Your data has answers. Let’s find them together.

I work with fractional CDOs to do the deep analytical work their client’s team doesn’t have time for — retention, CLV, sales patterns and the experimentation opportunities hiding in the results.

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The data is there. The answers aren’t coming fast enough.

Most fractional CDO engagements have data infrastructure in place or being built. The systems are running or taking shape and there are engineers doing the heavy lifting. What’s missing is someone who understands the business well enough to know what questions the data should be answering — including the ones nobody has thought to ask yet.

The client has no resources

The client has data systems but no dedicated analyst. The engineers can build and maintain but nobody is doing the work of understanding what the data is actually saying about the business.

You don’t have a team

You may have engineers on hand but not analysts with the capacity to sit in the data and do the sustained, deep work of finding what matters. You need someone with the SQL depth to work with complex data in whatever state it’s in and the instinct to surface what matters.

The client’s team has no time

There’s a data team but they’re maintaining pipelines and responding to ad hoc requests. Nobody has the capacity to step back and ask whether the business is even looking at the right things.

What you need is someone who can understand your client’s business well enough to find the answers they didn’t know they needed.

Here’s what I can do with your client’s data

Retention analysis

Understanding why customers stay or leave is one of the most valuable things a business can know. I dig into cohort behaviour, churn patterns and engagement signals to surface what’s actually driving retention — and what’s quietly working against it.

Cohort analysis and churn modelling via SQL
Behavioural signals that predict retention or drop-off
Plain-language findings the business can act on

CLV modelling

Most businesses have a rough sense of customer value. Few have a clear model of what drives it. I build CLV analysis that segments customers by real behaviour and identifies where the highest value relationships are coming from and how to find more of them.

SQL-based CLV calculation across customer segments
Identification of high value cohorts and acquisition patterns
Findings framed around business opportunity, not just numbers

Sales pattern analysis

The opportunities are usually already in the data. I analyse sales patterns to find what’s performing, what’s underperforming and where the gaps are that the business hasn’t noticed yet.

SQL analysis across sales, product and customer data
Identification of trends, anomalies and missed opportunities
Findings translated into clear business recommendations

Experimentation roadmaps

Where the analysis points to a hypothesis worth testing, I translate findings into a structured experimentation framework the client can use to validate what the data is suggesting.

Hypothesis development from analytical findings
Experiment roadmap tied to business questions
Handover ready for internal teams or external delivery

How we work together

Every engagement looks a little different. Some CDOs brief me directly and take the output to their client themselves. Others bring me into the room to present findings to the leadership team. Some prefer I stay in the background entirely. I’m comfortable with all of it. We agree the arrangement upfront and it can flex as the engagement develops.

What an engagement looks like

Week 1	Brief — The CDO gives me a clear question the business needs answered. I review the available data, assess whether it can answer the question as asked and flag any gaps that need resolving before analysis can start.
Weeks 2–3	Analysis — I work through the data using SQL, cleaning where necessary and building toward the answer. If I find related questions worth investigating along the way, I flag them.
Week 4	Findings — I document what the data says, what it means for the business and what I’d recommend as next steps. Where the findings point to hypotheses worth testing, an experimentation framework can be delivered as an optional addition.
Week 5	Playback — I present findings and recommendations to the CDO and, depending on the engagement, directly to the client’s leadership team. Optional retainer for ongoing analysis.

“Storm is a strong advocate for data lead decisions and is always able to back up her recommendations with detailed data insights.”
– Dan Churchill, Senior Product Manager, RedBalloon

Who this is for

You’re a fractional Chief Data Officer or Data Lead working with clients who have data infrastructure in place or being built. You may have engineers on hand but not analysts with the capacity to sit in the data and do the sustained, deep work of finding what matters.

You’ve been in engagements where the business thinks it knows what it wants to measure but the real questions are buried underneath. Or where there’s plenty of data but nobody has connected it to the decisions the business actually needs to make.

You need someone who can understand the business well enough to ask the right questions, go deep enough in the data to answer them properly and communicate findings in a way that moves the engagement forward.

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Common questions

How do I engage you?

The easiest way is to book a call using the link on this page. Tell me what you’re walking into and I’ll tell you straight whether I can help. If it’s a fit, I’ll turn around a plain-language scope within a few days.

How do we work together?

Flexibly. Some CDOs brief me directly and take my output to the client themselves. Others bring me into the room to present findings to the leadership team. Some prefer I stay in the background entirely. We agree the arrangement upfront and it can change as the engagement develops.

How do the finances work?

I work on a day rate or fixed project price depending on the scope. I can invoice you directly or the client, whichever works for the engagement structure. Payment terms are 14 days.

What is a fractional product generalist?

Most specialists go deep in one area. I work across the intersection of user experience, technology and business strategy. That means I can understand the full shape of a problem and connect the pieces rather than just solving my corner of it. In a world where AI can do a lot of jobs, the person who can bring multiple parts of the problem together is more useful than someone who only knows one piece of the puzzle.

How much experience do you have with data analysis and SQL?

Twelve years across agencies and in-house roles, covering customer lifetime value, retention, conversion and customer behaviour analysis. I work across multiple data platforms including Snowflake and SQL Server, and I’m comfortable working with data in whatever state I find it — clean, messy or mid-migration.

What happens at the end of the engagement?

Everything is handed over cleanly. Findings are documented, any dashboards or reports are explained and I walk you and the client team through what was built and what comes next. If ongoing analysis makes sense, I offer a retainer for continued support.

How long before you can start?

Usually within one week of an agreed engagement. Get in touch early if you have something coming up and I’ll let you know availability.

How do you use AI in your workflow?

I use AI to review code, support customer research and generate analytics artefacts. Your data is never shared in a way that trains models. If your client works with sensitive data I can work without AI entirely or in line with their existing company policies.

Got an engagement coming up?

Tell me what you’re walking into. I’ll tell you whether I can help and how. No commitment required.

Book a call

Tell me what you’re walking into and I’ll tell you whether I can help.

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or drop me a message

Week 1	Discovery — I work with the CDO and the client to understand what problems the business is facing and what decisions they’re struggling to make. The goal is to surface the questions the data should be answering, including the ones nobody has articulated yet.
Week 2	Data review — I assess the available data against the questions we’ve identified. I document what’s answerable now, what needs cleaning or enriching and what’s genuinely out of reach with current data.
Weeks 3–4	Analysis — With the questions defined and the data understood, I dig in. SQL-led analysis across retention, CLV, sales patterns or whatever the business problem demands.
Week 5	Findings — I document what the data says, what it means for the business and what I’d recommend as next steps. Where the findings point to hypotheses worth testing, an experimentation framework can be delivered as an optional addition.
Week 6	Playback — I present findings and recommendations to the CDO and, depending on the engagement, directly to the client’s leadership team. Optional retainer for ongoing analysis.