs with an attractive price, so also the sales of high-margin goods purchased together with KVIs increases. Gross profit grows.
All types of KVI — Soft, Hard, "false" and "hidden"— are described in detail HERE.
Important note:
Different stores within the same retail chain may have different KVIs.
It is recommended to make individual clustering for each region and each store format.
In Imprice, clustering for each individual cluster of stores is done using standard settings.
Different stores within the same retail chain may have different KVIs.
It is recommended to make individual clustering for each region and each store format.
In Imprice, clustering for each individual cluster of stores is done using standard settings.
Important note:
The list of KVI is not static.
Some positions may cease to be KVI after a certain period of time.
Conversely, "regular" products can quickly become KVIs.
As a result, it is recommended to update the clustering at least once a quarter.
In some domains, such as in food retail, re-clustering is necessary on a monthly basis.
The list of KVI is not static.
Some positions may cease to be KVI after a certain period of time.
Conversely, "regular" products can quickly become KVIs.
As a result, it is recommended to update the clustering at least once a quarter.
In some domains, such as in food retail, re-clustering is necessary on a monthly basis.
Clusters by roles. Foreground and Long Tail
In parallel to KVIs, AI algorithms identify two other segments:
● Foreground. Relatively popular items with low price sensitivity.
The algorithms collect them in a separate segment, and sort them by decreasing the demand dependence on price.
● Long Tail. Products with minimal/rare sales.
● Foreground. Relatively popular items with low price sensitivity.
The algorithms collect them in a separate segment, and sort them by decreasing the demand dependence on price.
● Long Tail. Products with minimal/rare sales.
These segments have significant potential for growth in margin and gross profit. Optimizing these segment prices, retailers recover the decrease in margin at KVIs:
➜ The price of many items in these segments is often undervalued; in fact, consumers are willing to pay more. Charging a lower price, the company misses out on profits. With an optimal price,
gross profit increases and sales in pieces remain about the same.
➜ The price of other items in these segments is overvalued. Demand is only inelastic within certain boundaries; when the upper price limit is exceeded, demand sharply decreases. When the retailer lowers ............ the price of such products to an optimal level, demand appears. The company begins to gain much higher sales and gross profit.
➜ The price of many items in these segments is often undervalued; in fact, consumers are willing to pay more. Charging a lower price, the company misses out on profits. With an optimal price,
gross profit increases and sales in pieces remain about the same.
➜ The price of other items in these segments is overvalued. Demand is only inelastic within certain boundaries; when the upper price limit is exceeded, demand sharply decreases. When the retailer lowers ............ the price of such products to an optimal level, demand appears. The company begins to gain much higher sales and gross profit.
Clusters by roles. Substitute goods
Products A and B are substitutes if there is a direct dependence of the demand for one product on the price of the other.
If the price of A decreases, its sales will increase, while the sales of B will decrease. Cannibalization of sales will occur — the demand will remain the same, but A will "eat" the sales of B.
And vice versa, if the price of B decreases, the sales of A will decrease.
If the price of A decreases, its sales will increase, while the sales of B will decrease. Cannibalization of sales will occur — the demand will remain the same, but A will "eat" the sales of B.
And vice versa, if the price of B decreases, the sales of A will decrease.
AI algorithms identify analog products (substitutes) and group them together.
Afterward, prices of all products in the substitutes group are calculated together, so that the gross profit for the entire group of substitutes is maximized, and no sales cannibalization occurs.
The optimization is not for a single price, but for the entire price vector for the entire group of substitutes at the same time.
Afterward, prices of all products in the substitutes group are calculated together, so that the gross profit for the entire group of substitutes is maximized, and no sales cannibalization occurs.
The optimization is not for a single price, but for the entire price vector for the entire group of substitutes at the same time.
2
Step 2. Increasing profits. Setting up and launching pricing strategies for each role
For Hard KVIs and Soft KVIs, you launch competitive pricing based on highly flexible rules.
✓ Imprice automatically collects ......... ... prices of your competitors or loads these prices from any price monitoring service you work with.
✓ For each KVI, you set a target price position relative to competitors:
first, second, third, fourth, or fifth minimum market price.
The target position is determined based on clustering results (on Step 1) or according to your pricing strategy.
✓ Imprice platform automatically ........ n ... recalculates your KVI prices considering changes in competitors' prices, all internal factors, and constraints (including minimum markup levels).
Result:
your KVIs' prices automatically follow competitors' ones.
The store's traffic and sales increase.
At the same time, your store is protected from negative marginality through markup constraints.
✓ For each KVI, you set a target price position relative to competitors:
first, second, third, fourth, or fifth minimum market price.
The target position is determined based on clustering results (on Step 1) or according to your pricing strategy.
✓ Imprice platform automatically ........ n ... recalculates your KVI prices considering changes in competitors' prices, all internal factors, and constraints (including minimum markup levels).
Result:
your KVIs' prices automatically follow competitors' ones.
The store's traffic and sales increase.
At the same time, your store is protected from negative marginality through markup constraints.
For Long Tail and Foreground, you launch AI-driven automatic price optimization:
2 – Algorithms study the dependence of demand for each product on the price:
● Analyze sales history at different price levels.
● If the price change history is absent or insufficient, algorithms run a short series of pricing experiments:
● Analyze sales history at different price levels.
● If the price change history is absent or insufficient, algorithms run a short series of pricing experiments:
Example.
We sell 10 types of laundry conditioners that are substitutes for each other.
On an average day, the group of conditioners sells 100 bottles.
If the price of one conditioner decreases, most customers switch to it, and sales of other conditioners drop. At the same time, an average of 100 bottles of conditioner are still sold per day.
"Optimizing the entire price vector" means that the AI algorithms search for a set of prices that increase sales and profits from the sales of the entire group of substitute conditioners.
We sell 10 types of laundry conditioners that are substitutes for each other.
On an average day, the group of conditioners sells 100 bottles.
If the price of one conditioner decreases, most customers switch to it, and sales of other conditioners drop. At the same time, an average of 100 bottles of conditioner are still sold per day.
"Optimizing the entire price vector" means that the AI algorithms search for a set of prices that increase sales and profits from the sales of the entire group of substitute conditioners.
3 – Result:
● Prices of products that were overpriced are lowered ...... to optimal levels. This leads to a significant increase in sales and gross profit.
● Prices of products that were underpriced are increased ....... to optimal levels. Sales volume remains about the same, but gross profit increases due to an increase in the marginality of sales.
● Prices of products that were overpriced are lowered ...... to optimal levels. This leads to a significant increase in sales and gross profit.
● Prices of products that were underpriced are increased ....... to optimal levels. Sales volume remains about the same, but gross profit increases due to an increase in the marginality of sales.
1 – You choose the optimization criterion:
● Maximum gross profit,
● Maximum revenue,
● Or maximum sales in pieces.
● Maximum gross profit,
● Maximum revenue,
● Or maximum sales in pieces.
– Your experts, together with Imprice experts, set the under and upper limits for the price.
– Algorithms set prices within the specified range and measure demand::
1. Put the most likely "best" price for the current day or week.
2. Study the demand.
3. If the demand increases, try raising the price. If the demand decreases, try lowering it.
And so on every day or other specified period until the most optimal price is established for some time. If the market situation changes, the algorithm immediately reacts at step 2.
– At the same time, they resolve the "Exploration-exploitation dilemma":
The task is to spend as little time as possible finding the optimal price
and to exploit this price for as long as possible.
– For substitute goods, the algorithms optimize the entire price vector of the substitute group, not just the price of a single product.
– Algorithms set prices within the specified range and measure demand::
1. Put the most likely "best" price for the current day or week.
2. Study the demand.
3. If the demand increases, try raising the price. If the demand decreases, try lowering it.
And so on every day or other specified period until the most optimal price is established for some time. If the market situation changes, the algorithm immediately reacts at step 2.
– At the same time, they resolve the "Exploration-exploitation dilemma":
The task is to spend as little time as possible finding the optimal price
and to exploit this price for as long as possible.
– For substitute goods, the algorithms optimize the entire price vector of the substitute group, not just the price of a single product.
3
Step 3. Further increase in profits. The optimal price positions of Soft KVIs and products with high competition.
For Soft KVIs, you launch a special type of optimization.AI algorithms calculate at what position among competitors the price of a given SKU should be to achieve the maximum target metric in current conditions.
Step 1, clustering, is the segmentation of products based on their roles in the shopping cart. Among other things, the AI algorithms sort Soft KVIs by the degree of their demand dependence on price. That is, they determine what position the SKU's price should take among competitors' offers.
Clustering is the result of analyzing sales data, pricing history, and the cash receipts' structure. However, algorithms usually have to deal with insufficient/poor price change history (i.e., the store changed prices rarely and within a limited range). If the store has never set a price close to the optimal one, part of the potential of its KVIs can only be revealed with the additional pricing experiments.
Clustering is the result of analyzing sales data, pricing history, and the cash receipts' structure. However, algorithms usually have to deal with insufficient/poor price change history (i.e., the store changed prices rarely and within a limited range). If the store has never set a price close to the optimal one, part of the potential of its KVIs can only be revealed with the additional pricing experiments.
- Price optimization based on demand sensing follows the same principle as Step 2, but with one difference. The algorithms study not demand at different prices in euros, but demand at different price positions among competitors:
the second market price, the third, fourth, fifth.
In this step, it's recommended to choose the "maximum revenue" optimization criterion.
- You launch the same optimization for the products with relatively rare sales and high level of competition.
An example of a product with rare sales and high competition.
A double-door refrigerator of the XXX model costs an average of 1,300 euros on the market. Average monthly sales are 1 unit. At the same time, consumers compare prices in different stores before purchasing.
The AI-driven algorithms will find at what price position on the market the store will gain the maximum gross profit.
For example, it is possible that three competitors with a lower price have a delivery time of 2-4 weeks, unclear warranty and return conditions, and ambiguous reviews.
And your store has delivery up to 3 days, easy returns, an extended warranty, and excellent reviews. And it is more profitable for you to sell this refrigerator at the fourth minimum market price.
A double-door refrigerator of the XXX model costs an average of 1,300 euros on the market. Average monthly sales are 1 unit. At the same time, consumers compare prices in different stores before purchasing.
The AI-driven algorithms will find at what price position on the market the store will gain the maximum gross profit.
For example, it is possible that three competitors with a lower price have a delivery time of 2-4 weeks, unclear warranty and return conditions, and ambiguous reviews.
And your store has delivery up to 3 days, easy returns, an extended warranty, and excellent reviews. And it is more profitable for you to sell this refrigerator at the fourth minimum market price.
4
Step 4. Further increase in profits. Revealing of the true competitors.
True competitors are stores whose pricing significantly affects your stores' sales. Awareness of the true key competitors is crucial for KVI pricing efficiency.
Example.
For KVI pricing, you consider the prices of all online stores in your location.
Two online stores sell their KVI at a price much lower than other retailers. However, these stores do not actually affect your sales:
● One store is located in another city, and its delivery time is unacceptably long for your customers, whose wish to receive the purchased product quickly.
● The other store has poor reviews. Orders take a long time to process, and they may deliver defective or fake products. Your clients will not risk purchasing from such a store.
If you consider these stores' prices and set your KVI's price close to theirs, you will lose profitability on each transaction.
Another example:
You are a major retailer with a wide range of products. You monitor the prices of other large retailers only, and consider only their prices in calculations.
A small online store sells a narrow range of KVIs at "best deal" prices, actively advertised through bloggers. The online store has fast delivery, competent staff, excellent reviews, and more enticing prices than yours. More and more of your customers prefer to buy these KVIs from the small online store, because it offers better prices and customer experience. This leads to a decrease in your sales of KVIs. Your sales of high-margin items that were usually bought together with these KVIs also decline.
Example.
For KVI pricing, you consider the prices of all online stores in your location.
Two online stores sell their KVI at a price much lower than other retailers. However, these stores do not actually affect your sales:
● One store is located in another city, and its delivery time is unacceptably long for your customers, whose wish to receive the purchased product quickly.
● The other store has poor reviews. Orders take a long time to process, and they may deliver defective or fake products. Your clients will not risk purchasing from such a store.
If you consider these stores' prices and set your KVI's price close to theirs, you will lose profitability on each transaction.
Another example:
You are a major retailer with a wide range of products. You monitor the prices of other large retailers only, and consider only their prices in calculations.
A small online store sells a narrow range of KVIs at "best deal" prices, actively advertised through bloggers. The online store has fast delivery, competent staff, excellent reviews, and more enticing prices than yours. More and more of your customers prefer to buy these KVIs from the small online store, because it offers better prices and customer experience. This leads to a decrease in your sales of KVIs. Your sales of high-margin items that were usually bought together with these KVIs also decline.
Consumer price awareness is now very high, which means that the number of KVIs in categories can reach 30 or even 50%. And for each KVI, the list of true competitors may vary.
These are consequences of digitalization.
It is clear that manually or in a semi-automatic mode it is impossible to analyze the full list of competitors for each product and determine the significance of each competitor for your sales. This inevitably pushes towards simplifications:
● either monitoring too narrow a list of the most obvious competitors,
● or "carpet bombing" — setting the lowest prices relative to all retailers on the market.
The first option causes loss of sales. The second way leads to an unnecessary loss of margin.
These are consequences of digitalization.
It is clear that manually or in a semi-automatic mode it is impossible to analyze the full list of competitors for each product and determine the significance of each competitor for your sales. This inevitably pushes towards simplifications:
● either monitoring too narrow a list of the most obvious competitors,
● or "carpet bombing" — setting the lowest prices relative to all retailers on the market.
The first option causes loss of sales. The second way leads to an unnecessary loss of margin.
Artificial intelligence helps solve this problem.
Machine learning algorithms can:
1. Analyze accumulated internal and external sales data, stock levels, demand fluctuations, and internal and external price changes.
2. For each KVI, form lists of competitors whose prices actually have a significant impact on your sales.
3. Automatically update these lists when the market situation changes.
Machine learning algorithms can:
1. Analyze accumulated internal and external sales data, stock levels, demand fluctuations, and internal and external price changes.
2. For each KVI, form lists of competitors whose prices actually have a significant impact on your sales.
3. Automatically update these lists when the market situation changes.
CASE STUDY
Price Optimization With Automatic Revealing Of True Competitors
Artificial Intelligence optimized prices for e-commerce store, increased gross profit by 20.1%, and reignited demand
© 2019-2023 — Imprice
cloud-based dynamic pricing platform
cloud-based dynamic pricing platform
DYNAMIC PRICING
Imprice Dynamic Pricing Platform
AI-driven Price Optimization
Competitive Pricing
Zone Pricing
KVI Pricing
Time Pricing
Intelligent Queue For Price Tags Changing
--
Dynamic Pricing in Grocery Chains (Hypermarkets, Supermarkets, Discounters)
Dynamic Pricing — A Practical Guide - 2023 For eCommerce And Retail
Pricing Rules For eCommerce And Retail Companies
KVI In Retail — 4 Common Errors In 2022-2023
Launching dynamic pricing: How to evaluate the pilot test results and not go wrong
Profit growth with dynamic pricing: Benchmarks by retail industries. Factors causing slowing down and accelerating growth
Pricing Engine
Price Management Software
Retail Pricing Software
--
Português: Plataforma dinâmica de precificação
Español: Plataforma de precios dinámicos basada en la nube
Italiano: Piattaforma di pricing dinamico
AI-driven Price Optimization
Competitive Pricing
Zone Pricing
KVI Pricing
Time Pricing
Intelligent Queue For Price Tags Changing
--
Dynamic Pricing in Grocery Chains (Hypermarkets, Supermarkets, Discounters)
Dynamic Pricing — A Practical Guide - 2023 For eCommerce And Retail
Pricing Rules For eCommerce And Retail Companies
KVI In Retail — 4 Common Errors In 2022-2023
Launching dynamic pricing: How to evaluate the pilot test results and not go wrong
Profit growth with dynamic pricing: Benchmarks by retail industries. Factors causing slowing down and accelerating growth
Pricing Engine
Price Management Software
Retail Pricing Software
--
Português: Plataforma dinâmica de precificação
Español: Plataforma de precios dinámicos basada en la nube
Italiano: Piattaforma di pricing dinamico
CASE STUDY
Hypermarkets increase gross profit by 6,6% with AI and KVI pricing
Leading eBook retailer hits +8% growth in revenue
Supermarket chain turns to data-driven pricing to add value to consumers and increase profits
Hypermarket chain lures customers back from competitors
Hypermarket chain automates pricing by leveraging artificial intelligence to stay on top of competition
Artificial intelligence calculated optimal prices for e-commerce, increased gross profit by 20.1%, and reignited demand
Leading optical retailer increased sales by 32%
Esteemed online beauty store increased gross profit by 30%
PLANS AND PRICING
Leading eBook retailer hits +8% growth in revenue
Supermarket chain turns to data-driven pricing to add value to consumers and increase profits
Hypermarket chain lures customers back from competitors
Hypermarket chain automates pricing by leveraging artificial intelligence to stay on top of competition
Artificial intelligence calculated optimal prices for e-commerce, increased gross profit by 20.1%, and reignited demand
Leading optical retailer increased sales by 32%
Esteemed online beauty store increased gross profit by 30%
PLANS AND PRICING
Contact us: