You have the idea. The hard part is turning it into something real. Hiring a developer runs around $4,000 US a month per head, learning to code takes longer than most ideas survive, and most AI builders spit out generic templates that fall apart the moment you ask for something specific to your business. They forget context between sessions, leave you stuck in debugging loops, and your launch window slides further away with every patch.

Then there’s the part nobody warns you about: the strategy gap. Even when the code finally works, what comes next? Which feature should you ship first? Where does your product sit against everything already on the market? The technical hurdles are loud, but the strategic ones quietly kill more projects.

Shipper

Shipper is an AI-powered app builder that turns plain-English descriptions into fully functional applications, with hosting, database, and deployment handled in one place. Pair it with The Advisor, a separate AI consultant that reviews your project and tells you what to build, fix, or grow next.

Building with Shipper means you stop paying for what slows you down. Coding, debugging, agency retainers, and the cost of waiting all shrink, while the things that move the needle start showing up: real users, real revenue, real launches. Because the underlying engine is agentic, it reasons through your full project context before writing anything, so the code understands your business logic rather than pasting generic scaffolding on top of vague intent.

The Advisor sits one layer above that, watching market trends and your past decisions to surface improvements you’d otherwise miss. The result is a workflow where someone with no engineering background can ship an MVP before lunch, and an experienced developer can compress what used to be a quarter of work into an afternoon.

What’s Inside Shipper

Shipper packs a wide range of capabilities into a single chat-based workflow. Here’s what comes standard:

• Advanced App Analytics: Track engagement, retention, and behavior inside any app you build, without bolting on a third-party tool.
• Advisor Co-Pilot: A separate AI that reviews your project from the outside and recommends features, design changes, and growth moves.
• Agentic AI Builder: The core engine reasons about requirements, writes code, and self-corrects before deploying anything.
• AI Audience Research: Pulls market signals and user behavior patterns to inform what to build for whom.
• AI-Improved Prompts: Rewrites your input behind the scenes so the build instructions stay clear, even when your description isn’t.
• Auto Bug Fixing: Detects and patches issues automatically, cutting reported defects by roughly 91% versus standard AI coding tools.
• Chrome Extensions: Build browser extensions through the same chat interface used for full web apps.
• Connectors: Plug into external services so your app can talk to tools your users already rely on.
• Cross-Device Access: Because the workflow runs in the browser, you can build, refine, or check on a project from a phone, tablet, or laptop without switching apps.
• Email Sending: Configure transactional and outreach email flows directly inside your project.
• Hosting Included: Every app runs on Shipper’s infrastructure, so there’s no separate hosting bill or DevOps lift.
• Messaging Bots: Build for WhatsApp, Telegram, Slack, and Discord without juggling separate platforms.
• Mobile and Web Apps: Generate applications that render properly across phones, tablets, and desktop browsers from a single description.
• Motion-Animated Websites: Add animation and interaction layers that would normally require a front-end specialist.
• One-Click Translation: Localize your app for new markets with a single command.
• Self-Fixing Applications: When something breaks in production, the AI diagnoses and resolves it without you having to step in.
• Smart Credit System: You only spend credits when the AI completes meaningful chunks of work, not on every individual step.
• Unlimited Collaborators: Bring teammates into any project on every plan, including the free tier.
• Video Generation: Create video content and embed it inside your app without external tools.

Beta users of The Advisor report 70% cost savings, 90% faster iteration cycles, and 10x faster MVP launches, moving from idea to live business in minutes rather than weeks. Source: Shipper.now beta user data.

The Whole Stack in One Window

Stitch these capabilities together and Shipper functions less like a tool and more like an entire product team compressed into one chat window: a builder that codes and self-heals, plus a strategist that watches your market and your code at the same time. Anything you’d normally pay for separately, including hosting, database, analytics, billing, and design, lives inside the same project. That’s why a Pro seat at $25 a month does the work of a developer who’d otherwise cost you closer to $4,000.

Ship the Idea You’ve Been Sitting On

You have an idea worth shipping. Whether it’s an MVP you’ve been postponing, an internal tool that would save your team hours every week, or a niche app you sketched out on a napkin, the gap between thinking about it and using it is now a conversation rather than a hiring round.

Try the demo, push an idea through it, and see how far you get before your coffee cools. The free tier gives you daily credits to test the workflow before committing to anything.

Start Building with Shipper/

Frequently Asked Questions

©2026 DK New Media, LLC, All rights reserved | Disclosure

Originally Published on Martech Zone: Shipper: Build Working Native Apps by Chatting with AI

Anyone working seriously with ChatGPT, Claude, Gemini, or Grok has felt the catch. Answers come back fast, the writing is fluent, and the model never sounds uncertain. That last part is the problem. Confidence without accuracy has become the most expensive failure mode in working with these tools, especially in research, legal work, finance, and other areas where being wrong has a cost.

The fix is not a better model. It is a habit of checking the work before you act on it. Below are ten checks that come up again and again among people who use AI well. None requires new software.

Re-ask the same question in a different way

Models anchor on the exact phrasing of your prompt. Ask Is {X} true? and you tend to get a confirmation back regardless of the truth. Ask the inverse instead. What’s the case against {X}? or What would I need to see to know {X} is wrong? If the model wobbles between the two framings, that wobble is the signal you were looking for.

Force the model to cite sources

Ask for citations every time, then check at least the first one. Fabricated URLs are a known failure mode across all major models, and the verification step is cheap. Trusting a fake citation in something a client or court will read is not.

When a model can’t produce a real source, treat the underlying claim as unverified. That isn’t a failure of the tool. It’s the tool being honest about a question it shouldn’t have answered with confidence.

3. Run the same question through a second model

This is the most reliable check on the list, and it’s the basis for tools that automatically perform AI Answer Verification. If ChatGPT, Claude, and Gemini all give substantively the same answer, your confidence should rise. If one of them diverges, especially with a hedge, you’ve learned something useful for the cost of one extra query.

Different models trained on overlapping but distinct corpora, with different reinforcement objectives, tend to make different mistakes. When their answers converge anyway, the convergence itself is a soft signal of truth.

4. Watch for the specific number, vague source pattern

A precise figure (revenue grew 23.4% year over year) paired with a hand-wavy attribution (according to industry reports) is the classic signature of a fabricated statistic. Real numbers come from specific sources. Vague sources usually mean the number was constructed to sound right.

The check is fast: ask What’s the source for the 23.4% figure? A real, locatable citation back means the number is usable. Another generic deflection means throw it out.

5. Test the math separately

Models are still better at language than at arithmetic, even now. Whenever an answer involves a calculation, pull the numbers into a calculator and redo it yourself. Percentages, compound interest, ratios, and unit conversions: all of these benefit from a quick separate verification.

For multi-step calculations, ask the model to show its work step by step. Errors usually occur in a single bad step rather than spreading across the whole sequence. If you can spot which step is wrong, you can correct just that part.

6. Probe the model’s knowledge cutoff

Every major model has a cutoff date for training. For anything that happened recently, the model may either confabulate or hallucinate confidently. Ask directly: When was your training data current as of? and Was [event] in your training data? A model that admits uncertainty is more useful than one that pretends to know.

7. Ask for a counterargument

After an answer comes back, ask the model what the strongest argument against it would be. A well-aligned model will produce a real counterargument. A model that’s overfit to your framing will produce a token disagreement that doesn’t actually challenge anything. The quality of the counterargument tells you how robust the original answer was.

This works especially well for strategy, decision-making, or any question that involves tradeoffs. If the counterargument is uncomfortable to read, the original answer was probably more thoughtful than it looked.

8. Verify the structure, not just the content

For procedures like instructions, code, recipes, or walkthroughs, the most common failure mode isn’t a wrong fact. It’s a missing step, a step in the wrong order, or a step that assumes you’ve already done something the model didn’t mention.

Read the procedure end to end and ask: If a person who knew nothing tried to follow this exactly, would it work? If the answer is only if they already know what they’re doing, the procedure is hiding errors.

9. Stress-test against your own expertise

If you know any part of the question well, drop in a small detail you already know the answer to and ask the model to comment on it. If it gets that detail wrong, your confidence in the rest should drop sharply. If it gets it right, your confidence should rise modestly. One right detail does not prove the rest is right.

10. Decide your tolerance per question type

The most experienced AI users don’t verify every answer the same way. They calibrate. What’s a good restaurant in this neighborhood? doesn’t deserve the same scrutiny as Is this contract clause enforceable? A quick mental ladder helps:

• Casual or low stakes: Trust the model and move on.
• Real work, reversible: A quick sanity check (technique 1, 2, or 5).
• High stakes or irreversible: A full multi-model cross-check, plus source verification.

The goal is not to verify everything. It’s to verify the things where being wrong would actually cost you something.

What Ties These Together

Most of the techniques above are versions of one habit: do not trust a single AI in a single context. They lean on comparison. Between phrasings, between sources, between models, between what the model says and what you already know.

That structural habit also underpins multi-model AI tools that embed verification into the workflow itself. Asking one question and getting one answer is what people did when AI was new. Asking one question and getting back four answers from four different models, with the agreements and disagreements highlighted, is closer to what serious work with AI looks like now.

You can run all of these checks with browser tabs and a calculator. After a few rounds of doing it manually, the friction will be obvious, and so will the reason verification is becoming its own product category. AI is a fast, fluent, occasionally wrong assistant. The cost of building one verification habit is small. The cost of trusting a confident-but-wrong answer in a deliverable that goes to a client, a court, or a customer is not. Spend the thirty seconds.

©2026 DK New Media, LLC, All rights reserved | Disclosure

Originally Published on Martech Zone: 10 Ways to Verify AI Answers Before You Trust Them

Marketing teams blame the tools when campaigns fall flat, but the tools are usually doing what they were built to do. The real bottleneck sits a layer down. Customer data is scattered across the CRM, the ESP, the analytics warehouse, ad accounts, and a half-dozen other systems that don’t cleanly talk to each other. Identities don’t resolve. First-party (1P) signals become stale before they reach the campaign team. Activation depends on a data engineer who’s already booked solid for the next two sprints.

What you’re left with is a marketing organization that knows what it wants to do but can’t move fast enough to do it. Segments take days to build. Attribution arguments drag on because every team trusts a different dashboard. Personalization gets watered down to whatever the lowest common denominator across systems will support. By the time a campaign reaches the customer, the moment that mattered has already passed, and you’re left guessing why the numbers came in flat.

Databricks Data Intelligence for Marketing

Data Intelligence for Marketing combines the Databricks Data Intelligence Platform with seamless out-of-the-box integration to leading martech tools and services. It’s the unified data and AI layer that lets your existing marketing ecosystem actually do what it was sold to do.

The platform gives you the most complete view of customer and campaign data your organization has ever had, with first-, second- (2P), and third-party (3P) signals unified, identities resolved, and profiles enriched by AI for depth and accuracy.

Every marketer on the team can ask questions of that data in natural language (NLP) and get answers without filing a ticket, which means the people closest to the work stop waiting on the people closest to the database. Decisions get faster because the data behind them is fresh, governed, and shared. Campaigns get more relevant because segmentation can finally use everything you know about a customer, rather than the slim subset that happened to make it into the activation tool.

And the architecture is composable, so you keep the martech investments that work and let the data platform handle the unification underneath, instead of ripping and replacing every time a vendor changes hands.

What’s Inside Databricks

Here’s the toolset that ships with Data Intelligence for Marketing:

• AI Agents: Automate planning, execution, and optimization tasks across marketing campaigns so human resources scale without proportional headcount increases.
• Brand Management: Monitor brand health and consistency across channels using a unified signal, so creative and messaging decisions stay grounded in what customers are actually responding to.
• Campaign Measurement: Connect spend to outcomes across every channel with attribution modeling that runs on your full customer dataset rather than aggregated samples.
• Campaign Optimization: Tune live campaigns based on AI-driven recommendations that factor in audience, channel, and creative performance as data comes in.
• Campaign Planning: Build media plans grounded in unified historical performance, scenario modeling, and AI-assisted forecasts of likely outcomes.
• Composable CDP: Build a customer data platform on top of your existing data rather than duplicating it into a separate vendor stack, eliminating redundant infrastructure and reducing the total cost of ownership.
• Customer 360: Real-time, governed customer profiles that pull together every relevant signal across systems, refreshed continuously rather than batched overnight.
• Customer Experiences: Power personalized experiences (CX) across marketing, sales, and commerce by activating unified customer data wherever the engagement happens.
• Customer Insights: Deep behavioral and predictive analytics built on the full dataset, surfacing the patterns that drive lifetime value rather than only the ones that fit on a dashboard.
• Data Management: Ingest, unify, and enrich first-, second-, and third-party data with identity resolution and AI-driven profile enhancement, all governed from a single platform.
• Martech Integrations: Out-of-the-box connections to leading martech tools and services so collection, unification, enrichment, and activation work without custom pipelines.
• Open Lakehouse Architecture: Unify structured and unstructured marketing data on an open foundation that avoids vendor lock-in and supports any analytics or AI workload your team needs.
• Self-Serve Insights: Marketers ask questions in natural language and get answers from customer and campaign data directly, without queueing requests with the data team.
• Solution Accelerators: Ready-to-use frameworks for media mix modeling, multi-touch attribution, customer entity resolution, and survival analysis and lifetime value, so the projects your team would otherwise build from scratch start most of the way done.

Skechers personalizes customer journeys to boost lifetime value, seeing a 324% increase in click-through rates, a 68% decrease in cost per click, and a 28% increase in return on ad spend.

Databricks Customer Story, Skechers

The capabilities work together as a system rather than a collection of tools. Data flows in once, gets unified and enriched once, and feeds every downstream marketing application from segmentation to measurement, which is why teams like HP, Pandora, Burberry, and HSBC see compounding gains as they expand from one use case to the next.

Get Started With Data Intelligence for Marketing

Your customer data is already an asset. The question is whether your platform is letting you act on it or quietly forcing you to settle for less. Databricks gives marketing teams a unified, AI-driven foundation that works with the martech stack you already have, scales with the work you’re already doing, and keeps the data engineering bottleneck from being the reason a campaign misses.

Talk to Databricks

Frequently Asked Questions

©2026 DK New Media, LLC, All rights reserved | Disclosure

Originally Published on Martech Zone: Databricks: Unify Marketing Data, Apply Intelligence, and Activate at Scale

For decades, digital discovery was dominated by Search Engine Optimization (SEO). We built sites for humans to read and Google to rank. However, the rise of Answer Engines like ChatGPT, Perplexity, and Gemini has fundamentally shifted the landscape. These tools don’t just point users to a URL; they synthesize information and provide direct answers. Some have adopted the term GEO as an advancement to SEO focused on AI Visibility.

What is AI Visibility?

AI Visibility is the measure of how machine-readable (MX) your website is. It is no longer enough to have great content; that content must be wrapped in a technical architecture that allows an AI bot to identify the primary topic, verify the author’s authority, and extract data points (like prices or specs) without hallucinating or losing context. If an AI cannot parse your site, your brand will not be cited in the answers that are increasingly replacing the traditional search results page.

Decoding the AI Visibility Report

When you audit a page for AI readiness, several technical layers determine whether an LLM will favor your content over a competitor’s. Here is how each section of a standard report impacts your visibility.

1. Crawlability & Bot Access

Before an AI can learn from you, it must be allowed in.

• Robots.txt & User-Agents: Modern reports check if you are blocking specific AI bots, like GPTBot or CCBot. While some sites block these to prevent training, doing so also excludes you from real-time browsing features that use the AI to find current information to answer a user’s prompt.
• Sitemap & Discovery: If a page is orphaned (no internal links), an AI agent is unlikely to find it during a live web search.

2. Core Meta & Topic Signals

AI engines use meta tags as the “primary topic signal”.

• The <title> Tag: This is the headline the AI uses to categorize the page. If it’s truncated or vague, the AI’s downstream interpretation may drift.
• Canonical URLs: These tell the AI which version of a page is the authoritative one, preventing the splitting of ranking signals across duplicate content.

3. Social Graph (Open Graph & X Cards)

While originally for social media, many AI chat apps (like Slack or Discord bots) use Open Graph tags (og:title, og:image) to generate rich previews. If these are missing, the AI falls back to ad-hoc scraping, which often results in broken or empty citations.

4. HTML5 Semantics: The Map for Machines

AI doesn’t see a page layout; it reads code. HTML5 landmarks like <header>, <main>, <nav>, and <footer> help bots skip boilerplate and jump straight to the core content.

• <section> and <figure>: These tags provide topical structure. Wrapping images in <figure> with a <figcaption> allows multimodal AI to accurately associate an image with its textual context.

5. Content Structure & E-E-A-T

• Heading Hierarchy (H1-H3): AI uses H2S and H3S for table-of-contents-style summarization. Well-structured headings provide clean answer chunks for the AI to extract.
• Author Signals: To combat AI-generated spam, engines look for E-E-A-T (Experience, Expertise, Authoritativeness, and Trust). Naming an author via Schema makes your content more citable and trustworthy to an LLM.

6. Structured Data (Schema.org)

This is arguably the most critical component. Schema provides a backbone of facts in a structured data format where AI does not need to guess nor interpret the context. Instead of the AI guessing your price, location, or product specs from prose, Schema provides the AI with the exact values in JSON format it can ingest instantly.

The “Invisible” Signals: The Case of llms.txt

As you dive into AI Visibility, you may encounter emerging standards like llms.txt. This is a proposed markdown file (similar to robots.txt) designed to provide a condensed, LLM-friendly version of your site’s information.

The Reality Check:

While many SEO audits and AI-ready checklists now recommend implementing llms.txt, it is important to note that as of mid-2026, it is not yet a standard implemented by any major AI vendor (such as OpenAI, Google, or Anthropic).

While it doesn’t hurt to have one for future-proofing, your time is currently better spent on:

1. Validating your JSON-LD Schema (which all AI bots currently use).
2. Improving HTML5 Semantics to help bots navigate your DOM.
3. Ensuring your Robots.txt explicitly allows the bots you want citations from.

In the world of AI Visibility, focus on the signals that machines are actually reading today before chasing the experimental tags of tomorrow.

Beyond the Page: Off-Site Validation and Trust

While the AI Visibility Score provides a deep dive into the machine-readability of a specific URL, it is important to remember that AI models do not look at your page in a vacuum. LLMs and answer engines are designed to synthesize information from across the entire web to determine the “truth” of a claim.

The Ecosystem of Trust

To increase the likelihood that your data appears in AI responses, ensure your on-site information is echoed and validated by authoritative third-party sources. This process, often referred to as triangulation, is how AI agents verify that the data on your page is accurate rather than a hallucination or an error.

• Business Listings and Directories: Consistent NAP (Name, Address, Phone Number) data across platforms like Google Business Profile (GBP), Yelp, and industry-specific directories provides a foundation of trust for local AI queries.
• Knowledge Graphs: Being present in structured databases like Wikidata or Bing’s Knowledge Graph helps AI entities connect your brand to a broader web of verified facts.
• Third-Party (3P) Citations: Mentions in news articles, press releases, or authoritative blogs serve as social proof for AI. When multiple reputable sources cite the same specs, prices, or services, the AI’s confidence in that data increases.
• E-E-A-T Consistency: If your on-site schema identifies an author, but that author has no presence on LinkedIn, Amazon, or professional journals, the AI may discount the expertise signal.

A Note on the Scope of This Report

It is critical to note that the audit provided here does not measure these off-site indicators. This report is strictly focused on the on-page technical infrastructure—the crawlability, structured data, and semantic markers that allow an AI to interpret the exact page you have selected.

Think of this score as a measure of your “speech clarity”: it ensures the AI can hear and understand exactly what you are saying. Off-site indicators, by contrast, are your “reputation”: they determine whether the AI actually believes you.

©2026 DK New Media, LLC, All rights reserved | Disclosure

Originally Published on Martech Zone: What Is Your Site’s AI Visibility Score?

Return on Investment (ROI) is more than just a buzzword; it is the fundamental metric that separates strategic growth from reckless spending. At its core, ROI measures the efficiency of an investment by comparing the net profit generated to the total cost required to achieve it. In an era of tightening budgets and doing more with less, accurately forecasting and tracking these figures is essential for any department head, business owner, or financial analyst.

The beauty of a comprehensive ROI calculation lies in its ability to normalize different types of value. While a direct increase in sales is easy to track, the soft benefits (such as hours saved through automation or reduced operational waste) often go unmeasured. By utilizing a structured framework, you can translate these abstract efficiencies into hard currency, providing a clear Go or No-Go signal for your projects.

The ROI Formula

Before diving into individual variables, it is essential to understand the underlying logic that drives these calculations. The fundamental formula for ROI compares the efficiency of an investment by dividing the net profit by the total cost. This provides a percentage-based look at how hard your money is working for you.

This equation ensures that, regardless of the size of the investment, you are looking at a relative performance metric that remains consistent across all departments and project types.

Defining the Investment: Costs and Timelines

The first step in any ROI analysis is accurately identifying the Cost of Investment. This involves looking beyond the sticker price and considering the project’s entire lifecycle. To ensure a comprehensive financial snapshot, we utilize several key metrics to establish the baseline expenditure.

The following list details the essential input fields required to establish a baseline for your investment expenditure:

• Investment Name: A descriptive title used for identification and internal tracking across different departments or reporting periods.
• Upfront / One-Time Cost: The initial capital outlay required to initiate the project, including hardware, licensing, and installation fees.
• Recurring Cost: The ongoing fees associated with maintaining the investment, typically including monthly subscriptions or maintenance contracts.
• Recurring Period: A selection determining the frequency of the recurring cost (monthly or annually), ensuring the calculator can project expenses accurately over time.
• Evaluation Period (Years): The specific window of time (e.g., 1, 3, or 5 years) over which you intend to measure the performance and profitability of the project.

By meticulously documenting these costs, you create a realistic Total Cost of Ownership (TCO) that prevents unexpected financial drains from eroding your eventual returns.

Capturing the Value: Analyzing Returns

While costs are often easy to track, defining the Return requires a more creative and comprehensive approach. Value in a business context isn’t just about direct sales; it also involves cost avoidance and the reclaiming of human capital. Our framework breaks down returns into three distinct value streams.

To understand the total financial benefit of your investment, you must quantify gains across these key areas:

• Annual Revenue Increase: The direct impact on the company’s top-line growth, such as increased sales volume or higher customer retention rates.
• Annual Cost Savings: The reduction of existing operating expenses, such as lower utility bills, reduced waste, or the elimination of redundant legacy software.
• Hours Saved per Week: A measure of operational efficiency representing the amount of time employees no longer spend on manual or inefficient tasks.
• Loaded Hourly Rate: The comprehensive cost of an employee’s time, including base salary, payroll taxes, benefits, and overhead.

Aggregating these figures provides a holistic view of the investment’s impact, ensuring that efficiency gains are given equal weight with direct revenue growth.

Interpreting the Results: Financial Indicators

Once the data is processed, the framework generates several key performance indicators (KPIs). These indicators serve as the narrative for the investment’s lifecycle, telling you not just if the project is profitable, but when and how that profit manifests.

The resulting breakdown provides the following five metrics to guide your final decision-making process:

• Total Investment Cost: The aggregate sum of all initial and recurring expenses over the selected evaluation period.
• Total Returns: The gross financial benefit calculated by summing all three return streams (Revenue, Savings, and Time) over the evaluation period.
• Net Gain: The actual profit remaining after the total investment cost has been fully subtracted from the total returns.
• Payback Period: The amount of time required for the investment to generate enough return to cover its own initial and ongoing costs.
• Annualized ROI: A percentage-based representation of the annual return, facilitating easy comparison with other financial benchmarks.

These metrics provide a clear, data-driven roadmap for stakeholders, moving the conversation from speculative interest to financial certainty.

©2026 DK New Media, LLC, All rights reserved | Disclosure

Originally Published on Martech Zone: How To Accurately Calculate A Return on Investment (ROI)

In late April 2026, attackers ran one of the most elegant phishing campaigns in recent memory against Robinhood customers. They didn’t spoof a domain. They didn’t compromise a mail server. They didn’t steal a single credential from Robinhood itself. Instead, they convinced Robinhood’s own email infrastructure to do the phishing for them — exploiting a quirk in Gmail’s address handling, combined with a missing input sanitization step to send phishing emails that came from the real noreply@robinhood.com, passed every authentication check, and looked completely legitimate to the people receiving them.

The mechanics matter, and we’ll walk through them in detail later in this article. But the Robinhood incident is worth opening with because it represents a shift in how brands need to think about phishing. The threat is no longer just attackers spoofing your domain from the outside. Increasingly, attackers are finding ways to make your trusted systems carry their messages. That changes the defensive posture required, and it raises the stakes for every brand that sends transactional email at scale.

Before getting into the playbook, it’s worth defining what phishing is, how it has evolved, and why its impact on brands is much larger than most non-security teams realize.

What Is Phishing?

Phishing is a category of social engineering attack in which criminals impersonate a trusted entity — a bank, retailer, employer, government agency, or service provider — to trick people into handing over credentials, financial information, or money, or into installing malware. The name comes from the analogy of casting bait into a pool of potential victims and waiting for someone to bite. The bait is almost always a message that looks legitimate, and the hook is almost always a link, attachment, or phone number that leads somewhere it shouldn’t.

The term originally referred to email-based attacks, but the tactic has long since spread to every channel where people receive messages.

• Smishing uses SMS.
• Vishing uses phone calls, increasingly with GenAI voices that clone real executives.
• Quishing uses QR codes embedded in emails, posters, or PDFs.

Callback phishing uses fake invoices to trick victims into dialing numbers controlled by the attacker. Browser-in-the-browser attacks render fake login windows inside real ones. The underlying technique is the same in every case: borrow a trusted identity, manufacture urgency, and capture whatever the victim is willing to give up under pressure.

For most of the public, phishing is a personal problem — a suspicious email, a bad click, a drained bank account. For brands, phishing is something else entirely. It’s a sustained assault on the trust you spent years and millions building, carried out by criminals who never have to touch your systems to damage your business. Every time a customer receives a convincing fake email carrying your logo, the cost of doing business with you goes up. Every time someone clicks one and loses money, your support inbox fills with people who blame you, regardless of whether you were technically at fault. And every time a regulator, journalist, or insurer asks why your name keeps appearing in fraud reports, you spend another week explaining what is and isn’t your responsibility.

The scale of this is hard to overstate. Phishing is the entry point for nearly every other category of cyber harm — credential theft, ransomware, payment fraud, supply chain compromise, insider exploitation. Brand impersonation is the engine that makes it work, because attackers have learned that borrowing trust is cheaper than building it.

Stolen credentials from phishing enable 53% of all breaches, while business email compromise alone caused $2.77 billion in FBI-reported losses from 21,442 complaints in 2024.

StationX, Top Phishing Statistics for 2026

The Impact of Phishing on Brands

The impact on a brand falls into four categories that compound. Customer trust erodes when people start associating your name with fraud, even when the fraud has nothing to do with your security. Acquisition costs rise because suspicious customers click less, convert less, and demand more reassurance. Support and fraud-handling costs rise as your team absorbs the operational fallout from incidents you didn’t cause. And when an incident is large enough to make news, the reputational damage outlasts the technical response by years.

The global average cost of a data breach reached $4.44 million in 2025, with phishing-related breaches averaging $4.80 million per incident — and 49% of businesses reported being impacted by phishing-related events in 2025.

Kaseya, The True Cost of Phishing Attacks

What makes the current threat landscape distinct is that attackers no longer need to spoof your domain to impersonate you. The most sophisticated campaigns have stopped trying to fake authentication and started exploiting legitimate authentication. They find ways to make your real systems send their fake messages. The Robinhood incident in late April 2026 was the most public example of this pattern, and the most instructive.

Robinhood’s Phishing Incident via Gmail

Robinhood’s attackers exploited Gmail’s dot trick — the long-standing Gmail behavior where periods in a username are ignored, so john.smith@gmail.com and johnsmith@gmail.com deliver to the same inbox. Robinhood’s signup system, however, treated each dotted variation as a distinct account. An attacker could create a Robinhood account using a dotted version of a victim’s real Gmail address, and any system-generated email would land in the victim’s actual inbox.

That alone wouldn’t have been a crisis. The crisis came from what happened next. During account creation, attackers injected malicious HTML into user-controlled metadata fields, specifically the device name. Robinhood’s automated recent login notification system then pulled that metadata into its email template without sanitization or escaping. The result was a phishing email sent from noreply@robinhood.com, passing SPF, DKIM, and DMARC, displaying Robinhood branding, and containing an attacker-controlled Review Activity button that led to a credential harvesting site.

For users, there was no tell. The email was, by every technical measure, legitimate. Robinhood patched the issue quickly and confirmed no accounts or funds were compromised, but the broader lesson stands: a single missing call to an HTML escape function turned a Fortune-listed fintech into a phishing-as-a-service platform for its own customers.

This article walks through the layered defenses that would have stopped this attack and that prevent its many variants. Each section explains the risk, the fix, and how to verify the fix actually works. If you operate a consumer-facing brand — fintech, e-commerce, SaaS, healthcare, anything with transactional email — this is your audit checklist.

Treat Every User-Controlled Field as Hostile

The Robinhood incident comes down to one engineering failure: trusting user input that gets rendered in an outbound email. Account names, device names, profile bios, custom labels, support ticket subjects, transaction memos, referral codes, shipping instructions — anything a user can type and your system later includes in a notification is a potential injection point.

The fix is straightforward in principle and tedious in practice. Every user-controlled value must be HTML-escaped at render time, not at storage time. Storage-time escaping creates double-encoding bugs and breaks when data flows between systems. Render-time escaping in a properly configured templating engine is the only reliable approach.

Modern templating systems handle this automatically when used correctly. Jinja2, Handlebars, Liquid, and React JSX all escape variables by default. The vulnerabilities almost always come from explicit overrides — a developer who needed to render rich text once and reached for {{ value | safe }} or dangerouslySetInnerHTML and never came back to harden it. Audit every escape-bypass in your email rendering code. Each one needs a documented justification and a content sanitizer like DOMPurify behind it.

Beyond escaping, validate aggressively at the input layer. A device name does not need to contain angle brackets, and a display name does not need to contain href attributes. Allowlists beat denylists. Reject values that don’t match the expected character set rather than trying to filter out everything dangerous.

How to verify: Run a controlled test. In a staging environment, create an account with the device name

<a href="https://example.com">CLICK ME</a>

Trigger whatever flow generates a confirmation or notification email. Inspect the rendered email source. If you see a clickable link rather than the literal escaped text, you have a vulnerability. Repeat for every user-controllable field that surfaces in any outbound message — this list is almost always longer than teams initially assume. Add these tests to your CI pipeline so regressions get caught before they ship.

Close the Account Creation Abuse Loop

The Gmail dot trick was the delivery mechanism that enabled the Robinhood attack to scale. Without it, attackers could only inject HTML into emails sent to themselves. With it, they could inject HTML into emails delivered to any Gmail user whose address they knew.

Email normalization is the right answer. Before storing an email or comparing it against existing accounts, normalize it: lowercase the local part, strip dots from Gmail addresses, remove plus-sign suffixes, and apply equivalent rules for other providers that have similar quirks. Treat john.smith+work@gmail.com and johnsmith@gmail.com as the same identity for the purpose of account uniqueness. Users almost never need to register multiple accounts with the same underlying inbox, and the rare legitimate use case can be handled with explicit verification.

Even with normalization in place, account creation deserves more scrutiny than most teams give it. Require email verification before any system-generated email goes anywhere beyond the verification message itself. Rate-limit account creation per IP, per device fingerprint, and per email domain. Watch for patterns that suggest abuse — bursts of signups within seconds, sequential variations on a single email, or signups from infrastructure ranges associated with VPNs and bulletproof hosts.

CAPTCHA helps, but is not sufficient on its own. Behavioral signals during signup (typing cadence, mouse movement, time on page) separate humans from automation more reliably than puzzles do, and they don’t add friction for legitimate users.

How to verify: Attempt to create three accounts in succession using test@gmail.com, t.est@gmail.com, and te.st+anything@gmail.com. If your system accepts more than one, your normalization is incomplete. Then attempt rapid-fire account creation from a single IP — fifty signups in a minute. If your system processes all of them without rate-limiting, throttling, or escalating challenge, you have a scaling problem waiting to happen. Finally, check whether your verification flow actually blocks system emails to unverified addresses, or whether unverified accounts can still trigger notifications.

Lock Down Your Email Authentication and Outbound Patterns

DMARC, SPF, and DKIM exist to prevent external attackers from spoofing your domain. They worked perfectly during the Robinhood attack — and that was precisely the problem. The phishing emails passed every authentication check because they were genuinely sent by Robinhood. Authentication tells recipients that an email came from your infrastructure. It says nothing about whether your infrastructure was used legitimately.

Start with the basics, because plenty of brands still haven’t. Publish a DMARC policy of p=reject once you have alignment confirmed across all your sending sources. p=none is a monitoring posture, not protection. p=quarantine is a transitional state. Reject is the destination. Configure DKIM with at least 2048-bit keys and rotate them on a documented schedule. Make sure SPF records cover every legitimate sender and don’t exceed the 10-lookup limit, which breaks evaluation entirely.

Then go further. Outbound email monitoring catches what authentication can’t. Build telemetry around your transactional email volume, the URLs in your templates, the recipient domains you send to, and the rate of new account creation that triggers messages. A sudden spike in login alert emails to addresses that don’t match your typical user demographics is exactly the signal Robinhood would want. So is a transactional email containing a URL that doesn’t appear in your template repository.

BIMI (Brand Indicators for Message Identification) is worth adopting once your DMARC policy is at reject. It surfaces your verified logo in supporting mail clients, which raises the visual cost of any phishing attempt that doesn’t come from your real infrastructure. It does not help against the Robinhood-style attack, but it raises the floor everywhere else.

How to verify: Check your DMARC, SPF, and DKIM records using any of the standard public lookups. The DMARC policy should read p=reject with pct=100 and a reporting address that someone actively monitors. Send a test email to an address you control at Gmail and Outlook and view the original headers — all three authentication checks should show pass. Then send a deliberately malformed test from an unauthorized sending source and confirm it is rejected rather than quarantined. Finally, audit your aggregate DMARC reports for the last thirty days. Unfamiliar sources sending emails that align with your domain mean you either have shadow IT or someone is testing your defenses.

Build a Brand Monitoring Capability That Actually Triggers Action

Most brand monitoring programs generate reports. The good ones generate takedowns. The difference is in the process, not the tooling.

Continuous monitoring should cover newly registered domains that resemble your brand (typosquats, homograph attacks, TLD variations), phishing kits that target your login flow, fake mobile apps in third-party app stores, social media accounts impersonating your support team, and paid search ads that bid on your brand name and redirect to suspicious destinations. The volume of these signals is high enough that human review of every alert is impractical. You need triage automation that prioritizes based on activity — a parked typosquat domain is a watchlist item, but the same domain with an active login form pointed at your brand is an emergency.

The takedown process is where most programs fall apart. Establish relationships with the major registrars and hosting providers before you need them. Document the abuse-reporting endpoints for Cloudflare, Namecheap, GoDaddy, AWS, and others. Keep template DMCA and trademark notices ready. Know who at your company has the authority to authorize legal action and reach them in hours, not days. The economic logic of phishing depends on uptime — a kit that goes down within 4 hours of going live is barely worth deploying.

Defensive domain registration should be included in the same budget. The hundred dollars a year to own the obvious typos of your domain is trivial compared to the cost of one successful campaign that runs from those domains for a weekend.

How to verify: Run a tabletop exercise. Pick a plausible phishing scenario — a fake login page hosted on a typosquat domain, an Instagram account impersonating your support team — and walk through the response from detection to takedown. Time each step. If the total elapsed time exceeds 4 hours, identify the bottleneck. Common ones: nobody on call has the authority to file a takedown, the legal template needs to be reviewed every time, the registrar abuse process requires information you don’t have ready. Fix the bottleneck and run the exercise again next quarter.

Train Users to Distrust the Inbox

The Robinhood attack succeeded because the email looked perfect. No amount of check the sender domain advice would have helped the victims. The only defensive habit that would have worked is the one you should be teaching anyway: never act on a security alert by clicking a link in the email itself.

This is the single most important user behavior to reinforce. If your bank, broker, or any other sensitive account sends an alert, the user should open the official app or type the URL directly. Every legitimate provider supports this workflow. The cost is ten extra seconds. The benefit is complete immunity to email-delivered phishing, regardless of how convincing the message is.

Make this explicit in your communications. Tell users in the emails themselves not to click — they should log in directly. Pair every notification with an in-app equivalent so the link is genuinely optional. Maintain a public page documenting the contents of your real emails and what they never ask for. Update it when your patterns change.

Push hardware-backed authentication wherever you can. Passkeys make stolen passwords almost worthless. Hardware security keys defeat real-time phishing proxies that intercept TOTP codes. SMS-based 2FA is better than nothing, but increasingly compromised by SIM swapping and phishing kits that capture and replay codes within seconds.

For high-value accounts, consider out-of-band verification for sensitive actions. A confirmation prompt in the mobile app for any login from a new device, with a clear deny option, catches credential phishing at the moment of exploitation rather than at detection.

How to verify: Audit your last ten transactional emails. How many contain a clickable button that performs a sensitive action? How many include explicit guidance to log in directly rather than clicking? How many have an in-app equivalent that means the user never has to click the email link? Then check your authentication options. What percentage of your users have passkeys or hardware keys enrolled? If the number is low, your enrollment flow is the problem — make it the default rather than an opt-in buried in settings.

Build the Capabilities That Catch Subtle Forgeries

The defenses described so far handle the bulk of the threat surface. The remaining gap is sophisticated impersonation that doesn’t trip volumetric or pattern alarms — small-batch attacks that look enough like your legitimate communications to slip through. This gap is widening as attackers adopt generative AI to produce communications indistinguishable from your own brand voice.

82.6% of phishing emails analyzed between September 2024 and February 2025 contained AI-generated content, and AI-aided social engineering incidents impacted 42% of organizations.

Bright Defense, 200+ Phishing Statistics for 2026

Modern email security platforms use machine learning to model what your legitimate communications look like and flag deviations. They catch tone and formatting drift, unusual link destinations, and templates that don’t match your historical patterns. These tools have matured significantly in the last few years and are no longer just enterprise budget items. They belong in the stack for any brand that handles money or sensitive data.

Red team exercises specifically targeting your notification and onboarding flows surface the issues that code review misses. Internal teams know the systems too well to attack them creatively. External red teams bring fresh assumptions and find the input field that nobody thought to test. Run these annually at a minimum, more often if you ship significant changes to authentication or onboarding.

Bug bounty programs do the same work continuously and at scale. The Robinhood vulnerability is exactly the kind of finding a researcher would have reported for a few thousand dollars if the program were structured to receive it. Make sure your scope explicitly includes email injection and account creation abuse — many programs implicitly exclude these by listing only “the website” or “the API” without specifying that downstream effects of API calls are in scope.

How to verify: Look at your last red team report or penetration test. Did it test email injection through user-controlled fields? Did it test account creation abuse, including normalization edge cases? If the answer to either question is no, that’s your scope for the next engagement. For your bug bounty, check the language of your scope document — if a creative researcher reading it would conclude that email injection is out of scope, rewrite it.

The Robinhood attackers didn’t do anything magical. They found one missing escape function and one normalization gap and turned them into a working phishing operation. The defenses against this category of attack are well-understood, mostly free of licensing costs, and almost entirely a matter of process discipline. The brands that get weaponized against their own customers are not the ones facing sophisticated threats. They are the ones who didn’t audit the boring parts.

©2026 DK New Media, LLC, All rights reserved | Disclosure

Originally Published on Martech Zone: The Complete Guide to Defending Your Brand from Being Used as a Phishing Source

Martech Zone readers can use code DELIVER23 to unlock 23% off PAYG credits and ZeroBounce One monthly plans. Hurry, this sale ends on April 30th!

You spend weeks crafting the perfect campaign. The subject line is sharp, the offer is dialed in, and the segmentation looks clean. Then the metrics roll in, and something is off. Open rates are flat, hard bounces are climbing, and a chunk of your list is sending mail straight into spam folders you’ll never see.

This is the part of email marketing nobody warns you about. Outdated contacts, spam traps planted in old purchased lists, abuse accounts, role-based addresses, and disposable inboxes quietly tank your sender reputation. Once mailbox providers start flagging your domain, even your best subscribers stop seeing your emails. Recovering that reputation takes months, and the revenue you lose during the slump is gone for good.

ZeroBounce

ZeroBounce is an email verification and deliverability platform serving more than 600,000 clients, including Airbnb, Intel, LinkedIn, PwC, and Strava. The platform handles the full lifecycle of getting email seen, from validating addresses before they hit your CRM to monitoring blacklists and warming up new sending domains.

The benefits show up fast. By stripping invalid addresses, spam traps, and disposable domains from your list before you press send, you protect the sender reputation you’ve spent years building. Your bounce rate drops, your open rate climbs, and mailbox providers start treating your mail the way they treat senders they trust.

Beyond the technical wins, the platform surfaces your most engaged contacts through internal scoring, so your campaigns land where the highest-value conversations are already happening. Add in the inbox placement tests against 20+ global providers and the warmup seeds that train new domains to look human, and you stop guessing whether a campaign will perform. With SOC 2 Type 2, ISO-27001, GDPR, HIPAA, and CCPA coverage built in, your compliance team also stops asking awkward questions.

The Tools Behind the Platform

Each tool tackles a specific piece of the deliverability puzzle, and you can use them through the bundled ZeroBounce ONE™ subscription or buy credits à la carte through Pay-As-You-Go:

• Blacklist Monitor: Scans your domain or IP every eight hours across major blacklists and alerts you the moment something flags, so you can react before damage spreads.
• DMARC Monitor: Simplifies email authentication and shields your brand from spoofing and phishing by giving you visibility into who is sending mail on your domain.
• Email Finder: Looks up valid email addresses and domain patterns tied to real leads, useful for outbound prospecting and account research.
• Email Scoring: Identifies your most active recipients (called Gold Contacts) so you know which addresses are worth your best content and which to suppress.
• Email Server Test: Runs over 200 diagnostic checks in a single test to surface configuration issues that quietly break deliverability.
• Email Validation: Verifies addresses with 99.6% accuracy, detecting invalid emails, abuse accounts, spam traps, catch-alls, and disposable domains in real time through the API or batch upload, with the mobile app on the App Store giving you list status checks on the go.
• Inbox Placement Test: Sends a test campaign across 20+ global mailbox providers and reports back whether you landed in the inbox, the promotions tab, or spam.
• Warmup Seeds: Trains new domains and IPs through real-recipient warmup conversations to build sender reputation gradually instead of cold-launching.

ZeroBounce has 4.8/5 Stars based on more than 2,565 reviews across G2, Capterra, and Trustpilot.

ZeroBounce Subscription Options

How you pay depends on how predictable your sending volume is. ZeroBounce ONE™ costs $99 per month and includes 10,000 validation credits, along with monthly access to all the tools above (10,000 email finder searches, 100 inbox placement tests, 100 email server tests, 250 warmup seeds, 10 blacklist monitors, and 1 DMARC monitor domain).

Members also get a 15% discount on any additional credits and faster file processing. Pay-As-You-Go is the alternative if you want to buy validation credits without the bundle, paying only for what you need with no monthly subscription. Either way, validation credits never expire.

Compliance, Speed, and Support That Holds Up

Behind the toolkit is infrastructure built for production workloads. The API maintains 99.99% reliability with an average response time of 1.09 seconds and supports 16 programming languages, including Python, Ruby, Rust, and JavaScript. Bulk validations on popular domain lists like Gmail and Google Workspace process up to 90% faster than complex domains, with 250,000 emails verified in under 10 minutes. Support runs around the clock with an average live chat wait of 20 seconds, and the API integrates with more than 60 popular CRMs, email service providers, and website builders.

Get Started with ZeroBounce

Cleaner data, stronger sender reputation, and more revenue per send all start with knowing which addresses on your list are worth the effort. Whether you bundle everything through ZeroBounce ONE™ or buy validation credits as needed through Pay-As-You-Go, the platform meets you where your sending program is today. New accounts on a business or premium domain get 100 free monthly verifications to test the system before committing.

Martech Zone readers can use code DELIVER23 to unlock 23% off PAYG credits and ZeroBounce One monthly plans. Hurry, this sale ends on April 30th!

Start Verifying for Free

FAQs

©2026 DK New Media, LLC, All rights reserved | Disclosure

Originally Published on Martech Zone: ZeroBounce: Your All-in-One Inbox Placement and Email Deliverability Toolkit

The digital marketing landscape has evolved dramatically over the past few years. What used to be a straightforward process of placing banners and buying clicks has transformed into a complex, data-driven ecosystem powered by artificial intelligence (AI), automation, and hyper-personalized user experiences (UX). Every website with ads now operates within an intricate network of technologies that determine which message reaches which user at what moment. Understanding these technologies is no longer optional — it is essential for any business that wants to stay competitive.

Artificial Intelligence and Machine Learning in Advertising

AI has moved far beyond the experimental stage in marketing. Today, it sits at the core of nearly every major advertising platform, shaping how campaigns are built, optimized, and measured.

Machine learning (ML) algorithms analyze billions of data points in real time to predict user behavior, adjust bids, and allocate budgets across channels without human intervention. Platforms like Google Performance Max and Meta Advantage+ rely entirely on AI to manage creative selection, audience targeting, and placement decisions simultaneously.

Key applications of AI in modern internet marketing include the following.

• Predictive audience modeling. Algorithms identify users who are most likely to convert based on behavioral patterns, purchase history, and contextual signals rather than relying on broad demographic categories.
• Dynamic creative optimization. AI assembles ad variations on the fly — testing headlines, images, and calls to action in thousands of combinations to find the highest-performing version for each audience segment.
• Automated bid management. Smart bidding strategies adjust cost-per-click (CPC) or cost-per-acquisition (CPA) targets every millisecond during real-time auctions, maximizing return on ad spend with minimal manual oversight.
• Conversational AI and chatbots. Intelligent assistants handle initial customer interactions on landing pages and social channels, qualifying leads, and answering questions around the clock.

The impact is measurable. Brands that have adopted AI-driven campaign management report significant improvements in conversion rates and a notable reduction in customer acquisition costs compared to manually optimized campaigns.

Programmatic Advertising and Real-Time Bidding

Programmatic technology has fundamentally changed the way digital ad inventory is bought and sold. Instead of negotiating deals directly with publishers, advertisers use demand-side platforms to bid on impressions across millions of websites and apps within milliseconds.

Real-time bidding works through a transparent auction model where supply-side platforms offer available inventory and demand-side platforms compete for each impression based on how closely the user matches the advertiser’s target profile. The entire transaction — from bid to ad display — happens in under 100 milliseconds.

Several trends are shaping programmatic advertising right now.

• First-party (1P) data activation. With third-party (3P) cookies disappearing, advertisers are building their own data ecosystems using CRM records, loyalty programs, and on-site behavior to fuel programmatic targeting.
• Connected TV (CTV) and digital audio. Programmatic buying has expanded well beyond display banners into streaming television, podcast advertising, and digital out-of-home screens.
• Supply path optimization. Advertisers are auditing the intermediaries between them and publishers to reduce hidden fees and improve transparency in the bidding chain.

Programmatic now accounts for the majority of all digital display spending globally, and its share continues to grow as new channels and formats are integrated into automated buying systems.

Privacy-First Marketing and Cookieless Solutions

The shift toward user privacy has become one of the most disruptive forces in internet marketing. Regulations like GDPR and CCPA, combined with browser-level restrictions on tracking, have forced the industry to rethink how it collects and uses data.

Marketers are adapting through a range of emerging approaches.

• Server-side tracking. Moving data collection from the browser to the server gives marketers more control over what information is captured while respecting consent frameworks.
• Contextual targeting. Rather than following users across the web, contextual technology analyzes the content of a page in real time and serves ads that match the topic, sentiment, or intent of the content itself.
• Data clean rooms. These secure environments allow brands and publishers to match and analyze their first-party datasets without exposing raw user-level information to either party.
• Google Privacy Sandbox. Google’s suite of browser-based APIs designed to enable interest-based advertising, attribution measurement, and fraud detection without individual cross-site tracking.

Companies that invest early in privacy-compliant infrastructure will gain a competitive edge as the industry transitions away from legacy tracking methods.

Marketing Automation and Omnichannel Orchestration

Modern marketing automation platforms go far beyond simple email sequences. They now serve as central orchestration engines that coordinate messaging across email, SMS, push notifications, social media, and paid advertising — all from a single workflow.

The most impactful capabilities of today’s automation systems include the following.

• Behavioral triggers. Actions like cart abandonment, product page views, or content downloads automatically initiate personalized multi-step sequences tailored to the user’s position in the buying journey.
• Lead scoring and segmentation. Machine learning models assign dynamic scores to prospects based on engagement patterns, allowing sales teams to focus on the highest-value opportunities.
• Cross-channel attribution. Advanced models track the complete customer journey across touchpoints, moving beyond last-click attribution to reveal how each channel contributes to the final conversion.
• Lifecycle campaigns. Automated flows manage the entire customer relationship — from onboarding and activation through retention and win-back — without requiring manual campaign launches for each stage.

Businesses that implement full-funnel automation typically see higher customer lifetime value and significantly improved retention rates, since every interaction is timed and personalized based on real behavior rather than guesswork.

What Comes Next for Internet Marketing Technology

The pace of MarTech innovation shows no signs of slowing. Several developments are already on the horizon that will reshape the field once again.

• Generative AI (GenAI) for content production. Large language models (LLM) and image generators are enabling marketers to produce high-quality creative assets at scale, reducing production timelines from weeks to hours.
• Augmented reality advertising. AR-powered product try-ons and immersive brand experiences are moving from novelty to mainstream, especially in e-commerce and retail marketing.
• Voice search optimization. As smart speakers and voice assistants become household staples, optimizing content for conversational queries is becoming a distinct marketing discipline.
• Blockchain-based ad verification. Distributed ledger technology promises to bring unprecedented transparency to impression tracking, fraud prevention, and advertiser-publisher payment settlements.

The marketers who thrive will be those who combine technological fluency with strategic thinking — using these tools not as ends in themselves but as means to build genuine, lasting relationships with their audiences.

©2026 DK New Media, LLC, All rights reserved | Disclosure

Originally Published on Martech Zone: Modern Technologies in Internet Marketing That Are Changing the Game

For telecom operators, sales automation matters most when products are complex, sales cycles are long, and several teams influence one deal. In that environment, delays rarely come from one weak salesperson. They come from fragmented approvals, disconnected customer data, manual follow-ups, and poor visibility across sales, service, and field operations. That is why automation in telecom should be evaluated as an operating model, not as a set of isolated tools.

When companies start investing in telecom sales automation, the real goal is not just faster lead handling. It is better control over long customer journeys, clearer coordination between teams, and fewer losses between first contact and signed revenue. BanzaIT builds telecom solutions on Creatio for enterprise clients, combining CRM, process automation, chatbot support, and no-code adaptability so business teams can evolve workflows without pushing every change onto internal IT.

Why Telecom Sales Cycles Become So Hard to Manage

Telecom sales are difficult to scale because the product itself is rarely simple. Offers may include bundled services, custom pricing, infrastructure checks, service availability, approvals, and post-sale coordination. In enterprise and B2B segments, one opportunity can pass through sales, customer service, technical teams, and field specialists before it is closed. If those stages are not connected, the company loses speed and predictability.

For business owners and commercial leaders, this becomes visible in missed follow-ups, slow proposal cycles, inconsistent pipeline data, and limited forecasting accuracy. Process automation is valuable here because it reduces dependency on manual coordination and keeps all participants inside one operating logic.

What Should Telecom Sales Automation Actually Solve?

A useful solution should help operators manage complexity without adding another interface layer. In practice, it should be able to:

• Connect customer service, sales, ticket handling, and field operations
• Keep the customer journey visible across teams
• Automate repetitive routing, follow-ups, and workflow transitions
• Support no code changes when business rules evolve
• Improve response time without sacrificing control

This is especially important in telecom because many operators already run multiple legacy systems. Replacing everything at once is rarely realistic. A stronger path is step-by-step automation in the places where process loss is highest first.

What Should Leaders Clarify Before Implementation?

Before launch, it helps to answer a short set of business questions:

• 1the biggest revenue leakage happen today? Identifying specific stages where prospects drop out (such as during complex infrastructure checks or pending approvals) allows you to apply automation where it will have the most immediate impact on your bottom line.
• Which teams create the most handoff delays? Mapping the friction points between sales, technical teams, and field operations helps you design a unified workflow that eliminates manual bottlenecks and ensures no lead is lost during a transition.
• Which rules are needed to stay flexible? Determining which processes require agility ensures that your automation remains a tool for growth rather than a rigid constraint, allowing business users to adapt to market shifts or custom enterprise requirements without deep coding.
• Who will own the process after go-live? Establishing clear accountability between business leaders and IT ensures that the system is continuously optimized, updated, and championed, preventing the solution from becoming stagnant over time.
• How success will be measured – speed, conversion, retention, or service quality? Defining these key performance indicators (KPIs) early aligns your automation strategy with specific business outcomes, ensuring the investment delivers a measurable return on efficiency and customer satisfaction.

Telecom sales automation creates value when it reduces coordination noise and makes complex sales journeys easier to manage. For operators with long cycles and layered products, that is often the difference between adding more software and building a sales system that genuinely performs better.

©2026 DK New Media, LLC, All rights reserved | Disclosure

Originally Published on Martech Zone: Telecom Sales Automation for Operators with Complex Products and Long Sales Cycles

Bringing sales automation into your company must be a gradual process. It begins with conversations and continues long after the software is up and running. For most leaders, the objective is straightforward: reclaim time, work more intelligently, and let the sales team do what they do best—talk to people and close deals. The problem is, the market is crowded, making it tough to know where to even begin.

Think of this guide as a conversation about your next steps. We’ll walk through each phase, from initial planning to getting the most out of your new tool. The focus here isn’t on a long list of features, but on a way of thinking that helps you pick a platform that genuinely fits your business. Approaching it as a journey with clear stages helps you make a choice that feels right.

Before You Buy: Laying the Groundwork

The most essential work happens before you see a single demo. This initial phase is all about looking inward. Rushing into sales pitches without this homework is like starting a road trip with no destination in mind—you’re moving, but you’re not getting anywhere. The aim here is to agree on what the actual problems are, what a successful outcome looks like, and how the team will handle a new way of working.

A common pitfall is viewing this as a simple tech upgrade. It’s not. It’s a decision that affects your daily operations and, most importantly, your people. You have to understand your own workflow before you can know where technology can help. This preparation is what makes the difference between a smooth transition and a frustrating, expensive mistake.

Auditing Your Current Sales Process

First things first: get to know your own sales cycle, inside and out. Sketch out every step a prospect takes, from the first contact to a completed sale. Where are the delays? Where do things get messy or complicated? This simple exercise will show you exactly where the bottlenecks are, whether it’s tedious data entry, inconsistent follow-ups, or leads that simply disappear.

This audit provides a specific problem to solve. You might find your team loses hours every week just copying information between spreadsheets and emails. That tells you a central hub for customer data is a top priority. For many, this exercise highlights the need for flexible cloud-based CRM platforms that can act as a single, organized place for all customer information. Without this clarity, you risk buying a tool that solves a problem you don’t really have.

Getting Your Team On Board with the Change

A great tool that no one uses is worthless. Often, the biggest barrier to success is your own team’s resistance to change. Before you start shortlisting software, talk to your salespeople. They are on the front lines and understand the daily frustrations better than anyone. Frame the new tool as an upgrade to their toolkit, something to eliminate boring tasks and give them more time to sell.

Ask them a simple question: What’s the most annoying administrative task you do every week? Their answers are gold. They’ll help you define your needs and make your team feel heard and included in the process. When people are part of the decision from the beginning, they are far more likely to actually use and champion the new system.

During the Selection: What to Look For

With your internal audit done and your team in the loop, it’s time to explore your options. This stage is all about active testing. The prep work you did now becomes your checklist for navigating the market. The goal is to see beyond the polished sales pitches and understand how each sales automation platform would really function in your day-to-day business.

This is where you weigh shiny features against simple usability. A platform can promise the world, but if it’s confusing or the support is lacking, it will create more headaches than it solves. It requires a hands-on, curious approach to see how each option feels in practice.

How to Run an Effective Software Trial

Most vendors offer a free trial. Use it well. A trial without a clear purpose won’t teach you much. Define a few essential tasks from your audit and have your team try to perform them. For instance, can a salesperson easily import a few leads, create a simple follow-up sequence, and see the results? Can they log a call and set a reminder in a couple of clicks?

Don’t just click around the features; try to simulate a real workday. Pay attention to how the software feels. Is the interface clean and intuitive? Is it fast, or does it lag? The trial is your best window into future user adoption. If your team finds it frustrating to do basic tasks, that’s a clear warning sign.

Asking Vendors the Right Questions

Your preparation gives you control of the conversation with software vendors. Instead of asking, So, what does your product do?, you can ask pointed questions. For example: Can you walk me through how your platform scores leads based on website visits? or I need to see how our conversion rates differ by lead source. How would I build that report?

Integrations are another key topic. You need to know how this new piece of software will talk to the tools you already use every day. Customers now expect fast, direct communication, so asking how a platform integrates with a WhatsApp Business API solution can tell you a lot about how modern its approach is. A vendor’s ability to give you straight answers to these specific questions is a good sign of both their product’s quality and the reliability of their support.

After Implementation: Getting the Most from your Sales Automation

Choosing and installing the software isn’t the finish line. In many ways, it’s the start of the real work. This final stage is about continuous improvement. The goal is to ensure the tool becomes a living part of your strategy, not just a program that runs in the background.

Your new platform is a source of valuable information. If you don’t look at the data, it’s useless. But when you analyze it, you find insights that help you refine your sales process, improve your messaging, and get better at predicting your results. This is what separates companies that simply use automation from those who build a real advantage from it.

Using Analytics to Refine your Strategy

Does your new sales automation platform come with an analytics dashboard? Get familiar with it. Make it a weekly habit to check your key numbers. Which email templates are people actually opening? At what stage of the pipeline do deals tend to slow down? The data helps you trade guesswork for clarity.

If you notice that leads from a particular source are more successful, you know where to focus your efforts. If a certain follow-up email isn’t getting replies, it’s time to try a new approach. This cycle of trying something, measuring the result, and refining your strategy is how you get the most from your new tool.

Evolving your Automation as Your Business Grows

Your business is going to change, and your software needs to be able to change with it. How you use your platform today won’t be how you use it in two years. Revisit your workflows from time to time. Are there new, repetitive tasks that have appeared? Does your team now need access to more advanced features that you skipped at first?

A good platform is scalable and can grow with you. When you treat your automation as something that evolves with your business, you ensure your technology remains a valuable asset, supporting your work for years to come.

©2026 DK New Media, LLC, All rights reserved | Disclosure

Originally Published on Martech Zone: How to Choose Sales Automation Software (Without Regret)

For a long time, businesses turned to demographics like age, gender, and location to understand their customers. While this data offers a starting point, it often misses the crucial why behind people’s actions. And today, that deeper understanding is essential. This is where behavioral customer segmentation comes in, offering a more insightful way to connect with your audience.

At its core, behavioral customer segmentation looks at what customers do. It groups them based on their interactions and engagement with a product, service, or brand, rather than just who they are on paper. This shift in focus can unlock fresh perspectives for marketing, product creation, and the overall customer journey. By understanding behaviors, companies can fine-tune their approach, making their communication more relevant and building more solid relationships.

Understanding the Why behind the What: The Core of Behavioral Customer Segmentation

Behavioral customer segmentation shifts the focus from just describing your audience to understanding their motivations. It means looking beyond surface details to find patterns that hint at future actions. This deeper insight helps businesses create more personal and effective experiences.

Moving Past Surface-Level Data

Demographics are useful for general outlines, but they don’t tell the whole story. Think about two people with the same age, gender, and income. One might be a loyal customer, the other a one-time buyer. Demographics alone don’t explain why.

Behavioral data, on the other hand, can show the different paths these customers took, like their engagement, features used, or content viewed. This approach lets businesses look past assumptions. For example, if many users abandon carts at a certain step, it signals a possible issue to look into. Likewise, knowing which content users engage with helps shape your content strategy. This level of detail is even more powerful when you are capturing TikTok Business engagement, as the specific ways users interact with short-form video provide immediate behavioral signals that traditional metrics often miss. Such a detailed understanding is vital for good customer segmentation.

Gathering and making sense of these signals often needs centralized data management. Businesses wanting to bring together customer interactions and discovery data might find that unified systems are helpful for this. They can act as a central point for understanding customer actions across all modern touchpoints.

Key Benefits of Looking at Behavior

Focusing on behavior offers several benefits for a company’s results and customer loyalty. A key benefit is more personal marketing. When you know what a customer has bought or shown interest in, you can make your messages highly relevant, boosting engagement and conversions.

It also helps keep customers. Tracking usage helps spot customers who might leave. For instance, a drop in activity could prompt a targeted message with support or an offer to re-engage them. Being proactive here is better than trying to win back lost customers.

For this type of personal touch, direct communication and storefront synchronization are useful. For brands in the retail space, optimizing digital storefront performance through Shopify allows you to send timely, personal messages to specific segments based on their actual shopping habits, significantly improving their experience.

Plus, this type of customer segmentation helps use resources more wisely. Instead of broad campaigns, businesses can target high-value groups, like frequent buyers or very engaged users. This improves ROI and ensures messages reach the right people.

Common Types of Behavioral Customer Segmentation and Their Applications

Behavioral customer segmentation isn’t a single idea; it includes different ways to group customers by their actions. Knowing these types helps businesses choose what behaviors matter most for their goals and use this info wisely.

Purchase Behavior: Uncovering Buying Patterns and Preferences

Looking at purchase behavior is a common and powerful type of behavioral customer segmentation. This means looking at how often people buy, how much they spend (AOV), what they buy, and where they are in their customer journey (new buyer, repeat, loyal). Understanding these patterns helps tailor promotions, recommendations, and loyalty programs.

For instance, a business might find a group of high AOV, infrequent buyers. The approach for this group will be very different from low AOV, frequent buyers. The first group might like exclusive offers on high-end items, while the second might prefer loyalty points or small discounts. It’s also key to spot customers who mostly buy during sales. They need a different message than full-price buyers. This shows how customer segmentation can be quite detailed.

Usage Behavior: How Customers Interact with Your Offerings

Usage behavior looks at how customers use a product or service. This is especially useful for SaaS, apps, and subscription services, but many businesses can use these ideas. Important things to track are how often features are used, how long sessions last, if main tasks are completed, and how users navigate. This info shows which features are popular and where users might be struggling.

For example, grouping users by feature use can help find power users (potential advocates) and casual users (who might need tutorials or guidance). If many new users leave after a certain onboarding step, that’s a red flag for product improvement or better support. Understanding usage isn’t just for marketing; it’s key for product development and better user experience. This makes it a vital part of good customer segmentation.

From Data to Action: Strategically Implementing Behavioral Customer Segmentation

Knowing the basics of behavioral customer segmentation is just the start. The real strength is in using it strategically—turning data into actions that boost growth and improve customer ties. Think of it as a cycle: customer actions shape business plans, and those plans improve the customer experience.

To do more than just collect data, you need a clear plan. First, set your goals: more retention, higher order values, better feature use? Your goals decide which behaviors to track. Next, make sure you have the right tools and methods to gather, group, and use this data. This could mean linking your analytics, CRM, and marketing tools for a complete view of customer behavior.

The main aim of behavioral customer segmentation is to build a more understanding and responsive connection with customers. By paying attention to their actions, you learn more about their needs and concerns. This helps you offer personalized experiences, making customers feel seen and valued.

As markets change and expectations grow, using behavioral insights will be crucial for success. Good customer segmentation becomes more than just a tactic; it’s a vital strategy.

©2026 DK New Media, LLC, All rights reserved | Disclosure

Originally Published on Martech Zone: Beyond Demographics: Behavioral Customer Segmentation