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How New Developments in Big Data Show Is Where B2B Marketers Are Headed Next

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09-Dec-2016 12:00 AM

 
By Rick Riddle
Category: Analysis
How New Developments in Big Data Show Is Where B2B Marketers Are Headed Next

Most people merely dismiss Big Data as a buzzword. However, the truth is that improved B2B marketing is increasingly becoming available. Better B2B data comes in the form of increased volume of an already existing kind of data as well as in the new kind of data. Intent signal is the latest and most interesting way of B2B marketing data. This is the ability to keep track of what people are doing while on the web, registering for webinars, downloading white papers and other similar activities as they carry on their work.

B2B marketers are now tasked with monitoring visitors to their sites. They simply monitor the activities by visitors across the internet. The purpose of all this is to pinpoint just when people in a particular organization are doing an online research, which is followed by a purchase, which of the implementations is taking the course and using this particular information to improve on the existing marketing campaign.

Predictive Vs. Fact Based Models

Each online action which can be monitored ought to be helpful in the evaluation as well as purchase intentions of people and the companies which they work for. The information concerning the latest intent signals fits quite well the digital body language model and hugely continues to appeal to B2B marketers.

·         Predictive analytics is the approach that combines intent data and sophisticated statistical modeling to enable organizations to prioritize their marketing to potential customers. The marketers who would like to take full advantage of predictive analytics should provide the necessary information to predictive analytics vendor. The vendor will build a custom statistical model with the use of marketing information, financial data, CRM and other data. As time goes by, the predictive model will gradually become accurate in that it will be able to identify the organizations that have increased action on the website that closely seems similar to the marketer’s present customers.

·      Fact base approach entirely concentrates on intent signals and has no statistical modeling component. While on the web, a person’s activity is in a position to be tracked, aggregated and offered to marketing. Fact-based intent signals have a great benefit in that it informs B2B marketers when a company they are prospecting on carries out the online research which marks the beginning of the purchase process. In addition to that, the records regarding the contract are provided for the company, thus getting rid of the huge data and modeling initiative which is in a predictive intent signal model.

Prioritizing High-Quality Leads

What is the importance of prioritizing leads while using fact-based intent signals as compared to predictive approach? The fact-based approach does not need traditional lead scoring as the prospect customers are in the start-up stages of the buying process. If marketers choose to avoid this process, they will save money and marketing time. Marketers can use the content records to automatically trigger custom made content to individuals via integrations with marketing automation solutions. The fact-based approach provides huge breadth and timeless because campaigns are started immediately and is on a continuous basis.

 Predictive analytics needs the prioritization of leads. Although this model adds a layer of prioritization, it does not show which prospective customers are in the purchasing cycle, as is the case with fact based intent signals approach.

Calculating Actual Marketing ROI

Back in the days, it was tricky for marketers to measure the rate of return of their efforts. An organization is able to make more informed decisions when it applies layered data to its marketing decisions. The decisions made are backed up by intelligence that comes from understanding big data as well as its application.

Making insightful and well-thought over decisions can have great financial implications and can as well be the difference between using money in a case where there is little or none at all to be made and targeted lower costs or higher return hotspots.

Identifying Viable Opportunities

B2B marketers have started to use big data to check out on what is happening in real time. Organizations are able to attain their objectives faster when they take out less probable conversion opportunities found in their mails and welcoming the prospects who have a strong interest in what they provide.

According to recent research, more than 66% of organizations do not have a revenue and industry data for their already existing customers. This is a huge percentage given the weight that this data has on the likelihood of prospects to convert. The access to distinct B2B marketing data is definitely a competitive edge.

Reducing Barriers to Entry

There have been barriers impeding the entry of B2Bs with major struggles. But thanks to big data all these are changing. New and fresh models are offering access to some advantages of big data without the need for the difficult algorithms and extensive data mining which has been a requirement for older models. Middle sized organizations can now make themselves available to the best information. The fresh and new ability to get into new markets easily is what Bid Data as a Service (BDaaS) organizations are delivering. Rather than a load of probabilities, we see a real-time view of people, based on factual and descriptive actions.

To wind up all that has been discussed, we can clearly say that the most exciting part about big data evolution is how it continuously changes with us. Online writing services that heavily rely on content marketing will take you through all you should know about new developments in the big data show. As the level of our knowledge and understanding becomes better, we continue to take advantage of the new types of data to improve our capabilities and the experiences of our customers. 

 

About the author: Rick Riddle is a marketing consultant, content manager at Smart Paper Help and an up-and-coming blogger from New York. His current interests include big data mining, machine learning, computer vision, pattern recognition and digital marketing. Follow @rickrddl on twitter and reach him out on LinkedIn to keep up with his latest publications.

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