- What are your campaign goals?
- What performance measurements matter most to you? Engagement? Cost Per Action/Lead? Clicks?
- Do you want to reach new customers? Re-engage existing customers?
- Are you pushing specific product/service categories or items?
- Is there a cost difference in the items your pushing?
- How will you measure success?
- For example, a CPA of $20? .1% CTR? ROAS? Link a measurable number to your goal(s)
- What’s the time frame?
- Are there any outside factors that will effect the campaign? Such as seasons, timing etc.
A recent survey conducted by the Economist Intelligence Unit examined trends in digital marketing and identified the gaps in understanding between consumers and marketers. The survey found that as marketing strategies rely more heavily on data analytics executives find themselves struggling to interpret big data. This got us thinking – how does this apply to display advertising? And, what’s the solution? In reference to display advertising we often speak of unstructured data as it relates to big data. Unstructured data, meaning data in it’s raw form – not bundled and packaged into groups. The reason we advocate unstructured data is that bundling data into a group(s) with other like data pieces strips data of its true value by forcing it to fit neatly into a segment. To close the gap between the large amounts of data available and gleaning insights from it marketers must learn how to interpret it. So, we’ve compiled a list of tips for starting you on the path to interpreting raw/unstructured data within your display campaigns: