Hansel and Gretel had it right: How to efficiently capture engagement context.
In my last post, I demonstrated how any customer engagement within a company can be categorized by six facets (Who, What, Where, When, Why, and How) and fewer than 500 sub-classifications. Despite being streamlined, it still involves collecting a significant amount of data. In this post, I’ll outline a strategy to capture the timely, accurate, and granular context AI needs without any additional work from your teams. The secret lies in capturing data as you go.
Customer engagements don’t just happen – companies invest substantial time, effort, and resources into strategizing, planning, developing, and delivering experiences to facilitate engagement. Each of these steps encompasses numerous smaller decisions made by various individuals and teams. Decisions like business challenge to address, product or service to support, playbooks to use, audience to target, content to create, channels to use, and calendars to consider. Each decision is a data point that someone might want to analyze later.
This data is valuable. Any one of these data points could be critical piece information necessary to make a better decision downstream, ensure that engagement instructions are properly executed, or lead to the insight that explains an engagement’s success or failure. And given the complexity of the data it’s impossible to predict which piece of information will be the one someone needs.
Most companies realize this and make attempts to collect parts of this data. Typically, they charge a team at the end of the development process to collect this data. While this might ensure all the decisions have already been made, it makes efficiently collecting the right data nearly impossible. The teams collecting the data are removed from the data generating decisions and lack the context, authority, or resources to do it right. The result is suspect data, wasted time, and resentful teams.
What to do? Don’t wait until you’re lost, think like Hansel and Gretel: drop breadcrumbs along the way – early and often.
Capture as You Go
The best way to ensure data is properly captured is to do it as it’s created. This method not only accurately records decision-makers' choices and intents but also can often be automated. With the adoption of digital communication and workflow tools, companies have a huge opportunity to let machines "listen in" on the decision-making process and capture relevant data.
Creative workflow tools are obvious starting points, but also consider business planning, audience management, and channel platforms. Each plays a role in the engagement process and contains data representing vital decisions. If your company lacks tools to manage these capabilities, data capture is a compelling reason to invest.
Capture Everything
It’s nearly impossible to predict exactly what data will be needed in the future, so don’t try. Instead, make a point of collecting all the data you can and describe it using a commonly understood data framework (see my last article for more information).
Capturing all data is more efficient than selectively collecting “critical” data. This might sound counterintuitive, but deciding whether to collect data requires effort—someone must stop and think about it, get feedback/approval, and execute the decision. This process might happen multiple times across multiple teams, leading to inconsistent data and redundant work. Simply collecting all data as a standard practice eliminates this issue.
Additionally, because the entire company follows the same requirement, common services and solutions can be developed, making implementation and maintenance easier while ensuring data quality and accuracy.
Next time: Managing Change.
