The Internet has changed business in several ways. But marketing objectives have remained the same through the centuries. Get more customers, make them spend more and keep them for longer.
What has changed is the marketing opportunity that online provides that I define as the access to billions of potential customers; a completely interconnected audience and a flood of behavioural data available to improve decision making.
Put in context: twenty-five years ago businesses had a small addressable market, their customers were barely connected and there was very little data to hand.
In this fourth instalment of Teaching Google Via Analogies, we’re looking at the data opportunity. Why the hype? Which data? And how can it improve decision-making?
Once we’ve covered the theory, we’ll focus on some practical insights on how to use Google Analytics to capture and interpret behavioural data. The learning can, however, be applied to any other web analytics software.
As always we teach by taking everyday objects or concepts to make sense of the digital world. In doing so, we make the unfamiliar, familiar and the new, memorable.
In this two-part article, we explore data analytics through the prism of winning races and thinking like airports. The racing part will set the scene by highlighting examples of large companies that have been usurped by smaller ‘data-driven’ competitors. The airport analogy, covered in the next part, provides a useful prism to view your company’s data opportunity.
Understanding both will empower you to win your race(s).
Business is a set of races. Some are longer and more like marathons while others are sprints. The race to acquire customers, for instance, or to get funding, to expand geographically or to finish Research & Development and launch before a competitor, are examples of real life business races.
History is full of incumbent companies that grew fat and complacent and let smaller, nimbler competitors take their market share. We’ve highlighted four examples taken from the previous three decades that highlight how a company or organisation was able to leapfrog the dominant player by using data effectively.
Tesco vs. the rest (1990s)
In the early 1990s, Tesco had around 15% of UK market share. It was in the middle of resuscitating a failed loyalty program and hired a small husband-and-wife company called Dunnhumby to assist. Dunnhumby managed to convince Tesco’s board to go back to first principles and change the way they understood their customers using data. They argued that collecting and interpreting purchase information would provide a more useful picture than location data.
Dunnhumby collected and interpreted hundreds of thousands of data points on the food Tesco customers purchased. Did someone buy cheese? Did the same person also buy meat? By studying purchasing patterns, they found that the assumption that someone bought all their food in one place at one time was flawed. The data suggested that people were shopping around to complete their food basket. Very few people, for instance, who buy meat, do not buy bread. So it was inferred that if they were buying meat but not bread at Tesco they must have been going somewhere else to shop.
When Dunnhumby presented the findings to the board, the then chairman Lord MacLaurin, pronounced notoriously that Dunnhumby knew more about his customers after three months than he did after thirty years. Tesco then rolled out the Tesco Clubcard based on behavioural data that exploited these purchase gaps with targeted offers. If someone wasn’t buying a certain category of food, Tesco offered a discount on that category, redeemable at his or her next shop. This rebalanced shopping baskets and significantly increased the average revenue per Tesco customer.
The collection and interpretation of behavioural data was also a major driver in Tesco’s diversification into selling insurance, mobile phones and electronics at their stores and helped catapult them to become the dominant supermarket five years later.
Sony vs. Apple (2000s)
In early 2015, Sony announced that it had buried the Walkman. This was a far cry from the 1990s, when the Walkman ruled the streets as the de facto portable music player. Over the years, it was jazzed up cosmetically and transitioned from playing just cassettes to CDs and then to MP3s. But Sony moved very slowly and did not adapt to the online environment that had transformed the way music was purchased and consumed. This intransigence was Apple’s opportunity.
In 2001 Apple launched the iPod. It was a beautiful and usable portable media player that could store thousands of music tracks. iTunes was launched in tandem as the media library that would sit on a desktop and manage the iPod’s inventory. The iPod was built from the bottom up for the online generation and exploited the transformations that the music industry was experiencing. Apple analysed the data that showed an exponential rise in digital media downloads and file sharing. Steve Jobs saw the fragmentation of albums into single track downloads and built a sleek product for this new consumer in a connected world, packaged and marketed for both music fans and artists.
As Time magazine puts it, ‘…the iPod wasn’t the first device of its kind. But like many examples of Jobs’ legacy, it was the product that did it best; it was the first to show listeners that a piece of technology could be more than useful; it could be cool, and eventually impossible to imagine living without. And it was the first to convince musicians that the same fans that had stolen their creative output would be the ones they should reach out to, and iTunes offered that opportunity in its most direct and personal form.’
Over the years, Apple improved both the iPod and iTunes and then integrated them into the iPhone and iPad with the rise of mobile connectivity. It is now investing heavily in on demand music streaming to keep up with services like Spotify – by acquiring Beats and recruiting telepathic radio DJ Zane Lowe – but still enjoys a dominant position in online, portable media.
While the music has stopped for Sony, Apple’s is still very much in crescendo.
Oakland Athletic vs. the rest (2000s)
Sport is increasingly big business. That’s why most large professional sports teams are now run like corporations, with org charts to rival bluechip conglomerates.
To make money, a team needs to do well on the pitch/field/court. This is because sporting success brings more fans, more merchandise and more broadcasting revenue. English Premier League is a case in point.The gap between the teams in the Premier League and Championship (the league below) is vast and described by the Wall Street Journal as ‘soccer’s wealth gap’.
But to achieve sporting success, a team needs the right players. This is where data comes into play. Teams are now collecting and interpreting behavioural data on an industrial scale to determine recruitment. Data enables a team to collect a holistic view of any prospective player to assess his or her viability to the team. Data is democratising as it enables teams to unearth players that are statistically strong but undervalued by the market. In other words, it gets great players at great prices.
In the film Moneyball, Brad Pitt plays Billy Beane, the coach of Oakland Athletic baseball team. It tells the story of how he, alongside a young Yale econometrist, pioneered the use of player performance statistics in player recruitment. Instead of relying on the subjective and often corrupt opinion of scouts, Oakland based all their hiring decisions on statistical analyses. Data suggested that ‘on-base percentage’ and ‘slugging percentage’ were better indicators of offensive success, and these qualities, among others, were cheaper to acquire than speeds and contact that were ‘priced in’ to the market.
Despite their meagre playing budget, Billy Beane took the Athletics to the playoffs in four consecutive years from 2000 through 2003, losing in the American League Division Series each year. In 2002, the Athletics became the first team in the 100 plus years to win 20 consecutive games.
The impact of statistics on sport recruitment has even changed the managerial structures of English football clubs. Rather than employing a manager to recruit and train the players, many football clubs opt instead for a Head Coach who is solely responsible for training, and a Director of Operations to oversee recruitment based on a growing army of statisticians.
Uber and taxis (2015)
At the time of writing, Uber – the smartphone taxi app – is valued at more than $18 billion and has changed the way people travel within cities. It has ambitions to move into courier services and expand to every city in the world. Based on data-driven decisions and smartphone connectivity, it is displacing traditional licenced cabs, as its service is cheaper, more transparent and convenient to use. Its ultimate aim, according to founder Travis Kalanick, is to replace private cars so that people no longer need to own their own transport.
Licenced cabs have been kings of the city for one hundred years. Indeed, London and New York cityscapes are incomplete without an image of either a black or yellow cab. But the ubiquity of the internet, smartphone connectivity and sophisticated data decision-making, mean that consumers can now get an estimate of cost and time for travel, understand the driver and the vehicle, hail the cab and watch it meander towards them in real-time, travel from A to B, pay and rate the driver, all within two clicks on their smartphone.
“The Knowledge” in the traditional cerebral ‘A to Z’ sense is redundant and has been replaced by ‘The Knowledge” of data and sophisticated algorithms that match consumers with the right cab, at the right time at the right price, democratising inner city travel for the masses.
How does this all apply to my business?
Now that we’ve established the power of data on business outcomes, the next part of this article will cover what it means for your business and how you successfully collect and interpret your data. We will look at how to do this through Google Analytics and use an airport as our analogy. It sounds strange but it’s a great way to structure one’s thinking around which data to collect and how to interpret it to make better marketing decisions.
This is an extract from my forthcoming book How Google Products Work.