A funnel framework for running your digital marketing channels

Give every channel one job in the funnel, track the numbers that prove it, and diagnose every missed target. A framework for display, search, email, social and web.

GTM
September 30, 2026
·
5 min
read
Abstract brand form artwork

Most digital marketing plans list channels. A working plan assigns each channel a job in the funnel, a short list of numbers that prove it's doing that job, and a routine for fixing it when it isn't. That's the whole framework: implement by funnel stage, track by funnel stage, and work on the gap between target and result.

The funnel has four core stages, plus a fifth that many teams add:

  1. Awareness: the buyer notices a problem or a brand.
  2. Interest: the buyer researches solutions.
  3. Desire: the buyer wants a specific offer.
  4. Action: the buyer converts.
  5. Retention and advocacy: the buyer stays and tells others.

Where should the framework start?

Start with the business, before any channel. Four questions do most of the work:

  • What are the business's current goals and priorities, and why?
  • What is the current sales motion, and why?
  • Has product-market fit been confirmed, and who are the ICP, the buyer personas and their top three pain points?
  • Is there a repeatable sales cycle pattern?

Then write objectives and key results. A key result is an outcome you can measure. If the objective is to increase brand authority in the tech industry, "launch three content campaigns" is a task and "get more followers" is a vanity metric. "Secure 10 guest post placements in top-tier industry publications this quarter" is a key result, because it proves the objective moved.

Which channel does which job in the funnel?

Think of the funnel as a relay race. Each channel runs its own leg and hands the buyer to the next one. Seven components cover the course, shown first as a funnel and then in detail:

Funnel diagram mapping display ads, SEO, paid search, landing pages, email and social media to five stages, with web analytics across all
Each channel runs its own stages of the funnel. Web analytics measures all five.
ComponentFunnel stagesIts jobWhat you build
Display advertisingAwareness, Interest, early DesireAttract attention and convey the brand messageBrand visuals, succinct copy, a clear CTA, offers and urgency cues
Web design and landing pagesInterest, Desire, ActionConvert visitors with a page aligned to the adLayout for usability, visual hierarchy, trust signals, answers to objections
SEOAwareness, InterestAttract relevant organic trafficEducational content, meta tags and headings
Paid searchAwareness through ActionDrive targeted traffic and conversionsKeyword-matched ad copy and aligned landing pages
Email marketingDesire, Action, RetentionNurture leads and keep customersResponsive, branded, personalized, segmented emails
Social mediaAwareness, Interest, Desire, RetentionBuild presence and communityImages, video, stories, polls and Q&A
Web analyticsAll stagesMeasure behavior and campaign effectivenessTracking and reporting

Three rules come out of the table.

Match the message to the stage. SEO content at the top of the funnel is educational and solves problems. Display and paid search add unique selling points and offers such as a free trial. Email at the bottom mixes educational, promotional and transactional messages, timed to where the customer is in the journey. Social media keeps one brand voice and a community-building, customer-support tone, with calls to action for engagement or conversion.

Protect the handoffs. Paid search and display both hand visitors to a landing page. Its messaging has to match the ad that sent them.

Treat analytics as the referee. Web analytics produces no creative and no message. It runs the length of the funnel and tells you which leg is slowing the race.

What should you track at each stage?

Keep the dashboard to four groups of metrics: traffic, engagement quality (how long visitors stayed and what they did), conversions and return on investment. Then give each channel the formulas that match its job.

  • Click-through rate: clicks divided by impressions. It measures whether an ad or link earns traffic.
  • Conversion rate: conversions divided by clicks. It measures whether traffic turns into action.
  • Cost per click: the CPM divided by clicks per 1,000 impressions.
  • Cost per acquisition: the CPM divided by conversions per 1,000 impressions.
  • Return on ad spend: revenue from ads divided by ad spend.
  • Bounce rate: single-page visits divided by total visits, which matters for SEO and landing pages.
  • Open and unsubscribe rates: opens or unsubscribes divided by emails delivered.

A worked example shows how the numbers connect. An advertiser pays a $20 CPM and gets a 0.8% click-through rate: 8 clicks per 1,000 impressions, or $2.50 per click. With a 2.3% conversion rate, those 8 clicks produce 0.184 conversions, which puts the cost per acquisition at $108.70. Drop the click-through rate to 0.6% and the cost per click rises to $3.33 with the same CPM.

Social media needs a tracking layer of its own. Monitoring tools capture brand mentions by context, sentiment, platform and engagement, including conversations that never name the brand. Arby's found through that kind of monitoring that one-third of its brand mentions were about its sauces, and the insight led to new products. Pick the tool by objective: a comprehensive platform such as Sprout Social for ongoing brand health, native platform analytics for real-time engagement, and a mention tracker such as Google Alerts for web-wide reputation.

Retail media networks take the idea furthest. In Adtelligent's Retail Media Market Outlook 2026, Anna Sursaieva writes: "Retail media is driven by measurable ROI, first-party data, and closed-loop attribution."

How do you work on what the numbers show?

A missed target is a question, and variance analysis is the way to answer it. Work through five steps:

  1. Collect the data: sales reports, customer feedback and market analysis.
  2. Set the baseline: targets from historical data or industry benchmarks.
  3. Compare actual to target: size the gap and mark it positive or negative.
  4. Find the root cause: ask "why" five times, or map causes on a fishbone diagram.
  5. Recommend the action: one specific change tied to the cause.

Root causes fall into three families. Market conditions cover competition and demand. Operational efficiency covers things like delayed content that pushes a campaign past its window. Customer behavior covers shifts such as buyers moving to mobile while your ads still target desktop.

Once you know the cause, the formulas point to the lever. Better creative and targeting raise click-through rate, which lowers cost per click. Better landing pages and offers raise conversion rate, which lowers cost per acquisition. Adjust bids and budgets after that, based on where cost per click and cost per acquisition land.

Test one element at a time. If an email variant lifts opens by 50% and clicks stay flat, the subject line worked and the body copy didn't. Change both at once and you learn nothing.

Key takeaways

  • Start from business goals and key results that measure outcomes.
  • Give every channel one funnel job and a message to match it.
  • Track traffic, engagement quality, conversions and ROI, with formulas per channel.
  • Diagnose every missed target with baseline, comparison and root cause.
  • Raise click-through and conversion rates, then adjust bids and budgets.

If your channels are live and nobody can say which funnel stage each one serves, bring your current dashboard to a free 30-minute call. We'll map each channel to its stage, pick the numbers that belong on the dashboard, and you'll leave with a tracking plan your team can run.

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Data Augmentation
Creating extra training examples by altering existing ones, such as flipping, cropping, rotating or recolouring images, so the model sees more variety.
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workflow
Workflow
The ordered steps and hand-offs by which work moves from start to finish, who does each one and what triggers the next.
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verification
Verification
Confirming that a result, label or component is correct by checking it against a reference or a second method.
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vectorized-execution
Vectorized Execution
A query-engine technique that processes whole columns of values in one CPU instruction instead of row by row, which is what makes engines like DuckDB fast.
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vector-space
Vector Space
The multi-dimensional space where embeddings live; distance within it stands for how similar two items are.
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uncertainty-sampling
Uncertainty Sampling
An active-learning strategy that sends the examples the model is least sure about to human reviewers first, because those teach it the most.
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transformer
Transformer
A neural network architecture built on attention, which weighs every part of the input against every other part; it underpins modern language and vision models.
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traceability
Traceability
The ability to follow any output back through the data, labels, code and model versions that produced it.
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tokenization
Tokenization
Splitting text into the units a language model works with, which may be words, parts of words or characters.
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token-limit
Token Limit
The maximum number of tokens a language model can take in and produce in one request, which caps how much context it can hold.
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threading
Threading
Running several parts of a program at the same time on separate threads so work overlaps instead of waiting in line.
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temporal-analysis
Temporal Analysis
Studying how things change over time across frames or records, to detect motion, events, trends or anomalies.
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sla
SLA
Service Level Agreement: the committed level of service a provider promises, such as uptime, response time or support hours, with consequences if missed.
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simulation
Simulation
Generating realistic scenarios in software to test or train a system where real data is scarce, risky or expensive to collect.
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similarity-search-vector-search
Similarity Search (Vector Search)
Finding the items whose embeddings sit closest to a query's embedding, which returns things that look or mean alike rather than share keywords.
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setup
Setup
The initial configuration of a project: connecting data sources, choosing models and settings, and defining the workflow before work begins.
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semi-supervised-learning
Semi-Supervised Learning
Training on a small labelled set plus a much larger unlabelled one, using the labelled examples to guide what the model learns from the rest.
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semantics
Semantics
The meaning behind data rather than its surface form, which lets a system treat "car" and "automobile", or two photos of one scene, as the same thing.
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segmentation
Segmentation
Dividing an image into regions by labelling every pixel, so the exact shape of each object or area is known.
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search-attributes
Search Attributes
The fields a search can filter or rank on, such as labels, tags, dates, camera id or model confidence.
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search-and-retrieval
Search & Retrieval
Finding the items in a collection that best match a query, whether by keyword, metadata or visual and semantic similarity.
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scene-understanding
Scene Understanding
Interpreting a whole image or video as a scene: what objects are present, where they are and how they relate.
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saliency-map
Saliency Map
A heat map over an image showing which pixels most influenced a model's prediction, used to explain and debug it.
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reward-function
Reward Function
In reinforcement learning, the rule that scores each action so the agent learns which behaviour to repeat.
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review-batch
Review Batch
A set of items grouped for a human reviewer to check in one sitting, often chosen because the model was unsure about them.
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relational-database
Relational Database
A database that stores data in tables with rows and columns and links them through keys, queried with SQL.
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regularization
Regularization
Techniques that stop a model memorising its training data, such as penalising large weights or dropping random neurons, so it generalises better.
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refactoring
Refactoring
Restructuring existing code to make it cleaner and easier to change without altering what it does.
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recipe
Recipe
A saved, repeatable set of processing steps or settings that can be rerun on new data to get the same kind of result.
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rag
RAG
Retrieval-Augmented Generation: a language model that first fetches relevant documents and then writes its answer from them, which cuts hallucination.
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quality-assurance-qa
Quality Assurance (QA)
The checks and processes that make sure a product, dataset or model meets its standard before it ships.
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predictive-maintenance
Predictive Maintenance
Using sensor data and models to forecast when equipment will fail, so it is serviced just before it does rather than on a fixed schedule or after a breakdown.
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policy
Policy
In reinforcement learning, the strategy that maps each state to the action the agent should take.
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pixel
Pixel
The smallest unit of a digital image, a single point with a colour or intensity value; images are grids of them.
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pgvector
pgvector
A PostgreSQL extension that stores embeddings and runs similarity search inside the database, so vector search needs no separate system.
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one-shot-learning
One-shot Learning
Learning to recognise a new class from a single example, usually by comparing against a learned notion of similarity.
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object-storage
Object Storage
Storage that keeps files as objects with metadata in flat buckets rather than folders, built for huge volumes and cheap durability.
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normalization
Normalization
Rescaling values to a common range or distribution so no single feature dominates and models train more stably.
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noise
Noise
Random or irrelevant variation in data that hides the real signal, from sensor error, compression, lighting or sloppy labels.
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mvcc
MVCC
Multi-Version Concurrency Control: a database technique that keeps multiple versions of a row so readers and writers do not block each other.
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monitoring
Monitoring
Continuously watching a deployed system's health and output quality so drift, errors and slowdowns are caught early.
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model-versioning
Model Versioning
Tracking each trained model with its data, code and settings, so any version can be reproduced, compared or rolled back.
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metrics
Metrics
The numbers used to judge a model or system: accuracy, precision, recall, F1, latency and others chosen to match the goal.
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metric-learning
Metric Learning
Training a model so that similar items end up close together in its embedding space and dissimilar ones far apart.
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meta-learning
Meta-learning
Training a model to learn how to learn, so it adapts to a new task from very few examples.
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mask
Mask
A pixel-level map marking exactly which pixels belong to an object or region, the output of segmentation.
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markov-decision-process-mdp
Markov Decision Process (MDP)
The mathematical frame for reinforcement learning: states, actions, rewards and transition probabilities where the next state depends only on the current one.
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loss-function
Loss Function
The formula that scores how wrong a model's predictions are during training; training works to make this number as small as possible.
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latent-feature
Latent Feature
A hidden characteristic a model learns internally that is not directly observed in the data but helps explain it.
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label-noise
Label Noise
Errors and inconsistencies in labels, from mistakes, ambiguity or disagreeing annotators, which degrade what a model can learn.
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keypoint-detection
Keypoint Detection
Locating specific points of interest on an object, such as joints on a body or corners on a part, rather than drawing a box around the whole thing.
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iou
IOU
Intersection over Union: the overlap between a predicted box or mask and the true one, divided by their combined area. 1.0 is a perfect match.
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inverted-index
Inverted Index
An index that maps each term to the documents containing it, which is how full-text search answers queries quickly.
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instance-object-instance
Instance (Object instance)
One individual occurrence of an object in an image, as distinct from the class it belongs to.
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information-extraction
Information Extraction
Pulling structured facts such as names, dates, amounts or relationships out of unstructured text or images.
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indexing
Indexing
Building a lookup structure over data so searches find matches without scanning everything.
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image-processing
Image Processing
Operations applied to pixels, such as resizing, filtering, thresholding or colour correction, usually to prepare images for analysis.
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hyperparameter-selection
Hyperparameter selection
Choosing the settings that control how a model learns, such as learning rate, batch size or network depth, which are set before training rather than learned.
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grid-search
Grid search
Trying every combination of chosen hyperparameter values in turn and keeping the set that scores best.
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gradient
Gradient
The direction and size of change in a model's error for each weight, used during training to decide how to adjust that weight.
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gpu
GPU
Graphics Processing Unit: a chip built for many parallel calculations at once, which makes it the workhorse for training and running neural networks.
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generative-ai
Generative AI
AI that creates new content such as text, images, code or audio from a prompt, learned from patterns in large training sets.
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generalization
Generalization
A model's ability to perform well on data it has never seen, rather than only on its training examples.
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feedback-loop
Feedback Loop
A cycle in which a system's outputs, or human reactions to them, are fed back as training signal so it improves over time.
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feature-map
Feature Map
The output of one layer in a convolutional network: a grid showing where in the image a particular pattern was detected.
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feature-engineering
Feature Engineering
Creating or transforming input variables so a model can learn from them more easily, using domain knowledge rather than raw data alone.
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fairness
Fairness
A model behaving without systematic disadvantage to groups defined by traits such as gender, age or region.
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exploration-vs-exploitation
Exploration vs. Exploitation
The trade-off in learning systems between trying new actions to discover better options and repeating actions already known to work.
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evaluation
Evaluation
Measuring how well a model performs against held-out data using agreed metrics, before and after it is deployed.
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efficiency
Efficiency
Getting the same or better result with less time, compute, money or manual effort.
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ec2
EC2
Amazon Elastic Compute Cloud: AWS's service for renting virtual servers by the hour, including GPU machines for training.
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domain-shift
Domain Shift
When the data a model meets in production differs from the data it was trained on, so accuracy drops even though nothing in the model changed.
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dml
DML
Data Manipulation Language: the SQL commands that read and change rows, such as SELECT, INSERT, UPDATE and DELETE.
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dino
DINO
A self-supervised vision method from Meta that learns strong image features without labels by teaching a network to match different views of the same image.
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ddl
DDL
Data Definition Language: the SQL commands that create and change the structure of a database, such as CREATE TABLE and ALTER.
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database-reliability
Database Reliability
A database's ability to stay available, correct and recoverable under load, failure and change.
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data-quality
Data Quality
How fit data is for its purpose: accurate, complete, consistent, current and free of duplicates, noise and wrong labels.
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data-lineage
Data Lineage
The record of where data came from and every transformation it went through, so any result can be traced back to its source.
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data-leakage
Data Leakage
When information from the test set, or from the future, slips into training, so the model scores well in the lab and fails in production.
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data-lake
Data Lake
A store that keeps raw data in its original format at any scale, structured or not, for later processing and analysis.
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data-integrity
Data Integrity
Data that is complete, accurate and unaltered over its whole life, protected from corruption, loss or unauthorised change.
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data-ingestion
Data Ingestion
Bringing data from its sources into a system where it can be stored, processed and analysed, whether in batches or as a live stream.
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data-governance
Data Governance
The policies, roles and controls that decide who can access data, how it is kept accurate and how its use is tracked.
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data-consistency
Data Consistency
Data that stays correct and in agreement across copies, transactions and systems, so every reader sees the same truth.
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data-balance
Data Balance
How evenly examples are spread across classes or conditions in a dataset; imbalance teaches a model to favour whatever it sees most.
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cross-validation
Cross-validation
Evaluating a model by splitting the data into several folds, training on some and testing on the rest in turn, to get a more honest accuracy estimate.
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cosine-similarity
Cosine Similarity
A score between -1 and 1 for how closely two vectors point in the same direction, used to compare embeddings regardless of their length.
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convolutional-neural-network-cnn
Convolutional Neural Network (CNN)
A neural network built for images: it slides small filters over the picture to detect edges, textures and shapes, then combines them into higher-level features.
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connected-components
Connected Components
Groups of items linked by similarity so that every member connects to the group through at least one close neighbour, used to find clusters and duplicates.
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compliance
Compliance
Meeting the laws, standards and internal policies that govern how data is collected, stored, used and audited.
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collaboration
Collaboration
People and teams working on shared data, models and decisions with a common view of the work, rather than in separate tools and silos.
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cloud-native
Cloud Native
Software designed from the start to run in the cloud, typically built as containers and services that scale and recover automatically.
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cloud-computing
Cloud Computing
Renting computing resources such as servers, storage and databases over the internet, scaling up or down on demand instead of owning hardware.
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cloud-ai
Cloud AI
Running AI training or inference on a provider's infrastructure rather than your own hardware, paying for compute as you use it.
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clip
CLIP
A model from OpenAI that learns images and text in one shared space, so a sentence can be matched to pictures it describes without task-specific training.
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caching
Caching
Keeping a copy of frequently used data in fast storage so later requests are served without recomputing or refetching it.
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buy-vs-build
Buy vs Build
The decision between purchasing an existing product and developing your own, weighing speed, cost, control and long-term maintenance.
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business-value
Business Value
The measurable benefit a project delivers to the organisation: revenue, cost saved, risk reduced or time recovered.
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black-box-model
Black-box Model
A model whose internal reasoning cannot be inspected or explained, so you can see what it predicts but not why.
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big-data
Big Data
Data too large, fast or varied for traditional tools to store and analyse, which calls for distributed storage and processing.
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