INBOX INSIGHTS, October 20, 2021: Effecting Change, Technical Content Marketing, Incrementality

INBOX INSIGHTS: Effecting Change, Technical Content Marketing, Incrementality (10/20) :: View in browser

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How Do You Effect Change Without Authority?

Recently asked our Slack community what questions they had about change management. This one stuck out to me as a very common issue:

“How do you effect change without authority?”

That’s an excellent question with a not so easy answer. Buckle up, this is going to be a long read.

I’ll start with – it depends. You have to factor in purpose, people, process, platform, and performance. The more complex each of those gets, the harder the change will be regardless of your role. The key points to remember are to 1) communicate the benefits of the change, 2) the risk of not making the change, and 3) options to approach the change. To do this, we’ll use the Trust Insights 5P Framework.

Let’s say you’re bottlenecked by executives. Maybe it’s your CMO that needs to approve all the social posts that go out or your plans to do an A/B test. It could be that you need to make changes to the MarTech infrastructure and you need IT to take part. Perhaps you’re locked into a 3-year strategy that your board approved but the market has changed and you need to adjust.

We’ll use the MarTech infrastructure example. This is a common challenge and has a lot of moving pieces.

Purpose: This is where you want to drive home the benefit, and even the risk of not making this change. For example – The purpose of making a change to the MarTech infrastructure is to increase the level of accuracy of our attribution models. If we don’t make this change, we run the risk of not being able to know which digital channels are working.

What you’re doing is helping the decision-maker understand the situation quickly.

People: Start with the bare minimum – the person who needs to execute the change and the decision-maker. You can build out from there. In this case, you’ll want to include those on the receiving end of the data, especially if the data that they have been getting will be changing. There needs to be a communication plan for those people, not only the decision-maker.

If you can get that group on board you’ll have an easier time convincing the decision-maker it’s not a big risk.

ProTip: Make sure you’re truly hearing the concerns of those involved. Talk through potential solutions to those concerns during planning. Factor those concerns and solutions into your plan.

Process: For the processes, you’ll want to outline what relates to your governance. This includes governance of the platforms, meaning who has access to what. Next is the governance of the data (current and future state), and how you’ll inspect the data for any issues once the changes go through. Last, outline if the method to extract the data change at all.

If there will be minimal changes to each process step, make sure you walk through that and why. Conversely, if the changes are more complex consider an incremental approach. Start with one setting or data point at a time. Let that change setting for a week or two. Making your process iterative allows time to get the people involved comfortable with the changes piece by piece.

Platform: If you’re wanting to make some kind of infrastructure change, be sure to think through all the other platforms the change will touch. Walking through all the platforms will help you get ahead of any possible side effects of the proposed change. This will also lower the risk of surprises.

Performance: This is your opportunity to reiterate the benefits in a measurable way. Emphasis on measurable. This is going to look different for each project. If your purpose is to change the data collection mechanism in a platform to make your attribution more accurate, be sure you can measure that. This means you need to talk through why it’s not currently accurate.

Defining the measure of success ahead of starting a project will help you track against the purpose and keep the project focused.

If you’re still with me, and I hope you are, to effect change when you have no authority, you have to make the case and defend it. Help people understand why the change is important to them, the business, and the customer. Outline the risks to not making the change.

The 5P structure operates as a mini business case for the change you want to make. Use this structure to organize your requirements and communicate to all involved. Provide options to approach making the change more palatable.

Have you tried to make a change in your organization? Let me know in our free Slack group, Analytics for Marketers!

– Katie Robbert, CEO

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Data Diaries - Interesting Data We Found

In this week’s Data Diaries, let’s talk for a brief moment about incrementality and the use of predictive analytics. Incrementality is a marketing analytics concept which answers how we spend our marketing resources – time, people, budget, etc. Within incrementality, we have our baseline, and we have our incremental lift as our core concepts.

Our baseline is the effect of our always-on marketing efforts, and it’s the marketing results we get from doing nothing extra. We blog, we post on social, we run always-on ads, and we get a certain level of performance.

Then, if we do something different – start a new campaign, change our tactics, etc. – what we get on top of that baseline is our incrementality, what we got that was extra. What marketing is always in search of is the biggest bang for the buck.

The challenge with incrementality is that A/B testing is very, very difficult, especially on channels where you have no ability to realistically do so. How do you demonstrate incrementality when you no longer have baseline data?

There are two different approaches we can take to addressing this issue. The first, which we’ve discussed in previous newsletters, is using propensity matching, matching similar days by all other factors that could influence an outcome and then looking at the difference between similar days when a campaign was running and when it wasn’t running. This is an excellent method for determining incrementality, but because it collapses time, it’s very difficult to illustrate the difference.

The second approach is to use the conceptually odd retroactive predictive analytics. This is when we forecast what should have happened using predictive analytics and then compare the forecast with what actually happened. As long as the forecast is statistically sound, we can illustrate the difference between what was baseline and what actually happened.

For example, in August I decided to try using LinkedIn differently, which we discussed on an episode of So What? previously. Now, how would we measure the incrementality of that effort? After all, I can’t A/B test my own account, behaving differently only some of the time. If we use predictive analytics to forecast what August and September SHOULD have looked like, it would look like this:

LinkedIn Forecast

For August through today, I should have earned 351 visits from LinkedIn, based on the forecast.

What actually happened?

LinkedIn Actuals

The campaign earned 1,887 visits. The difference – 1,536 visits – is the incrementality, what I earned that was above and beyond the baseline. That’s incrementality, what’s additive from our efforts. When you embark on efforts to measure incrementality in your own marketing, try this approach.

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