If I were a Business School Professor in Finance, I would assign the following exam: ‘How do you value Internet Companies?’ and I would fail any student that did not leave the answer sheet blank.
Warren Buffet, Chairman & CEO of Berkshire Hathaway
Mr. Elon Musk’s Twitter last week (screenshot below) on Twitter deal (US$ 44bio vs market cap of US$31.48bio and enterprise value of US$31.12bio as of 13 May 2022 as per Yahoo Finance https://finance.yahoo.com/quote/TWTR/key-statistics/) was put on hold to get more supporting calculation for spam-fake accounts of Twitter platform users.
Interesting to know that the spam users could put on hold such mammoth deal, as spam or fake accounts are not generally found in the internet-based business valuation metrics, for example, Corporate Finance Institute lists 17 most important metrics to know (Monthly Unique Visitors; Customer Conversion Rate; Bounce Rate; Average Order Value; Monthly Active Users; Average Revenue per User; Monthly Recurring Revenue; Revenue Run Rate; Contribution Margin per Order/Customer; Customer Acquisition Costs (CAC); Contribution Margin After Marketing; Churn Rate; Burn Rate (and Runway); Lifetime Value; LTV/CAC ratio; Payback (# of orders, or time) and Viral Coefficient), (https://corporatefinanceinstitute.com), related to the valuation of internet-based companies. And even from two IPO Prospectus, Bukalapak and Tokopedia, we will hear more about Annual Transacting Users, Total Payment Value; Net Promoter Score, Gross Transaction Value (from GoTo) and Annual Transacting Mitra/Users; Average Transaction Value; Customer Acquisition Costs and Total Processing Value (from BUKA).
Twitter itself estimated in a filing on early May 2022 that false or spam accounts represented fewer than 5% of its monetizable daily active users during the first quarter of 2022. Let’s say we assume not more than 5% for the spam or bot or fake accounts on Twitter platform, then the estimated spam accounts will be around 20 mio (using total monthly active users of around 400 mio in 2022 (see https://backlinko.com/twitter-users), or 11 mio (using total monetizable daily active users of around 217 mio – (https://www.omnicoreagency.com/twitter-statistics/)]
The interesting stuff about this spam account that it is directly an integral part of the calculation of how many active users on that platform from all total users. We need to be aware that total users here could be seen from several aspects, such as [monthly/daily] active users, monetizable active users, marketing-reach users, etc. To know how it is defined, it is really crucial in the valuation of such e-business.
Elon Musk’s concern seems making a lot of business and finance sense though Elon Musk twitted after the disclosure coming that Tesla Inc. who has inked a deal to buy Twitter for $44 bio (with US$ 31.48 of Enterprise Value as per 13 May 2022, https://finance.yahoo.com/quote/TWTR/key-statistics/), that one of his priorities would be to remove “spam bots” from Twitter platform, yet here spam is considered as one of the risk factors that related to Twitter business and reputation as well. We could read this in Twitter IPO filing which it is said that other than spam that could diminish the user experience on Twitter platform (potentially could damage Twitter reputation and deter their current and potential users from using Twitter products and services), more importantly, spam is mentioned as one of the assumptions being used and relied on calculating certain of Twitter key metrics (see Twitter https://www.sec.gov)
I copy herewith the whole paragraphs to understand the background of this risk factor.
The numbers of our active users are calculated using internal company data that has not been independently verified. While these numbers are based on what we believe to be reasonable calculations for the applicable period of measurement, there are inherent challenges in measuring usage and user engagement across our large user base around the world. For example, there are a number of false or spam accounts in existence on our platform. We estimate that false or spam accounts represent less than 5% of our MAUs as of December 31, 2014. [bold added for emphasis, and Twitter boasted to have 302 million average monthly active users, or MAUs, in the three months ended March 31, 2015. However, this estimate is based on an internal review of a sample of accounts and we apply significant judgment in making this determination. As such, our estimation of false or spam accounts may not accurately represent the actual number of such accounts, and the actual number of false or spam accounts could be higher than we have currently estimated. We are continually seeking to improve our ability to estimate the total number of spam accounts and eliminate them from the calculation of our active users, but we otherwise treat multiple accounts held by a single person or organization as multiple users for purposes of calculating our active users because we permit people and organizations to have more than one account. Additionally, some accounts used by organizations are used by many people within the organization. As such, the calculations of our active users may not accurately reflect the actual number of people or organizations using our platform. (note: bold added for emphasis purposes)
Our metrics are also affected by mobile applications that automatically contact our servers for regular updates with no discernable user action involved, and this activity can cause our system to count the user associated with such a device as an active user on the day such contact occurs. The calculations of MAUs presented in this prospectus may be affected by this activity. The impact of this automatic activity on our metrics varies by geography because mobile application usage varies in different regions of the world. In addition, our data regarding user geographic location is based on the IP address associated with the account when a user initially registered the account on Twitter. The IP address may not always accurately reflect a user’s actual location at the time of such user’s engagement on our platform. We present and discuss our total audience based on both internal metrics and data from Google Analytics, which measures unique visitors to our properties.
We regularly review and may adjust our processes for calculating our internal metrics to improve their accuracy. Our measures of user growth and user engagement may differ from estimates published by third parties or from similarly-titled metrics of our competitors due to differences in methodology. If advertisers, platform partners or investors do not perceive our user metrics to be accurate representations of our user base or user engagement, or if we discover material inaccuracies in our user metrics, our reputation may be harmed and advertisers and platform partners may be less willing to allocate their budgets or resources to our products and services, which could negatively affect our business and operating results. Further, as our business develops, we may revise or cease reporting metrics if we determine that such metrics are no longer accurate or appropriate measures of our performance. For example, we stopped disclosing timeline views as we no longer believed that metric was helpful in measuring engagement on our platform. If investors, analysts or customers do not believe our reported measures of user engagement are sufficient or accurately reflect our business, we may receive negative publicity and our operating results may be harmed.
As mentioned above, “Spam” is not really make it to a public highlight until this Elon Musk is making a fuss on that. Normally we are accustomed to some common metrics that in general linked directly to the platform or web-based business, such as
|HIT||One count per request for data. Highly subjective and easily manipulated|
|PAGE VIEW||One count per HTML page. A better measure of an advertising opportunity given that advertising banners are changed with each new page served|
|CLICKTHROUGH||Tracks the number and percentage of customers that follow an advertising link. Sites with higher click through numbers/percentages can drive higher advertising revenue. Specific to advertising potential|
|UNIQUE VISITORS||Counts unique IP addresses to determine the number of individuals viewing a site. A useful metric to an advertiser that wants to expose as many people as possible to their product.|
|REACH||The percentage of the Internet population visiting a particular site per month. Based on sample user-groups. Internet population is not well defined or accurately known.|
|LENGTH OF STAY||The average length of stay can identify sites who’s users spend little time per page and are not likely to read ads versus those sites that attract users that absorb the information presented. Could be affected by transfer rates and overall internet performance; slow transfer rates would artificially improve this metric.|
|REPEAT VISITS||A measure of the number of times a user may view a specific advertising banner.|
Source: Vonder Haar, Steven, (1999). “Web Metrics: Go Figure”, Business 2.0, June, pp.46-47.
However, this news about Twitter will open our eyes, to be aware of that all above numbers will somehow be linked to how many spam accounts that are there in the platform.
Unrelated but I am thinking about this?
Can we value a internet-based business from collecting the data about how many ping-pong tables that they have purchased for the past one year for their employees’ fun? How about how many pages slide deck do they have prepared for analysts? More pages, better? Or how many rockets do they have launched, landing and re-launched (https://www.spacex.com/launches/)?
Elon Musk to Twitter CEO : In God we trust, and for others, please bring supporting data.
Note: All websites mentioned above are accessed on 16 May 2022.