web analytics

Big Data and Marketing: A Revolution on the Move

To know everything about customers’ tastes, preferences, behaviours, desires, reflexes or rejections, companies are always collecting more data. This data makes it possible to better target the e-mailing campaigns according to the tastes and colours of each one (marketing automation), to attract prospects then to retain customers and to personalize Web contents (personalization of the experience user).

In the United States, a new discipline has already emerged, the ” Sales Intelligence “, which allows to understand, in depth, all the steps of the process that finalizes a sale.

Behavioural study, heart and key of “Sales Intelligence”

For mass-market sites, “Sales Intelligence” collects the behavioural data of a prospect and/or a customer, and then analyzes this information to offer images, advertisements or services corresponding to his/her profile, the tastes and experience as a user or buyer.

The Big Data forward is also used for B2B research, when it comes to a big company or a retail giant to identify markets and seek new opportunities in the event of tension or withdrawal. According to the most recent statistics and studies, it is the collection of commercial information or the process of identifying customers who benefits most from Big Data, well before quality monitoring and shipment tracking.

Leads, leads, leads: new customer data or track signs

Internal or external, these mega-data are analyzed and cross-checked in order to provide the company or the e-commerce site with knowledge of the prospect or customer, refined and contextualized. This massive data is combined with the behavioural analysis that determines the different paths users take in their search for information or in their buying experience.

Here, the detection of lead is fundamental: the collected data make it possible to identify the level of maturation of a potential customer and its “lead score”, that is to say its percentage of potential purchase.

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We have thus moved from a static information sheet to a search for signs of a runway in real-time situation. This is the era of experience and behaviour. Predictive lead scoring thus makes it possible not only to determine the attractiveness of a product, the ergonomics of a site or the prestige of a brand, but also to identify major trends over the long term.

Previously, classic lead scoring was based on a list of criteria that best describes the ideal target. Today, predictive lead scoring is based on algorithms that model behaviours and determine trends, based on the observation of existing customers. Segmentation algorithms also categorize prospects (age, gender, social background, occupation, interests, cultural level, tastes and preferences, information channel, consumer habits, purchase history, geolocation, etc.). This is called Machine Learning, the Machine Learning system for Artificial Intelligence (AI).

Machine Learning will thus exploit the Big Data and raw Big Data for advertising optimization purposes, product recommendations or purchase suggestions.

Detect the prospect, draw the customer profile

Once the prospect becomes a customer, Predictive Big Data continues to be effective in increasing sales. How?

By building loyalty and determining the most effective contact. Is the customer looking to go to a place of sale, is customer in the habit of buying online, is the customer delivered at home or in relay points, is the customer sensitive to marketing by e-mail, at what times of the day or night does he usually buy?

Making contact via the most appropriate method and at the best time makes it possible to find the client in the best possible arrangements and thus make him more receptive. Predictive analytics also improves customer service because it is based on a refined behavioural study: we will analyze the opinions and needs of each other (What is particularly emphasized in a quality survey?) and we can anticipate problems most frequently encountered by customers.

What are they complaining about? a lack of information, a non-compliant product, a long delivery, an ineffective or disrespectful advisor, etc., and what do they like the most? the quality and conformity of the products, the quality/price ratio, the ergonomics of the site, the repayment facilities, etc.?

All this data will enable a company to greatly improve the quality of its after-sales service and to establish a relationship with the privileged customer and more and more personalized.

The unconscious purchase in a click: towards a disappearance of the free will of the buyer?

According to a 2016 SalesForce survey, tracking customer journey and customer satisfaction is now the top priority and the primary success criterion for 88% of the top performing marketing teams, far ahead of the acquisition of new customers and increased revenues.

Anticipating the desires of the buyer has always been at the heart of Amazon’s strategy. But with the power of segmented analysis of Machine Learning, the force of suggestion is even greater.

Jeff Bezos, Chief Executive Officer of Amazon implied this in his annual letter of April 12 to his shareholders. This is not only anticipating the desires of the user, but to ensure that it passes to the act of purchase as quickly as possible, in a kind of unconscious reflex or compulsive immediacy in a click.

Are we going towards a total dematerialization of the act of purchase with the disappearance of the interface?

Are we going towards an ever greater manipulation on the part of the seller, who will eventually sign the death of any free will on the part of the user?

In other words, still, are we heading for a sale and a purchase that does not say their name?

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