INBOX INSIGHTS, November 10, 2021: Tiktok, Marketing Measurement Basics, 2022 Planning

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Getting Started With Marketing Measurement

This past weekend I was lucky enough to catch up with a friend I hadn’t seen in over four years. We decided to keep it safe and distanced and go hiking. It also meant I got to meet her super cute puppy!

Turn on images to see the super cute puppy

While we were talking through all the things that have happened over the years she started talking about her job. She mentioned that the part she loves is instant feedback. I asked her what that meant and she went on to explain:

In previous jobs, my marketing efforts would go into the void. I never knew what was working. The sales cycle was so long, years in fact, and we never had a good system to measure anything. Now, I get feel back on my campaigns, even if it’s negative. I love it!

I started thinking to myself, “How does that even happen? How can a company sustain if they never measure anything?”

Then I remembered, I’ve worked at that kind of organization. I was working at a company that was transitioning from government grants to commercial offerings. With grants, there was no need for sales or marketing teams. When the company started standing up these teams, there was so much that they didn’t know about what they didn’t know. There were tools and platforms purchased, people hired, and things created. However, there was no real strategy, tactics, or measurement plan in place.

I cannot speak to all organizations that struggle with measurement. But with the ones that I’ve had exposure to, it comes down to not being aware of what’s possible. It’s not a priority because the company is still moving along, making money, keeping customers. Taking a step back and learning how to better measure their efforts doesn’t seem important.

What if you could measure your efforts? What if you could make more money? Imagine a scenario where your team or company is doing well, you’re profitable, but you don’t know how to take it to that next level. Where do you start?

I asked our slack community what advice they would give and here is what they said:

“Line up your business goals and key principles. Then your strategies, tactics. With all that in place, you can then figure out which metrics are important to your goals, principles, strategy, and tactics. There’s so much to measure, so you have to have some alignment for the metrics to tell the right/best story.”

“Focus on only a handful of KPIs that truly have an impact on their business goals. There are a lot of things you can look at, but which indicators will really move the needle for them? They shouldn’t try to measure too much, and separate the nice to have from the essentials to simplify their reports.”

“Keep it simple. Select three to five metrics to track. The metrics must align with their business goals. Also, since they’re just starting out, they’ll need to align on how the metrics will be tracked and the cadence they’ll use for reporting. Ideally, the tracking would be transparent and open to stakeholders, but that may not be feasible. Alternately, they’ll share progress periodically. The team will then agree on who owns the tracking and reporting, and memorialize those decisions. Also, they’ll schedule a future conversation to check in and find out if the metrics are meaningful and applicable or if they should be modified.”

“start slow then ramp up if needed. You don’t have to become a measurement metric maven overnight. And you don’t have to marry the KPIs you choose right now. Consistency is key, but if you find that measuring”X” is not as valuable as a metric as you thought if was, allow yourself the opportunity to revise or upgrade your dashboard. And every KPI needs to have an owner.”

What advice would you give? 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 take a peek at some Tiktok data. Tiktok doesn’t have a public, sanctioned API, so any datasets around it have to be collected from software that crawls and scrapes Tiktok data. A number of enterprising data science enthusiasts have done so; for this look, we’ll be using a dataset published on Kaggle.

As with any exploratory dataset, we first should understand what’s available to us. In this particular Kaggle kernel (a dataset plus code) of the top 1,000 trending videos at the time of capture, we find basic metrics like views, comments, shares, and diggs (likes). We also obtain data like the music being played, the author name, and any accompanying text.

Tiktok features

The key question most marketers will inevitably have when looking at beginning analytics like this is, what outcome should we be aiming for? Generally speaking, with social media channels like Tiktok, our initial efforts should be awareness-based – getting people to even see our content. For that, there are two metrics worth considering. First, we have playCount – the number of times a piece of content is seen. That’s a useful metric, literally describing what we’re after. The second is shareCount, which is the number of times a piece of content has been shared. If we want social media efforts to be effective without having to spend extraordinary amounts of budget and time, we need the help of other people to distribute our content.

For today’s purposes, let’s use shares as our objective. Using data science tools like IBM Watson Studio or Dataiku, we can take all this data and ask the software to build a model that tells us what variables most correlate with the outcome we care about:

Machine learning model of outcomes

What we see from this initial dataset is that comments plus views, followed by comments alone, have the highest correlation with the outcome we care about. Thus, if we’re producing content on Tiktok, we might want to focus our efforts on encouraging comments and see if that then yields an increase in the number of shares, thereby proving causality. After all, it’s entirely possible that reverse causation exists – someone shares it, and that causes people to comment.

What’s missing from this data is any of the more sophisticated feature engineering that might guide our content efforts better, such as what the topic or subject of the video is itself. Because Tiktok is still a relatively new platform with no real, official data, we must rely on gathering the data ourselves and doing this work in lieu of it being provided.

If you’re producing content for Tiktok, let us know how you determine your analytics and content strategy in our free Slack group, Analytics for Marketers!

Methodology: Trust Insights used the Kaggle Tiktok top 1000 trending videos dataset provided by Kaggle. The timeframe of the data is December, 2020. The date of study is November 10, 2021. Trust Insights is the sole sponsor of the study and neither gave nor received compensation for data used, beyond applicable service fees to software vendors, and declares no competing interests.

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