Multiple teams, one dataset: How Bloom & Wild broke down its data silos with Contentsquare

About Bloom & Wild

What started as a letterbox flowers concept has grown into one of the UK's most loved gifting brands. 

Today, Bloom & Wild sells everything from plants and hand-tied bouquets to hampers, chocolates, baby gifts, and standalone greeting cards. The business runs across three brands—Bloom & Wild (UK), Bloomon (Netherlands and Europe), and Bergamotte (France)—and seven local websites.

With multiple brands, websites, and apps, the product team generates a huge amount of behavioral data. Making it work for every team, not just analysts, is a challenge. That's the problem Lottie Linter, Head of Product Analytics, has been solving since she joined in 2021. She’s focused on building the analytics infrastructure that powers decisions across the business—from product teams and user researchers to commercial leaders.

The challenge

When Lottie joined, Contentsquare was already in place, but its potential was largely untapped. Behavioral data existed, but it sat in isolation, disconnected from the wider business and mostly accessible only to analysts.

For a fast-growing business, that wasn't sustainable. Key pain points included:

• Fragmented data: Contentsquare behavioral data wasn't connected to business data like orders, customer tiers, and email engagement—making it hard to see the full picture

• Analyst dependency: without a clean, self-serve structure, every question got routed through the analytics team, creating bottlenecks and slowing down decisions

• Missing context: quantitative data alone couldn't explain the why behind customer behavior, making it difficult to design the right experiments or product changes

• Scaling complexity: keeping event taxonomies consistent across all their digital platforms needed a disciplined approach—one that didn't yet fully exist

The solution

Bloom & Wild made Contentsquare the foundation of a connected, multi-team data stack. 

The approach rests on two pillars: 

1. Product Analytics, which gives teams direct access to behavioral insights through tools like Session Replay and Funnels

2. Data Connect, which sends that data into Bloom & Wild's Snowflake warehouse, where it's combined with business data and made available to authorised teams across the business

The goal is simple: give every team the right data, in the right place, with the right context, so relevant teams can make informed decisions. 

How Contentsquare helps Bloom & Wild 

1. Powering data-backed decisions with a single source of truth

One of the most impactful things Bloom & Wild has done with Contentsquare is connect its behavioral data to revenue, stock, and email insights using Data Connect, giving everyone a combined view across the board and democratizing intelligence. 

Here's how it works:

1. Contentsquare exports behavioral data to Snowflake: critical events like order confirmed get updated every two hours; everything else, every 24 hours

2. Other data sources land in the same warehouse: email and push notification data from Braze, order and stock data from Bloom & Wild's own systems, and manual annotations from Google Sheets

3. Contentsquare generates a unique user key: this enables Bloom & Wild to combine behavioral and customer data within its own systems

4. DBT creates models that combine every source into a single, clean dataset

5. The unified data flows into Tableau, giving commercial and leadership teams dashboards that blend behavioral, financial, and operational data in one place

This gives senior stakeholders a unified business perspective that integrates behavioral, financial, and operational data into a single source of truth.

2. Understanding and maximizing product feature adoption 

To make Product Analytics work for every team, Lottie's team built a clear, consistent event taxonomy: naming events descriptively, writing detailed descriptions, and categorizing everything across their website and app. Their principle: anyone in the business should be able to open the tool, search for what they need, and find it.

"Someone who's not an analyst, who's not using the tool day in, day out, can go into the tool, type in what they're thinking about looking at on the website or the app, and find that event."

Lottie Linter

Head of Product Analytics

One of their most-used features is Funnels, which allows teams to filter their conversion funnels by user properties, and jump straight into session replays to watch a specific step.

Contentsquare’s Session Replay summaries have taken this further—summarizing hundreds of sessions and key user behaviors, so teams can act on patterns, not just individual recordings.

"The AI summaries feature in session replays has been super useful. It summarizes hundreds of session replays for you, the key behaviors that customers are doing, so you can make decisions accordingly."

Lottie Linter

Head of Product Analytics

3. Targeting the right customers for qualitative research

Using Data Connect, Bloom & Wild’s user researchers pinpointed the right customers to talk to. Not just based on what page they visited, but precisely where they dropped off, how long they've been a customer, and what their history with the brand looks like.

For example, when Bloom & Wild launched its new standalone greetings cards journey, Lottie's team built a funnel covering the key steps: 

Product listing page -> product details page -> personalization -> add-on -> delivery details

This funnel data flowed into their data warehouse, allowing them to cross-reference drop-off behavior with customer tier data, creating a targeted list of users for the research team to contact and interview further. 

Targeted interviews revealed something surprising: many customers hadn't realized they were looking at a new product at all—they assumed they were on the existing cards-with-flowers section of the site. 

This real customer experience insight led the team to: 

• Experiment with ideas (changing the word ‘personalized,’ because customers expected it to mean something different) 

• Rethink their proposition (creating card bundles rather than single cards) 

• Refine their marketing angles

"We wouldn't have been able to get [those insights] without being able to target these very specific users that had done very specific behaviors."

Lottie Linter

Head of Product Analytics

The results

With Contentsquare’s Product Analytics and Data Connect embedded into its data stack, Bloom & Wild has built an infrastructure that delivers value at every level:

• Improved time to insight: product managers, designers, analysts, and researchers all use Contentsquare across their workflows—reducing the time analysts spend fielding ad-hoc requests

• Faster decision-making: with behavioral and business data in a single warehouse, teams move from question to insight without switching tools or waiting on data pulls

• Refined product roadmap: deeper insight into customer journeys and behavior helps shape the experiment roadmap and product strategy

What's next

The next step for Bloom & Wild is A/B testing—powered by Data Connect. By running experiments directly on its fully joined Snowflake dataset. Because everything runs through the same connected data stack, business-specific metrics that would never make it into a front-end analytics tool become fair game.

"We've got all of that rich behavioral data joined up with all of our business data, and all of that can be used for A/B testing, which is so valuable. We can have any primary metric you want with any dimension, any filter on it—and all of the stats is done for us," says Lottie.

Alongside that, Bloom & Wild is laying the groundwork for what Lottie calls "democratizing AI insights"—making it possible for teams to query their data via Sense and MCP, without needing an analyst in the loop. But doing it responsibly matters. The risk isn't just that people get answers—it's that they get the wrong ones. 

"We're working on creating a centralized semantic layer, and then from there, creating an agent that has all the business context from the different data sources so that when you go and query, you get the right information with the right context, the metric definitions are all there, and you can add even more value."

Lottie Linter

Head of Product Analytics

It's an ambitious vision—built on the behavioral data foundation that Contentsquare helped create.