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What is Data Driven Marketing?
The document discusses how to transition an organization towards data-driven marketing. It recommends starting by setting goals focused on what matters most to the business. It also advises sourcing or hiring new skills in data and analytics. The document outlines implementing agile data processes through experimentation and a "data lab" concept. Building a marketing data platform is suggested by focusing first on actionable existing data and creating a "middleware layer" to integrate additional customer data sources over time. Adjusting vendor evaluation processes to prioritize "data openness" is also proposed.
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- 1. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 1 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph Data-driven Marketing? What the &*#^$ is
- 2. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 2 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph In marketing
- 3. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 3 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph But it’s not about the data, it’s all about decisions Making optimal decisions Understanding what they want and when Quantifying people’s behaviours
- 4. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 4 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph Does your organization have the right tools to make good decisions?
- 5. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 5 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph Most likely yes. Source: Teradata
- 6. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 6 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph Does your organization make optimal decisions all the time? Learn Examine Test Optimize
- 7. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 7 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph Data driven marketing leaders characteristics Use Attribution More “Digitally Mature” Have Defined new KPIs Have skilled personnel in “new Martech” Use new analytic models Foster Collaboration Agile & Flexible “Culture change”
- 8. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 8 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph What’s in it for them? Jackpot! Audience amplification Cross- channel measurement and Attribution Optimize every act AB Testing “Bonding” Predicting a person’s next action Can tailor 'magic moments' Agile & Flexible
- 9. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 9 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph “But our organization is not there yet”…
- 10. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 10 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph Which one comes first? Organizational culture? … or use of Technology?
- 11. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 11 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph Where should you start?!
- 12. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 12 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph Step 1: set goals What are the goals (that will meet your organization’s business goals)? We can’t measure EVERYTHING There’s a need to focus on what matters most
- 13. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 13 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph Step 2: Source, grow or hire new skills
- 14. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 14 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph Step 3: Implement agile data processes Experimentation, test-and-learn will become a core competency Start building a “fast-lane” process for ideas experimentation, testing, learning, and scaling Data Lab concept
- 15. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 15 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph Follow the yellow brick road
- 16. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 16 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph Step 4: Start building your marketing data platform 1. First focus on the actionable data you already have 2. Create a “middlewear” layer (“the glue”) 3. Add more pieces to the data puzzle customer ID is the “glue”
- 17. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 17 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph Adjust vendor examination process “Data openness” to be a top criteria for vendor evaluation Establish a vendor/solution checklist Which APIs currently exist? What is the data architecture? Does the SI partner “understand” the data architecture complexity?
- 18. STKI’s work Copyright@2016.Do not remove source or attribution from any slide, graph or portion of graph 18 STKI’s work Copyright@2016. Do not remove source or attribution from any slide, graph or portion of graph