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Strategy & Insight

Marketing, directed by data.

Strategy & Insight — service area visual

Strategy & Insight

01 / 04

We turn behavior into data, data into direction, direction into growth. AI accelerates the hypothesis; the decision stays with the brand.

The strategy conversation tends to collapse into two poles: on one side, annual deck-of-slides vision documents that get filed and forgotten; on the other, performance dashboards that hit daily numbers without any underlying thesis. In the middle — where strategy connects to the daily operating rhythm — there’s a real systems gap. beynart works in that gap: an operating partner that fuses strategic thinking with data science, lowers decisions onto weekly dashboards, and translates hypotheses into 4-week experiments before debating them further.

At beynart, strategy isn’t a market take — it’s a data-driven action plan. We build systems designed to understand who your customer is, why they buy, and what they’ll do next. One principle sits at the centre: every thesis ties to a measurable experiment; every experiment ties to a decision input; every decision becomes the input to the next thesis. We don’t have the strategy conversation until that loop closes.

With 10+ years of experience, we’ve worked with Türkiye’s leading brands. Textile manufacturer, e-commerce operator, financial services firm, B2B software company — the tactics change with the sector, the principle stays the same: measure first, then optimise. The value of a strategic partnership is not the year-end growth number; it’s the quality of the decisions made all year long.

Strategy and Insight is the first step of your digital transformation. Collecting the right data at the right moment, and meeting it with the right decision — that’s where the system that runs your marketing operation begins. Performance media, automation, content, and the underlying technology stack only start producing real returns once that decision system is in place. The reverse rarely works: teams that build performance campaigns first and ask “why didn’t this work?” later share the same root cause — there is no system feeding the decision.

If, over the next 12 months, you want marketing to stop feeling like luck — to have a team that argues from data in leadership meetings, and to treat growth as a repeatable engineering problem — let’s open an introductory call with our team. In the first 45 minutes we’ll map your current questions, the data you have on hand, and the 2–3 most critical decisions for the next quarter. If a reason to work together comes out of that conversation, we’ll prepare scope and a sprint plan. If it doesn’t, you’ll at least have sharpened the questions you’re carrying. Both are worth the time.

01

Why this layer matters

A poorly architected layer slows the brand. A properly built one compounds like interest.

  • 01

    Speed

    Modular infrastructure compresses time-to-market by 3-5x.

  • 02

    Scale

    We build foundations that carry today's operation into tomorrow's traffic.

  • 03

    Visibility

    Ready to make every decision at the right time, signaled by data.

02

Services in this layer

  • 01 / 03

    Marketing Strategy

    Brand positioning, message architecture, channel strategy, and execution roadmap. The decision doc spells out assumption, experiment, and threshold side by side.

    HubSpot Mixpanel Notion

  • 02 / 03

    Market Research

    Market sizing, competitive analysis, customer segmentation, and qualitative/quantitative research. A standing programme on a quarterly cadence — not a one-shot study.

    Dovetail Maze Typeform

  • 03 / 03

    Data Analytics & AI

    Data warehousing, BI dashboards, AI-powered customer insights, and predictive models. Infrastructure that keeps lowering the cost per decision.

    Looker dbt OpenAI BigQuery Looker Studio
Data network node composition — connected nodes representing the strategy layer

STRATEGY

Data leads the call, intuition confirms it.

03

How we work

We hold to the same discipline at every layer: listen first, measure, build, then improve.

  1. 01

    Discovery & alignment

    We clarify your brand goals, current state, and success metrics. We take the time to make sure we don't answer the wrong question well.

  2. 02

    Strategy & design

    We turn data-driven insight into an executable roadmap. AI accelerates the hypothesis at every decision point; we steer the direction together with your team.

  3. 03

    Build & integration

    We build the solutions and connect them to your existing systems. We ship without breaking your operations.

  4. 04

    Measure & iterate

    Launch isn't the finish line. We monitor performance, test hypotheses, and compound the wins.

04

Go deeper into this layer

use-case

Customer Segmentation Program

Behavioral clustering plus LLM-assisted personas — a testable ICP in four weeks.

Strategy & Insight — section visual

We move beyond demographic lists into segments built from behavior and product-usage signals. Each segment gets a channel, message, and offer hypothesis that can be measured.

Why it matters

In most companies, segments live in the marketer’s head — “premium customer”, “price-sensitive group”, “new joiner”. Those labels do not produce decisions on their own. They cannot answer who responds to which message, on which channel, at what cadence. So one message goes to everyone, and the budget splits evenly between the most valuable customer and the one who bought once and disappeared.

That even split is a quiet cost. A blanket discount takes margin from the loyal customer who would have paid full price, and it builds no habit in the one-time buyer. A loss on both ends. In the same way, a high-value customer about to churn gets the generic newsletter you think is “nurturing” them — when winning them back is far cheaper than re-acquiring them after they leave.

Teams that move to behavior-based segmentation typically see lower acquisition cost (CAC) on segment-led campaigns and higher email conversion. More valuable still: you know which customer is at risk before they leave, not after. At beynart, segmentation is not a slide. It is a system — derived from behavior data, refreshed weekly, and used as the shared language across marketing, sales, and product.

What we deliver

You finish the program with four concrete outputs, each designed to produce a decision:

  • Behavioral cluster map. We draw four to seven clusters from customer events — purchase, session, churn, and support. Each cluster reports size, economic value, and growth trend, so “which group is growing and which is eroding” rests on numbers, not opinion.
  • LLM-assisted persona cards. A one-page brief per segment explaining what the customer wants, what gets in the way, and which message lands. Synthesized from interview transcripts — not an invented archetype, but a profile grounded in real sentences.
  • Value and propensity scores. We isolate the high-value cluster with RFM (recency, frequency, monetary) scoring, then score the existing base by likelihood to repurchase and target the cluster’s twins as lookalike audiences on Meta and Google.
  • Segment-led experiment list. Two or three hypotheses per cluster for the first 90 days — which message, which channel, which offer. Not a blank table, but a ready-to-run playbook with success criteria written in advance.

Our approach

We treat segmentation as a capability that matures, not a one-off model. A four-level maturity model makes clear where most teams stand and what the next step is:

  1. Level 0 — Demographic. Splitting by static fields like age, city, or industry. Easy to apply, weakly correlated with purchase behavior. Where most businesses start.
  2. Level 1 — Value-based (RFM). Value segments derived from transaction history. The first real jump happens here, because you start allocating budget by value.
  3. Level 2 — Behavioral. Value combined with behavior. Cuts like “high value but dependent on a single channel” enable targeted intervention.
  4. Level 3 — Predictive. Forward-looking segments using churn probability and customer lifetime value (CLV). You work proactively rather than reactively.

The goal is not to leap to the top. It is to make a solid move to the next level your data and your team can carry. Predictive models produce noise without clean value segments underneath. So for every cluster we ask one decision question: “Once we know this segment, what do we do differently tomorrow?” A segment with no answer — however elegant — does not ship.

Process

Week 1 — Discovery and data. Data source inventory, event schema, and eight to twelve qualitative interviews. Output: an analysis-ready event table and raw material for personas.

Week 2 — Modeling. Event data is loaded into the warehouse; first clustering pass and persona narrative drafts. Output: a named cluster set with size and value measured.

Week 3 — Validation. A segment validation workshop with marketing and sales; a message architecture draft. Output: segment definitions the team owns and the field has confirmed.

Week 4 — Activation. Lookalike audience setup, the segment-led experiment list, and a dashboard launch. Output: live campaigns and a repeatable process that keeps segments current.

Example: an omnichannel retail brand

We worked with a mid-sized omnichannel retail brand that could not see store and online sales in a single view. The same weekly newsletter went to the entire email list; open rates were low and unsubscribes were high.

After behavioral clustering, they isolated an “at-risk loyalists” segment — customers with no purchase in the last 90 days who had been the top spenders the prior year. They built a discount-free “we miss you” flow just for that group. In the first quarter, the segment’s reactivation rate moved from single-digit percentages into the mid-teens and added a measurable share to overall email revenue. Not an inflated result — a sustainable gain from getting the right message to the right group.

FAQ

How much data do we need for segmentation? For meaningful behavioral and RFM segments, at least 12 months of transaction history and a few thousand customers are typically enough. You can start with less, but cluster boundaries are looser and need recalibration more often.

How often should segments be updated? Value and behavior segments shift as customers move. Monthly re-scoring is balanced for most businesses; weekly may suit fast-cycle e-commerce. We set the program up so this update runs automatically.

Will it work with our existing CRM? Yes. Segmentation is an analysis layer that reads your data; we write results back into your CRM or marketing automation tool. You do not have to buy a new system.

What is the difference between LLM-assisted personas and classic personas? A classic persona is usually written by guesswork in a workshop. An LLM-assisted persona extracts recurring themes from real interview transcripts — so it rests on the customer’s own words and maps cleanly onto a behavioral cluster.

Talk to us and let’s discuss how to start your segmentation program with concrete experiments.

deliverable

Data Warehouse Setup

Warehouse, dbt, and dashboards — a decision-grade data layer in eight weeks.

Strategy & Insight — section visual

We consolidate fragmented data from spreadsheets and tools into a single warehouse, model it with dbt, and feed dashboards that marketing, product, and finance use every day.

Why it matters

Without data, strategy is a guess. But “we have data” is not enough either. In most growing companies, data is written across 10 to 15 separate tools: one truth in the CRM, another in the ad dashboard, a third in finance’s spreadsheet. When the same question has three different answers, meetings are spent debating the data itself — and no decision gets made.

The cost of that fragmentation accumulates in two places. First, time: teams stitch the monthly report together by hand, version on top of version, and no one fully trusts the result. Second, and more expensive, wrong decisions: allocating budget on conflicting numbers means cutting the profitable channel and feeding the losing one — and it takes months to notice.

beynart’s warehouse setup gives the organization an answer to “what is the right number?” Marketing reads channel performance, product reads retention, and finance reads unit economics — all from the same source. The conversation moves from “is the data right?” to “what do we do?”

What we deliver

After eight weeks you own a working, four-layer system you can take over:

  • Cloud warehouse setup. A production-grade environment on BigQuery, Snowflake, or Postgres. With staging and production schema separation, role-based access policies, and cost alerts — so as you scale you keep both control and predictable cost.
  • Source integrations. Daily, automated ingestion from CRM, commerce, ad platforms, and product events using Fivetran, Airbyte, or, where needed, custom connectors. The manual export-import cycle ends.
  • dbt modeling layer. A staging, intermediate, and mart hierarchy. Every table has tests, documentation, and a named owner. A metric’s definition lives in one place; “active customer” does not come out two different ways across dashboards.
  • Four to six BI dashboards. Role-based views in Looker, Metabase, or Mode for the CMO, growth lead, product, and finance. Each dashboard has an owner and a weekly ritual — without one, even the best dashboard is abandoned within months.

Our approach

We build a warehouse as a maturing data capability, not as a technology that is either used or ignored. A four-level maturity model makes clear where most teams stand:

  1. Level 0 — Spreadsheet sprawl. Data lives in spreadsheets and each tool’s own panel. Every report is manual; the “right number” depends on who you ask.
  2. Level 1 — Central store. Data flows into a single warehouse but is not yet modeled. Access is easier; consistency is still missing.
  3. Level 2 — Modeled semantic layer. Mart tables cleaned, tested, and documented with dbt. Metric definitions sit in one source. Trust begins here.
  4. Level 3 — Decision rhythm. Dashboards are embedded in weekly meetings; data is an input to decisions, not decoration on a report. Activation (writing back to the CRM via reverse-ETL) arrives at this level.

For most projects, the right first goal is a solid Level 2. Building dashboards on unmodeled data only spreads the wrong number faster. For every mart table we ask one question: “Which decision will use this table?” A table that feeds no decision is left out of scope.

Process

Weeks 1–2 — Discovery and provisioning. Source inventory, semantic layer draft, and warehouse provisioning. Output: the access structure and a prioritized source list.

Weeks 3–5 — Integration and modeling. Connectors go live; staging-to-mart dbt models are written; data quality tests are added. Output: tested, documented mart tables.

Weeks 6–7 — Dashboards and validation. Role-based dashboard design, stakeholder workshops, and clarified access rules. Output: dashboards with owners, confirmed in the field.

Week 8 — Handover. Operational handover: runbook, alert rules, and team training. Output: a sustainable data layer that runs without beynart.

Example: an omnichannel retail brand

We worked with a mid-sized omnichannel retail brand that could not see store, e-commerce, and marketplace sales in one view. The month-end report required three separate teams to merge their spreadsheets by hand and took two to three working days to prepare — and the result was still disputed.

In seven weeks we unified seven data sources in one warehouse and shipped four role-based dashboards. Month-end report preparation dropped from days to under half an hour. More importantly, leadership now enters decision meetings on the same numbers; the agenda shifted from “is the data right?” to “which channel should we grow?” A modest but lasting gain: the same work done by fewer people, with more confidence.

FAQ

Which warehouse should we choose — BigQuery, Snowflake, or Postgres? It depends on your data volume, your team’s skills, and your existing cloud provider. For small-to-mid volume and cost sensitivity, Postgres can be enough; for large analytical loads and flexible scale, BigQuery or Snowflake. During the discovery week we evaluate all three against your case and recommend with reasons.

Do we have to replace our existing tools (CRM, ad dashboards)? No. The warehouse is a layer built on top of those tools; it pulls their data, it does not replace them. It is designed to work with your current stack.

Can our team maintain this on its own after setup? Yes, that is the goal. In the handover week we deliver the runbook, alert rules, and training; the dbt models are documented and tested. We can support maintenance afterward if you want, but we do not create dependency.

What does dbt actually do? dbt is the modeling layer that turns raw data into decision-ready tables. It embeds each metric’s definition in code, tests it, and documents it — so the same number comes out the same everywhere and changes stay traceable.

Talk to us and let’s see how this warehouse fits your stack.

deliverable

Experiment Program Setup

A hypothesis backlog and an A/B engine — the disciplined path from idea to decision.

Strategy & Insight — section visual

We turn the "let's also try this" instincts of marketing, product, and sales into a prioritised hypothesis backlog and a weekly decision rhythm. Every test has its success criteria and decision memo written in advance.

Why it matters

In most organizations, “let’s test it” is decided on instinct, and the result disappears two months later in a “what happened with that test?” meeting. There are many tests but little learning. A winning change is forgotten before it rolls out to all traffic; a losing one gets re-tried by another team. You buy the same lesson again and again, with money.

The cost of that loss has two layers. First, the direct missed gain: forgetting to ship a change that genuinely wins leaves money on the table. Second, and more insidious, false confidence: reading a test that never reached statistical significance as a “win” and turning it into a permanent decision steers the organization the wrong way for a long time.

An experiment program closes that gap. Why a hypothesis is being tested, what to do if it wins, and what to do if it loses are written before the test runs. The program beynart sets up produces one to three decision memos a week — “we tried, we learned, here is what we do next” on a steady cadence. The goal is not more tests; it is more decisions.

What we deliver

You finish with an engine that produces decisions, not just ideas — in four parts:

  • Hypothesis backlog. Thirty to sixty hypotheses gathered from marketing, product, and sales. Each scored on ICE (impact, confidence, ease) with a clear quarter-one queue. No more “what should we try?” debate — a ready queue instead.
  • Experiment audit. An inventory of tests run in the last 12 months: how many reached a conclusion, how many were abandoned, how many were misread. A pattern report so the same mistakes do not repeat — lessons from the past, rules for the future.
  • Test framework and tooling. Sample-size calculation, segment rules, and statistical significance thresholds on GrowthBook, Optimizely, or our own A/B infrastructure. When a test should end is known from the start.
  • Decision memo pipeline. A one-page memo at the end of each test: result, confidence interval, next action. Wired into Notion or Linear — the decision does not vanish; it becomes a searchable institution.

Our approach

We treat experimentation not as individual tests but as a maturing decision discipline. A four-level maturity model makes clear where the team stands:

  1. Level 0 — Instinct. Changes are made on opinion; no comparison. If something “feels good,” it ships.
  2. Level 1 — Occasional A/B. Individual tests get run, but there is no prioritization, rhythm, or documentation. Results live in personal memory.
  3. Level 2 — Programmed experimentation. A prioritized backlog, a weekly rhythm, written decision memos. Learning becomes institutional.
  4. Level 3 — Experiment culture. Multiple teams run tests in parallel; decision memos are shared memory; results feed planning. Experimentation is not a separate job but a way of working.

The goal is not to build a culture overnight; it is to make a solid move to the next level. For most organizations the right first target is Level 2: a small number of tests run cleanly, read correctly, and recorded. For every hypothesis we ask one question: “What will we do when this test wins or loses?” A hypothesis with neither answer does not enter the queue — because that is not a test, just curiosity.

Process

Week 1 — Hypothesis gathering. Ninety-minute workshops with three teams; definition of success and fail criteria. Output: a raw hypothesis pool and clear criteria.

Week 2 — Prioritization. Past-test audit and ICE scoring; ranking and ownership for the first 12 experiments. Output: a prioritized backlog.

Week 3 — Setup. Test infrastructure setup, the first two experiments live, and a dashboard launch. Output: a working test engine and the first running experiments.

Week 4 — Decision rhythm. Launching the weekly 30-minute experiment review; the first decision memos signed off. Output: a repeatable decision rhythm.

Example: an omnichannel retail brand

We worked with a mid-sized omnichannel retail brand whose product and marketing teams operated independently. Both ran A/B tests, but prioritization was not shared, results were not recorded, and winning changes were sometimes never even rolled out.

We set up a shared backlog, a weekly decision rhythm, and a decision-memo template. Over six months they ran more than thirty experiments; some won, some lost, the rest came back inconclusive. A portion of the inconclusive tests were redesigned and resolved cleanly. They earned a low-double-digit-percent improvement in product page conversion — but the real gain was this: “which change earned us money?” stopped being a guess and became a searchable record.

FAQ

Do we have enough traffic for a meaningful A/B test? The program works at low traffic too, but the approach changes: we focus on higher-impact hypotheses, run tests longer, or test flow-level changes instead of on-page tweaks. We calculate sample size up front; if there is not enough traffic, we say so before starting the test.

Which tool should we use — GrowthBook, Optimizely, or our own infrastructure? It depends on your budget, engineering capacity, and existing stack. For open source and cost sensitivity, GrowthBook is a good start; for broad enterprise needs, Optimizely. If you already have a warehouse, our own lightweight setup is an option. We recommend with reasons before setup.

How exactly does ICE scoring work? You score each hypothesis on three axes: impact, confidence, and ease. The combination of those three moves high-impact, quick-to-ship tests to the front of the queue — so limited resources go to the tests that teach the most.

What happens to tests that come back inconclusive? Inconclusive is not a failure; it is a signal. Often the sample is small or the effect is genuinely tiny. We either redesign those tests with a sharper hypothesis or close them and redirect the resource to a higher-expectation test. Either decision goes into the memo.

Talk to us and let’s discuss how to set up your experiment program.

05

Related work

by the numbers

We start at strategy and end in production.

90

Day roadmap

12+

GTM playbooks

100%

Data-driven decisions

4

Pillar integration

our approach

We turn the pillar into an operational architecture

From strategy conversation to implementation — case-specific, ongoing.

How we work

capabilities

Stand-out capabilities within this pillar

Each one independent or composed; shaped to the case.

Market analysis

We map the competitive landscape, customer segments and growth opportunities with data.

Product strategy

Positioning, pricing and roadmap — decisions sourced from insight, not opinion.

GTM playbook

Go-to-market pipeline: channel mix, campaign architecture and measurement plan.

Not sure where to start?

Let's find the layer that fits your need and map where the architecture begins.

Talk to us

customer voice

Working under Strategy & Insight, everything from strategy to execution is measured and evolves. beynart is an engineering partner running everything under one architecture.

Ali Rıza Tuncer

Founder, beynart

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contact

The right people, from the first message.

No gatekeepers, no brief relay. The team that talks strategy is the team that builds the system. Tell us where you want to grow — we bring the architecture.

let's talk → [email protected]
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