The Data Analytics Blog

Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.

All Posts

The Marketing Director And The Rise Of Technology And Data

February 1, 2016 at 4:11 PM

The aim of a marketing director is simple: 1) to get your brand noticed by the right people and 2) to get the right people to choose it over your competitors’. As we all know, that task is becoming both more difficult and easier every day. With the rise of social media and mobile marketing it has become increasingly easy and cost-effective to reach a large audience. And the time required to do so has been cut dramatically. As a result, however, it has also become more difficult to compete for attention as the traditional barriers to building a brand, such as budget and geography, dissipate.

Today’s Marketing Director needs to wear many hats to compete in a playing field saturated with brands, large and small, and a multitude of low-cost channels at their disposal. Two of those hats are Technologist and Data Scientist.

 From Technophobes to Technophiles

Marketing departments are have begun embracing technology to help them cope with the “Data Explosion” brought on by Big Data and the increasing number of channels of engagement with customers (think Social Media and Mobile). As a result, Marketing is investing more of their budget in technology and owning more of the technology budget overall within an organisation.  According to Gartner, Inc. 50% of tech spending outside of IT budgets is coming from Marketing departments and approximately 20% of the Marketing budget will be dedicated to IT spend to support Marketing initiatives. That’s a huge shift in where money is being invested by Marketing. Furthermore, that investment in technology is expected to grow: Gartner has predicted that by 2017, a CMO will spend more on IT than the CIO.

 Three Ways Technology is being used in Marketing

 Again, to cite Gartner, Marketers are relying increasingly on technology to:

  1. create and maintain a consistent, positive customer experience across various channels such as social, mobile and website,
  2. integrate data from an increasing number of channels, including internal data (such as transactions or customer behaviour) and external data (such as estimated household income, credit history and demographics), and
  3. support a growing number of marketing campaigns and programs running simultaneously across identified channels, such as paid search marketing and social marketing

From Binge Consumers to Selective Consumers of Data

Up until recently, most Marketers used data for cold calling a list of contacts they purchased from a list provider. “The more contacts, the better,” was the thinking, especially when it came to email marketing. Today, as the amount of customer data being generated via various sources grows, marketers can now be more selective – or smarter – about whom they choose to target. By incorporating additional data, such as credit history and purchase behavior, marketers can now whittle down a list using more than just demographics to determine who their most attractive targets are and how to customise their message or offering to improve response rates. You see, in a time where data is coming at us in large quantities, smart marketing focuses on curating quality insights that yield meaningful results.

Data analytics can help marketers reduce marketing costs while improving response rates to acquisition and retention campaigns. So, instead of targeting 100,000 contacts based simply on demographics and geography, marketers are now wising up and using data science to reduce that list to the 10,000 contacts with the most appealing profile who are most likely to respond to their campaign.  

Learn more - read our blog post "Four Successful Loyalty Programmes and why the Work."

The Next Phase in Marketing is both Technology- and Data-Driven

Soon, the ability to “out-market” our competition will no longer come down to how often we tweet or how high we rank for our chosen keywords, but how well we leverage technology and data to understand, reach and engage with our target audience in a consistent and relevant manner. Marketers must now go above and beyond today’s buzzwords of social media, SEO and mobile marketing. The next phase in marketing is technology- and data-driven. Will you be able to compete?

For a great example of how data is improving conversion rates in marketing, read my blog post on Conversion Data.

contact us

Image credit:

Post originally published 7 September 2015

Julian Diaz
Julian Diaz
Julian Diaz was Head of Marketing for Principa until 2017, after which he became Head of Marketing for Honeybee CRM. American born and raised, Julian has worked in the IT industry for over 20 years. Having begun his career at a major software company in Germany, Julian made the move to South Africa in 1998 when he joined Dimension Data and later MWEB (leading South African ISP). Since then, Julian has helped launch various South African technology brands into international markets, including Principa.

Latest Posts

[Slideshare] How To Make Your Business Data Work For You

Common barriers to success: Skills shortage: data scientists are in high demand and in low supply. Companies lack the skills to develop advanced data analytics or machine learning applications. Cost: recruiting and building up or training a team, as well as infrastructure costs are immense. Inefficiency and low ROI on: acquisition campaigns; re-activation and retention campaigns; outbound sales calls and debt collection. Resulting in: No or ineffective use of data. High cost to get insights from data. Low returns from campaigns. What’s the alternative? Machine Learning as a Service (MLaaS): removes infrastructure skills and requirements for machine learning, allowing you to begin benefiting from machine learning quickly with little investment. Subscription based pricing, allowing you to benefit using machine learning while minimising your set-up costs and seeing returns sooner. Answers as a Service: Use historic data and machine learning to allow answers to increase in accuracy with time. MLaaS with predictive models pre-developed to answers specific questions: Genius Call Connect: What is the best time and number to call customers? Genius Customer Growth: Which customers are most likely to respond to cross-sell? Genius Re-activation: Which dormant customers are worth re-activating? Genius Customer Retention: Which customers are most likely to churn? Genius Leads: Which contacts are likely to respond to my campaign? Genius Risk Classifier: Which debtors are most likely to pay or roll? Benefits of Genius: Quick and cost-effective ability to leverage machine learning: Minimal set-up time Minimal involvement from IT Subscription based service Looking to make your data work for your business? Read more on Genius to see how it can help your business succeed. 

5 Must-Join Facebook Pages For Data Science, Machine Learning And Artificial Intelligence In 2019

While LinkedIn has traditionally been thought of as the business or work focussed social platform, Facebook has been making headway into gaining market share in the space as well. With company pages and groups, Facebook is catering to every interest and aspiration that people might have – and combining that with their social interactions and news sources. Facebook aims to give users a one-stop-shop experience, and it’s very good at doing it.

Our 2018 Customer Acquisition And Engagement Blog Roundup

Our final roundup this year covers two of our main topics: customer acquisition and customer engagement. We’ve not covered these topics in depth this year, and so decided to combine these two to provide a roundup of the best of both.