In this article, we’ll cover data and the ecommerce industry, why data is so difficult to get right, and the pain points of a CDP as a data solution.
“We had such a complex [data] system. We had Snowflake, Fivetran, and so many different platforms. And then on top of that, to power those platforms, we needed a data scientist and a data architecture engineer. We just didn’t have those resources.” –Tran Ngo, VP of Marketing and E-Commerce, True Botanicals
Everyone in the DTC industry knows that their data is valuable, but unlocking the true value hidden in their data is a difficult challenge that every company experiences.
As a data company, we’ve seen first-hand how difficult data can be – many of our customers have tried hacking together existing solutions to make the data work for them, just like the example above.
In this article, we’ll cover data and the ecommerce industry, why data is so difficult to get right, and the pain points of a CDP as a data solution. Then, we’ll cover how we helped True Botanicals with their data problem and discuss how our no-code Data CRM is one of the best alternative data solutions.
Hundreds of millions of customers shop online – and every time they do, they are leaving a digital footprint.
Shopify is the fastest growing Ecommerce platform with over 1.75 million brands and 457M shoppers. They not only make it easy for brands to build their own online DTC store from the ground up, but also enable brands to be more data-driven as their platform captures all the important Ecommerce customer data.
The influx of new customer data being captured allows DTC brands to better understand their shoppers and their behaviors. But there’s a catch – it takes time, money, and data expertise to decipher the customer insights hidden in the data.
Before delving into the technicalities of why data is so hard, let’s first cover why data is important for Ecommerce brands.
A recent report by Twilio Segment found that 60% of consumers say they will buy again from the same brand after a personalized shopping experience. Driving repeat purchasers should be the top priority of any brand because reports show that customers who buy at least twice have a higher CLV, spend more over time, and refer more new business.
In order for brands to create tailored shopping experiences for each individual shopper, they not only need to know key customer data points, such as demographic data, engagement data, and behavioral data, but also need to know how to use that data to create meaningful insights.
Having these data insights readily accessible allows DTC brands to know what message to send to what customer at what time. This is key to creating personalized shopping experiences (e.g. a loyal customer buying hundreds of dollars worth of lipstick should be getting a different message than a first time customer buying the facial oil).
Now that we’ve established why data is crucial for brands, let’s talk about what makes data so hard.
With tightening data privacy laws and increasing advertising costs, knowing how to leverage this first party data can make or break an ecommerce company. And while there is a wealth of ecommerce data available for brands to capture, there are still quite a few data hurdles to overcome before reaping the benefits. These obstacles are: 1) data is difficult to initially set up, 2) data is time-consuming, 3) data is often siloed and fragmented, and 4) data is not intuitive.
If you’ve been in the ecommerce industry for a while, you’ve probably heard about Customer Data Platforms (aka CDP) as the golden standard to solve all ecommerce data problems. According to the CDP Institute, a CDP “is packaged software that creates a persistent, unified customer database that is accessible to other systems”.
To put it in simpler terms, a CDP allows for companies to build a single customer profile for each one of their customers by combining all their different data sources.
While CDPs are powerful and can really help a company unlock that next level of growth, there are still some drawbacks when we’re talking about CDPs for DTC ecommerce companies.
For an ecommerce company to analyze and understand their customer data with a CDP, they first need to get it all set up.
To begin with, brands need to know what type of data they want to collect. Then, they need to set up the actual technological infrastructure to not only capture and store the data, but also make sense of the data.
Without going into all the detail, this setup process includes building the database architecture, finding a data warehouse to house the data, connecting the data ETL pipelines to the data sources, then building algorithms on top to actually find actionable insights.
While platforms like Shopify are collecting most of the important customer data for ecommerce companies, brands still need a lot of upfront investment to start using their data.
In order to set up and maintain the data infrastructure to support a CDP mentioned in the above paragraph, a company would need a full team of data people: data engineer, data architect, data scientist, and a marketing analyst. According to Glassdoor, the average salary of a data scientist alone is around $123,928 USD / year.
Ecommerce stores typically do not have the budget to hire armies of data scientists like large tech companies like LinkedIn (fun side note: Tresl Segments co-founders were actually data scientists at LinkedIn).
Beyond the human resources needed to set up and maintain the data architecture, data pipelines, data warehouse, and CDP, a brand would also need to pay for all these digital technologies required.
Aside from a cost standpoint, designing the data architecture, setting up the data pipelines, and just getting everything up and running takes time. It can take anywhere from 3 to 6 months just to get everything setup.
Below is an image taken directly from Lexer, a popular CDP for retailers, that demonstrates the average timeline to get things up and running with a CDP.
“We had such a complex [data] system. We had Snowflake, Fivetran, and so many different platforms. And then on top of that, to power those platforms, we needed a data scientist and a data architecture engineer. We just didn’t have those resources.”
–Tran Ngo, VP of Marketing and E-Commerce, True Botanicals
Last but not least, data in ecommerce is often siloed and fragmented. What this means is that the various data points are often scattered across all the different digital technologies and apps that ecommerce stores use.
For example, there is email data, Google ad data, Meta Facebook data, product data, order data, shipping data, and more! According to Shopify statistics, there are over 8,000+ apps in their ecosystem, and an average Shopify merchant uses at least 6 apps – this leads to a lot of data!
And all this data is not housed in one database. So, again, it takes time to set everything up and connect everything so the data is actually usable.
If a brand has managed to overcome all the obstacles mentioned previously, they would still need to build complex algorithms using the data to find actual meaningful insights that can help them create better experiences for their customers.
And since data is not intuitive, usually a trained data scientist is needed to make sense of all the data. So we’re back to square one.
While we don’t argue the fact that CDPs do work and allow for companies to leverage their data and be more data-driven, it is just not always a feasible solution for growing DTC brands because they often do not have the same resources as large tech companies.
In the same DTC Podcast mentioned earlier, Tran Ngo from True Botanicals also discusses her experience working at Google versus working at her DTC brand. “Working at such a large company like Google, you have in-house data scientists, you have PhDs and behavioral scientists. You have literally everything at your disposal, but when you come to a direct to consumer brand, you have to be a lot scrappier. You have to hustle. You don't have as many resources at hand.”
So what is a scrappier alternative to a CDP? We think our no-code Data CRM “Segments” is the perfect solution.
“Powerful like CDP and approachable like an app.” -John Chao, CEO and Co-founder, Tresl Segments
Founded by former LinkedIn data scientists, Tresl Segments gives Ecommerce brands both clarity and control of their customer data. We make data analytics accessible to every business owner and DTC marketer, data savvy or starter, without ever needing to write a line of code.
Think of it as the power of a data team condensed in an app – enjoy the same analytical capabilities enjoyed by big tech companies, but at a fraction of the cost.
So what does this exactly entail?
Remember how a CDP is quite difficult to set up and can take months to get up and running? Well, Segments can get up and running with just a few clicks – no need for all the data pipelines, data warehouses, data architecture, etc.
Our platform is connected directly to Shopify’s API – we not only download all of a brand’s customer, order, and product data within just a few hours, but we already have all the algorithms pre-made. Once a store’s data is loaded, a DTC brand can immediately access the insights hidden within their data.
Segments is not only connected to your Shopify data, but we can also connect directly to your email data and advertising data. There is no need to build ETL pipelines for each individual data source as we’ve already done the hard work for you. Just connect your channels, and we’ll pull in the data to give you a unified view of your brand’s data. Monitor, discuss, and share without ever changing apps.
Our simple UI requires no learning curve or weeks of training. We believe that marketers shouldn’t have to learn how to be data scientists; DTC marketers should not be writing SQL queries to find data insights, they should be able to easily access them.
“Not only did it give me the data that I needed to inform my marketing strategy, but it was just beautiful, easy to use. I could take screenshots of some of the cohort analysis reports or repeat rates and just share that with my boss and the team. It's amazing.” -Tran Ngo, VP of Marketing and E-Commerce, True Botanicals
Our 30+ prebuilt customer segments are derived from machine learning and market intelligence. Answer your most complicated marketing questions. Pivot segments across different dates and customer groups to find new opportunities for a winning campaign.
Sync segments to your marketing channels so you can send the right message to the right customer at the right time.
So let’s bring it back down to Earth and discuss a more concrete example: how did we solve True Botanical’s data problem mentioned at the start of the article?
Using our existing data infrastructure, we created a prototype data warehouse and no-code Data CRM for True Botanicals to easily access the gems in their data. Within one year of leveraging Segments, True Botanicals saw a 400% increase in new subscribers and a 128% increase in purchases from existing subscribers.
“I feel very strongly that the marketers of the future are going to have to balance both human empathy, the brand side, and analytical rigor, which is the data science [side]. And that's how you really drive transformational business results, like True Botanicals. Tresl is a true gem for any small and medium-sized businesses looking for the same tools as their heavyweight peers. Tresl makes data analysis easy, digestible, and accessible” –Tran Ngo, VP of Marketing and E-Commerce, True Botanicals
Don’t believe us? Install a free 14-day trial, book a call with one of our data scientists, and see for yourself the transformative power of a No-Code Data CRM. Create personalized experiences for your customers using data.
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