As noted by Scott Brinker in his chiefmartec.com blog in June 2016, there are approximately 5,700 marketing technology solutions on the market; just five years prior, there were only about 100. While having all these options are great, it can also be overwhelming. More and more, marketers are slowed down by indecision and paralysis.
Marketing is increasingly expected to contribute to revenue. Marketing quotas are set at the various stages of the marketing/sales pipeline and include marketing qualified leads, sales accepted leads and sales qualified leads. In addition to reporting on these metrics, marketing must be able to measure their tactical efforts such as campaign performance and lead sourcing. Marketing may also be expected to execute campaigns rapidly to support business operations. These responsibilities place even more pressure on ensuring that the right technologies are selected and properly implemented, and that the right people and processes have been deployed to optimize these investments.
What are your marketing objectives and existing infrastructure?
Before determining in which new technologies to invest for your Revenue Marketing Stack, you need to ensure your marketing objectives are defined and aligned with your organizational goals. It’s not uncommon for marketing to establish some or all technology requirements based on resolving pain points in executing marketing campaigns. These may or may not necessarily align with marketing’s business goals, and this bottoms-up approach can lead to poor or irrelevant technology selections that result in a sub-optimal stack.
You also need to understand your current infrastructure; after all, you can’t manage what you don’t know. This isn’t limited to just making a list of your existing technologies. It is critically important to also identify the people and their skills in using these technologies, as well as the business and data processes that govern their use. Who runs the technologies and do they have proper training? Are the right processes in place to leverage the technologies? Are the technologies effectively integrated to optimize data quality and management?