I recently co-hosted a one-day event with Professor Andrew Stephen at Oxford Saïd Business School. I’m lucky enough to be invited to a lot of events where I can learn from people much smarter than me, but this event was something special. The Future of Marketing brought together a brilliant and diverse range of speakers from across industry and academia. It isn’t every day that you get to hear and debate with speakers from eBay, HSBC, STC, Gjensidige, Celebrus, Columbia University and Oxford Saïd Business School. We plan to run more of these events in the near future — and if this one is anything to go by, you should definitely find a way to attend the next one!
Writing my speech for the opening remarks, I began thinking about how marketing has changed in the 20-odd years that I’ve been in the data game. In this short series of blogs, I thought I would share those thoughts with those of you that weren’t able to join us in Oxford.
Let me say straightaway that I am principally a technologist. I started my career in data and analytics in retail a little over two decades ago. Within four months I was working on a major loyalty analytics project for a top-four UK grocery retailer. I later delivered multiple customer analytics projects at another UK retailer — and since joining Teradata 13 years ago, I have worked on yet more customer analytics projects across multiple industries and geographies.
An “outside-in” view of marketing
So, whilst I have lots of relevant experience, the lens I bring to all of this is defined by the frame of reference of a technologist. I am a data guy, not a marketing exec — and what follows is an “outside-in” view of marketing.
In the last two decades, lots of buzzwords have come and gone in customer-centric marketing. We used to talk about “1-2-1 marketing” and “segmentation of one”, before speaking about a “single customer view”. Later, we were concerned with a “360-degree view of the customer”, then “analytical CRM”. Today, no presentation about digital marketing is complete without a reference to “omnichannel”. But what really changed through all those successive hype cycles?
I think that four things have changed — and one thing has remained the same.
Mo’ channels, mo’ problems
The first thing that is clearly different is the rise of new digital channels — specifically the internet and, more recently, social and mobile.
Social and mobile are already so ubiquitous that it is easy to forget that these channels are only a decade old. Facebook was founded in 2004 — but was only made accessible to the public in September 2006. The iPhone was exactly 10 years old on 29 June this year.
Today, social and mobile are an exponential phenomenon. That means measurements intended to quantify their impact are obsolete almost as soon as they are published. What we can say for certain is that roughly half the global population is already online; that recent growth has been fuelled by new users in developing nations like India and China, many of whom have never owned a PC or a laptop, but who do own a smartphone, and that social channels and platforms continue to show exponential growth, especially IM and chat platforms.
The direction of travel is clear: Consumers are spending more and more time online — and more and more of that time is spent accessing social platforms using mobile devices. If we want to meet our customers where they are — well increasingly they are on Facebook, Twitter, Snapchat, Weibo, and WeChat.
Data, data everywhere
The second thing that’s new, or at least different, is that many of these new channels come with data. A lot of data. Importantly, these data also describe interactions, rather than data that merely record the fact of a transaction.
What we might refer to as ‘big data’s first new wave’ was mostly about how the big e-commerce properties — Amazon, eBay, Rakuten, Yahoo Japan, Alibaba and the rest — were able to leverage clickstream data to understand customer behaviour. In this way, the big e-commerce properties were able to understand not merely what customers had purchased — the story that that traditional transaction data could anyway already tell us — but what they had browsed and not bought, what they had bought for themselves and what they had purchased as gifts for others, whether customer reviews had been important in their decision-making process, the “golden paths” through websites that led to conversion, the paths that led to abandonment, etc., etc., etc.
In this way, e-commerce platforms were able to understand not just what customers had purchased, but how and why those purchases had been made, or what at Teradata we often refer to as “the customer journey”. This enabled “mass customisation”. When you and I visit an e-commerce platform, we now often have very different experiences, partly because the web pages we are served are customised to reflect the behaviours and preferences expressed during previous visits — and partly because these sites are effectively run as a continuous series of A/B experiments.
Arguably the big e-commerce platforms don’t have one primary website anymore, rather they have several — and they are able to continuously measure what is most appealing to different segments and to react accordingly.
Big data’s second new wave
‘Big data’s second new wave’ was all about social analytics — and note that for us at Teradata, social media analytics is a subset of social analytics. By leveraging clickstream data, plus social data in some cases, organisations like Amazon, LinkedIn, and Netflix have been able to understand not just our individual preferences as expressed in our interactions with their websites, but also the preferences and likes of our peers, friends, and neighbours. Every time you accept an invitation to a new professional network, or buy a product based on a “people like you” type recommendation, you are part of big data’s second new wave.
Years ago, Accenture coined the phrase “scientific retailing”. Today, the websites of the big e-commerce properties really are vast labs. Is customer conversion rate improved if the colour and location on the web page of the “buy it now” button are changed? Does algorithm A produce better product recommendations than algorithm B? Which segments are least price-sensitive — and how much of a premium can these customers be charged? Do scarcity counters increase revenue per customer? For which product / customer segment combinations? Today, the major e-commerce players can ask-and-answer these questions continuously and in near real-time.
Big data’s third new wave
Big data’s third new wave is all about the internet of things (IoT). As Andrew pointed out during his presentation at the Future of Marketing event, IoT is enabling the digitisation of analogue experiences — think Disney’s smart wristband or indoor-location services.
A decade ago, the internet pure plays could out-compete their bricks-and-mortar rivals — in part because they had actionable interaction data, whilst the bricks-and-mortar crowd did not.
Today, online companies are investing in bricks-and-mortar (witness Amazon’s physical bookstores and acquisition of Whole Foods). At the same time, the bricks-and-mortar brigade have gone online (you can now buy a BMW from the comfort of your armchair, stepping into the dealership only to grab the keys to drive away in your pride-and-joy). The distinction between “online” and “offline” is fast disappearing, as everything is becoming digitised.
Will this erosion of the distinction between “online” and “offline” favour the established e-commerce platforms, with their long experience of mining behavioural data, or will it act as a great leveller, enabling the more established players to achieve the same understanding of customer behaviour as their newer rivals?
That’s all for now. In the next instalment, we’ll pick up where we left off by examining how consumer expectations have been reset by the digerati — and what the internet and social media mean for brand ownership.
Note: This is the first in a series of posts from Martin on the future of marketing.