Predictive Growth · Customer LTV · Fractional Analytics Consulting

Predict your most
profitable customers
with Enterprise-Grade ML.

We bring the regression modeling firepower of $B+ brands to scaling Shopify stores. Science-backed rigor, translated into the strategic signals you need to grow.

3+ Years E-Commerce Analytics
$B+ Client Revenue Supported
DTC Focused Exclusively

Is this
right for you?

Kinetric brings enterprise-level data science to scaling brands — the kind of predictive modeling that used to require a full in-house team. You might be a fit if:

  • You're a DTC or e-commerce brand doing $1M–$10M in annual revenue
  • You've outgrown Shopify's native reports and need enterprise-level data science — without hiring a full internal data team
  • You know some customers are far more valuable than others, but you don't have a systematic way to identify them early
  • You want to know which customers are at risk of churning before they go silent — not 90 days after
  • You're ready to make acquisition and retention decisions based on predicted customer value, not just past purchase behavior

Three ways to work together

We don't sell hours. We scope to the depth your data actually requires — nothing more, nothing less.

01 One-Time

Predictive LTV & Growth Audit

A regression-based diagnostic of your customer base that identifies the revenue-driving 20%, scores your active customers by churn risk, and maps the behavioral signals that predict whether a new customer will become a repeat buyer. Every technical finding is paired with a plain-language recommendation your team can act on immediately. You walk away with a 90-day predictive growth roadmap — no ongoing commitment required.

03 Retainer

Fractional Data Scientist

Senior data science embedded in your business on a monthly retainer — 10 hours of strategic analysis per month with a direct line to a senior analyst. Built for board-level reporting and making sense of the noise: every engagement produces analysis your leadership team can read and act on in under five minutes. No agency overhead. No junior analysts. No shared queue.

  • Monthly cohort & segment performance analysis
  • Churn risk monitoring & proactive alerts
  • Campaign targeting recommendations
  • On-demand predictive modeling

See the Audit.
Then try it yourself.

Two ways to get a concrete read on Kinetric before any conversation. Walk through a full engagement a brand already received, or run the same diagnostic on your own data in under five minutes.

What a $1,500 Audit produces

A complete DTC analytics engagement, from raw CSV to recommendation memo.

A full audit for a $1.5M DTC brand — five stages, one coherent story. Every stage shows what the analysis produced, what the analyst corrected, and why the correction mattered. The output is what the client actually receives.

  • An acquisition channel retaining 22% of customers at twelve months against an email baseline of 70% — and the spend-shift recommendation that follows.
  • A product category with a 17% repeat rate against a 71% company baseline — isolated as a structural gifting problem, not a marketing one.
  • A prioritized At Risk intervention list with a specific recommended action per customer — and a founder-ready memo a retention lead can forward without editing.
A 5-minute read on what your data can answer

Signal Check: what does your customer data actually support?

Upload a customer or order file. Get a consulting-grade read on what it can answer today, what it can support in thirty and ninety days, and what it simply cannot tell you no matter how it's analyzed. The output that would otherwise take a discovery call.

CSV in  →  7-section consulting PDF out
  • Seven-section analytics roadmap covering insights available now, 30- and 90-day capabilities, hard limits, and critical gaps.
  • Closes with a single two-week first move — a defined output and decision attached, not a generic recommendation.
  • Formatted as a consulting deliverable: the same document structure Kinetric uses for paid discovery engagements.

Live capability demos

Two working tools that each solve a specific decision a retention or growth lead has to make every week. The full audit walkthrough is in the Proof section above.

Know Who to Target — and Why — Before Every Campaign Sends

● Live

Most teams build campaign lists from recency filters. We built a propensity scoring engine that ranks every customer by purchase likelihood, then explains exactly which behavioral signal earned them that rank — so creative and timing decisions are driven by data, not intuition.

  • The top-ranked 10% of the customer file converts at 2.95x the population average — sending to Priority and High Reach tiers captures the majority of expected revenue at a fraction of full-list cost
  • Each score is attributed to three behavioral signals (long-run purchase history, same-period seasonal history, and recent momentum), so your creative team gets a specific brief — not just a ranked list
  • Paired SQL patterns handle suppression (who should never receive a campaign) and dormant high-value reactivation (who to re-engage before they're gone permanently)
Python Streamlit Scikit-learn Pandas SQL

From Test Results to Decisions in Minutes

● Live

Most teams wait 3-5 days to interpret A/B test results before leadership can make a call. We built a tool that cuts that to minutes — upload your test data, get a plain-language recommendation any stakeholder can act on immediately.

  • Discovered that a personalization feature drove a 47.7% conversion lift for returning customers — but had no effect on new visitors. That single finding changed the rollout from "ship to everyone" to a targeted strategy that protected margin
  • Delivers a clear verdict — ship, don't ship, or test further — along with expected revenue impact and who it affects
  • Removes the bottleneck between your data team and your decision-makers, so experiments move from results to action in one meeting
Python Streamlit Claude API Pandas SciPy

Enterprise rigor.
Boutique attention.

The same ML methodology used inside $B+ DTC operations — delivered with the direct access and senior focus you'll never get from a high-volume agency.

3+ Years as dedicated analytics consultant for a multi-billion dollar DTC brand
$B+ In revenue informed by regression models and predictive analysis — embedded inside a leading national DTC brand
1:1 Direct partnership — no shared queues, no junior analysts, no hand-offs

The models built for a multi-billion-dollar DTC brand — regression-based customer targeting, longitudinal LTV analysis, churn-prediction scoring — are now available to Shopify brands doing $1M–$10M. Same scientific rigor. No $150k/year headcount required.

Data.
Decisions.
Dollars.

Data
Rigorous regression modeling and longitudinal analysis applied to your customer data — no averages, no gut feel.
Decisions
Model outputs translated into plain-language directives any stakeholder can act on — from results to action in minutes.
Dollars
Every recommendation ships with a dollar estimate — so you know exactly what each insight is worth before you act on it.
See the Decision Engine →

Data Discovery

Longitudinal time-series analysis of your customer base surfaces the behavioral patterns hidden inside your order history. So what: You'll see, in plain English, which early behaviors predict a high-LTV customer — and which predict a one-and-done buyer — before the second purchase ever happens.

Decision Engine

Regression-based churn prediction assigns a real probability score to every active customer — not a gut-feel tier, a statistical estimate grounded in your actual data. So what: Your retention team gets a prioritized list of who needs attention and when, not a guess.

Revenue Prescriptions

Incremental lift analysis quantifies the revenue impact of targeting your highest-LTV segments differently — in paid acquisition, retention sequencing, and budget allocation. So what: Every recommendation comes with a dollar estimate and a clear action, not just a direction.

About

A focused solo practice — not an agency juggling 30 accounts. Every engagement gets direct senior attention. No junior analysts. No hand-offs. The same person who scopes the work does the work.

Brendan Hoffman

Brendan Hoffman

Founder & Lead Analyst

Direct analyst for a multi-billion-dollar DTC brand. I build the models that drive $1B+ decisions — regression-based customer targeting, churn prediction, and A/B test infrastructure inside one of the largest DTC brands in the US. Now bringing that same enterprise-grade firepower directly to scaling Shopify brands — without the $150k/year headcount. University of Maryland, Business Analytics & Information Systems.

Regression Modeling Customer LTV Churn Prediction Python SQL Snowflake A/B Testing Executive Communication

Let's talk about
your data

Whether you need a one-time audit or an ongoing analytics partner — tell us what you're working with and we'll tell you how we can help.