"CORRELATION ANALYSIS FOR THE WOM COEFFICIENT"

"One of the key assumptions for the WOM Coefficient is that active users are predictive of new word of mouth users."

"WOM Coefficient Over Time"

"his will help us start to identify changes in the coefficient and understand what impacts it at a high level."

"Changes in the WOM Coefficient with seasonality."

"Changes in the WOM Coefficient as we make changes in other channels like paid acquisition."

"Changes in the WOM Coefficient with product releases."

"Calculating the Word of Mouth Coefficient Light To Heavy"

"Light Version - Using Google Analytics with some basic filters/segments."

"Medium Version - Refining the definitions of Returning Users and New WOM Users using qualifying actions."

"Heavy Version - Using Econometrics and multivariate regression analyses."

"Step by Step: How to find your Word of Mouth Coefficient - Light Version"

"STEP 1: DEFINE THE DENOMINATOR — RETURNING USERS"

"STEP 2: DEFINE THE NUMERATOR — DIRECT & BRAND SEARCH"

"To get the other half of the WOM Coefficient equation, we need an approximation of ‘word of mouth’ traffic."

"Step by Step: How to validate your Word of Mouth Coefficient"

"When Light/Medium Word of Mouth Coefficient Isn’t Enough"

"This is because as your business’ complexity increases, there’s more ‘noise’ in the data and additional variables need to be accounted for."

"In scenarios like this, one method that has proven useful for us is Econometrics."

"This is essentially expanding the analysis to use multivariate regression."

"Word of Mouth is critical to growth as channel saturation, competition, and platform control are all increasing."

"It’s tied to active users - a user metric every company tracks"

"It’s a stable metric - you can use it to forecast confidently"

"It can be influenced - you can break it down to its inputs and influence it with product and marketing activities"

"If the R^2 is high (close to 1) then we know that we can use the metric to forecast goals that are realistic."

"The second chart that we want to produce is looking at the WOM Coefficient over time.  This will help us start to identify changes in the coefficient and understand what impacts it at a high level."

"This primarily comes down to how complicated your marketing channel mix is."

"Step 1: Define The Denominator — Returning Users"

"Step 2: Define The Numerator — Direct & Brand Search"

"In Google Analytics we can simply choose ‘direct’ traffic, which is traffic without a marketing source, primarily consisting of sharing on ‘dark social’."

"To replicate, first filter for New Visitors, who came from Google / Organic, who landed on “/”, or the homepage (or you can import by clicking this link)."

"Step 3: Run The Analysis — Filling In The Template"

"Now all we need to do is export the data, preferably with at least a year’s data (or as much as you have)."

"If returning users is predictive of new word of mouth users, that means the best thing you can do for word of mouth is to drive repeat visitors."

"If the Word of Mouth Coefficient is trending up or down, then you can forecast where that leaves you versus your growth goals."

"Less than 0.7 requires more work and exploration – this means you’ll need to use the medium or heavy analysis to find better definitions for returning and new word of mouth users."

"Since there are many more users that visit and do not buy than those who visit and do buy, the non-buying visitors will dilute the Word of Mouth impact of the buyers.  In this scenario, a more valuable definition of active user is someone who makes a purchase"

"The point is to look at additional definitions thinking about your specific product and consumer and then validate by measuring each definition’s R^2."

"Definition 1: New WOM Actives - All users who performed at least one of the qualifying actions (studying or signing up) for the first time during a specific week."

"Definition 2: New WOM First-Time Visitors - Same as Definition 1, but with a technical exclusion that removed logged-out visitors who were actually just returning users."

"Definition 3: New WOM Sign Ups - All users who signed up during a specific week."

"All WOM definitions: For all three of these definitions, only users traced to ‘direct’, ‘branded search’, ‘social sharing’, or ‘social’ acquisition were counted as WOM new users."

"The best definition did not have the highest R^2. The ‘New WOM Actives’ definition had the highest R^2, but for reasons that are not necessarily positive."

"For example, companies running less directly trackable marketing campaigns (TV, billboards, podcast ads, content, PR) require techniques to tease out how much ‘direct’ traffic was really attributable to those campaigns."

"Practically the work involves measuring more variables (not just performance but actions taken on campaigns), enriching and cleaning the data in creative ways, and then trying various models until you find a way to improve accuracy."

"Let the R^2 metric guide you, though the true test of the model will be how it performs in predicting future levels of word of mouth."

"Facebook, Google, Amazon advertising dominance - 40% of all venture capital, and 70% of a company’s digital ad budget is spent on these 3 platforms."

"Apple is killing user advertising IDs - Apple has recently killed user advertising ID’s."

"Google Search decreasing real estate - Google continues to decrease the prominence and presence of organic search results in addition to decreasing the need for a user to click out of the search results."

"Word of Mouth is notoriously hard to measure because most of a brand’s Word of Mouth happens offline"

"80% of online sharing happens through dark social (sharing via untracked channels like whatsapp, text, and email)!"

"NPS - Most companies already try to measure the word-of-mouthiness of their existing users through NPS, but can’t quantify actual growth impact."

"Attribution Surveys - Another major method is to ask new users how they heard about the product."

"Social Listening Tools - Social listening tools crawl social platforms and the web to track mentions and engagement."

"“You manage what you measure.”"

"Less Capital Efficient - Many companies end up overly reliant on less efficient channels leading to poor unit economics and larger capital needs."

"Measures Word of Mouth as a function of what drives it."

"ARPDAU tells you how valuable an active user is in terms of revenue."

"So we created a metric called NOPDAU or “New Organics Per Daily Active User.”"

"The premise was simple: the people who are actively engaged with your product, talk about your product--and at a fairly consistent rate.  The longer you engage your users, the more word of mouth they generate. In other words, product retention drives growth of new organic users."

"if the denominator of your WOM Coefficient is WAU (weekly active users), and the coefficient is 0.1, that means every WAU generates 0.1 new users per week via word of mouth."

"New Organic Users: First time product users that we consider to be driven by word of mouth (i.e. they can't be traced to any trackable source such as a paid digital ad).  This is the numerator and what we are trying to predict."

"Active Users: The denominator of the ratio is everyone else: Returning Users + Non-Organic New Users.  The time period depends on your retention metric."

"The data is arranged in a scatterplot and shows a strong linear relationship between active users and new organics with an R squared of 0.958."

"It tells you for each new user, how many additional new users you expect the viral loop to generate."

"K-Factor Doesn’t Solve WOM - K-Factor was popularized during the early social network and social gaming days where the main viral mechanism were viral invite flows that were highly trackable."

"Active Users As The Input - Most viral growth models are focused on new users as the input, not returning users."

"Instead of focusing on new users, you shift to build your strategy around engaged users."

"One other important conclusion is that it shows one way to increase word of mouth is by increasing retention."

"You might look at this and think new organic acquisition is trivial compared to other areas of focus, but that is misleading. Each “new organic” is worth multiple future DAUs as those new organic users retain and the word of mouth loop spins."

"The more sophisticated developers build their growth models to be driven by retention, monetization, and paid CPA benchmarks."

"Edtech is a category where optimizing in-product viral flows can be notoriously difficult for products targeting teachers.  This is because teachers mostly share with each other offline."

"Stability: WOM Coefficient is relatively stable during the school year, with each WAU generally bringing in an additional ~0.15 new organic user in an average week."

"Seasonality: However, WOM Coefficient fluctuates seasonally based on school calendars."

"This same pattern happens for other core metrics (such as retention, monetization, virality) in e-commerce companies that scale. The more you scale paid, the lower quality the user."

"WOM Coefficient becomes more stable with volume - As volume increases, the month to month volatility decreases."