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.

The dark side of AI-powered marketing

Artificial intelligence (AI) has become a major tool in the growth marketing arsenal, helping businesses drive growth and increase revenue. In fact, as the world has been introduced to consumer versions of AI through tools like ChatGPT recently, businesses have been using AI-powered campaigns and marketing channels for a long time. From TikTok video feeds to Facebook ad delivery, machine-learned targeting models have already seeped into our daily interactions and experiences on our phones and devices. Even Siri and Alexa (as primitive as they seem) voice assistants are deep learning TTS (text-to-speech) model rooted in AI technology. Businesses use AI-powered marketing rooted in machine learning algorithms to analyze user behavior and personalize marketing campaigns in real-time. This allows businesses to reach consumers more effectively and efficiently than ever before. However, as AI becomes increasingly prevalent in marketing, there are emerging pockets of ethical risks that potentially pose harm to you and me.

One of the leading concerns about AI-powered marketing is a tale that we’re all familiar with: the spreading of misinformation and fake news through AI-powered social media campaigns. AI algorithms can be easily manipulated to create fake news and spread false information to millions of people. According to a study by MIT Technology Review, false information spreads faster and more easily on social media than true information, which can have serious consequences for public opinion, trust, and democracy (MIT Technology Review, 2018). For example, the 2016 US Presidential election was influenced by AI-powered media campaigns on social media, which helped shape public opinion and ultimately influenced the outcome of the election (The Guardian, 2018).

Another area of concern is the manipulation of consumer behavior through AI-powered personalization. AI algorithms can track and analyze consumer behavior, including their browsing history, search history, and social media activity, to personalize marketing campaigns. While this can be a powerful tool for businesses to reach consumers more effectively, it also has the potential to exploit vulnerabilities and sway consumer behavior in potentially unintended ways. According to a report by the World Economic Forum, as AI algorithms become more advanced, they will have the potential to manipulate human behavior to a much greater extent, creating new risks for consumer welfare (World Economic Forum, 2021). For example, brands or organizations can use AI algorithms can be used to target vulnerable populations and exploit data to drive adoption or education, leading to negative consequences.

Big data could become another driving factor behind unethical practices in AI-powered growth marketing. AI algorithms are fed massive amounts of data to analyze consumer behavior and personalize marketing campaigns. According to a study by McKinsey & Company, big data analytics has the potential to generate significant value for businesses, but it also presents significant risks to privacy and security (McKinsey & Company, 2016). Vast quantities of data collected by algorithms can be easily manipulated to create false patterns and trends, leading to skewed results and unethical practices. Having worked in technology companies with access to large data sets, I know that there are several levels upon which data can be biased, both in how we determine which signals to track, how we define them, and ultimately how we interpret what the data is telling us. The scary thing is that we don’t fully realize that we’re making biased decisions, because we think the data is objective.

One could even see a world in which perfectly fine-tuned AI, which is based on mathematical models and algorithms that are designed to analyze and interpret patterns in data, can lead to a worse or more mediocre customer experience overall. These algorithms struggle to understand the complexity of human behavior and the context in which these behaviors occur. According to a report by Harvard Business Review, AI algorithms are still limited in their ability to understand human behavior and the context in which it occurs, which can lead to homogenous and generic experiences for consumers (Harvest Business Review, 2020). As a result, AI-powered growth marketing can create homogenous and generic experiences for consumers, ignoring important cultural and contextual factors that can influence consumer behavior. Imagine a world where every experience on the Internet is a 3.5-star Yelp restaurant one – homogenous, not too good nor too bad. Optimized right for the crowd.

To address these ethical concerns and limitations in AI-powered marketing, I believe that businesses must take a responsible and ethical approach. This might come in the from of more businesses putting ethical guidelines front and center, in addition to their terms of services. We need a cultural system (or even formal regulation) that can ensure the right incentives for transparency and accountability in AI-driven marketing campaigns, protect consumer privacy and data security, and create meaningful and respectful experiences for consumers. Our best bet will be to find the sweet spot in combining AI with human intuition and creativity, and then putting together the charter upfront for how these interact.

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.

A New Sales Forecast Template for Consumer Hardware Startups (Spreadsheet Included)

This essay originally appeared on Hardware Massive Resources.

5 steps to building your first website sales forecast

Traditional sales forecasts are based on historical data and can be rather complex given different product types. These forecast models are based on “growth rates” that calculate a percentage change over time. If you’re a hardware startup launching a single product, you may find that you have no historical data to use for making assumptions.

In my experience working with over 250 hardware startups at Indiegogo, I’ve found that there is one thing that all hardware startups are doing as they’re building up their sales: digital marketing. Through Facebook ads or other types of digital marketing activities, companies are using direct response marketing to drive visitors to their ecommerce websites in order to drive more sales. Aside from one-off promotional activities or traffic spikes from visibility in the media, there are two things that don’t change drastically from day to day, and that is your website traffic and the conversion rate on your page. The conversion rate refers to the total number of people who purchase a product out of everyone who visited your website.

For hardware startups trying to estimate future sales and establish a plan to continue to grow, I have found that the number of qualified visitors you drive to your checkout page is one of the most accurate ways to determine how much more sales you could potentially drive.

Example: Let’s say you’re currently driving 1,000 visitors to your page, and you manage to convert 1% of them into paying customers – that would be 10 purchases per day. Assuming that you need to sell 20 units per day, you would try to double the traffic you get–that means 2,000 visitors–to acquire 20 paying customers. This is a sign to your team that you need to increase the traffic to your page, or improve the conversion rate by optimizing your page and converting more sales for each ad dollar you spend. You need to start tracking this information in order to understand where you can improve your sales pipeline.

In this forecast, I use a page conversion rate instead of a “growth rate,” which is the more common underlying metric in forecasts. This is because hardware sales are prone to more nuanced data anomalies compared to software or service businesses. Selling 100,000 units in one month does not mean that you will be able to sustain the same volume going into the next month, nor is it helpful to reference during the same period the following year. With these differences in mind, let’s start by understanding the goal of creating a sales forecast.

What is a sales forecast?

A sales forecast is a spreadsheet that you can use to estimate future sales for your company. You can make it as short-term or long-term as you want, and include or exclude as much details as you need. A forecast will help you explain the assumptions behind your valuation to investors, and help you rally for budget for specific initiatives. Even if you’re not presenting a deck to your board soon, it’s important to keep an up-to-date forecast to understand how well you’re trending against your goals.

The goal of this tutorial is to help you build a forecast for your website sales in a single month, so that you have a sheet to track your daily sales with. This template will apply best to startups who don’t have any data, and therefore marketing performance is the best data set you have to build your forecast. At some point you’ll have a fancy, color-coded financial projection spreadsheet. But, for first-timers, I strongly recommend building a forecast that is easy to understand and update, so that you can easily look at it at any point in time to assess business decisions. The best forecasts are made bottoms-up, which means that you calculate any projection trends based on actual sales and results, so it can accurately reflect your business’ unique operations. There is little benefit to running a tops-down forecast when you’re trying to assess actual business numbers.

Example: Tops-down forecasting means to identify the total size of the market, and assume that you will capture any percent of it. You could say that the total size of the market in 2017 was $1 billion, and capturing 1% of the market would yield $10 million in sales for your company in the first year. Without any marketing activities and actual customers, you will capture none of the market, so showing any investor a tops-down forecast is not grounded in reality.

A small set of historical data, a goal, and Excel (or a cloud-based spreadsheet solution like Google Sheets) are all you need to get started. I recommend starting with Google Sheets so that you can update and share with team members at any point in time.

Before we get started, I want to point out that there are no rules for creating a sales forecast. This tutorial is only one variation and tackles a very specific goal.

Let’s get started

[Access the sales forecast template here]

Some tips for using this spreadsheet:

Don’t edit anything in this template–go to “File”–>”Make a Copy” and edit your own version. On your own version, you’re welcome to make any changes to this outline and use this as your own.
Cells in yellow are ones you will need to fill out.
Cells in grey are anchored formulas, so you should not edit these.
Cells in blue are summations, and are used to emphasize total amounts.

STEP 1: Determine your reporting period, product price, and goal.

Start Date: Date when your company’s reporting period starts. A common way to track this would be from the start of a quarter to the end.
Forecast Start Date: Once you have at least two weeks of sales, you can start to make this forecast. This is the date in which you’re sitting down to make this forecast.
Reporting End Date: The end of your reporting period–many companies will set this as the end of the quarter. In this example, I set the end of a 4-week period to calculate a month.
Price Per Unit: This number will help us calculate the total sales amount.

STEP 2: Lay out your historical data, which are the units you’ve actually sold.

Total Site Traffic: Use your site analytics tool to track how much traffic you’re driving to the page.
Conversion rate: Divide the Total Units / Total Site Traffic so that you can calculate a conversion rate. Depending on the channel you’re selling in, this conversion rate will be different. As a general benchmark, if your page conversion rate is below 1.0%, then you should look out for ways to improve the user checkout experience or your page content.
Total Units: How many products you sell per day.
Price/Unit: This field is carried over from your product price set in Step 1. You can override these fields if you have sales on specific dates and lower your price point.
Total Sales: Total Units x Price/Unit ($)
Running Total: This is the cumulative sum of your total sales since the start of the reporting period. In order words, this is a snapshot view of how much your total sales are on a specific date.

STEP 3: Calculate your “run rates”, which is how you’re trending.

This is the part where it becomes less of a template, and requires you to think about your business. You’ll need to pick whether which set of performance data you want to use to project future sales with. Based on your historical data, I’ve calculated two averages here:

=TrimMean(): An average that removes the top 25 data points so you can exclude any outlier data that does not represent the rest of the data well. For example:

In Week 1, we had a day where the page conversion rate was 2.06%, and we drove over $11,960 in sales. This was because a partner put in a larger order and is not a repeatable sales event, so we want to exclude it. Using the TrimMean helps us remove data points like this so that our average trend numbers are more accurate in our forecast.
=Average(): Just your regular average calculation (add up all numbers, divided by how many numbers there are).

In this case, the numbers aren’t too different, so picking any of the two won’t give you wildly different forecast results. In this case, because I know that there are unrepeatable sales events such as the 2.06% conversion rate on January 4th, I’m going to use the TrimMean. This means that in my forecast, I’ll be using the data in Column A to carry across my future sales forecast, which I’ll do in my next step.

Before that, let’s calculate some numbers on what it would take to reach the goal we set up for this period:

Revenue to Date: This is how much in total sales we’ve generated to date (carried over from the running total in Cell P18).
Difference to Goal: Subtracting our sales to date from our goal amount of $150,000, we still have $91,037 left to sell.
Days Remaining: We set our reporting period for 4 weeks, so if today is January 14th, then we only have 14 days left before January 28th.
Revenue/Day: In order to hit our goal, we need to sell $91,037 in the next 14 days. That’s about $6,503 in sales per day.

STEP 4: Calculate where you’re actually trending to hit, and what you really need to be selling in order to hit your goal.

Trending (Rows 35-41): This is how much we’ll sell if we keep doing the same things we’ve done in the past. Based on Step 3, we will carry the TrimMean numbers (13 units per day and 1,994 visitors per day) across the rest of the upcoming two weeks. To do this, we will anchor the TrimMean cells (C24, C25, C26) to our forecasting table, and then copy and paste these same numbers across all future dates:

Looking at the “Trending” forecast, we’ll end the period with $111,916 in sales, which does not meet our $150,000 goal. This helps us understand that we need to somehow boost our sales in order to meet our goal.
Goal (Rows 43-49): In Step 3, we calculated that we need an additional $6,503 per day in sales in order to reach our goal. In order to achieve that, we will need to sell 22 units at $299 each. Doing some reverse calculation, we can figure out how much site traffic we need to drive in order to sell 22 units a day:

If we could drive 3,452 visitors and assuming they converted at an average of 0.63% on the page, we could sell 22 units per day and reach our $150,000 goal.

Difference (Row 51): Now let’s calculate exactly how much we’re short of traffic and sales.

Based on what we’re selling now and where we need to be, it appears we need 1,458 more visitors per day in order to drive an incremental 9 units of sales per day.

Assuming that I am running Facebook ads, I could do a simple calculation as such:

While it’s not always correct to assume that your cost-per-click for any digital ads platform will scale up linearly, this is a great way to provide a ballpark budget to your team. Instead of going to your head of finance and asking for as much money as possible, you could say, “Hey, I think we’re getting a pretty good return on our ad spend, so we should increase our daily ads budget by $700.”

STEP 5: Most people stop after filling out the forecast and consider it an idle spreadsheet, but in order to make this spreadsheet actionable, you should visualize the magnitude of the difference, and draw actionable insights.

First, let’s start by determining what kind of growth rate we’re seeing day-to-day, and week-to-week. Eventually, you could build a monthly and yearly forecast. These will be the numbers that your stakeholders care most about.

Growth rates tell you how much your sales are growing per interval of time, based on where you’re at in the beginning of the period and where you net at the end. Use these growth rates to set goals and show your investors how well you’re doing in the short-term:

Daily Growth Rate: You started with $2,033 of sales, and plan to sell $111,916 by the end of the period–this means that you increased sales by 15% each day across all 28 days.
Weekly Growth Rate: An increase from $2,033 of sales to $111,916 at the end of the 4th week means you grew sales by 172.4% per week, across 4 weeks.

Note: The “Actual” column growth rates will be filled in when the period comes to an end and you fill in the actual sales data from the past 4 weeks. These fields will calculate the growth rates on your actual sales.

Lastly, let’s plot these numbers and see how we’re trending:

Actual (blue): This line represents how much we’ve actually sold to date. As you fill out the forecast, this graph will populate so you can see how much you’re beating or missing your expected sales plan.
Trending (red): This is how much we’ll sell if we keep up what we’re currently doing. You’ll notice it’s very similar to the blue line, because it’s an extension of what we’re doing right now.
Plan (dotted, green): This is where we want to be in order to hit our goals. This line is higher than what we’re predicted to sell right now (red line).

And that’s it! You can draw a variety of insights based on the numbers you see, that you can use to inform your marketing and pricing strategies. I’ve included some examples of insights you can glean from this forecast (Row 92).

What’s next

Remember that with hardware sales, especially with a few SKUs to start, you’ll face more seasonality and outlier data compared to other types of companies. For example: when selling on Amazon, there are certain product categories where looking at December sales history is not going to help you forecast how many units you’ll sell in January. Or, if you’re selling a pair of ski goggles, you’ll likely find that November is much better month than June. You’ll need to factor these seasonal trends into your forecast. In this case, website traffic is not the best metric to use.

Eventually, you’ll need to track many, many different channels. You could be selling in over 10 different offline channels and 20 different e-commerce marketplaces at the same time. For hardware startups, the initial P.O. (purchase order) will often be modestly sized, with massive orders for thousands of units coming in later in the year. Within a few month’s time, many retail channels could be shut down, or you could have opened new ones. That’s why it’s important to regularly update the assumptions of your forecast.

Regardless of the channel or number of products you’re tracking, just remember to keep it simple, bottoms-up, and actionable. Separate the forecasts for each channel if you need to–the more lines you can build in with different concrete assumptions, the more accurate your forecast will be.

Happy spreadsheeting!

How to optimize your crowdfunding campaign for success

This originally appeared on the Indiegogo Entrepreneur Site.

The ONE Smart Piano was the most funded music tech campaign on Indiegogo back in 2015.

In this post, I will be sharing optimization tips for the time that it’s easiest to freak out: campaign optimization. If you didn’t have time to complete an exhaustive list of pre-launch activities, fear not: I have been there before and these tips below will help you achieve an equal amount of success. The core topics of campaign optimization include content, on-site conversions, and measurement.

At The ONE Smart Piano, we created a piano device that can connect to your mobile device to teaches you to play with light-up keys. We prototyped the product early January to gear up for a launch on Indiegogo three months later, but we were met with unexpected delays in shipping, last minute prototyping, and filming schedules. I sweated buckets of worry since other campaigners told me that pre-launch activities could make or break the campaign, and we didn’t have time to complete most of them. I had a few people tell me to delay the campaign indefinitely.

We ended up giving ourselves another two months to finish our campaign videos. There are many reasons to delay a campaign, but I decided that extra months would throw off our schedule for the rest of the year and that more time wouldn’t make me feel more ready. Delays are a natural part of the crowdfunding process and you should embrace them as part of the plan. Everything external from planning media hits to driving traffic to the page will be forecasts, and forecasts change.

Even if you’re feeling nervous that you didn’t put your best foot forward in the pre-launch campaign, the best thing you can do is to treat crowdfunding like another marketing project: your goal is to get backers behind your mission, and the measure of that is their confidence to fund your project. Like all other marketing activities on the Internet, it is governed by a funnel of activities that lead to you reaching your target audience. Your job is to optimize that funnel.

Content optimization

You’re sending all of this traffic to your Indiegogo page from media hits and paid advertising. Don’t waste this traffic – convert as many visitors into backers as possible. Let’s face it – there’s no way you will appeal to everyone that lands on your page, but for the visitors that count, make sure your crowdfunding landing page is compelling to them.

For The ONE Smart Piano, we had many different angles to go after. At first, we tried to make the piano the “cool” instrument so we showed the piano being used by celebrities and listed all of the pop songs you could play. While it was sexy to show the piano as the hippest new thing in town, our target audience cared more about the learning and classic features of the piano. Parents cared most about the Smart Piano being an “economical solution to piano lessons” that offered “gamified learning experiences” for their kids.

To improve your message to your target audience, do these three easy and low-cost things:

1. Run Qualitative Research

To figure out what your core taglines are, use Google Consumer Surveys to vet them. At 10 cents per response, you can survey 100 people for $10. You can even target specific demographics and view the results based on different audience segmentations. These responses are going to be a lot cheaper than running Facebook ads to acquire survey respondents, which is often a costly and time-consuming pre-launch activity. Use Google Consumer Surveys to gauge your mass audience messaging.

To set up the survey, come up with four to five of the strongest hypotheses you have about appropriate taglines. Above is an example of survey responses, with a result that surprised me. For mass audience messaging, it turned out that simply describing the core function was better than using provocative language.

2. Maximize your top-of-fold content

A visitor’s first impression of your page determines whether s/he continues to scroll down to learn more about your product, so make sure your video thumbnail above-the-fold is a compelling image. One of the most undervalued assets on your landing page is the video thumbnail that appears above the fold. This image is not only the first image that a visitor will see on your page, but it is also the one that propagates on social shares. Create this image carefully.

I’ve outlined a few principles for this image that worked well for us:

Add a call-to-action, whether it’s a button or a line of text at the top or bottom of the image. When your project link is shared to Facebook, people will see this image and click-through to your website.

Use a color that can stand out and look sleek relative to other Facebook posts. A bright color, a gradient pinpoint to the center, or even a clear product shot on a white background, can draw attention to your image among the flood of posts in someone’s Facebook Newsfeed.
Make sure your text overlay isn’t in the center of the image because that’s where the video play icon will be.

3. Make sure your page loads smoothly

You’re probably going to use a lot of GIFs and large images on your Indiegogo page, so make sure your page load time is fast. We used many long infographic-style images to showcase the Smart Piano’s design and details, which would have been a disaster for someone with a slow Internet connection. Compress your images to make sure your page isn’t bottlenecked in a visitor’s browser.

It’s simple to reduce image file sizes without reducing quality. I like to use Compress Jpeg since it’s free and I can compress images in bulk. With this tool, I can’t tell the difference even though it’s able to reduce my image sizes by a whopping 70-90%.

In the image below, I was able to compress a large 2MB image into 490K image just by using this tool.

Increasing on-site conversions

This section is focused on what you can do to get people who visit your page to convert into backers. You can find a plethora of guides on driving traffic from external sources such as paid and organic media (i.e. Facebook ads, Google ads, press hits, etc.).

You’re probably getting a lot of visitors to your page – now you need them to become your backers. Two of our most successful ways of converting backers includes providing extra communication to those with questions and making it easier for interested visitors to fund the project anywhere on the page.

1. Providing excellent support converts customers

Create a support alias or email contact so that you can provide personalized responses and increase your chances of converting backers. When we opened up an extra communication channel, people were able to ask specific questions that they wanted a personalized response to. With these answers, visitors could better understand the pros and cons of funding and they successfully became our backers. Providing personalized support via email converted visitors at a much higher rate than simply answer their questions through public comments and Q&A.

Create a free Gmail account, or create a new alias for support through your existing domain. If you’re looking for customer support interface, Zendesk has a simple interface for helping you to prioritize your inbound requests, and you can link it to your support alias. Here’s a look at how you can organize your inquiries with the Zendesk interface:

2. Make your call-to-action buttons easy to find

Add buttons and links to specific perks you reference throughout your project page so that visitors can fund your project as soon as they’re interested. Your page can get long – it could take a visitor minutes to finish reading through everything on your project page. When you’ve got their attention, allow them to back your project from anywhere on the page, so they don’t have to scroll back to the top and potentially lose interest.

We added different types of calls to actions: Buttons and hyperlinks directly to perks. About halfway down the page, which takes a few scrolls to reach, we added links to the perks so that people who were interested didn’t have to scroll back up and look through all the perks to find the one that they just read about.

In some cases, we used images of buttons and hyperlinked them. Buttons look like they can be clicked, so they help visitors take action and fund your project. Add these hyperlinks directly to perks often, especially towards the middle and bottom of the page where it could be confusing to associate a perk they just read about with the actual perk link.

This is an example of a text-based link:

This is an example of a button link:

Measure & Optimize

You can’t just set and forget your crowdfunding page. Once you launch a specific set of changes or even receive organic media coverage, give it a few days to take effect and then measure the results.

There’s a different amount of time you’ll want to allow a campaign to run before you hastily make changes. For us, we allowed a paid campaign or press hit to run 3-4 days before looking at the final numbers. With a press hit, we often say upticks happen on Day 2 instead of Day 1, so you’ll want to wait it out a bit. The same concept applies to changes in taglines or designs – let the change run for a few days before you freak out and change everything.

Here are two ways you can change and measure effectively, without spending any money:

1. Track the performance of each marketing channel

Use UTM, Bit.ly and other link trackers for your external media links to better understand what is working for you. The tools I listed here are all free to use.

We wanted to understand which type of media hits drove the most backers for our product: was it mass consumer media, technology-specific coverage, mommy blogs, or all of the above? When we shared links with the press, we would append a UTM codes to our URL with the Google URL builder, or shorten links using Bit.ly to count exactly how many people clicked through from another site to our project page.

Below is an example where we linked to a piano bench we were offering and distributed the link through someone else’s email newsletter. Divide the clicks by the email list size and you have your click-through rate.

Note that the Google URL builder results can only be seen in your Google Analytics if you are linking to your own website. If you are linking to your Indiegogo page, you can use the native link tracker, which is simply igg.me/x/[your-URL] and shows up in your dashboard, or you can use Bit.ly.

2. Understand your product’s value

Don’t undercut your own costs – raise your perk prices in step functions to get backers excited about funding today. For example, we started out offering a very early bird discount at $799. We eventually raised this price to $849, so we raised this by $50 without going through a smaller increment first, such as $829. That is a good amount to increase if you’ve got a product worth hundreds of dollars. This one can seem counterintuitive, but you have to remember that crowdfunding isn’t purely a sales machine. You’re trying to secure funds to carry your business forward, not so that you lose money on every transaction made.

It’s nice to offer a discount to early backers, but it comes at a cost. Don’t forget that you still have a delivery timeline to catch and you will need your contributors to help you with that. In our experience, pricing low doesn’t necessarily help backers understand your product. We wanted to showcase a premium piano offering, but we priced too low to start with. When we adjusted the price upwards to better align with our cost of goods sold, we found that backers were more excited to learn more about the premium features we were offering.

Equate your pricing with your value. Offer a good discount, but use discounted prices as a way to draw attention, and then raise that in a step function when you want to showcase the value that you will bring to contributors.

Other things to keep in mind

The biggest takeaway for crowdfunding is that there is no formula for success, and your product category and market fit matter more than you expect. There are some product categories that will allow you to find pockets of success easier, and some that will tough to grow an audience for right out of the gate.

There are a lot of free and low-priced solutions for measurement and optimization. You don’t necessarily need to dish out your marketing dollars for high-end solutions because there is not fitted model of success for everyone.

Personalize everything you say, design, and price. You can reach out to your hardcore advocates or the mass market, but just make sure your message is sticky with someone. You can’t have taglines that work for everyone but make sure it reaches the people who count.

Don’t panic, don’t try to change everything on Day 2, and get some sleep so you can let the data speak. Break a leg!

Glossary of terms used:

Landing page: Your Indiegogo project page.
Above-the-fold: This is the height of your page visible in your browser, and is the first image that your page visitors will see.
On-site: Things that happen on your Indiegogo project page, versus off-site like on Facebook, Google, press hits, etc.