Data Infrastructure for Brands doing <$1m

Tracking: The Lifeblood of Growth

Effective data tracking is crucial for business growth, particularly in online sales and marketing. Overlooking or under-valuing this crucial subject matter can decisively rout a business’s growth ambitions early on.

The first is that data tracking impacts investment optimization, or deciding how much time and money to spend on each marketing channel.

Inaccurate or unavailable data leaves marketers uncertain about where to invest time and funds. The resulting decisions could quickly lead to inefficient spends that hamstring the company’s ability to fund not only its growth ambitions but its operating expenses as well.

It could be tempting to sacrifice careful implementation of data tracking in the name of speed. But these seemingly small decisions to delay or forgo setting up the correct tracking could lead to much larger consequences.

For example, if a brand’s paid search channel is over-valued due to a tracking error, stakeholders and marketers might decide to increase spend into that channel the following year. If that increase does not produce a lift in revenue, profit margins will shrink, necessitating cutbacks in other areas. In other words, making spend decisions on faulty data can have effects as wide-ranging as personnel being laid-off from employment.

In addition to the impact on investment decisions, data tracking affects the day-to-day decision-making of marketing teams who optimize initiatives such as ad campaigns and events. By carefully monitoring the results of these initiatives, marketers can decide how best to further optimize the brand’s marketing program to hit growth goals.

Common Pitfalls

Having covered the most serious tracking-related mistake for brands (having little-to-no tracking at all), there are two more that are worth highlighting. Steer clear of these common pitfalls.

(1) “Patching” Tracking Over Time:

On the opposite end of the spectrum from brands that have no data tracking, are those that have messy and convoluted tracking infrastructures.

This is common in situations where the initial tracking setup was rushed, necessitating further attempts to patch-up tracking on the fly, perhaps by adding additional tracking pixels or bringing in new reporting tools.

Re-visiting existing tracking setups is encouraged over the course of a reasonable timeframe (perhaps quarterly), but existing tracking should be thoroughly audited and archived or removed before new items are added.

This will allow analysts and marketers who are new to the brand to correctly interact with the data and tracking infrastructure instead of dealing with confusing, inactive, or duplicate items.

(2) Only Using One Attribution Method:

In the quest for clear ROI, inexperienced stakeholders often rely on a single tracking source that they use to make critical decisions about spend and strategy.

For example, a stakeholder may decide that their “source of truth” for revenue production will be Google Analytics, where revenue is by default assigned to marketing sources based on the last source a customer interacted with.

The reason this approach is faulty is that no single data source or data model can perfectly capture the complexities of a consumer’s journey from awareness to paying customer.

Additionally, the platforms those consumers interact with throughout that journey are typically owned by businesses that compete with one another and do not tend to cooperate or share data (think large tech companies like Meta and Google).

Further, evolving attitudes and activism around consumer privacy mean that marketers may never have access to the type of hyper-clear shopper data that may have caused many eyes to light up with dollar signs during the advent of tracking online behavior. And rightly so. If the job of a marketer is to influence consumer behavior to enhance the value of the businesses they serve, then paying attention to the preferences and concerns of those users is in everyone’s best interest.

Therefore, the best approach to attribution is one that weighs input among various data sources, and combines them to triangulate the truth. This should be further paired with an understanding that there will always be some art mixed in with the science whenever human will is part of the equation.

For the foreseeable future, businesses will never know with 100% certainty which marketing inputs will produce optimal results. However, by implementing and mastering the tools discussed below, they can do a pretty good job of setting their business up for more efficient decision-making and more effective growth.

Basic Tracking Tools

The basic tools and systems required for proper tracking will be unique to your business. Below, we outline some of the most common.

(1) E-commerce Cart Platforms:

1. For D2C businesses, building your website on a trusted e-commerce platform like Shopify enables scalable management of web features, orders, products, customer information, and more.

Shopify also includes analytics features for exploring your data - though dedicated web analytics tools like Google Analytics should be used for anything involving web behavior and traffic sources.

E-commerce platforms like Shopify serve as a ‘source of truth’ for revenue and orders since all customer orders are processed through their platform. Shopify’s revenue and order tracking provide an accurate view of the orders and revenue your business is driving.

(2) Customer Relationship Management Platforms (CRMs):

CRMs serve as the system of record for sales interactions, opportunities, and customer information, particularly for B2B companies, such as those selling software-as-a-service (SaaS).

CRM software like Salesforce, HubSpot CRM, and Zoho CRM are essential for B2B companies to track leads, manage accounts, and build relationships over time.

Key data tracked includes:

• Lead and account records

• Contact profiles for individuals

• Interactions like emails, calls, and meetings

• Opportunities and deals in progress

• Sales pipeline status and forecasts

CRM adoption and usage is critical for B2B companies to optimize sales processes, account management, and measure sales performance over time. Proper tracking and management of CRM data ensures revenue goals can be monitored and forecasted accurately.

(3) Google Tag Manager: Google Tag Manager (GTM) is a free tag management system that allows you to quickly and easily deploy tracking tags on your website or mobile app.

It provides a graphical user interface to manage tags used for tracking and analytics without having to edit code directly on your site.

GTM is valuable for implementing web analytics, marketing tags, conversion tracking, and more. All tags are controlled through a single GTM container added to each page.

For any business, GTM provides flexibility to add new tools over time as needs change. Marketing tags, lead tracking, and other tags can be added without taxing development resources.

Proper GTM setup is required to capture all visitor data across platforms and channels. It serves as an excellent launching point for comprehensive analytics.

(4) Advertising Conversion Pixels

Conversion pixels are small tracking pixels installed on your website that allow you to measure conversions from your advertising campaigns.

Examples Include:

• Meta Pixel - Tracks conversions from Facebook/Instagram ads. Allows creation of custom audiences and optimizing ads based on site engagement.

• Google Ads Conversion Tracking - Tracks conversions from Google Ads. Used to optimize ads and remarket to site visitors.

Benefits of implementing conversion pixels include:

• Seeing which ads drive conversions on your site

• Building targeted remarketing lists based on site activity

• Optimizing ad spend toward better performing creative and audiences

Conversion pixels can be easily implemented through Google Tag Manager rather than editing site code. This provides flexibility to add/remove pixels as needed.

Advanced Tracking Tools

(1) Data Warehouses

A data warehouse is a centralized repository that aggregates data from multiple sources for analysis and reporting.

They provide a single source of truth for business data across different systems like CRM, e-commerce platforms, marketing tools, etc.

Key benefits of a data warehouse include:

• Consolidated view of data from disparate sources

• Ability to connect historical and current data

• SQL-based querying for flexibility

• Scalable to handle large data volumes

• Data modeling and schema optimization

• Role-based access controls for security

ETL (extract, transform, load) processes are used to pull data from sources, prepare it, and load it into the warehouse. ETL is essential for structuring quality data.

Common data warehouse options include cloud solutions like Snowflake, BigQuery, Redshift, and Azure Synapse. Many business intelligence tools integrate with warehouses to visualize and explore data.

(2) Multi-Touch Attribution (MTA)

Multi-touch attribution refers to modeling the impact of multiple interactions across channels and touchpoints on conversions.

For analytics, data warehouses allow you to analyze a wide breadth of business data in one place. This provides complete insights not possible with individual sources.

Well-designed data architecture ensures high-quality analysis and reporting as your data scales over time. Your warehouse strategy and design should anticipate future analytics needs.

Rather than attributing conversions to the last click or channel, MTA aims to distribute credit to all influences preceding a conversion.

Common MTA models include:

• Algorithmic - Uses statistical methods to assign weights to touchpoints

• Time decay - Recent touchpoints get more credit

• Position-based - First, last, or evenly distributed attribution

Solutions like Google Analytics, Adobe Analytics, attribution platforms, and pixel-based tools like Triple Whale's Triple Pixel provide MTA modeling capabilities.

MTA provides a more nuanced view of marketing performance than last-click models, better reflecting the influence of various interactions over time.

(3) Marketing Mix Modeling (MMM)

Marketing mix modeling is an analytical approach to measuring the impact of overall marketing spend and tactics on business results.

MMM statistically models how changes in marketing activities across channels like TV, radio, print ads, digital media etc. affect key performance indicators.

MMM quantifies the ROI of marketing tactics and how they work together to impact sales, site traffic, brand awareness and other goals.

Benefits of MMM include:

• Holistic view beyond just digital data

• Accounts for real-world business factors

• Optimizes the entire marketing mix, not just attribution

MMM uncovers optimized spending levels across every channel to maximize ROI. It brings clarity to marketing ROI in a privacy-focused landscape.

For strategic insight, businesses should leverage MMM to quantify how all marketing levers work together to drive growth.

What We Recommend

For brands in the first few years of operations, we recommend the following setup. With the exception of our recommendation of the Triple Pixel (starting at $300/mo. as of this writing), the tracking tools below are all free.

• MTA Pixel: A multi-touch-attribution pixel such as the Triple Pixel by Triple Whale to help analyze revenue contribution by channel. Longer-term, we recommend pairing MTA with a marketing mix model (MMM), but MMMs generally require about 1-to-2 years of data to produce valid insights.

• Post-Purchase Survey: A simple post-purchase survey, asking shoppers to choose between a list of 4-5 possible channels that they may have found the brand through.

• Google Analytics 4: Google Analytics should primarily be used to analyze website behavior and traffic sources.

• Ad Tracking: Refer to the tracking documentation for all relevant ad channels to implement tracking for that channel. All pixels - such as the Meta Pixel and Google Tag - should be housed within Google Tag Manager.

With that simple and affordable tracking infrastructure in place, your business will be equipped to collect the data that powers good growth marketing decisions.