Fireside Chat with TULA Skincare

Fireside chat with TULA Skincare 

Recently we had a chance to take a step back from the day-to-day grind and catch up with one of our customers—TULA Skincare—about their experience with data, Daasity and Looker. Pull up a chair for our fireside chat with Zack Abbell, VP of Digital Marketing and Ecommerce for TULA. 

Daasity: First off, for those who aren’t familiar, tell us about TULA. 

Zack: TULA is the leading probiotic skincare brand that offers clean & effective skincare products powered by probiotics & superfoods to give you healthy, balanced, glowing skin. Our higher-level vision is focused on inspiring confidence by making it easier to achieve a healthy balance, inside and out. 

Our founder, Dr. Roshini Raj, is a frequent contributor on Dr Oz and The Today show – has studied probiotics for 15+ years and seen first-hand the power of probiotics with her patients.  

We’re still a young company, growing fast and have over 1200 points of distribution including our own site, Amazon, Ulta, Nordstrom, Neiman Marcus and more. 

Daasity: You’ve been with TULA for ~6 months now, what kind of data challenges did you see coming in? 

Zack: As a high growth company we were challenged with having a lack of a centralized data structure. After joining the company, it was apparent, that like many other high growth companies, discipline about data structuring did not exist. Different teams were using different data sources and coming to inconsistent data across the organization. It became difficult to understand whose data was accurate, if anyone’s.  

We spent too much time in meetings talking about or debating whose data was right, rather than talking about strategies to continue the growth.  

We were at a point of trying to run large cohort analysis in excel files and just praying they don’t time out during their 8-hr run time. It wasn’t efficient or effective and was preventing us from continuing to accelerate. 

Daasity: We’re happy you found us; can you share why you ultimately became a Daasity customer? 

Zack: We knew we didn’t have the resources in house to build out a data architecture and weren’t in a position to hire. I’d been through a prior Looker project at a previous company, and knew how complex and labor-intensive the data-mapping journey could be. So I knew we were going to need to outsource the build out of our data warehouse and integration into Looker.  

We started by reaching out to connections in the industry to see who had found companies to help with similar projects. We only looked at a few after receiving some strong recommendations from companies within our venture-funded community with similar tech and data stacks regarding Daasity. Daasity came highly recommended in spinning up Looker quickly and integrating into our core systems. Working with Sean and the team in the pre-sales process, it was apparent that they knew how to approach the data mapping and had many prebuilt integrations on our Tier 1 systems.

Daasity: Where have you found the greatest value of being a data-driven retail / ecomm organization? 

ZackThe truth lies in the data. The deeper you can understand customer behavior, the more personalized you can approach marketing to your customers. We don’t rely on gut feel but let the data and our experience help us move forward intelligently.  

DaasityWhat advice would you give to someone in your position at a retail / ecomm company that is looking towards data modernization and adoption to improve their business? 

ZackIt is never too early to get started. We got started too late. We were already busting at the seams trying to run key reports to help us understand consumer buying behavior, LTR models and understanding how to build out customer personas. 

The past few years has seen the overall costs of building out data warehouses & BI tools come way down. Making decisions without a complete view of the data can be catastrophic in a fast-moving DTC world. The longer you wait, the more you may need to rebuild after making mistakes that would have been prevented by understanding your business better earlier. 

DaasityWhat data has been most impactful to the organization to date? 

Zack: There are a few key areas that have been most valuable for TULA: 

Acquisition marketing 

  • Which products to promote 
  • Which products have the highest LTRs, to help us define our target CACs 


  • Lifecycle campaigns for email understanding what was bought first vs. subsequent purchases 

Finance & demand planning 

  • Understanding velocity across channels (direct, amazon and wholesale – future) of specific SKUs 
  • Tighter forecasting and inventory allocation 

DaasityWhat departments / specific users do you see using Looker / data most frequently? 


There are definitely power users organizationally and others that just have dashboards built and emailed daily/weekly but everybody uses Looker to some degree within the organization.  

The digital marketing team, finance, and brand marketing are the primary users today but as we build out our reporting suite both wholesale and operations will become more involved with Looker. 

We are able to focus more on analyzing reports vs. pulling reports and debating numbers from different sources. 

DaasityWhat business outcomes and improvements have you been able to uncover? 

ZackIt’s early as we kicked off with Daasity in June, but were up within 6 weeks. The past few months has been focused in a couple different areas as we’ve started looking at data views that were completely new to the organization. 

We’ve started looking at what products to feature in ads not just from a conversion side but looking more at LTR by first purchase SKU. We’re starting to think more holistically about the entire customer experience and the best entry point for consumers into our brand that drives the highest repeat rate and lifetime revenue. 


A Big Thanks to Zack and the team at TULA for sitting down and chatting with us; looking forward to more conversations in the future! 


If you’d like to learn more about what Daasity & Looker can do for your business, check out this ebook or contact us. And make sure to check out TULA and the innovative products they are bringing to market. 

Preparing for the Holiday Season

How to tackle the short Holiday season: Preparation, Planning and Potential Pitfalls  

The Significance of Thanksgiving in the U.S Retail Economy

Thanksgiving is a crucial marker of the (un)official start of the holiday shopping season in the United States. Traditionally, many adults and students have the day after Thanksgiving off from work and are free to start their holiday shopping. In the past, this meant planning a trip to the local mall or store, and now is a combination of online shopping and in-store pickups.

Why the time between Thanksgiving and Christmas matters

The time between Thanksgiving and Christmas is incredibly important to US retailers because retailers often operate at a loss—or close to it—all year with the anticipation that the holiday season will lead to significantly more sales that gets them “into the black” for the year. This makes preparing for the holiday season one of the most important activities retailers can and should prepare for throughout the year. Both retail stores and online sites must be ready for the deluge of customers with the products they are looking for and deals that are compelling enough to convince those shoppers to open their wallets without giving away too much profit.

Retail benefits of a shorter selling season

One benefit of a shorter selling season, like 2019 when Thanksgiving was at the end of November, is consistent momentum. Because there is 1 less week in the shopping season, consumers tend to stay in the holiday shopping mindset, somewhat out of necessity, which means more steady sales throughout the season until it completely stops just before Christmas.

Another benefit is simply getting through it faster. Because so much is riding on a peak holiday season for many retailers, it means longer than typical hours and can be exhausting. Those long days don’t go away in a longer season, there are just more of them.

A final benefit is more time to prepare. The work seems to never be done and everyone can use those few extra days to put the finishing touches on processes or campaigns.

Retail pitfalls of a shorter selling season

Pitfalls of a shorter selling season include operational execution and fewer days available to ‘make up’ any sales that didn’t come in as expected.

Operations execution in a short season

A short season means that there are fewer days to sell all of the merchandise or services that needs to be sold. For physical goods, this means stores/retailers need to be ready to handle more volume than a typical day, or even a typical sale season. This includes having full inventory on-hand, more frequent inventory replenishment and the ability to checkout more customers. Online stores may need to ramp up their server capabilities to handle the increased traffic, but also must be able to pack and ship the orders that are placed. Each year that passes results in higher and higher expectations of immediate delivery of items ordered online. This can be an unattainable bar during the holiday season simply based on volume increasing exponentially and needing to move through the supply chain to get to a consumers doorstep. This can become particularly challenged in the last few days of the holiday when last minute orders are still coming in and the expectation is that they will arrive by Christmas Eve. Fedex and UPS felt the brunt of this volume back in 2016; they couldn’t keep up so packages were not delivered in time for the holidays.

Fewer days means fewer opportunities

When the season is flying and retail workers are just trying to survive until its over, fewer days seems like a great benefit. But, if holiday sales start out softer than anticipated, then its time to play catchup and implement plan B or C to get more sales booked. A shorter season means fewer days to actually execute on additional promotions, which could make or break the financials for the year.

How to forecast for a shorter selling season

To set the appropriate forecast for a shorter selling season, gather the following components of the sales forecast:

  1. The natural increase in sales that the NRF or others are predicting for the entire season YoY, if applicable
  2. Recent company sales trends, have they been up or down YoY
  3. If you are an online retailer, expected shipping cut-off dates
  4. How many fewer days are in the selling season than the previous year
  5. Last year’s daily sales for the holiday season
  6. Any other trends that may be occurring that are large enough to impact a sales forecast

The easiest way to put together a forecast is to determine the YoY change in the season’s sales based on the factors above. For example, if sales have been +20% all year, odds are, they will be +20% for the holiday season. Layer on that fact that there may be predictions from the NRF or others, such as an overall growth or decrease in sales.

Let’s walk through an example.

Company X has been having a great year and sales have consistently been +25% YoY. Last holiday season, Company X generated $800,000 in sales over the 30-day promotion period. They expect to continue their success and see a 25% increase in holiday sales, so their sales forecast this year is for $1,000,000. If there were the same number of days in this year’s promotion period, they could just apply a 25% increase to each days’ sales over last year and the forecast would be done. However, this year there are 23 days in the promotion period.

The $1,000,000 in sales must then be spread across 23 days instead of 30 to arrive at a daily forecast. This means each actual day is going to be much higher than 25% because more sales will need to occur each day to achieve the 25% seasonal growth. To simplify even further, let’s assume that consumers shop equally each day of the promotion. The table below shows the actual dollar sales each day based on either a 23 day period or 30 day period to get to the same result.

Day 23-day period Daily Revenue 30-day period
1 $43,478 $33,333
2 $43,478 $33,333
3 $43,478 $33,333
$43,478 $33,333
TOTAL $1,000,000 $1,000,000

How to forecast for a longer selling season

A longer selling season will go through a similar exercise as above. It depends how much longer the season is. In the case of the holiday season, 2020 will have 2 extra days between Thanksgiving and Christmas than it did in 2019.

Sticking with Company X from the example above, if sales were going to be forecasted as flat YoY in 2020, then that $1,000,000 would be spread across 25 days instead of 23.

Not All Promotions are equal

The Q4 holiday season is unlike others. Consumers tend to trend in the same way and it is the time of year that the most people are doing the most shopping. Because of the cultural norms to both give gifts and wait to make large purchases for this period, and because the season is anchored by two key national holidays, it is more predictable as to how consumers will respond.

The same methodology of shortening or increasing a promotion length during other times of the year that are specific to your business may not be the same. The general principles apply, but consumers may not be as apt to act exactly the same way this year as last year for a different sale period.

Always remember that forecasting is both art and science.




About Daasity
Daasity is transforming the way companies access and use their data. It is the first and only company to design a proprietary platform specifically for the direct-to-consumer industry that makes business-critical data accessible and usable for strategic decision-making. The company’s mission is to make business-critical data accessible for all DTC brands. Visit or follow us on Twitter and LinkedIn.



6 BIG trends in Data 2020

Last month, Daasity sponsored and attended the Looker JOIN conference, one of the premiere events of the year for data enthusiasts worldwide. Tech based conferences are as fun as they sound, really. Not only do we get to spend 3 days with hundreds of like-minded folks, learn newer and better ways to use a software tool we love, there’s also a lot of data talk from some of the smartest and most passionate folks industry-wide 

Looker’s Chief Product Officer opened one of the keynote speeches with a simple message: “THE DATA NERDS HAVE WON!”. Cue applause. What he said afterwards was something many can relate to. To paraphrase, the time has come where debating data’s importance is a thing of the past; data IS important and everyone knows that. Hiring an analyst is no longer a debate on if that is truly necessary, it’s “how many do we need?”. Positions that used to rely on gut instinct, “the way it’s always been done” or even creative process and production now are embracing the use of data to help guide or determine outcomes.  

Below are some of the 2020 trends in data we found interesting from the 2019 JOIN Conference. 

1.Shift to cloud means more fragmented data 

Everyone is shifting to the cloud; even industries like healthcare. Over the next few years, $500 billion will be spent in cloud infrastructure! Since shifting everything to the cloud is so easy to do, data has become MORE fragmented. There are so many cloud SaaS applications to choose from that do one or many things. The average enterprise has 1100 SaaS applications deployed; Looker itself has 200! Combining all this data from numerous applications is now a requirement as databases are getting bigger and faster, but combining that data is not as easy as it sounds. This exact problem is the genesis of Daasity for ecommerce and direct-to-consumer brands. We saw this problem occurring and have been building out more and more integrations to service this pervasive problem that prevents business users from truly seeing the full picture of their business due to data fragmentation.  

2. Data is growing exponentially 

According to IBM, 90% of data has been created in the past 2 years. We are increasing our data 10 times every 2 years. That means in 6 years our data will be 1,000 times what it is now! It’s hard to comprehend that type of velocity, but that is where we have been and database performance needs to be increasingly improved to keep up. 

3. Traditional Business Intelligence (BI) is fading away 

When it comes to data, business intelligence is a common adjacency. But the BI market is actually shrinking because traditional BI platforms aren’t used in the cloud. The average age of traditional BI tools is 28 years old. The BI tool your company may be using is a millennial. Think about that for a minute and how much has changed in technology in 20+ years.  Because of how these legacy platforms are built, they have not been able to keep up with cloud expansion and processing.   

4. People want more data  

Users are discovering new ways to use data. As people understand data more, they want more options, more ways to use it, to help drive the business. With technology evolving at such a rapid pace, and infrastructure is easier than ever before to implement, it’s significantly easier to empower employees with the data they need for something specific. But, the requests and needs appear to be increasing at an even more rapid pace. The cloud may be an accessible place to store data, but when it comes to leveraging the data, it is tough jumping from one SaaS platform to another to analyze results. With so many data sources, it becomes difficult to know which data source or sources is the source of truth. We’re back to data fragmentation being a challenge that will continue to accelerate. 

5. Data is a product 

Regardless of the industry or company, data is big enough and important enough that it deserves to be treated and managed as a product. With so many data sources, uses and different teams that need it, data infrastructure must be thoughtfully designed and flexible. Doing this early will save time and re-work down the road as a company—and its use of data—grows.   

6. Data is not just backwards looking anymore

Data was traditionally viewed as a way to understand the past, in order to make more informed future decisions. That hasn’t changed, but with the acceleration of AI and machine learning, predictive capabilities are becoming increasingly available. Now your past data can not only help guide you to the answer, but give you a pretty darn accurate answer. This trend will continue to grow at a rapid pace and be utilized in new and interesting ways. 

All of these topics resonated for us at Daasity. This is an exciting time to be involved in this industry. We’re actively working on predictive tools that will launch in 2020. We love helping brands solve the analytics issues they face and make wading through their data easier so that they can focus on strategy and execution rather than drowning in disparate platforms and spreadsheets.  

If you’re a direct-to-consumer brand struggling with data fragmentation, we can help. Contact us for a demo! 




About Daasity
Daasity is transforming the way companies access and use their data. It is the first and only company to design a proprietary platform specifically for the direct-to-consumer industry that makes business-critical data accessible and usable for strategic decision-making. The company’s mission is to make business-critical data accessible for all DTC brands. Visit or follow us on Twitter and LinkedIn.

Daasity Reaches a Milestone and Successfully Raises $2.3M

Daasity Reaches a Milestone and Successfully Raises $2.3M

I’m thrilled to announce that Daasity has reached a major milestone by successfully raising $2.3M in our first round of funding. I believe in what we are building and am elated that our investors do too. Our debut investment round was oversubscribed and led by Okapi Venture Capital.  Serra Ventures, Cove Fund II, 1855 Capital, and Mooring Ventures LLC also participated.

Doing a capital raise, regardless of how large or small, is not for the faint of heart. We’ve been working to continue to deliver excellent value to our current clients while pushing to bring our vision to market faster with a capital infusion. As a result of the funding, we will be expanding our team both in San Diego and on the east coast. I’m excited to accelerate hiring for software engineers and data scientists, among other roles we will be posting in the coming months. What I’m most excited about is the new products and enhancements we can bring to market faster with this addition of working capital.

We are currently working on some exciting new predictive analytics solutions that will launch in 2020 and supplement our existing “D2C Analytics Suite.”

I’m humbled to think back to the days when Daasity had just a couple of customers, and as word spread more D2C brands started to contact us. Soon we doubled our customer base and then doubled again. I’m proud to say that our solutions are becoming a core part of business for 40+ fantastic brands in the ecommerce and D2C space and I expect that number to double again soon.  It’s exciting to see our technology solving their complex data needs, helping them to make smarter business decisions and accelerating revenue growth.

The e-commerce ecosystem is booming, and there is no other tool out there that does what our proprietary data model can do for businesses. It makes complex data from a variety of sources accessible, accurate and easy-to-manage in one tool. It gives a holistic view across all areas critical to growing businesses.

I’m delighted with how far we’ve come this past year and am looking forward to quickly expanding with the success of our fundraising efforts. I’m grateful to our clients and our nimble but mighty team that helped get us to this landmark moment in our company’s history.

For more information on our funding news, click here.

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.


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.


CPO, CPA or ROAS: Which is a better Ecommerce KPI?

What is the difference between CPO and CPA? 

The difference between Cost per Order (CPO) and Cost per Acquisition (CPA) is whether the transaction is from a new customer or any customer. CPO is a commonly used metric among companies in many industries. It is easy to calculate and easy to understand. Cost per Acquisition requires knowing if the transacting customer is a new customer to the business or not. You might also hear or see the metric CAC which is the same metric, standing for Cost per Acquired Customer. 

Which metric is more important? 

Determining which metric between CPO and CPA is most important for your business depends in part on the stage the business is in. If a company is brand new and in high growth mode, the majority of orders are going to be from new customers, so the two metrics will be very similar and focusing on CPO is likely easier to access. As a company builds it’s base of customers but is still in growth mode, breaking out the two metrics will be important to understand how much it is costing the business to continue acquiring customers. Neither metric is more important than the other, they simply are more useful at different stages of growth. 

What is the Cost per Acquisition formula? 

The cost per acquisition formula is variable marketing cost / new customers acquired. This metric can be viewed across the company or segmented by channels or marketing activations. It’s not uncommon to have certain activations or channels delivering different volumes of new customers to file and at different costs. As a rule of thumb, a first transaction from a new customer is always more costly than a transaction from an existing customer. When testing new audiences or new methods of reaching prospects, results can vary wildly. A good practice is to check in on results and costs early and periodically. 

What is the Cost per Order Formula? 

The cost per order formula is variable marketing cost / all orders in the same period. This metric can be viewed across the company or segmented by channels or marketing activations. Different activities will yield different results. Reviewing by segments or activities may indicate where a company should spend more or less to generate the highest return. 

What does ROAS mean and what is it used for? 

ROAS is an abbreviation for Return on Ad Spend or Revenue on Ad Spend. It helps to determine which marketing efforts are driving the most revenue for a business.  

What is the ROAS formula? 

The formula to determine ROAS is Revenue / Marketing Spend. There’s no ‘right’ way to represent it. Some people represent it as a dollar figure, such as $3.12. Others represent it as a percentage, such as 312%. And still others represent it as a number, 3.12. It also may be written or referred to as 3X, loosely translated to 300%. 

When to use ROAS vs. CPO 

Use ROAS rather than CPO if your company sells a large catalog that has widely varying price points. In instances when product price varies largely, average order value can be misleading and measuring by a single metric like CPO can be costly. For example, if the CPO target is $30, and you sell items that retail for $5 and $10, you could be paying upwards of $30 for an order that only drove $10 in revenue. In this case, you lost money on the sale. Using ROAS instead helps to relate actual revenue to how much is spent on media, leading to more effective budget management. 

An alternative approach to using ROAS when product price varies is to set category or sub-category CPO targets. Perhaps the average CPO is $30, but CPO targets for $10 items might be $6 and CPO targets for items that retail over $100 may be $50. 

How to determine your CPO or CPA goal 

Determining what your CPO or CPA goal should be depends on your business and financial objectives. Some questions to consider when determining what a CPO target should be are: 

  1. What is the average customer lifetime value?
  2. Is the company willing to invest to acquire customers that take months or years to pay back?
  3. Does the average revenue or margin per order vary across categories or customer types? 
  4. What types of sales or marketing activities does the company currently or plan to invest in? 
  5. Are those activities upper funnel (awareness generating) or lower funnel? 

What is a good cost per conversion? 

A good cost per conversion depends on your company’s financial goals. For a new company, cost per conversions are typically much higher as the brand or product has low awareness. If there are fast growth goals, a higher cost per conversion will be tolerated. Established or more mature companies tend to have lower cost per conversion both because the brand may be more established and repeat customers buying through low cost channels such as directly, branded SEM or email help to bring the overall cost per conversion down. 

Established brands typically strive for cost per conversion that is lower than the product gross margin. 

The best way to use CPO, CPA and ROAS 

The best way to use CPO, CPA and ROAS is based on business objectives. Work with category or finance leaders to determine what an acceptable CPO or CPA should be to meet financial objectives. Segmenting by channel, category or product can also give more flexibility, greater accuracy and increase profit. When segmenting, ensure stakeholders understand the methodology being used to determine acceptable ranges and communicate updates. Ultimately, there is no wrong way to use these metrics, but segmenting should yield the most positive results. 




For more information or a demo, visit

About Daasity
Daasity is transforming the way companies access and use their data. It is the first and only company to design a proprietary platform specifically for the direct-to-consumer industry that makes business-critical data accessible and usable for strategic decision-making. The company’s mission is to make business-critical data accessible for all DTC brands. Visit or LinkedIn.

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: {



last_order AS




MAX(order_date) AS last_order_date

FROM order







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






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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Enabling An Omnichannel Data Solution with Daasity 

“How are sales this month?” Seems like a straightforward, simple-to-answer question, especially with so much data and tools right at our fingertips. If you are an omnichannel retailer, which most are, this question probably takes some time to answer. 

 What does omnichannel mean? 

Being an omnichannel retailer means you sell your product or service across multiple sales channels. For example, you might have an online site that you sell direct to consumers, and via Amazon or other marketplaces. Some brands also have their own branded stores and sell through other big box stores. The advancements in technology and an easy user experience across platforms has made it easy for companies to set up multiple storefronts and many are using at least 2 sales channels today. 

How to measure omnichannel sales 

Measuring omnichannel sales effectively requires a data warehouse that combines all existing sales sources. Data warehouses are typical of large enterprise corporations, but are not as typical in smaller brands. The reason a data warehouse is required is because each sales channel (i.e., online store, Amazon, POS system) will have a different way of storing and coding the data. Data can only be combined when the attributes match exactly. But, with the right logic applied to each data source, the data from each source can be transformed to one unified source to provide a holistic sales view. 

Why having an omnichannel sales view is important? 

An omnichannel sales view is important to enable better decision making for both day to day management and long term strategies. Looking at each platform in a vaccum or silo can cause companies to miss important trends or make decisions that may not be the best for the overall company.  

Let’s explore a few examples 

Example 1: Marketing Impact 

Let’s face it, consumers don’t do what we want them to. Even in the digital world of trackability, accountability and pixels upon pixels, the directly measurable impact of marketing activities are never the entire story. Running ads on facebook, for example, might lead a consumer to look up your product on Amazon. Because these platforms are separate, it would be difficult to make a connection. Having all data connected in a data warehouse makes it easier to see connections across platforms and channels.  

Example 2: Product Inventory Management 

How about looking at product sales holistically? You probably have an inventory management system or warehouse management system (IMS/WMS) that lets you know the general in-stock quantity. Surely someone is reviewing reports to be aware of fast and slow movers, right? RIGHT? Where products are selling (ecommerce, marketplace, retail) and how quickly can lead you to make different decisions. Are you running low on a top product in retail? If retail customers are more valuable than Amazon, for example, then you may make a decision to not sell that particular product on Amazon in the short term to preserve inventory for retail customers. However, if you are just looking at a top level in-stock number, then you may end up letting this product sell out and miss out on more valuable customers and profit.  

These are just two small examples of the power of an omnichannel view of your business.  


A holistic view of your company’s performance can dramatically change how you make decisions to run your business. If creating or enabling a centralized data warehouse seems like an overwhelming or out of reach project—it doesn’t have to be. Daasity’s proprietary platform is designed with D2C brands in mind. The platform can create your data warehouse in a matter of days, not months all at an affordable cost. If you are drowning in spreadsheets, or don’t even know where to start, contact Daasity today. 

For more information or a demo, visit

About Daasity
Daasity is transforming the way companies access and use their data. It is the first and only company to design a proprietary platform specifically for the direct-to-consumer industry that makes business-critical data accessible and usable for strategic decision-making. The company’s mission is to make business-critical data accessible for all DTC brands. Visit or LinkedIn.