As digital retailers increasingly rely on data to drive their business decisions, implicitly and explicitly acquiring data from your customers is essential in delivering a personalized experience that customers have come to expect.
There is immense opportunity in collecting two types of data: “zero-party data” (data shared directly with you by the customer) and “first-party data” (data you collect when your audience engages with your website or app.)
Collecting and managing this data can be a challenge but doing so provides a powerful way to gain insights into your customers' behavior and preferences and can help you tailor your marketing mix to be more efficient and profitable.
In this blog post, we'll define and explore the importance of first and zero-party data sets, their benefits, and some tips on getting started.
First-party data is customer behavioral data collected on your website or app, not through a third-party database. These datasets in ecommerce describe what campaign brought a user to your site, how much time they spent on site, what products they browsed, what products were added and then abandoned in the cart, how often they returned to the site, and from what device they visited. In short, it’s observable-interaction data that you own because it’s been captured directly on YOUR website.
Unifying this type of behavioral site information helps brands map out which channels drive the most sales, be it SEO strategies, paid media, email, SMS, or influencers. Thriving ecommerce businesses activate first-party data to reliably predict consumer actions.
Next, first-party data is used to create a more personalized brand experience onsite and within owned channels like email or SMS. Without engaging in these strategies, digital retailers risk not recognizing the behavioral patterns of their most valuable customers. First-party data can also be used to determine buyer affinities, with reasonable confidence, without capturing that information directly in a form. For example, if a customer spends most of their time in a certain category of your website, or filters for certain colors, we can determine some of their shopping preferences. This information helps retailers make an educated guess about curating content for designated audiences.
Zero-party is data that a customer provides you directly. Examples in ecommerce are data capture experiences where a customer fills out a preferences form, shares post-purchase comments or ratings, submits a Net Promoter Score, among others.
Having this information directly from the consumer is often more reliable than first-party data since it was given intentionally.
However, collecting zero-party data can be tough and requires creative ways to get it from your site visitors, given that most users blow through form requests. It also comes with elevated customer expectations, given they expect to receive something remarkable in return for providing detailed information about their likes, dislikes, and interests.
When zero-party data is layered with first-party data, we can create highly personalized and profitable experiences that keep customers coming back for more, whether that be products, content, or specific offers.
Post iOS, brands are waking up to the fact that owning their customer datasets is a critical component to accurately assess what's working in their marketing mix. Meta, Shopify and Google will provide lots of data, but they all have a vested interest in sharing the data points that keep their own businesses profitable. Furthermore, the data is splintered among the various platforms. There is no single source of truth. Many companies were blindsided by Apple's unexpected data policy changes. Overnight, DTC growth curves were completely wrecked. No brand (or board) wants to have that experience again, so a customer data strategy is a smart hedge.
It's falling back on companies to examine their customer data to determine what's working and be able to deploy marketing capital in smart ways. With the advent of big data and analytics, marketers can now track and measure the ROI of their marketing campaigns with unprecedented accuracy. However, even with this newfound ability to track and measure ROI, many marketers are still wasting money on campaigns that don't work.
The reason for this wastefulness is twofold: First, there's a disconnect between what marketers think works and what actually works. Second, there's a lack of coordination between different marketing channels. Too often, marketers treat each channel in isolation without considering how it fits into the overall marketing mix.
The solution to this problem is twofold as well: First, marketers need to better understand which channels are actually working for them. Second, they must coordinate their efforts across all channels to create a more cohesive and effective marketing strategy.
By doing these two things, marketers can reduce wasted spending and finally start getting the most out of their budgets.
Brands that build and activate these datasets will have a powerful asset for raising capital and building long-term value. As they gain a deeper understanding of their customers, they can anticipate changes in the marketplace, capitalize on buying behavior shifts and deliver remarkable and profitable experiences. In today's competitive landscape, this type of agility can be the difference between success and failure.
A brand with a deep understanding of its customers is better positioned to deliver predictable results. By acquiring high-value customers through efficient campaigns and using first- and zero-party data to finetune their economic engine, brands can position themselves as attractive investment opportunities.
Don't write a line of code for a database until you have buy-in at the most senior level that data is a strategic asset and needs to be treated as such with proper investment. Like capital, data has compounding benefits.
Collecting, organizing and activating first- and zero-party data is not a small feat. Without senior management's buy-in on the vision, the projects won't have the support to get long-term returns, given the time it takes for datasets to mature and yield powerful insights.
Like a 401K, data compounds over time, and it's much better to start investing at 25 versus 45.
One of our favorite case studies around this effort is with a mid-market omnichannel retailer that has been in business for nearly 80 years and underwent a digital transformation about five years ago. The ecommerce engine was fueled by clean data, and as a result, the company grew 42 percent after being flat for almost five years. Most importantly, this retailer competed with publicly traded companies and expanded dramatically during the pandemic despite the brick-and-mortar store being closed.
Without this kind of profitable growth, the business would have likely expired. The fuel behind everything was customer, campaign, and product catalog data humming together to create a flywheel.
Those that build these datasets will have a powerful asset for raising capital and building long-term value. They will acquire high-value customers through efficient campaigns.
As they gain a deeper understanding of their customers, they can anticipate changes in the marketplace and deliver remarkable and profitable experiences. In today's competitive landscape, this type of agility can be the difference between success and failure.
By understanding their customers more and more, they'll be able to capitalize on buying behavior shifts and capture market share by delivering remarkable experiences that also deliver remarkable revenue and profit with predictability.
Investors are always looking for predictability, and a brand with a deep understanding of its customers is better positioned to deliver predictable results. By acquiring high-value customers through efficient campaigns and using first-and zero-party data to fine-tune their economic engine, brands can position themselves as attractive investment opportunities.