Growth strategy – @sandydiao

How to use AI writing tools to your advantage

Artificial intelligence (AI) has revolutionized the way we live and work, and writing is no exception. With the help of AI-powered writing tools, writers can generate ideas, improve their writing skills, and automate time-consuming writing tasks. Even prior to the advent of ChatGPT and other conversational AI bots that now act as personal writing assistants, several companies have for months and even year, developed some of the leading AI writing tools available today such as Writesonic, Jasper (formerly known as Jarvis.ai), Grammarly, ProWritingAid, Copysmith, Rytr, Articoolo, and more. These tools use machine learning algorithms to analyze large amounts of data, learn from it, and generate human-like content as the output. In this article, we’ll explore how you can use AI writing tools to your advantage, and which tools are the best fit for practical parts of the writing process.

What is AI Writing?

AI writing is a technology that uses natural language processing (NLP) algorithms and machine learning (ML) models to generate content without human intervention. AI writing software are usually designed to help with specific writing tasks, such as generating blog articles, email newsletters, social media posts, and even creative writing.

AI writing tools have proven to be a game-changer for many creators and businesses. For instance, the team at HubSpot has used AI writing tools like Writesonic to create dozens of social media posts in just a few minutes. Content creators have also creatively applied AI writing tools – in 2020, a group of digital marketers used GPT-3 to generate several blog posts that received thousands of views and shares. The tool’s ability to generate content quickly and efficiently helped them reach a broader audience, resulting in increased traffic and engagement. Even traditional authors have used AI writing tools to generate outlines and ideas for their books. A popular tool for this is Articoolo, which allows writers to input a topic or keyword and generates a full-length article on that topic in just a few seconds.

Can an AI Write My Essay?

Yes, in theory and practice, AI can write your entire essay (and I don’t even want to think about what teachers and professors are doing now to grade students’ work with this as a much more accessible technology). Still, while AI writing tools can be useful in generating content, they’re not yet advanced enough to write an entire essay without any human input. Students and other essay writers will still need to guide the writing as much as possible to ensure that it addresses the objectives of the essay.

Where essay writers can use AI tools most effectively today will be in assisting with idea generation, improving writing, and providing suggestions for revisions. For example, tools like Grammarly may provide suggestions for essay structure or offer ways to improve the essay’s clarity, style, or grammar. Another creative application could also be to use AI to search for research sources to support an essay’s arguments.

Can an AI Write a Script?

AI writing tools can assist in scriptwriting by providing suggestions for character dialogue, plot development, and even story arcs. While an AI writing tool probably can’t write an entire Oscar award-winning script on its own, they can be useful in providing ideas and improving the writing process.

The entertainment industry has already started using AI writing tools to generate scripts for movies, TV shows, and video games. In 2016, a short film titled Sunspring was created using an AI writing tool called Benjamin. Benjamin, which was developed by filmmaker Ross Goodwin, used a recurrent neural network to analyze hundreds of science fiction screenplays and generate a script for the film. Even media giants like Netflix has been using AI writing tools like LDA (Latent Dirichlet Allocation) to analyze viewer data and generate script ideas for its original shows. The streaming giant also used an AI writing tool called AIVA to generate a musical score for one of its shows.

How Do I Create Content for My Blog Using AI?

Creating content for a blog can be a time-consuming process, especially if you’re looking to produce high-quality, engaging content consistently. Fortunately, AI writing tools have made it easier to create blog content quickly and efficiently.

One popular AI writing tool for blogs is Jasper. Jasper offers a variety of features, including content ideation, writing, and editing. For example, if you’re struggling to come up with a blog post idea, you can use their content ideation feature to generate multiple ideas based on a keyword or topic. The tool uses machine learning to analyze thousands of blog posts and suggest ideas that will perform well.

AI writing tools can also help with writing SEO-optimized blog articles. AI tools can help improve the quality of your blog content by suggesting relevant keywords, optimizing meta descriptions and titles, and analyzing the content’s readability. SurferSEO is an AI-powered SEO writing tool that uses machine learning algorithms to analyze search engine results and suggest relevant keywords and phrases for your blog posts. It can also provide feedback on the length of the content, its structure, and readability. Clearscope is another tool that uses AI to analyze your content, and the app will suggest keywords and topics that can help improve its relevance and ranking on search engines. It’s like a real-time grader for SEO.

Practical Ways to Start Using AI Writing Tools

Now that we’ve covered the basics of AI writing tools, let’s discuss how you can use them to your advantage. Here are some tips for using AI writing tools effectively:

Use AI writing tools like Writesonic or Jasper to generate ideas quickly. These AI-powered tools can analyze your keyword or topic and provide you with content ideas that you can build upon.
Refine your writing style and grammar using Grammarly or ProWritingAid. These tools use AI to scan your writing for errors and provide feedback to help you improve your writing.
Automate your writing process using tools like Copysmith or Rytr. These tools can generate content for you quickly, freeing up time to focus on other tasks. Copysmith can create high-quality product descriptions, blog intros and outros, and more, while Rytr can help you create blog posts, social media posts, or even ad copy.
Experiment with new writing styles and genres using tools like Articoolo or Writesonic. Articoolo can create high-quality articles based on your keywords, while Writesonic can generate content for social media, ads, blogs, and more.

While AI writing tools like the ones mentioned above are not capable of writing an entire book or essay without any human input, they can certainly assist writers in generating ideas and creating content quickly and efficiently. By using these tools to your advantage, you can improve your writing, save time, and even have fun exploring new creative ideas.

What the future of performance marketing holds

There is a lot of buzz around AI-driven marketing today. Tools like Jasper and ChatGPT have caused many go-to-market professionals to question whether their jobs will be replaced by robots in the near future. It can be unsettling to think about, but I’m also excited about the potential of these technologies to drastically improve our work and productivity. In my daily work, I’ve been able to use Jasper to quickly generate strategy statements and ad taglines with minimal input. It’s even helped me proofread parts of this write-up.

While this is all novel and exciting, AI-driven marketing is already making an impact on performance marketing channels. In fact, I’ve been using an ML-based performance ad channel over the last year in my role running growth at Descript, and these campaigns have broken our performance ceilings month after month. Let’s explore what’s happening now and how it affects our digital marketing and go-to-market decisions.

ML-based performance marketing is already here

Channels are all converging toward ML-based conversion optimization for targeting. Example: Google’s Performance Max campaigns; Facebook/Instagram’s broad targeting with conversion optimization campaigns
- For those who aren’t familiar: Google Performance Max is a paid search campaign type that uses machine learning to optimize ad delivery and targeting in real-time, which results in achieving better performance and ROI outcomes. Instead of setting up targeting, you set up ‘”anti-targeting’” by telling the system what’s not considered a valid conversion.
ML-based targeting is ideal from the lens of ROI – the machine-learned system will most certainly be more precise in targeting and use each ad dollar in more effective way than human allocation could. But, it’s not ideal from the lens of gaining customer insights.
Perhaps in 6-12 months time, all paid search and paid social will utilize blackbox targeting where we don’t define or know who the platforms are targeting, except understanding broad filters geography, demographics, or platform, to name a few examples.
This creates a reliance on ads, where you can get a ton of results but you don’t know what drove its success and you can only count on the blackbox targeting system to continue working well.
- A good parallel might be to understand what Amazon does with brands/sellers today. You pay Amazon to sell on your behalf, and the units sell or they don’t, but you don’t know who the customer is at the end of the day.
- Overall, the Amazon-esque system can work incredibly well, but it rules out a certain type of seller that isn’t savvy enough to learn and exploit the system.

Skills for the future are technical and strategic

Performance marketing becomes a technical sport

Less focus on hiring those with ads operating experience, which tends to be the background of most junior performance marketers or agency hires. The things that performance marketers spend time on today, such as building the post, creating targeting parameters like frequency caps and daily budgets, setting up an a/b experiment, or optimizing based on results, will all become automated. Instead, people will spend more time defining clear optimization signals and structuring campaigns across these signals. Think of it almost as prompt creation.
Performance marketing as a whole will become a technically performant function where the operators behind the scenes will need to be data-minded in order to understand how utilize targeting and ranking mechanisms.
Instrumenting marketing data systems isn’t a common skillset for data engineers. That’s why the performance marketer will need to become an acting product manager to help ensure accurate instrumentation, and define a data taxonomy that becomes useful for marketing teams.

Content marketing and product marketing work requires more precise customer segmentation

It will be imperative to develop strengths across multiple niches in order to gain scale. Otherwise, we’ll hit the ceiling within the constraints of our economics – i.e. willingness to pay for a conversion – and time horizon.
More micro-targeted content positioned to the customer that will be likely to convert. This could mean developing more landing pages with focused content that gives greater chance of fitting into the blackbox targeting match.
Having a multi-faceted product that spans customer segments should play to our advantage in that it allows us to compete in multiple audience segments at one time, which increases our chances of exposure and conversion.
This increases our need to have a clear data-driven signal customer segments that can be exposed externally somehow.

The chasm between digital performance and brand marketing tactics will widen, yet the function of these are intertwined

Today, performance marketers look toward increasing reach as a mechanism to improve the likelihood of finding a converting audience. Tactics include bidding on a CPM basis to achieve a broader set of conversion outcomes. However, in the world of ML targeting, the system will reach as few users as possible to reach conversion objectives, which minimizes total reach and frequency of reach.
Brand and performance assets are thought of as separate entities right now, but these are becoming inseparable parts of the customer’s experience. For example, a user that is abruptly shown a use case landing page will most certainly want to explore the brand’s larger offering on the homepage or other branded pages (in my experience, this happens 20-30% of the time). Both types of pages will be relevant to cold prospects who are looking to learn about the brand while trying to get more information about their use cases. It’s about identifying the ideal navigation path and understanding what happens after a user interacts with a brand for the first time, or vice versa.
The bridge from brand marketing to performance marketing can be built via a multi-touch attribution model that companies either develop themselves by centralizing all data within a CDP like Segment, or they can be outsourced and imported to a 3rd party tool that does this for you, like Branch.

Preparing for the future means hiring for technical skills & building for channel-market fit

In a world of ML-driven customer acquisition, digital marketing teams must create clear signals and high-quality user experiences to be successful. This means that the better you perform today, the better you’ll perform tomorrow – it’s a performance flywheel. This wasn’t the case in the past, when performance marketing was limited by target audience ceilings and manual optimization decisions. The more signals you have, the better these platforms will be able to acquire new customers for you.

To accomplish this, you should hire ad managers from diverse backgrounds, with an emphasis on those with technical expertise. Content quality is still paramount for delivering persuasive messages that convert, but product marketing, content marketing, growth marketing, and product teams must work together to create a well-managed journey from first touch to product entry.

Why monetization is one of the most important yet underutilized growth levers

What is monetization strategy?

First, let’s understand: what is monetization? Monetization strategy refers to how a company decides to make money by offering its product or services to customers. This is a company deciding what they’re going to start charging users for them to use their products or services. Figuring out how you make money is tied in with what you’re naming it, when you charge, how often you charge, and how much it costs. For most software companies, revenue is the ultimate metric, and monetization is the strategy that touches revenue the deepest. The key reasons for a company to prioritize monetization strategy are:

Monetization allows companies to make money, which enables the team to reinvest into growth.
Monetization completes your product-market fit: it’s a reflection of your product’s positioning and packaging that ultimately allows you to build a sustainable and profitable business.
A focus on monetization strategy forces companies to focus on growing the set of users with the highest ARPC.
Rather than cost cutting, monetization strategy is a lever for accelerating revenue through its compounding effects on acquisition and activation.

To grow a company, product and marketing teams will think about several strategies, including but not limited to common ones such as acquiring paid users, reducing product friction, and improving product retention. When it comes to revenue, it seems like sales alone is somehow responsible for numerical dollar amounts, and it can feel as if product and marketing will dust their hands and work on user and retention problems (often times as proxy or leading indicator) rather than directly on revenue ones.

For product teams, the first default tactic to impact the bottom line is often to “cut costs”. This is also true for marketing teams that track metrics like LTV:CAC, where after a 4-5 years, LTV rarely grows and instead we look to decrease the cost basis of the users we acquire. Product teams often look at the traction of their time investments and determine whether products and features are additive or detrimental – if the latter, we shut down the feature, or roll it back. If the product or feature is neither good or bad, then usually nothing happens and is handed over to sales and marketing to grow adoption. These are problematic perspectives that are miss the mark on distributing a product to an end customer. Ultimately, a user will purchase a product that they need if the price and packaging are right in their moment of need. Companies can inspire users to get to that moment, but for the most part, a product’s core job is to get users to that point.

Pricing strategies today – SaaS & enterprise

Pricing is a very relevant topic today, where companies need to adjust prices due to the rising costs of doing business as a result of decades-high inflation rates. Companies like Netflix or Beamer for example, have announced their price increases. Some of these companies are taking extra care to word their messages by addressing value-added features to justify the increases (Netflix explained that they are providing a lot more value through an extensive new collection of movies and TV shows). Yet others simply saying that their costs have risen and they need to increase prices as a result (Beamer announced that COGS have increased). Regardless of the circumstances, price changes will impact users in different ways, such as affecting the satisfaction of existing users or changing the perceived value of new potential users. Prices also affect how users perceive a company’s competitive positioning. In the examples above, companies expressed that their prices were forced due to broader economic conditions, and that they had no choice but to begrudgingly change. But while these example are driven by an increased cost basis, there are many benefits to thinking about pricing and packaging proactively relative to positioning.

In a world where user growth is typically seen as a proxy to revenue growth, there are many missed opportunities when it comes to driving growth through the components of monetization. Let’s break those down into the raw pricing and packaging components that a monetization strategy represents:

If we only rely on growing the # and composition of users to grow revenue, we missing out on a big part of our conversion engine. For example, what if your business only offered two plans today – one for consumers and the other for businesses, and you found out that the prosumers using your product were looking more for business-type features, but were paying for the consumer plan? That’s both money and potential users left on the table, so there’s a ton of value in exploring how you could create a separate package for your prosumer users. The company Webflow, a low-code website builder platform, does a great job of identifying several distinct segments of users that would want to pay to create a website. Though the monthly prices are each within a low range of prices, they individually speak to the specific type of customer who is looking to create a website through the tool – a blogger would look at the CMS plan, whereas a small business would start with the business plan, and someone who is just getting started and doesn’t have the need to collaborate with others or utilize scalable page types would go for the starter or Basic plan.

Why don’t companies use pricing as a growth lever?

While it makes sense that we would want to package appropriately for our target customer segments, there are still reasons why companies don’t prioritize monetization as a growth lever. Based on my experience working at large companies like Pinterest and Facebook, to smaller startups like Indiegogo and Descript, these are the key reasons why companies don’t prioritize pricing changes as a growth lever:

It seems customary to focus on getting customers first, then getting monetization right later. For many consumer companies, we’re trained to think of the goal as a hockey-stick shaped user growth curve. SaaS companies track ARR, but even then, revenue growth is driven by assuming changes in the number of paid seats rather than increasing revenue per user.
Pricing and packaging isn’t a typical skill set for anyone on product or growth teams – it feels like an abstract decision that is driven by experienced people.
Pricing changes aren’t easy to make. Not only are these changes deeply embedded within existing product flows, such as checkout experiences, feature gates, or pricing pages, they are also decisions that no single team can make alone. Changing prices also risks the satisfaction of your core customers, if you’re not doing it right.

Given the potential hurdles of getting buy-in from internal stakeholders such as the CEO, product and marketing leaders, to your own customers, updating your company’s pricing can be hard to get started. But the changes of a company having gotten pricing and packaging right on the first try is low, and particularly as a company is scaling its growth, there are many reasons that make pricing changes incredibly advantageous.

How do you build a monetization strategy for your product?

There’s an art and science to landing a monetization strategy. There’s no one-size-fits all strategy, even as a baseline, that will apply to the specific context of the product and market that you’re building for. The best thing to do is to start with the intuition you have for your customer set, and begin to validate it through experiments. Start with your own baseline and go from there. There’s no one else’s pricing strategy that you can copy and instantly become successful with (in fact, it can often be detrimental to just follow what your peers are doing without considering how it impacts your customers). That said, here are a few of the most important considerations for creating your baselines and experimenting from that point:

Monetization isn’t just price changes, it’s about positioning relative to your understanding of customer value and how your product delivers it. Think about how packages are separated, and make the decision points clear for customers. Your goal isn’t to confuse them on what they want, but rather to offer them a clear way to make a decision on how pricing scales value for their usage of your product.
Don’t bet all of your eggs in one basket, test changes where you can. Geographical or audience roll outs are a great way to test without disrupting the entire user base.
Don’t reengineer all of your product SKUs right out of the gate. Painted door tests are a way to get real reactions that mimic true conversion rates.
If unit economics / profitability don’t work yet, don’t rely solely on cost-cutting. Look toward pricing and packaging as a lever proactively, as it could change your COGS considerations.

Implementing a monetization strategy at your company

So then how do you know if you even have the right monetization strategy before you’re a prime candidate for pricing and packaging changes? The reality is that pricing and packaging is never going o be a decision made in a vacuum. Being at the bottom of the funnel, monetization strategies will see the compounded effects of acquisition and activation for better or for worse. You might be charging the wrong price or on the wrong value metric for the right customer, even if it gates access to the right thing.

Monetization is an incredibly interdisciplinary sport. In this topic alone, you’ll end up running through at least these functions:

Product marketing view of different segments.
Qualitative and quantitative research to run pricing surveys such as Max diff or conjoint analysis.
Data science-led pricing tests, with test vs. control audiences, a/b variants, or other tests of statistical significance.
Customer support management to manage new and existing user questions and sentiment.
Sales & marketing will need to update pricing and packaging messages across the website, emails, and in external publications.
Finance to work through implications of how it impacts bottom line and other company numbers.
Product and design to identify the right flows to introduce pricing and packaging.

Not only does pricing require a strong general manager skillset, it also requires driving alignment amongst many stakeholders. Monetization isn’t going to be as simple as one person’s decision – because it ties to revenue and product, it will likely require working with the CEO in earlier stage and growth stage companies. This will involve bringing data, customer insights, strategic insights, risk management, and more to the table just in the process of writing a proposal alone. Given the complexity of pushing through pricing changes, testing every change isn’t always possible. Sometimes, you just have to roll it out and see how it works. If it doesn’t work well, you can roll back as long as you’re transparent and keep your customers updated each step of the way.