In 2006, Avinash Kaushik, coined the term the 10 / 90 Rule for Magnificent Web Analytics Success.
The reasoning behind this rule was based on several studies that highlighted that while most large companies have made substantial investments in analytics software, they continue to struggle to make any meaningful business decisions based on the data.
If this is the first time you are hearing about the 10 / 90 Rule, let’s summarize it first before we move on:
In short, the 10 / 90 Rule says that for every $10 spent on analytics tools and professional services, $90 is required to invest in intelligent resources/analysts a.k.a smart people.
We are quickly approaching 20 years from the date when Avinash introduced the 10 / 90 Rule and we are not any closer to reaching that benchmark of the investment we should be making in smart people. In fact, with the explosion of new marketing and analytics technologies on the market, it would be fair to say that, in general, we are spending even more on software, in comparison to people, than we did back in 2006.
Why is this happening?
Perhaps analytics software has become so sophisticated that we no longer need humans to architect how data is collected or how that data is analyzed. The software has matured so much in the last 20 years that it does the job of data collection, data hygiene, data analysis, for us, all we have to do is click a button. BOOM…THAT WAS EASY!
Perhaps we as analytics professionals are the problem. Have we somehow failed to be remarkable? Have we failed to deliver on the promises that “data was the new oil?” Have we got so caught up in the technology wars that we have failed to recognize that our primary goal is to use data to better understand how business works and how consumers buy?
So why is it more important than ever to invest in really smart data people?
Let's go back to the early months of 2020. The World Health Organization announces that an outbreak in Wuhan, China is threatening to create a pandemic that has since become know as the COVID-19 Pandemic.
In February 2020, during a cruise of the Western Pacific, a massive COVID outbreak occurred on the Dimond Princess, a luxury cruise ship operated by Princess Cruises. The ship quarantined off the coast of Japan for 2 weeks before all the passengers and crew could be evacuated. Of the 3,711 people on board the ship, 712 became infected and as many as 14 may had died from the virus.
The Dimond Princess incident not only sent shockwaves through the travel and tourism industry but throughout the entire business world. Companies panicked and began cutting costs wherever they could, including wide scale RIFs that hit digital analytics teams very hard.
Why were these digital analytics teams among the very first teams to be let go? Does it say anything about the perceived value we are delivering for the company? Did this mean that our teams were not critical to the operation of the business?
And, is the answer to all these questions a bit of a Catch-22 in which we have been unable to deliver to the level of expectations because we have historically been so under funded from a people standpoint?
Analytics teams are REALLY frustrated.
Not only were analytics teams on the frontlines of layoffs, the teams remaining (whom were already woefully short of people) now where being asked to do even more work, with smaller teams.
These smaller teams, with smaller budgets, are taking over analytics practices that are dated, falling apart, and buried in mountains of technical debt. They are being asked to DO more and more, with less and less. They are overworked, burned-out, and producing less insights into consumer behavior than ever before.
And to make things worse, these small analytics teams are being bombarded by other teams and external agencies that are themselves worried about their futures, so they are putting high pressure tactics on the analytics teams to produce results that will make them, the other teams and external agencies, look good.
Analytics teams are finding themselves at the mercy of digital agencies pushing to deploy more 3rd party marketing pixels, but why? Did we ever slow down and ask what good we were creating with all this code and data we were collecting at the behest of these agencies? Several companies we have worked with have gone so far as hire an FTE that all they do is deploy and manage 3rd party marketing pixels at the direction of marketing agencies. Gross.
These teams, often 2-3 people at the most, are being asked to maintain robust anlatyics practices for multibillion dollar businesses, that often span multiple websites, native mobile applications, and agency led microsites. We are asking these understaffed teams to be:
Solution Architects
Technical Implementors
Mangers with HR responsibilities but no budget
Project Managers
Relationship Managers
Web Analysts
Mobile App Analysts
Product Analysts
Optimization Strategists
Front-End Developers
Graphic Designers
Emergency Website Hot-fixers
Is it any wonder these analytics teams are frustrated?
😰They are overworked, often putting in 10+ hour days
😰 They are dissatisfied as their work isn't tied to business outcomes, which means they are the first to go when times get tough
😰 They find it difficult to make any real business impact as they are constantly left out of important conversations
😰 When things do go right, the analytics team is forgotten about, it’s always someone else’s win
😰 When things go wrong with the website, of course it's the analytics/optimization team's fault
So what is the end result?
People, at the least the good ones, get tired of working in such a toxic, unhealthy environment, so they quit. Companies, many of which do not now how to identify and hire quality analytics talent, hire someone that uses the right words on their LinkedIn profile yet often times are unqualified to lead. Sure, they may be useful on a 10 person team, with strong leadership, but being asked to run an entire practice on their own, it's a recipe for disaster. They erode an already delicate analytics program, the business loses even more trust in the program's ability, the business stops using the data (we've heard on more than one occasion where concern was expressed about the quality of the data, "that's ok, we aren't really using the data anyway, at least not for critical business decisions."), and it creates this vicious cycle.
Does this sound familiar to you?
Do we have any chance to break this ugly cycle?
It's a bit of a chicken-n-egg scenario, where the analytics teams needs more investment to hire and staff a proper team but the business is skeptical, based on years of poor data and poor performance from under staffed analytics teams, so they don't want to invest more in the program. Well, they are ok buying more software but they are hesitant to hire more people.
This means it's up to the analytics teams to figure out, in a very precarious situation, how they can become more:
1. Remarkable
2. Resilient
3. Memerable
4. Insightful
5. Entertaining
It's only after teams have put in the work to rebuild trust, trust in the team, trust in the data, that they will be in a proper position to ask for more money, to build out a proper analytics team, to finally start producing the promises that have been given for more than a decade.
Once that trust is rebuilt, once we build proper teams, then the pressure is on us to maintain them, otherwise, after all this hard work to clean up decades of poor performance, will be destroyed.
What do we need to do to properly maintain our analytics ecosystems? What do we need to do to think more sustainably about our data solutions?
1. Stop focusing on buying more and more technology, instead focus on maximizing the tools you already have.
2. Invest more in people, really smart people, than in tools.
3. Set proper expectations, stop being blown off balance by slick talking SaaS vendors, and start setting a proactive approach for how your analytics team will operate.
4. Prioritize documentation and routine maintenance.
5. SLOW DOWN.
6. Do "less work" but create "more value"
This is a systematic problem that effects almost every business on the planet and because the problem is so wide spread, it isn't something that we can just throw more software at to solve, it's not something that we can solve with a weekend leadership retreat. The only way we solve this is by making a commitment to do something really difficult, something that will take time, something that will test your resolve, something that will require you to be a leader rather than an order taker, but if we can solve this, we will all benefit. Not just all of us as analysts but all of us as businesses, as employees, as partners, as agencies, and as customers.
We may want this to be solved by the businesses we work for pouring more money into people but we have to face reality, that isn't going to happen, at least not until we prove that we are worthy of that investment.
Let’s go to work!
