AI KPIs Will Evolve, as did Those of Internet Firms

Since artificial intelligence is so new as a driver of firm revenues, growth and valuation, professionals have few ways to model AI equity reward and risk, as was true for professionals in the early days of the internet. 

Traditional valuation methods, such as price-to-earnings (P/E) ratios and discounted cash flow (DCF) analysis for firms with negative earnings or a clear path to profitability.

Instead, assessments will have to turn on other more-subjective angles, such as business model “potential," traffic or user engagement, much as early internet-watchers had to rely on the number of unique visitors, the average time spent on site, and the click-through rate for advertisements. 

Click-through rates were important proxies for apps in the early days of the internet. And customer acquisition cost might be less important for AI firms than repeat usage (lifetime value). 

Model owners and suppliers of computing as-a-service will measure volume of transactions and data processed. 

But key metrics may evolve. In the early days of the internet, user engagement and traffic seemed more important. In 1995, for example, page views, unique visitors, time spent on site and click-through rate were arguably the most important.

With greater maturity and actual profits, standard metrics such as revenue growth, profitability, customer acquisition cost or customer lifetime value became relevant.

In its mature phase, active users and user engagement seem more important. So is loyalty, as often measured using the net promoter score. 

Internet, 1995	AI (2023)
Website Traffic: Measured the number of unique visitors to a website. Indicated the popularity and reach of the website and its potential to attract users and advertisers.	User Engagement: Measures the level of interaction and involvement of users with an AI-powered product or service. Indicated the effectiveness of the AI in providing value to users and its potential for long-term adoption.
Page Views: Measured the number of times a page on a website was viewed. Indicated the depth of user engagement and the potential for advertising revenue.	Data Volume and Processing: Measures the amount of data processed by an AI system. Indicated the system's ability to handle large amounts of data and its potential for generating insights and value.
Click-Through Rate (CTR): Measured the percentage of users who clicked on an advertisement on a website. Indicated the effectiveness of the advertisement in attracting user attention and driving clicks.	Accuracy and Precision: Measures the ability of an AI system to produce correct and consistent results. Indicated the system's reliability and its potential for generating valuable insights.
Unique Visitors: Measured the number of individual users who visited a website. Indicated the reach of the website and its potential to attract a diverse user base.	Revenue Growth: Measures the increase in revenue generated by an AI-powered product or service. Indicated the commercial viability of the AI and its ability to generate financial returns.
Time Spent on Site: Measured the average amount of time that users spent on a website. Indicated the level of user engagement and the potential for monetization through advertising or subscriptions.	Return on Investment (ROI): Measures the financial return generated by an AI investment. Indicated the value of the AI in driving business outcomes and profitability.
Customer Acquisition Costs (CAC): Measured the cost of acquiring new customers for an internet-based business. Indicated the efficiency of marketing and sales efforts and the potential for profitability.	Customer Lifetime Value (CLV): Measures the long-term financial value of a customer to an AI-powered business. Indicated the ability of the AI to retain and grow a customer base and generate recurring revenue.
Brand Awareness: Measured the recognition and perception of a company's brand among potential customers. Indicated the company's ability to attract and retain users in a competitive market.	Industry Recognition: Measures the recognition of an AI company's achievements and innovations within the AI industry. Indicated the company's reputation and potential for leadership in the field.

Presumably, AI firms will move through similar stages as they mature. Early on, analysts will try to quantify the value of intellectual property, data assets or the danger competitors represent. 

In some cases, volume of data processed, accuracy of AI models, or adoption of AI solutions by customers could be proxies for growth. Customer acquisition costs could be another metric when traditional metrics do not yet apply. 

Revenue mix or reliance on few or broadly-diversified customers might also matter. But key metrics will change as the AI industry matures. Then the more-traditional financial metrics will apply. 

Usage matters now, but engagement will matter later.

Social Media Can Drive 20% to 40% Higher Spending

A recent survey of more than 3,000 consumers by Bain & Company found that customers who engage with companies over social media spend 20 percent to 40 percent more money with those companies than other customers. They also demonstrate a deeper emotional commitment to the companies, granting them an average 33 points higher Net Promoter score, a common measure of customer loyalty.

Up to this point, a disproportionate share of those results appear to have been reaped the big early adopters.

The gap between the early adopters and those waiting to take the plunge has actually widened. While the average billion-dollar company spends $750,000 a year on social media, according to Bain & Company analysis, some early adopters such as Dell, Wal-Mart, Starbucks, JetBlue and American Express invest significantly more. In some instances, the investment is tens of millions of dollars.

Bain argues that social media can create value at virtually every stage of the sales funnel, from awareness to retention.

Putting social media to work

AT&T Sees Record Low Churn, With Bundles

AT&T now is seeing such low churn rates in its postpaid mobile business and especially with customers on bundles that the “customer life cycle” lengthens. Where accounts in the past might have remained in service for three years or so, AT&T now expects that account relationships could last  eight years, a level previously unheard of for consumer services (mobile, video, internet access). Churn at these levels is implying 100-month and more lives.

Basically, AT&T finds churn cut in half when customers bundle services including either their mobile phone service and gigabit internet access, and often both of those core products.

The company also believes it will see similar benefits for customers bundling video service with other products.

Half a decade ago, for example, mobile subscriber churn  was far higher than it is today. About 2010, monthly churn of two percent to three percent a month among some of the largest four U.S. mobile service providers was not unusual.

source: IP Carrier

source: IP Carrier

That has clear implications for the profitability of accounts, since the rule of thumb is that accounts with longer tenure are higher value, both because such accounts tend to spend more, and also because such accounts represent very low customer service costs.

Research done by Frederick Reichheld of Bain & Company (the inventor of the net promoter score) shows that increasing customer retention rates by five percent increases profits by 25 percent to 95 percent.

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