" What can we expect from organic?
Your boss is building a budget proposal and wants to know: what contribution will we get from organic? We need to figure out how to predict word of mouth.
GoolyBib Office - Thursday Afternoon
Your boss messages you: this is probably about the budget proposal he's putting together. Let's see what he wants.
Hey, what’s the expectation on organic traffic for Q4?\nIf we can get a handle of how much traffic to expect from word of mouth, we can pair that with our paid ads forecasting and get a realistic picture of what’s realistic for setting Q4 targets.\nCan you take a look at seeing how we can predict the contribution we can expect from word of mouth?
Word of Mouth
Before the invention of mass media, word of mouth was the only way people learned about new products. However recommendations from friends and family still influence 78% of purchase decisions, more than any other channel. The internet made going viral more accessible and more easily trackable, thanks to social media. However public social networks are just the tip of the iceberg. Private sharing via Whatsapp, Email or Slack account, known as ‘dark social’, accounts for as much as 69% of traffic according to Alex Madrigal of the Atlantic.\nWord of mouth is becoming more and more critical thanks to Facebook, Google and Amazon’s dominance of ad budgets, and Apple’s recent moves towards user privacy, which have hurt our ability to track the performance of ads. We can't easily track this directly because these platforms don't pass a marketing source or referrer — they show up as 'direct' traffic in analytics platforms.\nSince a significant portion of your traffic comes from word of mouth, you have a big hole in your performance forecasts unless you can predict it. If you can measure word of mouth you can calculate how much traffic you can count on from virality and make sure future forecasts are realistic, before you agree to performance targets.
If you do build a great experience, customers tell each other about that. Word of mouth is very powerful.
I read something on Reforge the other day that might help\nThey claim you can predict word of mouth with a few simple calculations\nLet me know if it works for you because I was planning to try it too
Word of Mouth Coefficient
In 2014 Yousuf Bhaijee worked at Zynga with Tomas Pueyo (author of coronavirus articles with 50M+ views) where they created a simplified way of measuring word of mouth. They found a metric that was stable enough to use in forecasting and insightful enough that teams could figure out how to influence it. Yousuf teamed up with Mike Taylor (co-founder at Ladder and Vexpower) to apply this metric to products in a range of categories and found it to be highly predictive.\nMost models of virality assume that new users invite X number of people on their first day or week, then the cycle continues - this is analogous to the spread of a disease where people are only infectious for a specific time period, and if the R0 is above 1 (the number of people each person infects) the virus grows exponentially.\nThe key insight behind the word of mouth coefficient, is that word of mouth is a function of the number of active users you have: every day you get a user to come back and use your product is another day where your product is top of mind, increasing their chance of making a recommendation to a friend. We’ve found returning users to be a reliable, realistic and stable predictor of new organic users. This is important because when making forecasts you’ll know many users to count on organic delivering for you, and will be able to justify higher budgets based on virality assumptions.
Calculating Word of Mouth Coefficient
How do we measure and predict word of mouth? If we can figure this out we can make more realistic forecasts, and figure out ways to move the needle.
GoolyBib Office - Thursday Afternoon
Having learned about the word of mouth coefficient, you're excited to try and calculate it. Let's work through the steps.
To calculate Word of Mouth Coefficient (WoMCo) you take new organic users and divide them by returning users plus inorganic users. The WoMCo metric can be interpreted as how many new word of mouth users you get for each inorganic user, i.e. if you drive 10 people to the site via a paid ad campaign and your WoMCo is 0.1, you’re getting 1 new user for free from word of mouth.
The key to finding a stable predictor of word of mouth is how you define the numerator: ‘organic’. For example direct traffic and google search traffic that lands on the homepage should count, but search traffic to product or blog pages should be categorized as inorganic. It's also important to think about how you define the denominator: 'returning + inorganic'. You get the best results by only including users likely to refer, for example users that have done at least one qualifying action, like add to cart or wishlist.
Define the Denominator
The first step is to define the denominator you'll use in your calculation. This is the segment of your userbase that you expect to refer new users. Typically it makes sense to start with a simple definition, like 'Returning Users'. This will give you a good starting point from which you can then refine further if the R2 isn't high. Usually you get better results from further refinement down to just users who have completed at least one qualifying action. For example a returning user who hit a paywall and bounce without becoming a member isn't likely to refer new users, but active subscribers who log in to read each week are highly likely to keep making recommendations. It doesn't have to be only returning users you include in the denominator: in many cases new users who complete a qualifying action are just as likely to refer.
Which of these segments are likely to tell friends, coworkers or family about the product?
Define the Numerator
The second step is to define the numerator you'll use in your calculation. This is the segment of your userbase that you expect to be from word of mouth. The simple definition is 'direct' traffic, i.e. any user which is unattributed as they have no marketing source. However due to user privacy rules and adblocking a lot of users who are actually driven by paid ads might end up in this bucket. It sometimes makes sense to narrow this definition down to just direct users who land on specific pages that indicate someone shared a link with them. Alternatively you might have more luck with broadening the definition to users who search for your brand term on Google. It's hard to see this in Analytics since Google no longer passes keyword information, so a good proxy for this is users from Google who land on the homepage - the rest of the pages on your site likely owe their traffic to SEO campaigns, not word of mouth. Finally the definition of 'new' is important. Did they perform a qualifying action or did they just bounce? Did they sign up (so you can tell they're a new user) or did they just not have a returning user cookie (due to adblocking, clearing cookies or switching devices)?
Which of these segments would be mostly driven by word of mouth?
Once you have your definitions for new organics and inorganics, and have pulled the data, you can plot the WoMCo metric over time to see how stable it is. You can also identify its predictive accuracy by calculating the R2 value, which will tell you how much you can rely on forecasting using this metric. We've provided sample data for this next exercise, and you'll need to pivot that data to get it in the right format (one column per segment, one date per row), in order to calculate your word of mouth coefficient.
Create a pivot table that shows one column per segment, with a row for each week. Then use the resulting table to calculate word of mouth coefficient. Explain your process.
Plot a stacked area chart on the left axis of each segment that adds up to all users (Direct, Brand, Returning, Inorganic). Then add a line chart (make the original chart a combo chart) on the right axis that shows WoMCo over time. Is the metric relatively stable over time? What noticeable periods are there?
It's possible to find the correlation of two variables without doing any math: simply add a trendline and choose the option to add the equation to a scatterplot chart, and you'll have the R2, slope and intercept calculated for you.
Calculate the correlation between new organics (direct + brand) and returning users by creating a scatterplot and adding a trendline with the equation. What is the R2?
The coefficient from the trendline equation (the number multiplied by x) is the inverse of the average word of mouth coefficient. So for example if the coefficient is 9.54, that means we get 1 word of mouth user for every 10 returning users. The inverse (1/9.54) is 0.10, which is the number of word of mouth users we get per returning user, which was the metric shown on our line chart.
This is great\nKnowing we get 0.1 new users for each returning one is the last thing I needed to forecast budgets\nIt's amazing how stable this metric is: we should start tracking it for anomalies and overall trends.\nThis is a potential game changer!"