Data-driven marketing: from big data to smart data

In the past, large data volumes weren’t just a challenge for marketers to deal with. While data mining made it possible to process older data, it required manual analysis and was only able to offer limited insights on the gathered in­form­a­tion. Recent technical de­vel­op­ments, however, have made it possible to process data at in­creas­ingly faster rates, so auto­mat­ic­ally analysing data is no longer a problem in the age of Big Data. In addition to these trends, more and more consumers are coming into contact with a growing number of digital contact points (i.e. Like buttons, tweets, etc.), which produce even larger sums of data and contain valuable in­form­a­tion. Data-driven online marketing exploits these de­vel­op­ments, allowing data to be in­ter­preted within different contexts; it also helps marketers recognise their chances among potential customers or client bases and find out how cor­res­pond­ing marketing efforts should be adjusted to reach these in­di­vidu­als. 

Digital trans­form­a­tion has helped create a world in which users leave behind trail of in­form­a­tion wherever they go. Companies are able to gather this in­form­a­tion and use it for them­selves. Following this, it’s easy to un­der­stand why more and more marketers are beginning to refer to data as the new currency in the digital age. Col­lect­ing customer data—Big Data—is also a fun­da­ment­al aspect of data-driven marketing. Some of the most relevant data includes:

• Demo­graph­ic data: general in­form­a­tion on groups of people, including: age, sex, place of residence, socio-economic in­dic­at­ors (career, marital status, income). These points help create a fuller picture of the target group.
  
  
• Behaviour-related data: originate from web analyses and are released in the form of KPIs (key per­form­ance in­dic­at­ors), like user paths, bounce rates, average length of time spent on different sites, etc.
  
  
• Qual­it­at­ive customer state­ments: these include voluntary customer in­form­a­tion that have been gathered through various methods, like telephone surveys or online ques­tion­naires. 

Data analyses make up the core of data-driven online marketing. These help make sense of enormous stacks of data and help recognise patterns, such as a user’s click behaviour. Data analyses help fa­cil­it­ate the use of different data models and al­gorithms, giving structure to the data and re­cog­nising cor­rel­a­tion.

Analyses help marketers make pre­dic­tions on the future pur­chas­ing behaviuor of users based on their current search behaviour. Doing this helps create a clear advantage, as correctly using data allows companies and busi­nesses to better un­der­stand their customers. Knowing the needs, wishes, and ex­pect­a­tions of customers also generates better-matched products and services. The struc­tured gathering, eval­u­ation, and in­ter­pret­a­tion of data is crucial for a business to succeed and reach out to its customers.

The whole process depends on solid planning and co­oper­a­tion between data sci­ent­ists, who extract relevant in­form­a­tion from available data with the help of analysis tools, and the campaign’s cor­res­pond­ing marketing team. Together, these two groups have to answer relevant questions, such as:

• What’s the basis for this in­form­a­tion? Which data has been provided for the project?
• Which re­la­tion­ships are we looking for? And which analyses do we need to run in order to find these?
• What value do the potential results from this in­form­a­tion offer the company?
• What type of workload is involved?

The shared tasks between the data sci­ent­ists and the marketing team is to evaluate the flood of incoming data and to visualise facts gathered from this in­form­a­tion in a user-friendly way; it’s important to make sure that the most important details remain present in these de­pic­tions.

The main goal of data-driven marketing is to better un­der­stand customer be­ha­vi­ouir and to remain informed of all current events. Trends, short or long-term changes in pur­chas­ing behaviour, or a general change in a brand’s per­cep­tion can be monitored with the help of this marketing method. Those who are able to make quick use of this in­form­a­tion and change their ap­proaches do more than just increase customer loyalty; ul­ti­mately they also increase turnover. By sifting through raw data and filtering out the latest trends and specific plans for action, marketing teams are able to spare them­selves a con­sid­er­able amount of work.

Many marketers are familiar with losing potential customers that may have shown interest in the past, for example by visiting a site or even filling up a shopping cart, but fail to return. But which inactive customers can be won back? Analysing contact points helps marketers obtain in­form­a­tion about their company’s re­la­tion­ship to their customers. When properly timed, contact points that have been neglected for longer periods of time can be re­act­iv­ated by im­ple­ment­ing measures to per­son­ally address lost customers, po­ten­tially re­kind­ling the customer re­la­tion­ship in the process.