The data rush: the 20th century’s colorless gold The data rush: the 20th century’s colorless gold

The data rush: the 20th century’s colorless gold

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on 28 May 2018

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If I were to tell you that the world market for data for marketing purposes in 2016 was 28.65 billion dollars(1) would you consider that huge? Well then, hold on to your seats because the estimated world market for data in 2021 will be greater than 67 billion dollars(2). Truly! Your data have never been more expensive than today. In terms of symbolic value, data have become the most valuable resource in today’s world.

In the middle of the 19th century, had you been American, you could have come across an old man, hat over his brows, hands damaged by the task, face weathered by the sun, searching for what was most precious at the time: gold! Today, such a chance meeting is highly unlikely and what we now consider “precious” is completely dematerialized.  I’m making reference to data. Ah, data yet again! With the GDPR taking effect and the Cambridge Analytica affair, users are becoming aware of their data’s impact and what it represents for brands. As you’ve observed, some large companies use data for unjustifiable and completely abusive ends, however, for the vast majority of businesses, good non-abusive use is made of data, and it affords you personalized shopping experiences without trespassing into your private life.

Did you say Big Data?

First, let’s talk about an expression that uses rough wording and which simultaneously means both a lot and very little. Big Data is a recent term born of a new issue: the volume of numeric data has literally exploded over these past years, and the treatment and storage of this data has led to new means of analyzing and apprehending the digital world.  Big Data allows us to group the mass of data that we exchange but also create every day, under the same term: GPS signals, videos, messages, files, information, published works.

The importance of data has multiplied since the rise of social networks: where before our presence on the web was limited to a few pseudonyms, passwords, and email addresses, we have begun to supply more data to the web giants (contacts, gender, address, workplace, and even more personal information). This large volume of data is worthless without analysis. The added value for brands, as for us, resides in its use and the ability to send the right content at the right time (to simplify).

A fascinating intrigue

You’ll have understood that the very interest in analyzing data is to make it profitable, and to reach a specific user, at a specific time, and quite often in a specific context also. In that situation, we refer to Fast Data! The principle of Fast Data is to use the data that was just generated and to use it before it loses value. Henceforth, we no longer wish to analyze data in batches, in a deferred manner, but to process the data in real time.

“Today, individuals buy the experience… not a product” to cite Shantanu Narayen, CEO Adobe Systems, during the Adobe Summit. You and I know, personalized buying experiences are one of the fundamental points in a digital marketing strategy for competitor differentiation. It is more reassuring to buy a product from a brand that best knows our tastes and preferences and suggests products and advises us.  “First impressions count” also applies to e-commerce! The relationship between the brand and the consumer starts with the first visit or the user’s first purchase on a brand’s site. The business’s first challenge is to create loyalty as a loyal customer is a customer who has the potential to recommend your offer to his or her entourage. It is less costly for a company to create customer loyalty than to spend its time recruiting new customers to then lose them. But back to data. That’s precisely where it comes into play, in the personalization of the buying pathway. Personalizing a customer’s experience with the brand will strengthen the relationship s/he maintains with the latter.

Advertising is the less pleasant flipside of using a consumer’s data but here I’m speaking of data with the aim to personalize and perfect an experience. The issue is to target a problem even before users have identified it. To under their needs, to offer them products or offers consistent with their expectations.

And what can we expect next?

In a few words: AI, automatization, data science, and predictive commerce!

AI and blockchains are 2 technologies that everyone’s been talking about since the beginning of the year but in our case, we’ll exclude the latter and focus on the use of AI in the field of data. However, if the subject of blockchains interests you, I leave you the article by our acquisition expert, Nicolas Francisoud, who will explain the interest of blockchains in advertising.

But let’s get back to our data. More and more tools are using AI to automate and optimize analysis. Certain tools such as Ascend are beginning to appear and with AI, they make it possible to industrialize certain practices such as AB testing in the present case. The tool will explore your audience on its own and conduct a multitude of tests in order to define which user segment will best respond to which version of a page or content.

Knowing your customer types is good. Knowing their feelings and their personality is better! Because even if demographic profiles offer buyer segmentation that’s always more precise, buyers don’t always behave the same way. Take Martin for example. Thirty years old, in a relationship, lives in the 14th arrondissement of Paris, and loves cooking and skateboarding. Even if he ticks off the same boxes (from a demographic and social point of view) as another person, he does not have the same expectations with regards to a brand. And in this specific case, we can cite  Optimizely X or the new Einstein function from Salesforce. Optimizely X, the personalization platform of the famous AB test, also has an exploration brick that enables identification of user segments on the site with similar behavior and so potentially similar reactions to the same stimuli!

This trend in tools towards artificial intelligence doesn’t stop there whether it’s Salesforce which recently launched its Einstein AI or yet again Google Analytics which has been testing its intelligent analysis for some time.

The use of data results in customers experiencing better buying pathways, and for brands that allows them to gain better customer knowledge. These inseparable components are levers in a digital marketing strategy which participates in building user loyalty. In the future will there be versions of sites that are fully personalized for consumers as a function of their profiles?

Thank you to Anthony for his help on these indications and article’s essential information!

Speaking of data and customer knowledge, our last CRM project perfectly illustrates the different points I explained. Our collaboration in a few words: centralization of customer data, implementation of new pathways, and creation of new digital support materials, nothing less!

(1) Figure from Mediapost Communication

(2) Figure from MarketsandMarkets

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The data rush: the 20th century’s colorless gold