Data Driven Marketing: Using Big Data in your Digital Marketing Strategy

According to the Advanced Performance Institute, humans now generate the same amount of data every minute as they did between the dawn of humanity and the year 2000 combined. Accessing this data presents a huge opportunity for business to gain valuable insights regarding their target customers.

Before the digital age, the role of the marketer was to create captivating ads for print media, television and billboards, with a focus on appealing to the masses. Nowadays, marketers have a different kind of job – collecting data, analyzing trends and finding the right channels to reach out to their target clients within the masses.

Organisations large and small are leveraging data sources to enhance their digital marketing campaigns and drive engagement at an individual level. But how can this data be used in your business, and from where does it come?

Data collection techniques

The term “big data” has become a buzzword in the information technology sector, and basically refers to any set of data so large that it becomes difficult to process using traditional data processing applications. It is generally collected from multiple sources (mobile devices, satellites, cameras, microphones, websites, social media and more), and the information needs to be analysed and stored in order to gain insights of any value.

There are 6 key sources of big data for the purpose of digital marketing explained below as follows:

  1. Web mining: The application of data mining techniques to discover usage patterns from web data, such as browsing habits, server data and transactional information.
  2. Search information: Data available as a result of search history and intent behaviour, which may also identify individual users through their online ID.
  3. Social media: Personal preferences can be collected through clicks, likes, check-ins, shares and analysis of comments.
  4. Crowd sourcing: Data compiled through forums, surveys, polls and other user-generated behaviours, often compiled from multiple related sources.
  5. Transactional: Financial and logistical information collected when an organization conducts business, such as purchases, enquiries, financial transactions etc.
  6. Mobile: The fastest growing source of data, collected through apps and services running in the background, particularly with smart phones.

The problem with collecting big data is that you need a big data source, which is generally not readily available to small businesses and entrepreneurs. As a solution, a number of companies have developed systems solely dedicated to collecting data and distributing it for commercial use.

Data as a Service (DaaS)

DaaS providers offer specialised data assets collected through various sources to create a digitally addressable set of customers, enabling marketers to quickly identify prospects for highly targeted messaging. Not only does this afford business owners and marketers detailed insight into customer habits, it also enables them to identify and reduce the barriers to purchase.

For small businesses, DaaS can be a cost-effective solution to access customer insights at a relatively low cost, however the benefits will only be seen if the marketer has the knowledge to implement these insights at the right time. Rather than gathering the statistics of their thousands of Facebook followers, small business owners now have the ability to access data sets to rival even their biggest competitors. Through agile digital marketing strategies, small businesses have a significant opportunity to distract customers from their larger, less flexible competitors.

Implementation

Business owners are bombarded with data from various sources every day, but to use this data it must be organized and selected to address specific problems or queries. By analyzing customer data (or that of competitors), businesses can determine interesting patterns in customer habits and use these insights to develop targeted marketing campaigns.

As a recent example of successful use of big data, Target in the USA used big data obtained through data mining to predict the purchasing habits of customers going through a major life event (eg. pregnancy, marriage, divorce). Target identified approximately 25 products that, when analysed together, indicated a “predictive pregnancy score”. They then targeted high scoring customers with baby related products and saw a steep increase in sales of baby related products soon after the introduction of this targeted campaign.

Through big data analysis, marketers are able to determine the social influence of digital users. This information can then be used to target “influencers” in certain categories, expecting that their social clout upon followers will act as social proof to influence their buying habits. For example, Virgin America used social media data to identify 120 individuals with heavy social media followings and offered them a free flight on their new Toronto route. The individuals were not obligated to write about their experience, however the campaign resulted in a total of 4,600 tweets, 7.4 million impressions and free PR coverage in top news outlets, thereby creating a high awareness of the new route.

 

The advantage that small businesses have over their larger competitors when analyzing big data is their agility and ability to adapt quickly to changes in the market. It is important to remember that having access to digital data is less important than your ability to use it. In future articles we will look into further practical uses of data, big and small, so subscribe or follow us on LinkedIn to stay up to date.

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