3 Important Ecommerce Metrics that are NOT Visits, Conversion Rate and AOV Strategy

There are many important ecommerce metrics to track when running a business or managing a store. In addition to visits, conversion rate (CVR) and average order value (AOV), here are a few more less commonly discussed metrics that should be incorporated into your ecommerce analytics.

  1. Repurchase rate
  2. Cost per acquisition
  3. Inventory turns

What is Repurchase Rate?

Repurchase rate is the percentage of first-time customers that come back to your store and purchase at least one more time. This is a universal customer metric that can be applied across product companies, service companies, D2C, retail, B2B and so on. The length of time for a second, third or fourth purchase to occur will depend highly on the industry that you operate in.

For example, a large purchase like a vehicle will have a very long repurchase cycle, potentially 10 years between the first and second purchase. However, there is value in finding ways to get smaller or subsequent purchases, for example, does the owner have children or a spouse who will be in need of a vehicle sooner?  Or perhaps there are complementary products you can offer like vehicle service or accessories to retain customers and provide them with additional experiences with your brand?

Smaller value or consumable items, such as apparel or personal health and beauty products, might have a repurchase rate <1 year and have more levers available to the business to shorten the time between purchases.

Why is Repurchase Rate important?

Repurchase rate is important because it is always less costly to get one incremental purchase from an existing customer than it is to acquire a brand new customer. Regardless of the industry, product or segment, competition will eventually enter if it is not already present. Increased competition drives up acquisition costs. A business built solely on acquiring new customers will not be sustainable long term as costs rise.

Another related metric to look at is how quickly existing customers are lapsing, or becoming dormant. Many companies have definitions of lapsed starting at 13 months with no purchase, but this can vary by industry. If your customers are lapsing faster than you can afford to acquire new customers, this can have a compounding negative affect and eventually make your business completely unsustainable.

What is a good percent of repeat customers?

A good percent of repeat customers will depend on your business, but 30%-50%+ tends to be a good goal for smaller value items and consumables. The higher the number, the better for your business. You will always want a balance of new and repeat customers to balance out those who lapse and continue building a pipeline of future purchases.

However, if you have a new business, then your repeat rate will be low. In early growth stages, the goal is to acquire new customers and build a base of customers for your business. After ~1 year, repurchase rates should be something you begin paying more attention to as one of your key ecommerce KPIs.

Two ways to measure repeat rate

Repurchase rate is measured as a percentage. The percentage needs to be based on a timeframe that has a distinct start and end point. A customer who bought for the first time this month is unlikely to buy again this month, including them in a repurchase calculation would severely dilute the results. Typically repurchase cohorts (groups of customers) are based on either the month or quarter that they were acquired. This group is then measured together going forward over the same time period. For many businesses, an analyst has created a model that will do this math, combine all the relevant cohorts and produce one percentage that represents your repurchase rate.

Another way to work with this data is to visualize the concept and add more context through a layer cake graph like this.

Revenue Driven by Cohort

The layer cake shows the revenue driven by each cohort over time. With a new cohort of customers, they drive the most revenue in the time they are acquired, as customers from that group come back and purchase, you can see how they are adding to the overall revenue each quarter in ‘layers’ for each cohort.

These metrics and visualizations come standard with Daasity’s base D2C Suite package.

What is Cost per Acquisition

Cost per acquisition (CPA) is the marketing spend a company invests to acquire one new customer. Acquiring new customers tends to be an expensive proposition in many marketing channels, however, you may be able to spend more than you think. When factoring in other aspects of your business including gross margin, customer lifetime value and purchase frequency, it may make sense for a business to spend significantly more to acquire new customers than what it costs to incite an existing customer to buy again.

Why Cost per Acquisition is important

Cost per acquisition is important because building a pipeline of new customers is essential to a thriving business, but the costs can be expensive and sabotage the brand’s budget. Acquiring customers at any cost is not advisable, even for a new company in growth mode. There are many ways to reach potential customers that all have varying costs. Knowing your company’s cost per acquisition target is crucial to efficiently allocating spend between various marketing channels.

What is Inventory turn rate?

Inventory turns is a metric used by supply chain personnel to determine the amount of product sold in a one month period. This can be looked at for your entire assortment, by category or at the SKU level.

Why is Inventory turn rate important?

Understanding your inventory turn rate is important to ensure that you are purchasing enough inventory to meet sales demands, but not more than you need.  On the one hand, purchasing inventory is costly, and buying too much inventory can create cash shortages. In particular small and high growth businesses can quickly find themselves strapped for cash if they purchase more inventory than is necessary.

At the same time, not purchasing enough inventory means your business is missing out on sales.  Every customer that comes to your site looking for a product that is out of stock is lost revenue that you may not recover. The cost of losing those sales may outweigh the cost of carrying the product. The goal for all businesses should be to always be in stock, especially on best sellers. This might mean having different suppliers and PO schedules for certain products or categories.

Further complicating your inventory decisions are seasonal effects on demand.  Most inventory software management solutions don’t know your business and what makes your product sales spike–this is where the team must make manual adjustments to prepare for big sale periods. Holiday season is a large sale period for many businesses, but throughout the year there are others that will be unique and you will need the inventory on-hand to support the revenue goals of the business.

Having a perfect inventory system may not be achievable, but optimizing your supply chain, supplier relationships and inventory forecasting models can both save you money up front and make sure you aren’t losing revenue to out of stocks.

Conclusion

There are many metrics that are important to track no matter what business you are running. Conversion rate is a hugely popular metric for ecommerce and can be a good indicator of brand and site health. But other lesser used metrics are also critically important and can be instrumental in your business’ success. At Daasity we spend a lot of time educating clients on their repurchase rates, cost per acquisition and inventory turns in order to help them better manage their business. Understanding these metrics and using them to drive decision making can efficiently drive both top and bottom line results. Daasity’s D2C Suite contains the most important ecommerce metrics in pre-built dashboards so clients can spend more time making decisions and less time building dashboards. Contact us for more info.

 

Click Attribution: Types of Models & Attribution Strategy

What is click attribution?
Click attribution is a way to determine what sources or campaigns are driving the most results for online companies. Many people like click attribution because it is trackable back to its site, email, or source, and click-through links can be programmed to include several attributes. Click attribution also allows people to see the relative performance of different messages, executions, or marketing techniques. Plus, it is a strong signal of intent or interest. Whatever content was clicked, you can assume it was compelling enough to incite action by that user.

What types of click attribution models are there?
The most common click attribution models are first-click attribution, last-click attribution, and linear attribution. There can be many variations of attribution algorithms that assign different values based on the type of transaction and channels involved. These three attribution models are common and not proprietary or algorithmic, so they are a great introduction to attribution.

What is first-click attribution?
First-click attribution is a model that assigns 100% of the credit for a sale to the first channel that a user clicked through. Some customers convert on the very first interaction with a company, but many will have at least two interactions during their journey to purchase. The first-click attribution model rewards the marketing channels or activities that are deemed introducers to the brand.

What is last-click attribution?
Last-click attribution is a model that assigns 100% of the credit for a sale to the last known channel that a user clicked through. This is in some ways a time-decay model: rather than giving fractional attribution to the last channel a user touched, it gives all the credit to it. Last-click attribution tends to be common among many companies regardless of their web analytics platform.

What is linear attribution?
Linear attribution breaks the credit for a sale or action into equal parts pending how many touchpoints were measured in the course of the customer’s purchase journey. If the user had four marketing channel interactions that ultimately resulted in a sale, each channel would be assigned 25% credit for the sale.

How to choose click attribution models
Most companies will choose one attribution model to use in standard reporting, and often this is last-click attribution. Last-click attribution will favor channels or marketing activities that are lower in the funnel, meaning that the customer is ready to make a purchase rather than being in their discovery or shopping phase.

When evaluating the results of last-click attribution, companies should consider their entire marketing mix and targeting strategies. The truest measure for last-click attribution is an email or text channel. Almost immediately upon receiving these messages, customers or clients either do or don’t take action. Other channels, such as search, paid social, podcasts, etc. are likely driving one another. How your attribution rules are configured can make a difference in the end result of which channels or activities get ‘credit’ for the conversion.

Click attribution example
Company A runs a marketing campaign that includes paid social ads, podcast ads, and online display banners. The customer hears a podcast ad and is curious, so they look up the company in a search browser and visit the website to learn more, but they do not make a purchase. After visiting the website, the customer begins receiving online display banners and paid social ads advertising the company and product. They later hear another podcast ad the following week and note that there is a promo code offered for a discount. Later that day, the customer clicks a paid social ad, shops on the site, and at check out they enter the promo code from the podcast before submitting their order.

Pending how attribution rules are configured, this order could be attributed in two ways. Either it would either be classified as Paid Social, since that was the last channel that a click occurred, or it would be classified as Podcast, since that it’s that channel that had the associated promo code. Ultimately, the order can only be classified to one channel. Which channel do you think should be deemed responsible for driving this purchase?

This example may seem complex, but in reality, this is a simple example. It does not include more complicating factors like marketplace sales or brick and mortar.

It’s for this reason that understanding attribution is both art and science. There are many algorithms available on the market and countless companies trying to crack the code to having the most accurate tracking, but none of them can solve this for every piece of information or every touchpoint a consumer has. This is why comparing first click and last-click attribution models is a good place to start. Google Analytics Attribution Models are great for this too, because it includes first click and last-click in their default suite. With this, you can easily compare sales that were measured both ways side by side across multiple channels.

Additional ways to improve attribution
As the example above shows, promotional codes are another method for improving attribution. They’re often used as a measurement and attribution tactic for social influencers, on podcasts, radio, tv, and in direct mail. A great way to add an additional attribution layer is to ask customers what caused them to purchase or how they learned about the company. By introducing this one question, you can gain a better understanding of which interaction the customer found most memorable.

Combining all of these data sources to draw insights using a marketing analytics platform will give you a good idea of how your marketing activities are performing. Ultimately, you will have a range of performance pending which data sources you have. Understanding which activities are upper funnel (introducing your brand to new potential customers) and which are lower funnel (capturing the sale from someone ready to purchase) will further help you determine what the corresponding metrics should be.

At the end of the day, there is no silver bullet to having the perfect attribution model. By collecting as much data as possible and considering the role your media mix plays in a customers’ path to purchase, you can optimize your marketing spend to customer conversion based on what your optimal channel mix looks like.

Check out these tips to learn more tips on creating an effective attribution model.

Learn more with Daasity + Looker
Daasity has approached attribution analysis in multiple ways in our direct-to-consumer (D2C) Analytics Suite, which integrates seamlessly with Looker. The data model can use additional data beyond Google Analytics to prioritize attributes such as specific promo code usage, post-checkout survey results, or map orders to marketing channels. Using that data mapping with Looker to visualize results, users can slice and dice data by initial order marketing channel to better determine financial metric targets.

Additionally, the D2C Analytics Suite allows users to easily view results by first click, last-click, and ad platform (view + click) in one simple graph to help gauge results.

Daasity and Looker continue to find ways to make it easier for eCommerce and D2C brands to access and see the data they need to inform strategies and tactics for growth.

Conversion Funnel: How to Build, Analyze & Optimize

What Is A Conversion Funnel? 

A conversion funnel, also referred to as a site funnel, is the path to purchase in an eCommerce store or site. In some ways, it can be compared to the traditional marketing funnel, but different than a traditional funnel, most of the steps are occurring on your site. 

 

Ecommerce Purchase Conversion Funnel

Conversion Funnel Comparison

 Generally, there are five steps in a site funnel for any given website. You can add additional steps depending on your particular website and what makes it unique. 

Five Steps In A Site Purchase Funnel 

  1. Site Visit – A customer arrives at your site 
  2. Product View – Customer views a specific product page for more detail 
  3. Add To Cart – Customer shows a strong preference for a product by adding it to their cart
  4. Enter Checkout – Customer shows strong intent that they will purchase your product 
  5. Purchase – Customer goes through the entire checkout process and completes the transaction. 

Many web traffic measurement tools will offer a breakdown of these five steps. For example, in Google Analytics, you can find the site funnel in the Shopping Behavior area of the eCommerce section.  

 Although five steps may seem simple, a myriad of factors influence the customer journey from the initial site visit to a completed purchase. The reality is, the vast majority of visitors will not complete the transaction. However, there are ways to improve on these odds to result in more purchases. 

If eCommerce transactions are not your first priority, a similar framework can be applied to whatever your website goals are. Perhaps gaining more sales leads is of higher importance, for which a typical funnel for a goal of increased sales leads would be: 

  1. Site Visit 
  2. View content 
  3. Request to learn more 
  4. Enter contact info 

 You can create a goal of ‘acquiring leads’ in Google Analytics or another web traffic tool. Adding steps to the goal will allow you to see the goal flow similar to how a conversion funnel flows. 

The Importance Of A Conversion Funnel Analysis 

A conversion funnel analysis is a powerful tool any company can use to improve their business. By evaluating each of the stages mentioned above, you can begin to understand how many of your site visitors are progressing through each of the stages in the funnel. To make your analysis more valuable, you may also want to evaluate the funnel flow based on consumer groups, the device used to visit, or other segments that make sense for your business.  

 Once you’ve completed your first conversion funnel analysis, you will have a baseline measurement to benchmark against and use to improve and optimize your site experience. 

Considering More Than Just Conversions 

If looked at in a vacuum, optimizing within the funnel based on a single metric can seem relatively easy. However, increasing one metric could have long term negative impacts on the other KPIs of your business. While conversions, transactions, and leads are the ultimate goal, there are other factors that are important to understand when driving towards success. Metrics such as revenue per visitor, average order value, gross margin; for leads, and quality measurement of a lead are useful and important metrics to understand since they all can affect the outcomes of funnel-related actions.  

 A great example of this is a hyper-focus on lead gen. Although you may find a system that works for consistently improving your lead generation volume, if the new leads never become customers, the significance of this optimization loses its value against the overall goal of trying to generate more customers for the organization. 

How Do You Optimize A Conversion Funnel? 

Depending on your business and website, there are countless changes that could result in improved performance in your conversion funnel. That being said, you should keep in mind that conversion funnel optimization is an ongoing effort and something that is never truly ‘completed’. 

 To conduct a conversion funnel analysis, you need to have: 

  • a specific area you’d like to focus on improving 
  • a hypothesis that could improve the performance of this part of the funnel, and  
  • an ability to A/B test the hypothesis  

 Since not all hypotheses will generate positive results, the A/B testing component is critically important to guide the decisions and strategies that lead to fully-implemented changes.  

 Two areas on your site that could improve conversions or increase lead generation are product/service pages and the checkout experience. 

  • Product Pages 

Having pages specifically for your products allow you to test hypotheses based on questions about the page that affect site conversions and leads, which can include questions like: 

  • Is the Request Info or Add to Cart button prominent on all devices? 
  • Are there product reviews or testimonials that could be added to the product page? 
  • Is there certain information (i.e., product size, trial details) that could be made available on the page? 
  • Are there videos that could be embedded on the page that showcase the product in use? 
  • Checkout Experience 

A seamless checkout experience, especially on mobile, is absolutely necessary for improved site conversions. Consider tactics to remove friction and improve conversions, such as: 

  • Implementing mobile wallets like Apple Pay 
  • Adding alternative payment methods such as PayPal, Venmo, or Affirm financing 
  • Assessing whether all the data collected during the checkout process is required or if there are parts that could be eliminated 
  • Reducing the number of pages in the checkout flow by removing data collection or redesigning the page 

Continuing To Build, Analyze & Optimize Your Conversion Funnel 

To recap how to start converting more site visitors: 

  1. Identify the goal (transactions, sales leads, etc.) 
  2. Measure your baseline for each of the five conversion funnel steps 
  3. Brainstorm hypotheses and ways to improve the customer experience 
  4. A/B test your hypothesis 
  5. Roll out positive test results to all users 

 Improving your overall site experience and conversion funnel is an ongoing process that has many solutions. But at the end of the day, it’s important to remember that the leads you’re looking at in the funnel are human beings in real life, so each part of the conversion funnel you can improve means improving the experience of a consumer. By keeping up with consumer preferences and expectations, the changes you make – no matter how big or small – can add up to long-term improvements for your entire organization. 

Creating Actionable Customer Segmentation Models

What is customer segmentation?

Customer segmentation is a way to split customers into groups based on certain characteristics that those customers share. All customers share the common need of your product or service, but beyond that, there are distinct demographic differences (i.e., age, gender) and they tend to have additional socio-economic, lifestyle, or other behavioral differences that can be useful to the organization.

What type of information is used in customer segmentation:

Any information you can acquire about individuals can be used to create a customer segmentation. Direct-to-consumer brands and B2B companies are at a distinct advantage because of the amount of information they can obtain about their customers just from their transaction data alone.

Basic data types typically include:

  • Geography (billing info, shipping info (if applicable), browser info)
  • Product(s)/Service(s) purchased
  • How customers found you (referring URL and/or campaign info, promo codes)
  • Device used (device type, brand (if mobile), browser)
  • If this is a customer’s first purchase
  • Payment method

Beyond these basics, companies may choose to collect more information as part of the selling or checkout process that can augment their customer data, such as:

  • Reason for purchase
  • Marketing or advertising channel that drove purchase*
  • Intended use: business, personal, self-consumption, gift, etc.
  • Company industry segment
  • Job title
  • Age/Gender

*Important Note:

This has become more common, especially with direct-to-consumer businesses trying to assess their marketing efficacy and offer another viewpoint besides last-click in Google Analytics. There is always a healthy margin of error applied to data reported in this way from a customer, but it certainly indicates what they believe to be the most memorable or important reason for their purchase. Daasity has built out specific logic for processing this information, along with other data, to help determine the most likely marketing channel responsible for purchases.

From here, there is the opportunity to either infer additional attributes or purchase additional attributes. Inferring attributes means you have already collected data that results in a strong correlation to another attribute. For example, you might infer gender from name.

The other option is to purchase data and append it to your customers’ existing profile data. Companies like Experian, Acxiom, and others happen to have significant amounts of purchase data from credit card transactions, as well as demographic data that they have mapped to certain behaviors. They have strong match rates to provide additional data, (referred to as 3rd party data) such as:

  • Estimated household income
  • Presence of children
  • Homeownership
  • Amount of spend in your company category or other retail categories
  • Lifestyle or behavioral interests

6 types of customer segmentation models

Common customer segmentation models range from simple to very complex and can be used for a variety of business reasons. Common segmentations include:

  1. Demographic

At a bare minimum, many companies identify gender to create and deliver content based on that customer segment. Similarly, parental status is another important segment and can be derived from purchase details, asking more information from customers, or acquiring the data from a 3rd party.

  1. Recency, Frequency, Monetary (RFM)

RFM is a method used often in the direct mail segmentation space where you identify customers based on the recency of their last purchase, the total number of purchases they have made (frequency) and the amount they have spent (monetary).  This is often used to identify your High-Value Customers (HVCs).

  1. High-Value Customer (HVCs)

Based on an RFM segmentation, any business, regardless of sector or industry, will want to know more about where HVCs come from and what characteristics they share so you can acquire more of them.

  1. Customer Status

At a minimum, most companies will bucket customers into active and lapsed, which indicates when the last time a customer made a purchase or engaged with you. Typical non-luxury products consider active customers to be those who have purchased within the most recent 12 months. Lapsed customers would those who have not made a purchase in the last 12 months. Customers may be bucketed even further based on the time period in that status, or other characteristics.

  1. Behavioral

Past observed behaviors can be indicative of future actions, such as purchasing for certain occasions or events, purchasing from certain brands, or significant life events like moving, getting married, or having a baby. It’s also important to consider the reasons a customer purchases your product/service and how those reasons could change throughout the year(s) as their needs change.

  1. Psychographic

Psychographic customer segmentation tends to involve softer measures such as attitudes, beliefs, or even personality traits. For example, survey questions that probe how much someone agrees or disagrees with a statement are typically seeking to classify their attitudes or perspectives towards certain beliefs that are important to your brand.

5 Benefits of customer segmentation

There are several benefits of implementing customer segmentation including informing marketing strategy, promotional strategy, product development, budget management, and delivering relevant content to your customers or prospective customers. Let’s look at each of the benefits in a bit more depth.

  1. Marketing Strategy

Customer segmentation can help inform your overall marketing strategy and messaging. As you learn the attributes of your best customers, how they are alike, and what is important to them, you can leverage that information in messaging, creative development, and channel selection.

  1. Promotion Strategy

An overall promotion strategy (i.e., our customers are deal seekers, therefore we should offer frequent deals) for sending promotions for specific segments can be made better with information from a broad customer segmentation scheme. You may find that certain cohorts of customers don’t require discounts when you use certain messaging, thereby saving you from having to offer a discount for those groups at all.

  1. Budget Efficiency

Most companies do not have unlimited marketing budgets, so being precise about how and where you spend is important. You could, as an example, target similar customers to segments of high value or those most likely to convert to get the most return from your marketing investment.

  1. Product Development

The more customers you acquire, the more you learn about what is important to them, what features they want, and which customers are the most valuable. Your company can use these insights to prioritize product features that either appeal to the most customers, those categorized as high-value customers, or other characteristics that makes sense for your industry.

  1. Customers Demand Relevance

Whether it’s D2C, B2B, Millennials or GenZ; it seems that there is a study or resource on every possible group of customers stating that relevant content is important to them. These customer segments are more likely to respond, buy, and respect the brand and feel connected if provided with relevant content. By performing some level of segmentation, you can ensure that the messages you are delivering via email, on site, through digital ads, or other methods are targeted and relevant to the individual seeing it. It is almost counter-intuitive to the hyper vigilance of data privacy to use so many pieces of data in this way, but with so many marketing messages coming at people today, no one has time for something that isn’t relevant to them.

How to make customer segmentation actionable

To make your customer segmentation actionable, first, you must start with a goal in mind. As previously mentioned, segmentation can be simple, complex, or anything in between – and you aren’t limited to one set of segments. With the ease and accessibility of data today, you can devise different customer segments for different purposes.

The amount of information that can be obtained from various sources is endless. But, it’s only useful if you can use it. This requires questioning, being curious, and analyzing the data you have. From there, as you find treasures buried in the data you have, design a test to confirm that is in fact a useful finding.

Examples of customer segmentation

Target has perhaps the most famous story of using customer segmentation, analytics, and marketing techniques to increase their share of wallet with pregnant women. In 2012, the incredible story broke of Target accidentally informing a young woman’s father that she was, in fact, pregnant, before she had broken the news to him herself.

Once a customer has a child, his or her purchase patterns and basket contents suddenly change to contain diapers and other products consistently. That is a whole segment of customers: people who’ve just had babies. Add gender to it and you have women who have just had babies. As the analysts evaluated this segment’s history, they started to see purchase patterns emerge as markers of the pregnancy’s milestones. From here, they surely built predictive models that would classify customers as they hit some of these markers and flagged those customers as newly pregnant. The action that Target took was to market very specifically to these women with highly targeted ads and direct mail for baby items, baby clothes, and supplies. When a young woman received one of the mailers addressed to her, her father was astonished at how foolish and careless Target would be…until he found out that his daughter was indeed expecting, and Target knew before him.

This example is extreme but memorable. Segmentation can be employed using knowledge of your customers, knowledge of your business, common sense and perhaps a few creative variations – even if you don’t have a Target-sized team of data scientists pouring through the data.

An easy way to use segmentation and to start collecting data for immediate results is through email campaigns. Let’s say you are planning a campaign series and really want to learn how different customer groups react to various messaging and offers. You have a healthy database of emails that includes a mixture of customers and non-customers. Using the code below, you can group customers into non-customers, and then groups based on recency of last purchase being 0-3 months, 3-6 months, 6-12 months, and >12 months.

view: customer_recency {

derived_table: {

sql:

WITH

last_order AS

(

SELECT

customer_id,

MAX(order_date) AS last_order_date

FROM order

GROUP BY

customer_id

)

SELECT

c.customer_id,

CASE

WHEN DATEDIFF(day, CAST(lo.last_order_date AS DATE), CAST(current_timestamp::timestamp AS DATE))  BETWEEN 0 AND 90 THEN ‘1: 0-3 Months Active’

WHEN DATEDIFF(day, CAST(lo.last_order_date AS DATE), CAST(current_timestamp::timestamp AS DATE))  BETWEEN 91 AND 180 THEN ‘2: 3-6 Months Active’

WHEN DATEDIFF(day, CAST(lo.last_order_date AS DATE), CAST(current_timestamp::timestamp AS DATE))  BETWEEN 181 AND 365 THEN ‘3: 6-12 Months Active’

WHEN DATEDIFF(day, CAST(lo.last_order_date AS DATE), CAST(current_timestamp::timestamp AS DATE))  > 365 THEN ‘4: 12+ Months Lapsed’

ELSE ‘Non-Customer’

END AS customer_recency_group

FROM customer c

LEFT JOIN last_order lo ON c.customer_id = lo.customer_id

GROUP BY

c.customer_id,

lo.last_order_date

;;

}

dimension: customer_id {

sql: ${TABLE}.customer_id ;;

primary_key: yes

}

 

dimension: customer_recency_group {

type: string

sql: ${TABLE}.customer_recency_group ;;

}

 

measure: num_customer {

type: count

}

}

Customer Recency Visualization Dashboard

You can then evaluate the performance of each group against sent content to determine if there are specific messages that resonate more.

Accomplish more with actionable customer segmentation models

Customer segmentation is an important part of any business aiming to grow revenues, repeat rates, share of wallet and profitability. Segmentation does not have to be incredibly complex or expensive, and it can be easily accomplished using a Looker dashboard with readily available transaction or demographic data. And customer segmentation benefits your customers and your organization, allowing your customers to feel more connected to your brand because they’ve been received relevant content, and in turn your company should see increased positive results.

 

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