Guides
I worked with a colleague to prepare this set of product guides in consultation with the product manager and the product marketer for each market. The guides—for a fintech platform’s newest feature—were used by account managers for upselling and as post-webinar lead-conversion and mid-funnel materials.
We reused content common to all three markets and then added market-specific content to each guide. The graphs, with abbreviated text, were used in LinkedIn carousels to drive website traffic.
See the Sales Enablement page for an example of a best practices guide.
See the Partner Marketing page for an example of a white paper.
Blogs
For each blog post I interviewed a subject matter expert, then combined the transcript with my knowledge of the product and market to ghostwrite a blog post that ran under the SME's byline. I also created bullet points for key takeaways, SEO title tags, page/meta descriptions, and a one-sentence teaser blurb for each, and added internal and external links to improve SEO. The blogs included self-running videos of the software, static screenshots of which are included below.
Agentic AI: The superpower underlying GrowthLoop’s Compound Marketing Engine
Every company—from ecommerce behemoths like Amazon and eBay to five-person startups running on angel investor funds and caffeine—is eager to find the most effective way to incorporate AI into all its operations. And we’ve all encountered AI customer service bots that are more annoying than useful, and seen cool AI parlor tricks like generating sonnets in the style of Shakespeare.
But at the end of the day, if AI isn’t producing measurable business value, it’s just another obstacle to meeting your monthly sales KPIs, revenue generation, and realizing a healthy ROI; a company’s marketing function is central to all three. That’s why agentic AI in marketing is gaining traction—not as a gimmick, but as a scalable way to boost efficiency and performance.
The GrowthLoop Compound Marketing Engine helps your marketing team drive growth more quickly. It doesn’t replace marketers: It gives them superpowers, leveraging their knowledge to rapidly scale and optimize campaigns. And it frees them to do the kind of meaningful, creative work that differentiates your brand, maintains long-lasting relationships with your customers, and unlocks new business opportunities.
It does so through the power of compound growth fueled by agentic AI on the data cloud. We call this compound marketing.
What is agentic AI?
Agentic AI is a form of AI that operates autonomously, allowing it to achieve specific goals or tasks without constant human intervention. AI agents can make decisions on its own and act in real-time.
An “agent“ in this context refers to an autonomous entity within the AI system that perceives its environment, processes information, and takes actions to achieve predefined objectives.
Agentic AI can handle complex tasks that require reasoning, planning, and problem-solving, making it ideal for tasks like optimizing ad placements across multiple platforms. Continue reading on the GrowthLoop Blog >>
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How the GrowthLoop Compound Marketing Engine powers retail media growth
Marketers at retail media networks are at a crossroads.
Online retail sales are booming and offer the potential for high-margin revenue even as profit margins in traditional retail get thinner. But to realize this potential, retailers (and other enterprises with a large ecommerce business) must find ways to monetize the mountains of valuable first-party customer data at their disposal. By offering brands access to audiences built from their first-party data, they can develop a parallel revenue stream, capturing ad dollars from brand partners to supplement their product- and service-based sales by offering ad placement on their retail media networks (RMNs).
The decline of third-party cookies and data privacy regulations have only increased the value of RMN real estate. That’s because without these cookies, it’s becoming harder for advertisers to collect the first-party data needed to meet the level of personalization that consumers have come to expect.
But an RMN’s value depends on an advertiser’s ability to create and sell precisely targeted audiences—and, ideally, measure the success of campaigns and optimize those campaigns continuously.
The rise (and fall) of manual and in-house solutions
RMNs have traditionally relied on manual data transfers or a semi-automated system built in-house to deliver a dataset of customers from which advertisers can create targeted audiences and orchestrate marketing and advertising campaigns.
Unfortunately, these solutions bring a host of challenges that impede a retailer’s ability to capitalize on the potential of its first-party data.
The manual approach is arduous. It requires IT and data teams at both the retailer and the advertiser to manage the data exchange. The process involves exporting the subset of first-party data from the retailer’s data management platform to a shared clean room accessible only by this advertiser. The advertiser then imports its own data to the clean room, finds relevant overlaps through identity resolution, and identifies the best customers with which to build an audience.
Not only is this approach time-consuming, it’s also difficult to automate or scale: Updating the data to reflect new information from a campaign must once again be transferred manually from customer database to clean room. Continue reading on the GrowthLoop Blog >>
