24 questions every retailer should be able to answer

To capitalize on lift and maximize profit, products that are frequently purchased together, known as complementary products, should be exposed or marketed together. For example, a company might market a new DVD player and a DVD rental service together to increase sales. Additionally, upselling and cross-selling techniques can be used to increase profits by encouraging customers to purchase additional products or upgraded versions of existing products.

To determine how much inventory is not getting viewed on your website, you can track the number of views or clicks on each product page. If certain products have a significantly lower number of views or clicks compared to others, it may indicate that they are not getting as much attention from visitors.

You can quickly identify these products by analyzing website traffic data and conducting user research to understand why these products are not getting viewed. Potential problems could include poor product placement, lack of product information, or poor product photography.

Additionally, A/B testing and heat maps can be used to understand how visitors interact with your website and identify areas that may need improvement. It's also important to review customer reviews, as they can provide valuable feedback on what's working and what's not.

It's important to note that low views on a product doesn't mean it's not selling or it's bad, it could be seasonal or targeted to a specific audience. Therefore, it's important to have a well-rounded view of the product's performance.

To determine which products are receiving too many or too few views given their inventory levels, conversion, profitability, review ratings, time on site, and fragmentation, you will need to analyze a combination of data from multiple sources.

First, you can start by comparing the number of views for each product to its inventory level. If a product has a high inventory level but a low number of views, it may indicate that it is not getting enough attention from visitors. Conversely, if a product has a low inventory level but a high number of views, it may be selling quickly but not restocked frequently enough.

Next, you can analyze conversion data to see which products are generating the most sales. If a product has a high number of views but a low conversion rate, it may indicate that there is a problem with the product page or the product itself.

Then, you can look at profitability data to see which products are generating the most revenue. If a product has a high number of views and a high conversion rate but a low profitability, it may indicate that the product's cost is too high.

In addition, you can also analyze review ratings, time on site, and fragmentation to gain more insights. For example, If a product has low review ratings but high views, it may indicate that the product is not meeting customer expectations. If a product has a high time on site but low views, it may indicate that the product page is not easily accessible. And fragmentation could mean that your website's navigation is not optimized for the product placement.

It's important to note that these metrics should be analyzed together in order to gain a comprehensive understanding of how each product is performing. Also, it's important to consider the context of the data, such as a new product might have fewer views and reviews, but it doesn't mean it's not performing well.

To determine for which product categories your company is under or over-optioned, given viewing demand, you will need to analyze data on product views and inventory levels for each category.

First, you can track the number of views for each product category on your website. If a category has a high number of views but a low inventory level, it may indicate that the category is under-optioned and that there is high demand for more products in that category. Conversely, if a category has a low number of views but a high inventory level, it may indicate that the category is over-optioned and that there is low demand for products in that category.

Another way to analyze the data is to look at the conversion rate of each category. If a category has a high number of views but a low conversion rate, it may indicate that the category is over-optioned and that the products are not meeting customer expectations.

It's also important to consider other metrics such as profitability, review ratings, and customer feedback. For example, if a category has low profitability and low view rates, it could be a sign that the category is over-optioned and the products are not profitable.

It's important to note that these metrics should be analyzed together in order to gain a comprehensive understanding of how each product category is performing. Also, it's important to consider the context of the data, such as a new category might have fewer views and reviews, but it doesn't mean it's not performing well.

To determine the percentage of product views that land on in-stock, non-fragmented items, you will need to track the number of views for each product and compare it to the product's inventory level and fragmentation status.

First, you can track the number of views for each product on your website. Then, you can compare it to the product's inventory level to determine if the product is in-stock. If a product is out of stock, it should be excluded from the calculation.

Next, you can check the fragmentation status of each product, fragmentation means if the product is available in different sizes, colors, or variations. If a product is fragmented and not all variations are available, it should be excluded from the calculation.

Finally, you can calculate the percentage of views that land on in-stock, non-fragmented items by dividing the number of views for in-stock, non-fragmented items by the total number of views for all products.

It's important to note that the percentage of views that land on in-stock, non-fragmented items can vary depending on the context, such as during a sale or a seasonal event. It's also important to keep in mind that although a product may be in-stock and non-fragmented, it doesn't necessarily mean that it's performing well, other factors such as profitability, reviews, and customer satisfaction should also be taken into account.

There are several technologies that can be used to make automatic changes to inventory processes. Some popular options include:

1. Inventory Management Software: This type of software allows companies to track inventory levels, set reorder points, and generate reports on inventory status. It can also be integrated with other systems such as point-of-sale and accounting systems to automate many inventory processes.

2. Warehouse Management Systems (WMS): These systems are designed to help manage and optimize warehouse operations, including inventory management, order fulfillment, and shipping. They can also be integrated with other systems such as transportation management systems (TMS) and enterprise resource planning (ERP) systems to automate inventory processes.

3. Artificial Intelligence and Machine Learning: These technologies can be used to analyze data on customer demand, inventory levels, and other factors to predict future demand and optimize inventory processes.

4. Robotic Process Automation (RPA): This technology can automate repetitive, rule-based tasks such as data entry and inventory tracking.

5. Barcode scanning: This technology allows to automate the inventory process by scanning barcodes on products and updating the inventory levels automatically.

It's important to note that the technology you are using to make automatic changes to inventory processes depends on the specific needs of your business and the systems you currently have in place. It is also important to work with a professional that can help you to choose the best option for your business.

There are several actions you can take to reduce returns and improve customer satisfaction:

1. Improve product descriptions and images: Provide detailed and accurate product information, including dimensions, materials, and care instructions, to help customers make informed purchasing decisions. High-quality images that show the product from multiple angles can also help customers get a better sense of what they are buying.

2. Offer free returns: A free returns policy can help reduce the risk for customers and increase their likelihood of making a purchase.

3. Provide clear return policies: Make sure your return policies are easy to find and understand.

4. Encourage customer reviews: Encourage customers to leave reviews, as this can help others make informed purchasing decisions. Responding to customer reviews can also help to address any concerns they may have.

5. Implement a product recommendation system: By recommending products that are similar to items that a customer has already shown interest in, you can reduce the chances of the customer returning the product.

6. Implement a product-quality check: By checking the quality of the products before they are shipped, you can reduce the chances of customers returning defective items.

7. Monitor customer feedback: Keeping an eye on customer feedback can help you identify common reasons for returns and take steps to address them.

8. Use predictive analytics: By using predictive analytics, you can identify patterns in customer behavior that may indicate a likelihood of returning a product, allowing you to take proactive steps to reduce returns.

It's important to note that reducing returns is a continuous process and it's important to keep monitoring and adjusting your strategy accordingly.

The percentage of customers who return 100% of what they purchase, also known as "free rentals", can vary greatly depending on the industry and the specific business. It's important to track this metric and compare it to the industry average, to understand how your business is performing.

To determine this percentage, you can track the number of total purchases and the number of full returns (items returned in the same condition as they were purchased). Then divide the number of full returns by the total number of purchases and multiply by 100 to get the percentage of customers who return 100% of what they purchase.

It's important to note that having a high percentage of customers returning 100% of what they purchase could indicate that customers are not satisfied with their purchases, or that the products are not meeting their expectations. Therefore, it's important to analyze this metric together with other metrics such as customer satisfaction and product reviews, to gain a comprehensive understanding of how the business is performing and what can be done to improve it.

The percentage of customers who frequently return the majority of any order containing substitutable items, also known as "home dressing room" can vary greatly depending on the industry and the specific business. It's important to track this metric and compare it to the industry average, to understand how your business is performing.

To determine this percentage, you can track the number of orders containing substitutable items (e.g. clothing items, accessories) and the number of those orders where a significant portion of the items were returned. Then divide the number of frequently returned orders by the total number of orders containing substitutable items and multiply by 100 to get the percentage of customers who frequently return the majority of any order containing substitutable items.

It's important to note that having a high percentage of customers frequently returning the majority of any order containing substitutable items could indicate that customers are not satisfied with their purchases, or that the products are not meeting their expectations. Therefore, it's important to analyze this metric together with other metrics such as customer satisfaction and product reviews, to gain a comprehensive understanding of how the business is performing and what can be done to improve it.

The percentage of loyal customers who return products with a return rationale under your control (e.g. damaged, differs from web description, wrong items, etc.) can vary greatly depending on the industry and the specific business. It's important to track this metric and compare it to the industry average, to understand how your business is performing.

To determine this percentage, you can track the number of returns from loyal customers and the number of returns that are due to reasons that are under your control (e.g. damaged, differs from web description, wrong items, etc.) . Then divide the number of returns that are under your control by the total number of returns from loyal customers and multiply by 100 to get the percentage of loyal customers who return products with a return rationale under your control.

It's important to note that having a high percentage of loyal customers returning products with a return rationale under your control could indicate that there are issues with your product quality, shipping and handling, or customer service. Therefore, it's important to analyze this metric together with other metrics such as customer satisfaction and product reviews, to gain a comprehensive understanding of how the business is performing and what can be done to improve it.

To determine how much money your company is spending on marketing campaigns that send customers to products that are sold out or highly fragmented, you will need to track the cost of each marketing campaign and compare it to the inventory levels and fragmentation status of the products that are being promoted.

First, you can track the cost of each marketing campaign, this includes the cost of creating the ad, the platform fees, and any other cost associated with the campaign.

Next, you can analyze the inventory levels of the products that are being promoted in each campaign. If a product is sold out or has a low inventory level, it should be considered as a campaign that sends customers to products that are sold out.

Then, you can check the fragmentation status of each product. If a product is fragmented and not all variations are available, it should be considered as a campaign that sends customers to products that are highly fragmented.

Finally, you can calculate the money spent on marketing campaigns that send customers to products that are sold out or highly fragmented by summing up the cost of all campaigns that fall into those categories.

It's important to note that this calculation will give you a rough estimate of the money spent on those campaigns, and other factors such as the return on investment (ROI) of each campaign should also be taken into account. Additionally, it's important to keep in mind that the inventory level and fragmentation status of a product can change frequently and it's important to keep monitoring it to make sure the campaign is sending customers to products that are available and not fragmented.

To determine which products you should cut marketing spend on because you will sell through the item without the paid exposure, you will need to analyze data on product sales and marketing spend.

First, you can track the sales data for each product to determine which products have consistently high sales without any paid marketing exposure.

Next, you can analyze the marketing spend for each product to determine which products have high marketing costs but low sales.

Then, you can compare the sales data and marketing spend data for each product to identify products that have high sales without any paid marketing exposure.

It's important to note that some products may have a natural demand, such as hot sellers, seasonal items, etc. that don't require paid marketing. Additionally, it's important to consider other factors such as customer satisfaction, reviews and product profitability when making the decision to cut marketing spend on certain products.

It's also worth noting that cutting marketing spend on certain products doesn't mean discontinuing the promotion altogether, it could mean reducing the marketing budget for those products and reallocating it to other products that require more promotion.

To determine which marketing initiatives, promotions, and customer segments are the most profitable, you will need to analyze data on costs, sales, and returns.

First, you can track the costs associated with each marketing initiative, promotion and customer segment, including the cost of creating the ad, the platform fees, and any other cost associated with the campaign.

Next, you can track the sales data for each marketing initiative, promotion and customer segment.

Then, you can track the return data for each marketing initiative, promotion and customer segment and calculate the return rate.

Finally, you can calculate the profitability of each marketing initiative, promotion, and customer segment by subtracting the costs from the revenue, taking into account the return rate.

It's important to note that some marketing initiatives, promotions, and customer segments may have a higher return rate, but also a higher revenue. Therefore, it's important to analyze profitability together with other metrics such as customer satisfaction and product reviews, to gain a comprehensive understanding of how the business is performing and what can be done to improve it.

It's also worth noting that profitability can be affected by many factors, such as the time of the year, the economy, and the competition. Therefore, it's important to keep monitoring and adjusting the strategy accordingly.

There are several ways technology can be used to optimize marketing campaigns efficiently, such as:

1. Marketing Automation: This type of technology allows businesses to automate repetitive tasks such as email marketing, social media marketing, and lead nurturing. This allows businesses to focus on other important tasks such as creating content and analyzing data.

2. Predictive Analytics: Predictive analytics uses machine learning and data mining to analyze historical data and predict future outcomes. This allows businesses to identify patterns in customer behavior and optimize their marketing campaigns accordingly.

3. A/B Testing: A/B testing allows businesses to test different variations of a marketing campaign to determine which version is most effective. This allows businesses to make data-driven decisions and optimize their campaigns for better performance.

4. Personalization: Personalization technology allows businesses to deliver personalized content to each customer based on their browsing history, purchase history, and other data. This increases the chances of customers engaging with the campaign and making a purchase.

5. Attribution Models: Attribution models track the customer journey and attribute conversions to different touchpoints in the customer journey, allowing businesses to understand the effectiveness of different marketing campaigns and allocate budget accordingly.

6. Ad optimization: Ad optimization technology uses data and machine learning to optimize the delivery of online ads, ensuring that they are shown to the right audience, at the right time, and in the right format.

It's important to note that these technologies are not a one-size-fits-all solution, it's important to choose the one that best fits your business needs and to keep monitoring and adjusting the strategy accordingly.

To determine the percentage of orders from high valued customers that get shipped within 24 hours and the percentage that are delivered after their delivery promise date, you will need to track the shipping data for each order from high valued customers.

First, you can track the shipping data for each order from high valued customers, including the date the order was placed, the date the order was shipped, and the date the order was delivered.

Next, you can calculate the percentage of orders from high valued customers that were shipped within 24 hours by dividing the number of orders shipped within 24 hours by the total number of orders from high valued customers and multiplying by 100.

Then, you can calculate the percentage of orders from high valued customers that were delivered after the delivery promise date by dividing the number of orders delivered after the delivery promise date by the total number of orders from high valued customers and multiplying by 100.

It's important to note that having a high percentage of orders shipped within 24 hours and a low percentage of orders delivered after the delivery promise date is a sign of a well-functioning logistics and shipping operations, that can be beneficial for customer satisfaction and retention. On the other hand, a low percentage of orders shipped within 24 hours and a high percentage of orders delivered after the delivery promise date could indicate issues with logistics and shipping operations and could lead to customer dissatisfaction.

To determine if your company is decreasing the average days to ship for new customer orders, you will need to track and analyze data on the shipping times for new customer orders.

First, you will need to track the shipping times for new customer orders over a period of time, for example, over the past month or quarter.

Next, you can calculate the average days to ship for new customer orders by dividing the total number of days it took to ship all new customer orders by the total number of new customer orders.

Then, you can compare the average days to ship for new customer orders over time to determine if it is decreasing.

It's important to note that the average days to ship for new customer orders can be affected by many factors, such as the time of the year, the economy, the competition, and the logistics and shipping operations. Therefore, it's important to keep monitoring and adjusting the strategy accordingly.

Additionally, it's important to track this metric alongside other important metrics, such as customer satisfaction, return rate, and delivery promise date to gain a comprehensive understanding of how the business is performing and what can be done to improve it.

To determine which stores and/or warehouses you have over or under-allocated for your most profitable products, you will need to track and analyze data on inventory levels and sales for each store and/or warehouse.

First, you will need to identify your most profitable products. You can do this by analyzing data on product sales and profitability.

Next, you will need to track the inventory levels for each of your most profitable products at each store and/or warehouse.

Then, you can compare the inventory levels for each store and/or warehouse to the sales data for the same store and/or warehouse.

Stores and/or warehouses that have high inventory levels for your most profitable products but low sales should be considered as over-allocated, while stores and/or warehouses that have low inventory levels for your most profitable products but high sales should be considered as under-allocated.

It's important to note that inventory allocation can change frequently, and it's important to keep monitoring it to make sure the stores and/or warehouses are properly allocated to meet customer demand. Additionally, other factors such as the time of the year, the economy, the competition, and the logistics and shipping operations should be taken into account when analyzing this metric.

To determine which items you should consider a price reduction for, given inventory levels, conversion, profitability, review ratings, time on site, fragmentation, and competition, you will need to track and analyze data on these factors for each item.

First, you will need to track the inventory levels, conversion rates, profitability, review ratings, time on site, fragmentation, and competition data for each item.

Next, you can compare these data points for each item to identify items that have high inventory levels, low conversion rates, low profitability, poor review ratings, low time on site, high fragmentation, and high competition.

Items that have these characteristics should be considered as candidates for a price reduction.

It's important to note that price reduction should be used as a last resort and it should be considered carefully, as it can have an impact on the profitability of the products and the perception of the brand. Additionally, it's important to consider the reason behind the low performance of the product, if it's a product issue, a marketing issue or a demand issue before taking a decision.

It's also worth noting that price reduction should be accompanied by other marketing strategies, such as promotions, discounts or bundling to increase the chances of a sale.

For an overstocked product, whether increased exposure or a price reduction leads to a more profitable action will depend on a variety of factors such as the product, the market, and the target audience.

Increased exposure, through marketing and advertising campaigns, can help increase awareness and demand for a product, leading to increased sales and profitability. This strategy can be especially effective for products that have a high demand but low visibility, or for products that have been recently introduced to the market.

On the other hand, a price reduction can also be an effective strategy for an overstocked product, as it can help increase demand by making the product more affordable to customers and more competitive in the market. This strategy can be especially effective for products that have a low demand but are still considered high-quality or desirable.

It's important to consider the cost of each strategy, as well as the potential return on investment (ROI) when deciding which strategy to use. Additionally, other factors such as the time of the year, the economy, the competition, and the logistics and shipping operations should be taken into account when analyzing this metric.

It's also worth noting that it's important to consider the combination of both strategies, for example, reducing the price and increasing the exposure by promoting the product with discounts or bundles, to increase the chances of a sale.

To determine which products require a pricing reduction due to lower competitive prices, you will need to track and analyze data on the prices of your products compared to those of your competitors.

First, you will need to research and identify the prices of similar products offered by your competitors.

Next, you can compare the prices of your products to those of your competitors to identify products that are priced higher than the majority of similar products offered by your competitors.

Products that are priced higher than the majority of similar products offered by your competitors should be considered as candidates for a price reduction.

It's important to note that a price reduction should be used as a last resort and it should be considered carefully, as it can have an impact on the profitability of the products and the perception of the brand. Additionally, it's important to consider the reason behind the high price of the product, if it's a product issue, a production issue or a marketing issue before taking a decision.

It's also worth noting that the market and competition can change frequently, so it's important to keep monitoring the prices of your products and your competitors' products to make sure you are pricing your products competitively.

There are several ways technology can be used to make pricing changes more efficient:

1. Automated Pricing: Automated pricing technology uses algorithms and data to automatically adjust prices based on factors such as inventory levels, demand, and competition. This allows businesses to make pricing changes quickly and efficiently, without the need for manual adjustments.

2. Price Optimization: Price optimization technology uses machine learning and data mining to analyze historical data and predict future outcomes. This allows businesses to identify patterns in customer behavior and optimize prices accordingly.

3. A/B Testing: A/B testing allows businesses to test different prices for a product to determine which price is most effective. This allows businesses to make data-driven decisions and optimize prices for better performance.

4. Dynamic Pricing: Dynamic pricing technology allows businesses to adjust prices in real-time based on factors such as inventory levels, demand, and competition. This allows businesses to make pricing changes quickly and efficiently to respond to changes in the market.

5. Competitive Pricing: Competitive pricing technology allows businesses to track and compare the prices of their products to those of their competitors. This allows businesses to make data-driven decisions and ensure that their prices are competitive.

It's important to note that these technologies are not a one-size-fits-all solution, it's important to choose the one that best fits your business needs and to keep monitoring and adjusting the strategy accordingly. Additionally, it's important to consider other factors such as product margins, market trends, and customer demand when making pricing changes.

To determine which first-purchase brands lead to lifetime customers, you will need to track and analyze data on customer behavior, including brand loyalty, repeat purchase rates, and customer lifetime value (CLV).

First, you can track the brand of the first purchase for each customer.

Next, you can analyze the customer behavior data, such as repeat purchase rates, brand loyalty, and CLV, for customers who made their first purchase from each brand.

Then, you can compare the customer behavior data for customers who made their first purchase from each brand to determine which brands have the highest repeat purchase rates, brand loyalty, and CLV. These brands are likely to lead to lifetime customers.

It's important to note that some brands may have a natural demand, such as hot sellers, seasonal items, etc. that lead to lifetime customers. Additionally, it's important to consider other factors such as customer satisfaction, reviews, and product profitability when making the decision to target certain brands.

It's also worth noting that this analysis should be done over a certain period of time, such as a year, to ensure that the findings are relevant and reflect the current market trends.

To determine which campaigns and promotions perform the best at luring back previously churned customers, you will need to track and analyze data on customer behavior and campaign performance.

First, you can track the customer behavior data for previously churned customers, including the reason for churn and the time elapsed since the last purchase.

Next, you can track the performance of various campaigns and promotions aimed at luring back previously churned customers, including the type of campaign, the offer, and the response rate.

Then, you can compare the customer behavior data and campaign performance data to determine which campaigns and promotions have been most successful at luring back previously churned customers.

It's important to note that customer behavior and market trends can change frequently, so it's important to keep monitoring and adjusting the strategy accordingly. Additionally, it's important to consider other factors such as customer satisfaction, reviews, and product profitability when making the decision to target certain campaigns and promotions.

It's also worth noting that different campaigns and promotions may perform better for different segments of previously churned customers, so it's important to segment the data and analyze the results accordingly.

There are several ways technology can be used to enable organizations to create optimized customer experiences that precisely match customers with products that generate maximum revenue and profit:

1. Personalization: Personalization technology allows organizations to deliver personalized experiences to customers based on their browsing history, purchase history, and other data. This can help match customers with products that are most likely to generate maximum revenue and profit.

2. Predictive Analytics: Predictive analytics technology uses machine learning and data mining to analyze historical data and predict future outcomes. This can help organizations identify patterns in customer behavior and optimize their product offerings to match customers with products that are most likely to generate maximum revenue and profit.

3. Recommendation Systems: Recommendation systems use algorithms and data to recommend products to customers based on their browsing and purchase history. This can help match customers with products that are most likely to generate maximum revenue and profit.

4. Artificial Intelligence: Artificial Intelligence technology can help create a more personalized and efficient shopping experience for the customer, by understanding their needs and preferences, and recommending products that are most likely to generate maximum revenue and profit.

5. Machine Learning: Machine learning technology can help to optimize product pricing, inventory, and marketing strategies based on customer behavior and market trends, leading to a better match of products to customers.

It's important to note that these technologies are not a one-size-fits-all solution, it's important to choose the one that best fits your business needs and to keep monitoring and adjusting the strategy accordingly. Additionally, it's important to consider other factors such as customer satisfaction, reviews, and product profitability when making the decision.