Artificial intelligence (AI) is the buzz term of 2017. The industry is prophesizing that AI will be the tool to revolutionize marketing. Every day I hear about some new AI that enables software and that will solve all of marketing's problems. Artificial intelligence can be defined as the capacity of a computer to perform operations analogous to learning and decision making in humans (Dictionary.com).
An aspect of AI technologies that is often overlooked, is that AI requires good data to perform effectively. Data is at the heart of AI being able to provide meaningful results for marketing. Without good data, there is nothing for the AI algorithms to churn through to provide new insights or to act on. Imagine what it would be like to make a decision if you were in a sterile white room with only the front page of the newspaper and access to no other information. Artificial intelligence is supposed to mimic human thought and actions. AI tools, like the brain, need access to a wide variety of data to be able to make effective recommendations.Some of the marketing disciplines that AI can perform or assist with are:
- Media mix optimization
- Attribution analysis
- Consumer insights
- Recommendation engines and next-best action
- Guided selling through intelligent agents
These functions all require capturing data from the marketing, sales, and service channels (data capture); bringing that data together (eliminate silos); and associating it with known and anonymous individuals.
Data capture refers to an organization's ability to capture all relevant marketing, sales, and service data. This includes all web site activities, conversions, impressions, search, sales calls, call center interactions, chat sessions, surveys, orders, etc. The data can also include service calls and financial information.
The more information that is available to the AI tools, the better they can find insights and make recommendations. Data capture does not need to be perfect to start working on the other areas.
It is necessary for all the information being captured to be collected in a central repository. Many organizations continue to have silos of data being stored in the capture system but they are not integrated together.
This central repository could be a structured environment such as a marketing database; it could also be done in an unstructured environment such as a data lake. Many organizations are starting to stand up data lakes to enable the capture of unstructured data, such as social media posts or call center transcripts. The data lake allows for future data types to be added easily, which then can be accessed by AI tools as they are introduced into the martech stack over time.
As you eliminate the silos and bring the data together, it is essential to associate the marketing data with individuals. Identity management is the ability to match all the data captured with first-party data. Are you able to associate on-line behavior with known people? When someone identifies themselves on-line, do you match up their previous anonymous behavior with their identity? Another area where identity is important is chat. Brands should associate chat sessions with individuals.
When your data is captured, gathered into a common repository, and associated with the appropriate identities, the AI tools will be able to assist marketers in identifying micro-segments that are most likely to respond to specific offers and determine which channels are best for those offers. AI tools will then continue to analyze the incoming data to re-evaluate each decision and make quick changes to segments, offers, and channels.
Having perfect data is not essential to implementing AI solutions. However, it is important to understand the marketing challenge you are trying to solve with AI and what data is essential to making that solution effective. You can then determine the gaps in your data to determine the roadmap necessary to address them, and then leverage the full power of your AI solution.