Display, PPC, SEO and email: what changes when you build each

All four campaigns start with the same audience, message and call to action. Here is what changes when you build display ads, PPC, SEO pages and email.

GTM
September 30, 2026
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6 min
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Abstract brand form artwork

Every campaign starts with the same three decisions: who you're talking to, what you want them to do, and how you'll know it worked. After that, the four channels split. A display ad is built around a picture. A PPC ad is built around a search term. An SEO campaign is built into the page itself, and an email is built around a list of people who asked to hear from you.

This post covers creation only: what you decide and produce before launch.

What do all four channels have in common?

Five build-stage habits hold across display, paid search, organic search and email.

  • Research the audience before you write any copy.
  • Write one relevant message with one clear call to action.
  • Tie the campaign goal to a business objective and to the brand voice.
  • Use the data you already have to set targets, pick keywords and choose segments.
  • Plan a creative test before launch, one element at a time.

Change the headline and the image together, and you can't tell which one moved the result.

How do you create a display ad campaign?

Display advertising works like a poster on someone else's wall. The reader came for something else. The visual leads, and the copy has to read in a second.

Four decisions make up the build.

  1. Format. Banners suit brand building. Interstitials, floating ads, wallpaper ads and map ads cover the other placements.
  2. Targeting. You can target by location down to IP address, by device and browser, by connection speed, by time of day, by browsing behavior, or by the content of the page. Contextual targeting puts mountain bike ads on a blog about mountain biking holidays.
  3. Network and placement. An ad network matches you with publishers, and you skip negotiating site by site. The Google Display Network covers more than a million websites, reaches over 80% of internet users, and can reuse the keyword lists from your search campaigns.
  4. Payment model. Match it to the goal. CPM, paid per 1,000 impressions, suits awareness. CPC suits traffic. CPA charges only for a lead or a sale, and smaller publishers sometimes sell a flat monthly rate.

Finish with the landing page. When someone clicks, the page they land on should look and read like the ad they clicked.

What goes into building a PPC campaign?

Paid search reverses the display problem. The searcher already typed what they want, so the build starts with keywords.

The ideal keyword has high purchase intent, high search volume and low competition. That keyword rarely exists. Settle for two of the three.

Then choose match types. Take "tennis shoes" as the keyword:

  • Broad match can show your ad for "red tennis shoes" or "history of tennis shoes."
  • Phrase match shows it for any search containing the exact phrase, such as "buy tennis shoes."
  • Exact match shows it only for the exact term.
  • Negative keywords such as "history" block the searches you don't want to pay for.

Group related keywords into ad groups, with a text ad for each. A PPC text ad has a headline, ad copy, a display URL, your unique value propositions, a call to action and optional extensions. Google's editorial guidelines cap character counts and ban excessive punctuation, all caps and superlatives you can't verify. Write inside those limits from the first draft.

Add extensions while you build. Brooke Osmundson of Search Engine Journal describes them plainly: "Google Ads extensions (assets) are pieces of content that complement an existing ad with useful business information." Sitelinks, 25-character callouts, call buttons and lead forms cost nothing to add.

Last come the money and the destination. Set a budget and a target cost per action, then set maximum CPC bids by what each keyword is worth to you. Point every ad at its own landing page. A dedicated page improves your quality score.

Where does SEO creation start when there's no ad to write?

SEO has no ad. The page you publish is the creative, and it has to explain itself to a search engine. Picture a library where no book has a title, chapter headings or a description on the back cover. That's how an unoptimized page looks to an algorithm.

Start from keyword research, then build five on-page elements into every page:

  • Title tag: primary keyword in the first 30 characters, 60 characters or fewer in total, brand name at the end.
  • Meta description: 150 to 160 characters with the keyword, a clear benefit and a call to action.
  • Headers: one H1 with the main keyword, H2s for major sections, H3s beneath them.
  • Image alt text: 100 to 125 characters on what the image shows, plus a descriptive file name in place of IMG_001.jpg.
  • URL: a short, readable path with the keyword, such as /seo-audit-services.

The meta description won't move your ranking. It decides whether people click, so write it for intent. Someone searching "enterprise SEO audit" wants a business solution, and a definition won't win that click.

Stay with white hat methods: keyword stuffing and hidden text get penalized. And plan for upkeep, because an SEO page needs continuous optimization for as long as it's live.

What makes an email campaign effective before you hit send?

Email is the only one of the four where you own the audience. The list is the foundation, and it has to be permission-based: opt-in forms, lead magnets such as a checklist or a free guide, event and webinar registrations, and referrals. A purchased list skips the consent that GDPR and Canada's CASL require and damages your sender reputation.

Build the campaign itself in four layers.

  1. Segmentation. A bakery that knows some customers love chocolate sends those customers the chocolate email.
  2. Subject line and sender name. Together they decide whether the email gets opened.
  3. Content and template. Personalized, readable, on brand, with one call to action.
  4. Timing and frequency. Set both before the first send.

Test the layers separately. If a new subject line lifts opens by 50% and clicks stay flat, the subject line did its job and the body copy didn't.

Compliance goes into the template on day one. The US CAN-SPAM Act requires an honest subject line and sender, a visible unsubscribe option and a valid postal address. GDPR and CASL require consent before the first marketing email. The discipline pays: a DMA study puts email's average return at $36 for every $1 spent.

How do the four compare side by side?

Build decisionDisplayPPCSEOEmail
Lead creativeImage, animation or videoText ad plus extensionsThe page itselfSubject line and body
Targeting throughLocation, device, behavior, page contentKeywords and match typesKeywords and search intentSegments of your own list
Hard constraintReadable at a glanceGoogle's character caps and editorial rulesTitle up to 60, description 150 to 160 charactersConsent and unsubscribe rules
Money set at buildPayment model: CPM, CPC, CPA or flat rateBudget, target CPA, maximum CPC bidsResearch and content timeList building and content time
Where the click landsA page matching the adA dedicated page per adThe searcher is already thereA page tied to the call to action

Key takeaways

  • Audience research, one message, one call to action and a planned test apply to all four.
  • Display is built around the visual and the placement.
  • PPC is built around keywords, ad groups and bids.
  • SEO is built into the page: title, description, headers, alt text and URL.
  • Email is built on a permission-based list and its segments.

If you're planning a launch and haven't decided which channel to build first, bring the brief to a free 30-minute call. We'll match each channel to the goal it serves, and you'll leave with a build list for the first one.

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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
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
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
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
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
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
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)
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
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
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
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)
The checks and processes that make sure a product, dataset or model meets its standard before it ships.
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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
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
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
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)
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
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
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
Errors and inconsistencies in labels, from mistakes, ambiguity or disagreeing annotators, which degrade what a model can learn.
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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
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
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
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
A model behaving without systematic disadvantage to groups defined by traits such as gender, age or region.
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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
Measuring how well a model performs against held-out data using agreed metrics, before and after it is deployed.
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Efficiency
Getting the same or better result with less time, compute, money or manual effort.
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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
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
Data Manipulation Language: the SQL commands that read and change rows, such as SELECT, INSERT, UPDATE and DELETE.
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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
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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