While GAFA excels in the exploitation of the data, it is in no way reserved to them. All companies must now work on this, especially their marketing and sales departments.
Without the deployment of a big data and predictive strategy, they will not be able to perform in terms of lead identification, opportunities, customization of offers, anticipation of consumption, churn or optimization of their campaigns …
If 90% of French companies are conscious of the importance of data to produce better, faster, and in an optimal manner, only 48% have used Big Data and 9% have used Predictive Tools.
However Business Teams and notably Marketing Teams call for a stronger integration of these technologies in the process of identifying leads, spotting opportunities, customization of offers, anticipation of consumption, churn, optimizing cross or upselling.
So if these tools are long-awaited by business teams, why are they so little deployed within companies (intermediary-sized companies, small and medium sized companies).
The reason is simple: All these businesses consider that the deployment is reserved for GAFA and market index companies. The don’t dare to take the leap, and esteem that they don't have the skills and knowledge or financial capacity. But this is a mistake.
Marketing 3.0 is today the scope of all structures. For this to happen companies need to put themselves in a data-driven configuration. Data-driven means to organize and equip themselves tools to collect, centralize and exploit data.
Companies have to become data-driven The first step in the implementation of a data-driven strategy consists of retrieving data issued from numerous internal sources (internet sites, mobile, social networks, databases, tables,excel, ERP, CRM) and external stakeholders of the company (open data, partners, suppliers, studies and polling etc) and to break all the silos to put into place an inclusive and multidisciplinary organization significant to the data.
Although being in marketing, sales, IT, digital, if they are data scientists, data analysts, or various business areas, all profiles have to collaborate together.
The data scientists and data analysts enrich the data thanks to algorithms, their trades bring out useful information to their missions.
Thanks to Big Data, machine learning and AI, data professionals can analyze the behavioral signals left by consumers, elements enabling businesses to deploy different marketing and commercial strategies.
Thus, data collected on the problems of consumers exposed to customer service, the typology of customers, searches for information on products or services on the website associated with data from outside the company (offers competitors, behavioral studies, seasonality, weather, etc.) provide the business with elements enabling them to anticipate a churn, or to target potential customers with adapted offers.
Exploitation of the data by all
If at first sight, mid-cap companies and SMEs can be intimidated by all the upheaval imposed by a data driven strategy, they should be reassured. Today, preconfigured data science tools allow businesses to work on large volumes of data from various sources, and to deduce information that can be used in their missions.
Simple to deploy and use, these tools allow them to become familiar with the data and to identify use cases, then to industrialize and operationalize business processes thanks to AI enterprise platforms.
Considered until recently as a discipline reserved for experts, data science is democratized with the arrival of structured and unstructured data centralization platforms integrating machine learning and AI algorithms.
Today, all businesses can become data driven as soon as they drop the silos and encourage each employee to share and use the data.
Like Mr Jourdain and his prose, employees can now do analytics without knowing it!
Article Taken from Co-marketing News