How to Generate Big Data Revenue Without the Big Investment in a Team of Data Scientists

July 28, 2015
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data science

data science

How to Generate Big Data Revenue Without the Big Investment in a Team of Data Scientists

If you are like most companies, you know you need to get started with Big Data – and sooner than later. Companies today are more customer-focused, are trying to outpace the competition, and are seeking new ways to grow revenue. These objectives are all fueling the need for Big Data insights.

In a February 2015 study conducted by Vanson Bourne for CA Technologies, improving the customer experience (60%) and the need to get new customers (54%) were the leading factors driving the need for big data projects.

Other factors included:

  • Increasing top-line revenue growth (46%)
  • Entering new markets (42%)
  • Keeping up with the competition (41%)
  • and outpacing competitors (34%)

However, lack of trained data professionals and the need for better infrastructure were cited as the biggest barriers to actually implementing Big Data projects.

According to another study by CA Technologies, companies are making the investments to overcome these challenges. Respondents cited training existing resources on big data technologies (57%), new infrastructure (49%), and hiring new resources with the required skills (47%) as the top three objectives.

Major Investment Areas Required for Big Data Projects According to IT Managers Worldwide, Feb 2015 (% of respondents)

However, let’s put this into a bit of perspective. The resources required to hire new talent can quickly add up to a hefty price tag. The median salary for a data scientist is $80,000 – $150,000/yr. And this is only if you are planning to hire one. The biggest and most intensive drain on resources lay in the actual implementation – new infrastructure, new technologies, and a lengthy project timeline to get your Big Data initiatives up and running.

big data, data science

However, what if there was a more efficient way? A solution to implement a Big Data strategy today – without the need for a slew of data professionals or a complete re-haul of your infrastructure.

Have You Heard of Data-as-a-Service?

In case you haven’t, Data-as-a-Service (DaaS) fits this need perfectly. DaaS mines the Big Data ecosystem and delivers “just the right” data to your marketing channels and automation programs in real-time.

htfd

DaaS unlocks a vast new world of opportunities. Imagine getting streams of highly qualified prospects and even your own customers who are ready to purchase now based on their online searches or information they are sharing on social platforms. What if you could market to consumers who are searching at the moment for your competition? Or imagine the power of being able to enhance your internal marketing database with highly specialized and unique data sources for real-time multi-channel marketing campaigns.

How Does DaaS Work?

DaaS is a service approach in which a vendor sources, structures, and delivers unique and hard-to-find data assets on a real-time basis. It comprises three types of data, uniquely customized to each company:

  1. Foundational Data: Internal data combined with additional demographic and firmographic enhancement and specialty data.
  2. Onboarded Data: Offline data transformed into addressable online identities.
  3. Fast Data: Real-time behavioral data.

Take a look at the following example of a truck manufacturer who used a DaaS program to generate more leads and revenue.

trucks

Challenge:

The truck manufacturer needed to generate higher quality leads and in higher volumes. They also had many incomplete customer records that could not be enhanced with traditional data sources.

Solution:

Advanced Data Acquisition

Through a variety of proprietary data sources, incomplete customer records were enhanced and additional missing information crucial to strategic and operational efficiencies was compiled through a proprietary database. Beyond simple data enhancement, these sources of data were not available through list brokers or traditional compiled data sources, and could only be obtained through highly specialized, private data sources.

trucks, mfg big data

Alternative Lead Generation

The second phase of the program included looking at real-time data sources through patented web mining technology. These were based on key triggers that indicated readiness to purchase specific to this manufacturer.

mfg lead gen

Social Lead Generation

The third part of the solution included social behavior monitoring, based on a set of keywords and phrases. By monitoring social media for purchase power signals, customers and prospects who were ready to buy were sourced, providing the manufacturer with a dramatic increase in lead acquisition.

social media purchase signals

As high quality prospects are sourced, it is crucial that they are marketed to in real-time, before the opportunity is lost. DaaS delivers a constant stream of these “in-market” prospects to a company’s channel systems so they can be targeted with real-time messaging and offers.

While this is just one example of how a company has used DaaS to access real-time Big Data opportunities, DaaS can be implemented across a range of industries, for both B2B and B2C. The point is truly that DaaS provides an alternative. While it may still make sense for a company to invest in data scientists and a Big Data infrastructure, a solution exists to access these insights NOW and to start outpacing the competition TODAY.