A way to Calculate Your commonplace name market (TAM) for seo

When launching a new product / company or proposing additional fundsforecasting according to your TAM (common addressable market) is a key component.

TAM is a key part of a gaining users both the strategy and your SEO campaign.

It can help you prioritize your optimization activities and predict traffic potential based on your predefined keyword sets.

Your common addressable market is different from your entire market, as your common market is your biggest potential opportunity if there is no competition or alternative products with different USPs (unique sales proposition).

Your TAM is part of this market, but it is more closely related to yours persons and potential users whose needs are more in line with your product / service.

What is the Total Address Market (TAM) formula?

The defined formula for calculating your TAM (in general business terms) is:

Potential market x competitive position = TAM

Your potential market is the number of potential users, e.g. the number of email users in the world is estimated at about 4.03 billion, but if your competitive position is for U.S. users only, your TAM is about 250 million.

Your TAM may also be affected by users using adjacent products.

Let’s say you’re an online service that focuses on user privacy as a core USP.

In this case, users of other privacy-focused products, such as Bravely (browser) and DuckDuckGo may belong to your general TAM.

However, when we look at our SEO TAM, we can use existing tools and data to create estimates of our entire TAM and then break it down according to the potential needs of users (that match your personalities).

Setting up your TAM

Setting up your SEO TAM is a three-step process for me:

• Fully identify and identify product USPs and capacity (current and forecast).
• Comprehensive keyword research around your product / service / offer.
• Traffic estimates (click curves and opportunity gap analysis).

For the first step, you can achieve this by meeting with product owners and managers and asking them about product details, functionality, features, and product design.

You can then use this information to inform about your keyword research create better contentbut also a more valid user experience.

It allows the user to more accurately predict their product experience, thus reducing both outflow and redundancy in the pipeline of unqualified potential customers.

From experience, these meetings can also help identify some potential aspects and messages that can be included in content that competitors overlook.

The second step is implementation extensive keyword research and categorization. In addition to categorization by purpose, this is also a good opportunity to categorize by funnel levels.

The third step is to create traffic estimates – and you can do so from your keyword research, the current positions of your landing page (if any), and whether or not SERPs (search engine results pages) for keywords contain SERP features.

To illustrate this process, I will use Narmi as an example (using publicly available data via SEO tools).

The Narmi website currently ranks for about 800 keywords in the US and is based on rated CTR SERP data, deadlines produce about 500 sessions per month.

If the domain ranked first for all 800 search terms, the total potential organic traffic is around 81,000.

We are now taking a set of raw data. To get a more realistic TAM estimate, you’ll also need to:

• Add additional search terms that you are not currently ranked for, but want.
• Remove irrelevant search terms from the dataset, e.g. the random brands you rank 81st were chosen because you once mentioned them in a blog post.

Ranking 1st for all potential search terms is not realistic.

But with your data, you can create a step-by-step approach to show what improvements can be made if things were 10% better, 20% better, and so on.

Based on this, you can show other stakeholders and potential investors what improvements are needed to achieve the specific goals of organic transport – and then link the values ​​of effort back to the resources needed.

Use your TAM

In addition to forecasting traffic, your TAM data can be further used to predict potential customers and transactions.

Forecasting potential customers

For most SaaS and leading generation model companies, pipeline is the most important metric pointed out by most Level C and other stakeholders.

This can be calculated in a similar way to a potential traffic opportunity and can be modeled using your existing lead data.

Using Narmi estimates and assuming that they currently receive 11 SQLs (potential eligible customers) each month, we can model that each SQL requires an average of 38 sessions.

Based on this conversion rate, the potential opportunity for potential leads for a set of keywords is estimated at 2116 per month.

Again, this is modeled at 100% first position, but as with traffic estimates, this can be modeled based on a gradual increase in performance:

This can be further extended if the estimated number of potential customers is multiplied by the value of the potential customer and used for long-term forecasting based on outflow rates in LTV (life value).

This can also determine if there are storage problems.

If the pipeline shows enough to sign up for the free trial, but does not redirect enough to paid users, you can focus on the product or customer service / SDR (sales development representatives) and discover potential problems with on-site content and product messages.

Transaction forecasting

If you are an e-commerce store, you can make a similar forecast for the number of transactions you can achieve and total revenue (based on your current or projected AOV).

If your data fluctuates greatly seasonally or due to the nature of the products you sell, you can break this down by product category or even by seasonal category and combine forecasts.

Again, you can compare the total organic transaction and potential revenue data with incremental profits:

For example, are the current user path and conversion rate of the site sufficient to generate the potential customers / transactions needed to generate return on investment (return on investment) and growth?

Predicting transactions or leads on existing ones can help identify opportunities and problems that might not come to light until you miss or experience them.

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