Customers Dashboard

Putler provides predefined filters(Facets) to filter your data based on sales/orders.

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If you are not satisfied with the predefined filters, you can filter data by creating customized segments. Follow this link to know more.

Predefined Filters (Facets)

Segment customers based on location

Geo-targeted segmentation/Location-based segmentation is a segmentation technique that divides customers based on their geography. Putler provides a very simple way to segment customers based on their geography. It lets you filter customers based on the Continent, Country, and County. You can segment customers right up to the street level.

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This is only possible if you collect your customer's address information during checkout.

How to use this metric?

• You can filter customers based on their location and then export a CSV. You can use this CSV to -
• Run geo-targeted ads
• Send out festive offers
• Create FB lookalike audience
• Send out targeted emails
• Make decisions to add translation to your app/webpage
• Make decisions to accept local currency on your website

enefits

• Increase conversions: Targeting based on location leads to more relevant content and offers, boosting conversion rates and satisfaction.
• Cost efficiency: Allocating resources to high-potential areas reduces wasteful spending, maximizing marketing budget effectiveness.
• Better localization: Knowing customer locations informs localization efforts, improving user experience and market expansion.

Segment customers based on product

Segmenting customers based on the products they purchase is a common approach in marketing and can provide valuable insights into customer behavior and preferences.

How to use this metric?

• Cross-Selling: Identify frequently paired products to create targeted campaigns promoting complementary items to customers.
• Targeted Communication: Segment customers based on specific product purchases for personalized communication, like feedback requests for product improvement.
• Upsells: Figure out which products are frequently bought together with the selected product. For example: If Products A and B are bought together. Filter out customers who have bought either Product A or B and upsell them the other product.
• Ask for feedback: Filter customers who have bought a particular product and write them a targeted email. Ask them what they like/dislike about the product.
• Market research: For instance, if you're introducing a complementary product to an existing one, you can filter out customers who have already purchased the product and provide them with a timely heads-up about the upcoming complementary offering.

Benefits

• Increased Sales: By identifying cross-selling opportunities, businesses can boost revenue by targeting customers with complementary products, raising average order value.
• Improved Engagement: Tailored communication based on purchase history enhances satisfaction and fosters stronger customer relationships.
• Efficient Resource Use: Market segmentation allows businesses to allocate resources more effectively, maximizing ROI on marketing campaigns.

Segment customers based on Product Attribute

Understanding the attributes (variations) associated with your products is crucial for effective product management and marketing strategies.

How to use this metric?

Putler's attribute filter allows you to categorize and analyze products based on their attributes, such as color, size, material, or any other relevant characteristics.

Example

Let's say you manage an online bookstore. Using Putler's attribute filter, you discover that books with the attribute "mystery genre" and "hardcover format" are the best-sellers among your customers. Armed with this insight, you decide to increase your inventory of mystery novels in hardcover format, leading to a boost in sales.

Benefits

• Targeted Inventory Management: Identify which product attributes are most popular among customers to optimize your inventory and ensure you stock items that align with their preferences.

• Efficient Marketing Strategies: Tailor your marketing campaigns based on popular product attributes to attract your target audience more effectively and increase engagement.

• Improved Product Development: Utilize attribute analysis to inform product development decisions and introduce new products or features that resonate with customer preferences, enhancing overall satisfaction and loyalty.

Segment customers based on Revenue Contribution

Putler one of its most valuable features is the customer dashboard, which includes the "Revenue Contribution" metric. This metric allows businesses to understand how much revenue each customer or group of customers contributes to the overall sales, providing a clear view of the most valuable customers.

How to use this metric?

• Identifying High-Value Customers: The dashboard highlights top revenue contributors, enabling targeted marketing and personalized engagement.
• Analyzing Purchase Patterns: It reveals purchase frequency, product preferences, and average order value of top customers, aiding in tailored product offerings and promotions.
• Segmenting Customers for Marketing Campaigns: Understanding revenue contribution allows businesses to segment customers and design effective marketing campaigns.

Benefits

• Enhanced Customer Retention: Focus on high-value customers to boost loyalty and reduce churn.
• Increased Revenue: Use targeted marketing and personalized offers to increase sales and order values.
• Efficient Resource Allocation: Optimize resources by prioritizing high-revenue customers and segments.

Segment customers based on “Customer Since”

The "Customer Since" filter facet in Putler's customer dashboard allows users to filter and segment their customers based on the date they became customers.

How to use this metric?

• Targeted Marketing Campaigns: Tailor campaigns for new and loyal customers with personalized messages and offers.
• Customer Retention Analysis: Analyze retention rates, behaviors, and trends over time to understand customer loyalty.
• Sales Trend Analysis: Track sales from different customer cohorts to measure the impact of marketing and sales efforts.
• Loyalty Program Management: Reward long-term customers, develop programs for specific segments, and boost repeat business.

Benefits

• Enhanced Customer Segmentation: Refine segmentation using acquisition dates for precise targeting, enabling more effective marketing strategies.
• Improved Customer Insights: Understand customer lifecycle stages, identify key milestones, and map touchpoints in the journey for better engagement.
• Increased Marketing ROI: Optimize spending by prioritizing high-value segments, minimizing waste on ineffective efforts.

Segment customers based on Customer Type

Putler's Customer Dashboard offers a comprehensive suite of features designed to streamline your understanding of customer behavior and preferences. One key feature is the ability to filter customers based on their type, whether they are new or returning customers.

How to use this metric?

• Segmentation: Easily categorize customers into 'New' or 'Returning,' allowing for targeted marketing campaigns and personalized communication.
• Performance Analysis: Assess the effectiveness of your strategies by comparing the behavior and engagement of new versus returning customers.

Benefits

• Enhanced Targeting: Precisely target marketing efforts towards either new customer acquisition or nurturing existing relationships, leading to improved conversion rates.
• Improved Customer Retention: Identify returning customers swiftly, enabling proactive measures to enhance their experience and loyalty.

Segment customers based on the number of Orders

Segmenting customers based on the number of orders they've placed is a common practice in customer relationship management and marketing strategies. This segmentation helps businesses understand their customer base better and tailor their marketing efforts accordingly.

How to use this metric?

• Identify top customers: Find customers who have placed the highest number of orders in your store. They are your best customers. Give them top priority and early bird access every time you launch a new product.
• Upsells: People who have bought more than once from your store might end up buying more. So use this information to send out relevant upsells.
• Pitch offers: Appreciate and acknowledge people for patronizing your store and offer them discounts/loyalty rewards.

Benefits

• Target Marketing: Tailor promotions and recommendations to engage high-order customers effectively.
• Retention Strategies: Implement loyalty programs and perks to foster loyalty and encourage repeat purchases.
• Product Development Insights: Gain valuable insights into popular products and prioritize enhancements or new offerings.
• Revenue Growth: Drive revenue more efficiently by focusing on customers who have demonstrated a higher level of engagement and propensity to spend.

Customer Metrics

Top 20% customers

Top 20% Customers typically refers to the subset of customers who contribute the highest proportion (80%) of sales or revenue to a business. In other words, these are the customers who generate the most income for the company. It is often referred to as the 80/20 rule or the Pareto Principle. To find the net sales from the top 20% of customers, calculate the total sales, sort customers by purchase amounts, select the top 20%, and sum up their sales contributions.

Let's illustrate with an example Calculate the total sales for the selected date range, let's say it's $100,000. Sort your customers based on their purchase amounts. Identify the top 20% of customers by revenue. If you have 100 customers, this would be the top 20, based on their spending. Suppose the top 20 customers contribute $70,000 to the total sales. Therefore, the net sales from the top 20% of customers for the selected date range would be $70,000.

How to use this metric?

• Targeted Marketing Campaigns: Customize marketing efforts for top customers to boost effectiveness.
• Priority Support: Provide dedicated channels or managers for top customers for prompt assistance.
• Data Analysis and Segmentation: Analyze purchasing behaviors to segment the customer base for targeted strategies.
• Facebook Lookalike: Export the top 20% of customers and create a lookalike audience based on them
• Persona: Study the personas of the top 20% of customers, this will give you more insights into what type of customers you should try to attract to your business.
• Grow customer relationships: Connect with the top 20% of customers and try to build better customer relationships with them. Such relationships can go a long way for your business.

Benefits

• Revenue Maximization: The top 20% of customers generate the majority of revenue.
• Cost Efficiency: It's cheaper to serve and retain these high-value customers.
• Customer Loyalty: A stronger relationship with top customers leads to loyalty.
• Customer retention: Reduce churn by strengthening relationships with high-value customers.

New customers

In Putler's customer dashboard, "New customers" represent those who make their first purchase within the chosen date range.

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Even if they make additional purchases within that same timeframe, they're still categorized as "New" customers.

Returning Customer

In Putler, a customer who has previously made purchases before the selected date range and then makes additional purchases within that range is categorized as a "Returning" customer. This classification system helps identify and analyze repeat business patterns. By distinguishing returning customers, businesses can better understand their loyal clientele and tailor marketing strategies accordingly.

Refunded

In the customer dashboard of Putler, the refunded stats display the number of customers who are no longer active (refunded or canceled) to the total number of customers for the selected date range. Additionally, the dashboard displays the total amount deducted due to refunds, providing a comprehensive overview of customer engagement and financial performance.

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Green & Red stands for % increase and % decrease in the number of customers respectively compared to the same number of days in the corresponding previous date range.

Example If the metric shows 182 customers and a 116.67% increase for the date range June 16, 2023, to June 30, 2023 (15 days), there will be 84 customers in the previous date range June 1, 2018, to Jun 15, 2023 (15 days).

Customer Breakdown Chart

In the Putler customer dashboard, the breakdown chart provides a comprehensive view of the performance metrics for New, Returning, and Refunded Customers. This visualization allows users to assess the distribution and behavior of different customer segments quickly.

Additionally, Putler offers the flexibility to view this customer chart based on specific timeframes, such as days, months, or years. Users can gain deeper insights into customer trends by selecting different time intervals and making informed decisions to optimize their business strategies accordingly.

How to use this metric?

• Behavioral Analysis: Track new, returning, and refunded customers for different timeframes.
• Tailored Marketing: Tailor marketing strategies based on customer behavior analysis.
• Customer Retention: Analyze returning customer metrics for enhancing loyalty.
• Personalized Incentives: Offer personalized incentives to encourage repeat purchases.

Benefits

• Performance Evaluation: Segment breakdown aids in assessing customer base performance, revealing acquisition, retention, and satisfaction strengths and weaknesses.
• Targeted Marketing: Identifying customers into different categories such as new, returning, and refunded can help in crafting more effective marketing strategies. Here's how you can tailor your approaches for each:
  • New Customers: Onboarding Emails: Series of emails introducing products/services and guiding on how to start.
  • Returning Customers: Loyalty Programs: Offer points or rewards for repeat purchases.
  • **Refunded Customers: ** Win-back Offers Special incentives to regain refunded customers, showing value and commitment.
• Risk Management: Keep an eye on refunded customers to catch revenue and reputation risks early, so we can quickly fix things and avoid more losses.

RFM analysis

RFM Analysis segments customers based on Recency, Frequency, and Monetary values, crucial for targeted marketing. It involves analyzing three key aspects of customer transactions:

• Recency (R): How recently a customer has made a purchase. This metric helps identify customers who are actively engaging with the business.
• Frequency (F): How often a customer makes a purchase within a specific period. This metric helps identify loyal or repeat customers.
• Monetary (M): The total amount of money a customer has spent within a specific period. This metric helps identify high-value customers.

Here's a brief overview of the typical RFM segments:

• Champions: These are customers who have high recency, frequency, and monetary value. They are the most valuable segment and should be rewarded and retained.
• Loyal Customers: These customers have high frequency and monetary value but may not have made a purchase recently. They should be encouraged to make repeat purchases to maintain their loyalty.
• Potential Loyalists: These customers have made recent purchases and have a high monetary value, but their frequency of purchases is low. They have the potential to become loyal customers with targeted marketing efforts.
• New Customers: These customers have made their first purchase recently. They should be nurtured to encourage repeat purchases and loyalty
• Promising: These customers might have made recent purchases and show promise in terms of frequency or monetary value, but they haven't quite reached the status of loyal customers yet. They need attention and nurturing to encourage repeat business and increase their value to your business over time..
• Need Attention: These are customers who might have shown some interest in your products or services but haven't made a purchase recently. They require targeted marketing efforts or personalized communication to re-engage them.
• About to Sleep: These customers have been active in the past but are showing signs of decreased engagement. They may be on the verge of becoming inactive if not prompted with incentives or reminders.
• Hibernating: These customers have been inactive for a significant period. They require special attention and tailored strategies to revive their interest and bring them back into the fold.
• Can't Lose Them: These are high-value customers who are currently engaged but might be at risk of churning due to certain factors such as dissatisfaction, increased competition, or changing needs. Retaining them should be a priority.
• At Risk: These customers are showing signs of decreased engagement or dissatisfaction. They need immediate intervention to prevent them from churning.
• Lost: These are customers who have churned or shown no response despite efforts to re-engage them. While it's challenging to win them back, analyzing their behavior can provide insights to prevent similar cases in the future.

note

• Putler RFM calculates at runtime.
• Using these metrics, you can create segments based on their RFM scores. For example, customers with high recency, frequency, and monetary value are your most valuable and engaged customers. On the other hand, those with low scores across all three dimensions might be at risk of churning or already lost.
• The count in each segment of the RFM chart is based on all the data in Putler. To see all customers in a segment, adjust the date picker to either 2 years or 5 years, depending on your Putler plan.

SaaS Customer Metrics

Avg. LTV

Average Lifetime Value (LTV) represents the expected revenue generated from a customer before they churn. It combines both recurring and non-recurring revenue to predict the total value a customer brings to the business over their lifetime.

How to use this metric?

• Predicts future revenue: By estimating how much money each customer will bring in before churning, businesses can make informed decisions on resource allocation and marketing strategies.
• Customer segmentation: LTV helps segment customers based on their potential value, allowing businesses to focus more resources on high LTV customers for retention efforts.

Average Revenue/Customer

Revenue per Customer is a metric calculated by dividing the total net sales within a specific date range by the total number of customers acquired during that same period. It provides insights into how much revenue each customer contributes on average.

How to use this metric?

• Performance evaluation: Provides a clear metric to assess the effectiveness of marketing campaigns or product offerings in generating revenue from individual customers.
• Pricing strategies: Helps in setting prices that ensure profitability while maximizing customer value, by understanding the average revenue generated per customer.

Acquired/Day

Acquired per Day measures the rate of new customer acquisition within a given date range. It is calculated by dividing the total number of new customers acquired during the selected period by the total number of days in that period, providing a daily average of new customer acquisitions.

How to use this metric?

• Growth tracking: Allows businesses to monitor the effectiveness of their acquisition efforts over time, helping to identify trends and adjust strategies accordingly.
• Resource allocation: Provides insights into how many new customers are gained daily, enabling businesses to allocate resources such as customer support or infrastructure accordingly.

Total Customers

This refers to the overall number of unique customers who have interacted with a business or service within a given period. It's a key indicator of the customer base size.

Average Customer/Day

This metric calculates the average number of customers served by the business per day. It helps in understanding the daily flow of customers and can be useful for planning resources and staffing.

Orders/Customer

This metric denotes the average number of orders placed by each customer within the specified period. It provides insights into customer behavior and loyalty, as well as the effectiveness of marketing strategies in encouraging repeat purchases.

Total Revenue

Total Revenue: Total revenue refers to the overall income generated by a business from its goods or services over a specific period. It's calculated by multiplying the quantity of goods or services sold by their respective prices.

Free vs Paid

Free vs Paid: This refers to the pricing model used for products or services. "Free" typically means that the product or service is offered at no monetary cost to the user, while "paid" means that users must pay a fee to access the product or service.

One Time vs Recurring

One Time vs Recurring: This refers to the payment structure. "One-time" payment means that customers pay for the product or service once and have permanent access to it. "Recurring" payment means that customers pay at regular intervals, such as monthly or annually, to maintain access to the product or service.

Customer List View

The Customer List feature in Putler offers a comprehensive analysis and management of customer data. It encompasses essential details such as Customer Name, Location, Products Purchased, Last Purchase Date, and Revenue Generated.

How to use this metric?

• Users can sort the list based on the Last Purchase Date or Revenue to analyze customer activity and revenue generation.
• Users can apply filters to view only the details that meet certain conditions/criteria.
• Additionally, Custom Segmentations can be created to cater to specific requirements.

Benefits

• Data Organization: The customer list helps in organizing and presenting customer data in a structured manner, facilitating easy analysis.
• Customization: Filtering and sorting options allow users to customize their view based on specific criteria, enhancing user experience and efficiency.
• Total Customer Count: Display the total number of customers within selected date ranges to analyze trends over time.

Export Options

On the top right-hand side of the order details, users have access to an export feature. They can export the list in various formats, including: CSV Mailchimp

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For a detailed understanding of customer data and seamless analysis, utilize the export feature to export the list.

Customer Details Card

The "Customer Details Card" in Putler refers to a specific feature or section within the software that displays detailed information about individual customers.

Here's what you might typically find on a Customer Details Card in Putler:

• Personal Information: This section includes essential details about the customer, such as their name, email address, contact number, and any other relevant contact information. This allows businesses to easily identify and reach out to their customers when needed.
• Purchase History: Putler provides a summary of the customer's purchase history. This includes information about the products they have bought, the dates of their purchases, and the total amount spent. Having access to this information can help businesses understand their customers' buying behavior, preferences, and frequency of purchases.
• Activity: This section displays specific details about the customer's activity on your store, such as the product names, dates and times of purchases, refund activity if any, and the amounts spent. This real-time data can be valuable for businesses to track customer interactions and monitor sales performance.
• Website: Putler smartly maps the customer’s main website linked to the customer's email ID and shows it in the Customer Detail Card. You can use this information to know more about your customers and tailor your marketing activities based on that.
• Address Details: Putler includes the customer's address details, which are important for shipping and delivery purposes. Having this information readily available ensures smooth order fulfillment and customer service.
• Current Period Sales, Orders, Refunds, Items: Putler provides data on the current period sales, orders, refunds, and items purchased. This information gives businesses insights into their current performance metrics, allowing them to track progress, identify trends, and make informed decisions to optimize their operations.

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If a customer purchases three items in one order and two items in another order, the order count will be 2, and the item count will be 5.

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This document provides an overview of the features and benefits of the Customer Dashboard in Putler. For more information or assistance, please refer to the Putler FAQ docs or contact our support.