The Importance of Big Data in Your Content Marketing Strategy
Among the options a brand can choose when looking to reach its customers while also pay attention to trends, content is one of the most relevant and useful techniques in 2019.
As described by the PR agency GoodNoon, through diverse content presented under different formats and distributed through various platforms or media, brands make sure that the public appreciates the attributes of their offer. In such regard, companies gain trust, preference, and interest, something that puts it above those competitors who haven’t implemented a specific content plan.
To date, using content marketing has gone from being a trend to a pillar in every strategy or marketing plan in companies of goods and services from various parts of the world.
According to Forbes, only in the US, in 2018 53%% of the companies use this type of marketing, with a lot of them saying it had a positive impact on their market and promotion efforts. That’s why today we are going to tackle one of the essential components in content creation, the Big Data, and what’s its role amid the marketing trends.
Big Data and content as a must
When a brand decides to create and distribute content, starting from the tactics designed by its own team or in conjunction with an expert / specialized agency, the Big Data element cannot be obliterated, since much of the success of BTL marketing actions depend on it. Why?
Big Data refers to the formation of large data collections that brands obtain from different media, to find patterns that allow them to discover consumption behaviors, preferences, and habits. That is a potential knowledge that comes from listening to customers, understanding what their needs, what they respond to, what they ignore, and find out what needs to be done as a “salesperson.”
So, what makes Big Data a key element, when creating contents of great value for the consumer? Think of it in the following way; through Big Data, companies can:
Segment prospects intelligently.
Customize each interaction and the entire journey of the client.
Optimize the marketing budget and maximize its impact.
But first, to implement Big Data a series of elements and steps are necessary, among which we find:
Analyze audience interests. Tools like Google Analytics facilitate the monitoring of user behaviour on the company’s channels (sites), determining those that are viewed the most:
Define the total number of visits, as well as the time a user spends reading a specific content, the type of device utilized, time of the day users saw or shared the content. All of this among other data, to correctly program the editorial calendar that guides the creation and distribution of each publication.
Analyze what, when and where they consume the contents.
According to figures collected in 2017, the global market value of Big Data was US $ 13.5 billion; while for 2018 it increased to reach 18,200 million dollars.
What are the challenges faced Big data marketing face?
Following the previous section, the data that is being collected from customers is propagated from a wide variety of sources. The challenge lies in obtaining clean, complete and reliable customer data and associating it with accurate profiles.
If such procedure is already complicated in terms of big data, it’s even more so when it comes to multiple sources, with different names, email addresses, and devices, and is riddled with incomplete forms, significant data gaps, duplicates, and other quality problems.
Many times, the companies end up accepting a fragmented vision of the client due to their inability to overcome this challenge. It’s the price they pay to do marketing. However, in reality, they should avoid this conformism since solving the problem is within their reach.
The big break for Big Data
As can be seen, the data most companies work with are the same as those used by their competitors. Big data marketing will make a difference, between leaders and laggards, based on their ability to take advantage of the value of that information.
Thus, the competitive advantage will be consolidated in organizations that are able to carry out an optimized marketing data management, something that has to do with collecting, cleaning and validating, enriching, using and administering in this way:
Collect. Incorporate all the data into a data lake, for example, a Hadoop cluster, hosted in virtual machines in the data center itself or through web services. Start up a registry marketing automation system, which will help create programs, feed flows, record pages. Then, capture the response data and load all that activity into the data lake, without wasting even a bit of information.
Cure and confirm. Consolidating data from several sources means that there will be a lot data duplication and possible conflicts with small variations in names, among other quality problems. Master Data Management (MDM) is the best ally, as it is an automated process guided by the rules of business matching. Thus, if the system sees two records for the same individual, it will automatically collapse them together, as long as the confidence level is above the established threshold. If you aren’t sure, you will throw the exception to a data manager that can decide. In addition to this, it is essential to clean up the data. It can never be assumed that the data collected is correct and usable: people make mistakes when entering their addresses, give incorrect phone numbers and email addresses, put the state and the postal code in a field … and the result can lead to disaster. Once again, we must use the right tools to correct this kind of errors, a common thing in Big data marketing.
Improve. With the help of partners and suppliers, the data is improved with additional information. This process is quite simple: it is loaded, compared with existing records, information is combined, and sets are imported using a data integration platform. Then it is convenient to validate and clean the data once again, to ensure its quality.
Use. Deploying data-driven marketing programs targeted at specific segments is a crucial step. Segmentation can be based on the interest of the product, which helps to aim more accurately and increase commitment.
Supervise. If you only clean and care for the data once, that strategic asset will depreciate quickly. To avoid this impairment, evaluate the state of the data, act accordingly and define a clear set of policies supported by good communication and training so that all those who interact with data know the rules and understand its relevance.
In summon, Big data marketing is a source of opportunities for companies that seek to increase their income and their customer base, but if the data is not clean, nor complete and reliable, management and information will take the business in the wrong direction.
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