When it comes to your decisions and your data, one tool I like to use is called a Decision Log.
By recording your important or ambiguous decisions in this format – the game changers, the ones where your decision-makers disagree, the ones where it’s not so clear what the best decision is or what the influencing factors might be – you provide for yourself a means of auditing, reflecting on, and redirecting yourself towards your goals by getting really clear on how you’re using data, both in the moment of decision-making and as part of post-process analysis.
This can be particularly useful for times when the result of your decision was unexpected, you want to repeat results, or you want to better understand the context in which you’re working.
- Was this decision logical?
- Would you do it this way again?
- Were you looking at the right data, or using the right data for what you wanted to achieve?
- Did you misassess the relationships between variables and clues in the data?
- Was there anything you overlooked when you made the decision?
So that I can apply the tool as I talk about it, let’s use a simple networking scenario. Suppose that getting in front of people is important to your business, so you join networking groups and start scheduling a lot of coffees and other meetings with the people you meet there, and the people that these people connect you with.
Is it working? How do you know if this tactic is having an impact on your sales? How will you go back and assess if your reasons for joining these networking groups and scheduling these meetings made sense?
To answer these types of questions, you use this template to begin an auditable trail of your networking decisions so that you can retrace your steps and assess them in hindsight.
Let’s work through the template for this.
When is this happening?
In the Decision Log, start with the calendar date of the decision itself, as well the date on which you implement the decision and it can start having some effect. Things can change so quickly, and sometimes, in hindsight, knowing these dates can make all the difference in how you contextualize the rest of the information you’ve recorded.
For example, if you made a decision in February 2020 in the US, and it didn’t turn out the way you expected it to, you probably have a lot of COVID-19 related explainers for that. For the networking example, these dates will be the same, because your networking will begin immediately.
What’s the “big picture” context?
Document what you think the context is, that is, what’s happening around this decision.
In this scenario, you’re working to expand your company’s reach, increase brand awareness, and establish some client funnels so your company can grow. All of the business advisors have told you that networking groups and 1-on-1s are the way to do this, but you’re not really sure how to focus your efforts.
What’s the decision? What will the result look like?
Talk about the decision you need to make, followed by the actual decision you’re making, and what you expect to happen as a result of your decision. The more explicit you can be about that result, the better, but this isn’t a huge, scholarly book on the topic. Keep it highly focused and as short as possible. What are the specific clues or signs that you’ll be able to observe, or what will be the impact on certain of your data points, when the result you expect has occurred?
In this scenario, you need to select at least one networking group to join, and develop an elevator pitch to use there. To start with, you’ve picked the (100% fictional) local networking group with the most events, Generic Network Group South (GNGS), in the hopes that this will lead to the most interactions. In your CRM, you’ll record these meetings and begin to establish a dataset regarding your networking efforts, including when these people become clients, referral partners, or other strategic partners for complementary business services and products. You expect to see that sales increase as a result of these new connections, that is, that you’ll be able to tie new sales directly back to these new people in your CRM.
What are the variables or clues you used to choose this decision and reject alternatives?
Document what the variables or clues are that made you decide that the decision you made was the correct decision to make, and why those are the things you decided to base your decision on, including why you decided to ignore certain other factors or bypass alternatives. Again, be explicit.
Were you drawing on the data for a certain segment of your client base? An experience you’ve had? Things you’ve observed in other companies Instinct?
In this case, you don’t have any datasets to point to yet, because you’re just starting out. But, by the time you review your data, you expect to see some trends emerging in terms of people you can classify as possible or active strategic partners, people you can classify as referral partners, people you would like to refer, and clients. While there are other networking groups you could have joined, your budget is limited, and so is your time, and so you decided to go with the single networking group that seemed to offer the most opportunities for 1-on-1s.
Who are the decision makers? When will you come back to review?
Record who the decision makers are – not all of the stakeholders,
but the actual people in the room making the decision – and the date on which you will all come back and review this log entry. This review date should be set after you expect the result to have occurred, so that you can assess whether your expectations about the effect have been met. However, if the date is more than 3 months away, which is reasonable for some business decisions, you can schedule in quarterly reviews of this decision so you can catch divergence earlier rather than later.
In this case, you’ve made the decision yourself, and you’ll review your data in 3 months to see if this approach gets any traction, and if you need to fine tune.
On that review date, come back and assess. This is the audit.
- Did you get the outcome you expected?
- Are you seeing the specific clues and signs you expected to be able to observe?
- Have your specific data points been affected as expected?
- Did you overlook clues?
- Did you misassess the relationship between clues?
- Did you use the clues that were appropriate to the decision you made?
- Is there a nuance or a characteristic you need to capture in your dataset, one that’s not there right now?
In this scenario, your efforts with the chosen networking group, GNGS, have been somewhat successful in being a source of business, but have also led to two unexpected results. According to your CRM data, you have one referral partner, Doug, that’s brought a lot of business your way. What’s interesting is that some of Doug’s referrals are also members of GNGS, but they only approached you for business after speaking with Doug about you. What’s going on here? You realize that your simplified model of the world, your CRM, might be too simple to support your strategic decision-making. At the very least, it should be modified to include what Doug is talking about with people that prompts him to bring you up and inspires them to reach out, in addition to the fact that this referral came from Doug. Because people aren’t great about remembering things, you won’t try to backfill this data after the fact, but you’ll start collecting it from this point forward. Now you’re ready to capture that data, analyze it, and see if you need to modify your elevator pitch or the other things you’re talking about to better resonate with people’s pain points and the problems that you solve.
Since you’re still figuring this out, and it’s still ambiguous, you’ll record these changes in another Decision Log entry.
It’s not something to simply file away
If you skip the audit, you’re still recording the institutional memory of why things are the way that they are at your company, but you’ve skipped over the log’s power to help you reassess and redirect your pathways to your goals, the data you collect and your metrics, and how those intersect with your decision making.
Once you’ve amassed a few of these log entries for similar decisions and actions, you also give yourself a fuller picture of how you might address new things that have similar characteristics, because you’ll have the history of the decisions you’ve made, the factors you’ve considered, the datasets you’ve utilized, and alternatives you’ve rejected.
This blog post is a reworking of the transcript of a video on the Blou Designs YouTube Channel. If you would like to “watch” this post, you can find it at: https://youtu.be/GA7sbGRmSKI (new window)
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