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Customer Journey

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Until relatively recently, the information available about a consumer or client depended, in most cases, on the analysis of structured declarative data (surveys, consumer interviews, a loyalty program, or service quality, etc. .) and a non-declarative part that came to tell us what the client had not stated and that generated a first wave of what was called Business Intelligence, where structured data was worked from databases created on telecommunications or energy consumption, contracting insurance, banking, etc. This last part has taken a radical turn in recent years thanks to Big Data and elements such as the mobile phone, credit card purchases, social networks, web browsing itself or geolocation. Now we know about the real consumer what we call the 'customer journey', all his movements during the phases of the relational model between a market, a brand and the consumer himself, any 'touchpoint' along the way, from purchasing influencing elements, to the consumer itself. purchase and future prescription of the product or service. Leave a mark with every click, with every action, with every application. All this, multiplied by millions, and always treated in an aggregated manner, gives us a much more accurate portrait than any other information available, this is real, tangible and strategic. In 24 hours, 2.3 trillion gigabytes of information are generated on the Internet; We live in the era of 'Think fast'.

But if we leave the scope of the aggregate, brands like Amazon are already acting predictively on specific consumers, offering them offers based on their profile, their consumption habits, their searches... The growing trend is to reach the recipient in a individually, through the ideal channel to reach it and with the most relevant and useful message. One-to-one marketing with specific and differentiated messages not only for each audience, but for each specific member of the public. The essence of micromarketing brought to a palpable reality thanks to technology.

The huge repercussions of this have not yet reached the company. Recently, a study indicated that only 7.2% of the main companies with powerful marketing and communication departments acknowledge using Big Data technology in their processes, but if you asked them how many of them were going to incorporate this technology in the next and immediate years, 47% answered affirmatively. This is not a new religion or a new invention, behind Big Data there is an irrefutable scientific consistency and it is possible that the 47% forecast falls short.
From all this, I would like to highlight the following useful conclusions both for those who do political or corporate communication and for those who focus on marketing:

1. We have to generate engagement and with all the information we have, we can do it. Today, data (and the five Vs it carries: volume, variety of sources, speed of processing, veracity and value, especially that generated by that data) can be used as a tool to help achieve strategic objectives.

2. In addition to doing descriptive and predictive analysis of behaviors, we must think about doing prescriptive work with the data. Influence decisions (purchase, voting) that help companies achieve their objectives.

3. Recruiting the necessary talent will force us to modify the percentage of technical profiles in the marketing and communication departments (mathematicians, programmers, physicists, etc.). If today the proportion is 39% technical profiles and 61% creative profiles, in In the future the tendency will be to balance in a 69% technical profiles, and a 31% creative profiles. It won't be a quick process, but it will eventually happen, and this makes data technicians the profession of the future. Preparing these profiles is difficult and the educational system should also adapt to these emerging capabilities.

·    4. Profiles will change at all levels in companies. The Chief Data Officer will appear at the management level. The data scientist will be at a slightly lower level and his or her job will be to extract knowledge from the information. The data engineer (Data Engineer), who must safeguard the databases, make them accessible, design architecture, processing, etc. The pure data analyst (Data Analyst) and, finally, the Data Artist, who will be the one who tells how to tell that to make it visible and understandable.

In the coming years, it is foreseeable that we will experience a certain price inflation in the coming years with respect to these professionals, as happened with dotcoms in the year 2000, but everything will be adjusted and after exponential growth there will be 10 or 15 players who survive a future initial decline once this early adoption phase is over.

Jordi Crespo
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