Guest Post | Advt. | Query:

Why and How to Invest in Data Science: A 3-Step Guide

Adam A. Glenn

The days of putting your ear to the ground are over. Modern marketing is robustly data-driven like never before.

Marketing campaigns not only generate big data but depend for their success on the science of mining, organizing, analyzing, and deriving business value from the huge amounts of information about brands and buyer behavior circulating online.

What this means in practical terms is that failing to invest in data science puts your organization at a competitive disadvantage in the marketplace of 2018 and beyond. The right investment varies according to your needs, but can involve working with software vendors to find the right machine learning software for your situation or hiring data science experts to build digital tools for your organization.

But what are the signs your business is ready to take the big data plunge? Read on to find out.

The Why of Big Data

Data science offers plenty of benefits for your organization’s marketing efforts. We’ll focus on three.

#1: Improving the Budget/Sales Ratio

Compared with what the tools of modern data science makes possible, traditional advertising is slow, laborious, and imprecise.

The old-fashioned approach began with identifying your brand’s main audience, then proceeded to locate and engage them with content that marketing staff believed would likely appeal to their desires and expectations. There was much to be said for this approach, but it was heavy on both legwork and guesswork.

Modern data science platforms require far less staff than the old, manual methods and have a proven track record, with one 2017 study showing a greater than 5% revenue increase among 8 out of 10 companies that use big data tools. Over 6 in 10 businesses surveyed in the same study report the highest share of their markets among their competitors.

#2: Grow Your Brand without Growing Your Staff Budget

The substantial costs associated with recruiting, hiring, training, and provisioning staff are well-known. When data science is used to perform many key tasks traditionally requiring specialized staff, its contribution to the efficiency of your business’s operations is significant. These tasks include:

  • Identifying potentially fraudulent activity by automating the monitoring of transactions for red flags.
  • Identifying your target audience, including their preferences, typical research and purchasing behavior, and even the times when they are normally online and accessible for outreach or engagement.
  • Streamlining and optimizing the logistics around transporting goods. Some automated functions cut personnel costs and enhance your brand’s quality of service at the same time, such as the way in which e-commerce tools can help customers locate items not available in their area and arrange delivery to their location.

It’s worth adding that reduced personnel costs can be passed onto customers in the form of lower prices, which gives your company a further competitive edge over rival brands.

#3: Nail Down Your Numbers

It’s difficult to fine-tune a marketing campaign when you can’t precisely measure performance and define success. It turns out that nearly half of businesses have a hard time quantifying social media marketing ROI.

The good news is that there are data science tools to help you do precisely those things. It’s even possible to measure the effectiveness of your marketing efforts in specific campaigns and audience categories.

The basic principles of successful marketing haven’t changed. It’s still crucial to know and connect with your audience. But the old tools of the trade can’t make your brand competitive in the era of big data. Data science solutions that boost efficiency, automate e-commerce, and streamline budgets are what your business needs to gain the upper hand in today’s competitive digital marketplace.

Adam A. Glenn writes about tips and trends in digital marketing and social media for TrustRadius. He is an avid reader whose extracurricular interests include hiking, vintage pop culture, and messing around with music.

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