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January 19, 2017

Leveraging Data Science for Deeper Marketing Decision-Making

Kavitha Mariappan


It’s no surprise that Gartner has reinforced its prediction that CMOs will outspend CIOs on IT in 2017, as marketing technology plays a critical role in top line enterprise revenue growth. As the head of marketing for a high growth tech company, my day revolves around data – sourcing and curating the most qualified leads, analyzing and discovering hidden trends and patterns from this data, and providing real-time guidance to my team on strategies and tactics to maximize our conversion rates. A typical marketing technology stack goes across a broad range of tools and platforms ranging from marketing automation, to data warehousing, to web and social analytics, content and email marketing, etc.

Additionally, key marketing metrics today have evolved far beyond the traditional Cost per Lead (CPL) to now encompass not only Customer Lifetime Value (LTV) and Customer Acquisition Cost (CAC), but also Cost per Acquisition (CPA) of Product Qualified Leads (PQLs) i.e. leads that are activated by users of freemium or trial offerings, and many others. With all these fast changing requirements, and the need to be laser precise in predicting lead development to opportunity creation, marketing executives are often faced with three major challenges:

  1. Tools and Technology: Investing in the optimal marketing technology stack and tools to help in their decision making process.
  2. Skills: Building a skills toolkit and team that equips them to delve into data analytics, customization, personalization and optimization technologies, to help them drive highly targeted, sophisticated, digital-led campaigns and activities.
  3. Scale: Building a repeatable and scalable lead to deal engine based on predictive indicators.

The common theme across all three aforementioned challenges and a must-consider for marketing execs in 2017, is data analytics. Some common questions that are top of mind for for marketing execs in 2017 will be:

  • What are the right data analytics solutions and tools for me to invest in?
  • How do I integrate these disparate sources to provide me with a single-pane of glass in terms of insights and reporting?
  • How can I quickly ramp my team and myself up to embrace these new analytics and growth marketing skills to ensure we remain ahead of the curve?
  • How do I receive real-time feedback from sales on the performance of our lead pipeline that is validated by analysis and not based on anecdotal information?

Drawing from my personal experience building and scaling marketing teams, I have found that a data-first culture across marketing and sales teams, focused on real-time metrics and open feedback, is imperative. This allows teams to be more agile and adapt to new methodologies to skin the same cat – from lead generation, to opportunity creation, to closing deals. As the saying goes, “if you torture the data long enough, it will confess to anything.”

One of the most exciting transitions for me and my team this past year has been making data science an integral part of our marketing workflow. As much as we deploy some of the common tools for marketing automation, data enrichment, social listening, etc., we leverage our data science team on every aspect of the workflow – lead scoring, real-time dashboards, top of the funnel predictive analytics, and A/B testing every stage of the funnel.


The insights we have been able to glean from our data science efforts have heavily influenced our content creation, campaign development, and marketing spend. This initiative has enriched our marketing to sales workflow, and improved productivity and ROI. It has helped us focus on the predictive insights we see from our data, removing the reliance on siloed, off-the-shelf marketing software that at times is under-performing, and most importantly,takes the guesswork out of the equation.

As we enter 2017, I am excited for what the year brings in data analytics to marketing teams. Some parting thoughts for marketing executives:

  • Data Science for Marketing: Embrace and leverage data science as an integral part of your workflow and marketing tech stack. It is an essential tool in your toolkit that will relieve you of sunk costs and wasted cycles on under-performing tools and processes.
  • Test Everything: Evaluate and test often, A/B test both your current stack, and put off-the-shelf solutions through rigorous testing. Ensure that they stand up to your data-first KPIs.
  • Data-first Culture: Create a data-first culture within your marketing and sales teams. Use data analytics to constantly validate the funnel, and to optimize the performance of your lead pipeline, rather than relying on disparate marketing tools and anecdotal sales rep information.
  • Build the Toolkit: Get your hands dirty. Marketing today is more about technology than brand, communications, and creative-led services. The role and the acumen required to lead a marketing team today has evolved. Hire the best and the brightest to help bridge these gaps within your team, and bring data-first capabilities to your team.
  • Democratize Data Access: As a company, you have common goals and metrics. Break down those departmental siloes and let data science help your organization create a tighter and more transparent integration between your marketing, sales, engineering, customer success, and product teams.

About the author: Kavitha Mariappan is vice president of marketing for Databricks, where she heads end-to-end global marketing efforts. Kavitha brings more than 20 years of extensive industry experience in product and outbound marketing, product management, and business development to Databricks. Prior to Databricks, Kavitha was the vice president of Marketing at Maginatics (acquired by EMC), where she built and led the team responsible for all aspects of marketing and communications. Her previous professional experience includes leadership roles at Riverbed Technology and Cisco Systems, Inc. Kavitha has a Bachelor of Engineering in Communication Engineering from the Royal Melbourne Institute of Technology, Australia.

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