So, I was chatting with Rebecca the other day, and we were diving deep into the mess that is social influence ROI in the crypto world. You know, those in-depth articles I’m working on for token founders? The ones about how to actually make social influence work, not just spout the usual buzzwords? Well, we got properly stuck on attribution models. It’s a headache, right? But crucial if you want to know if your hard-earned crypto budget is actually doing anything.
“Seriously,” I said to her, stirring my tea, “how do you even begin to track this stuff effectively? It’s not like traditional marketing where you can easily see a direct click-through to purchase.”
Rebecca, ever the pragmatist, nodded. “Exactly! That’s why we need to get past the basic metrics like likes and retweets. They’re vanity metrics. We need to understand causality. Did that influencer tweet actually lead to someone buying the token?”
And that’s where attribution models come in. Think of them as different ways to assign credit for a conversion (in our case, a token purchase) to the various touchpoints a potential buyer has had with your social influence campaign. Let’s break down the main contenders, like we did over that pot of tea.
First-Touch Attribution: This is the simplest. It gives all the credit to the first interaction a user has with your brand. So, if someone sees an influencer’s post, then visits your website, then joins your Telegram group, and finally buys the token, that initial influencer post gets all the glory.
How to implement it: Use UTM parameters in your influencer links. Track the first UTM source for each user in your analytics platform (like Google Analytics or, even better, something crypto-specific if you can find it).
Pros: Easy to understand and implement. Good for understanding what initially attracts people to your project.
Cons: Ignores all the other touchpoints that contributed to the final decision. Overly simplistic in a complex journey.
Last-Touch Attribution: This gives all the credit to the last interaction before the conversion. So, in the same scenario, the Telegram group join would get all the credit, even though the influencer post started the whole process.
How to implement it: Similar to first-touch, but track the last UTM source instead.
Pros: Easy to implement. Often the default setting in many analytics platforms.
Cons: Ignores all previous touchpoints. Doesn’t give a full picture of the customer journey. Could lead to you incorrectly deeming that the telegram group is the only point of relevance.
Multi-Touch Attribution: This is where things get interesting (and complex!). Multi-touch models attempt to distribute credit across all the touchpoints in the user’s journey. There are different ways to do this:
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Linear: Each touchpoint gets equal credit. If there are four touchpoints, each gets 25% credit.
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Time-Decay: Touchpoints closer to the conversion get more credit.
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Position-Based (U-Shaped): The first and last touchpoints get the most credit (e.g., 40% each), and the remaining touchpoints share the remaining 20%.
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Algorithmic (Data-Driven): This uses machine learning to analyse your data and determine the optimal weighting for each touchpoint. This is the most accurate but also the most complex to implement.
How to implement it: This requires more sophisticated analytics tools and potentially custom tracking solutions. You might need to integrate your marketing automation platform, CRM, and website analytics to get a complete picture. Many of the crypto marketing platform offer this functionality in a simplified way.
Pros: Provides a more holistic view of the customer journey. More accurate attribution than single-touch models.
Cons: More complex to implement and requires more data. The algorithmic models can be expensive.
“Okay, so we’ve got these models,” Rebecca said, “but how do we actually track the data?”
Good question! Here’s the general process:
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UTM Parameters: Use UTM parameters in every link you share on social media (and anywhere else you’re running marketing campaigns). These parameters allow you to track the source, medium, and campaign of each click.
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Landing Pages: Direct users to specific landing pages related to your campaign. This allows you to track conversions related to that specific campaign.
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Conversion Tracking: Set up conversion tracking on your website to track token purchases, wallet connections, or any other key actions you want to measure.
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Analytics Platform: Use an analytics platform (like Google Analytics or a crypto-specific alternative) to collect and analyse the data.
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Attribution Modelling: Choose an attribution model and configure it in your analytics platform (or use a separate attribution modelling tool).
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Data Analysis: Regularly analyse the data to identify which social influence activities are driving the most conversions. Also look for signals such as negative comments or wallet addresses found to be selling large blocks of tokens quickly.
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Optimisation: Use the insights you gain to optimise your social influence campaigns. For example, you might find that certain influencers are more effective than others, or that certain types of content resonate better with your audience.
It’s crucial to remember that no attribution model is perfect. They’re all based on assumptions and estimates. However, using a robust attribution model is far better than just guessing which social influence activities are working.
Ultimately, measuring the ROI of social influence in crypto is about more than just tracking clicks and conversions. It’s about understanding the entire customer journey, from initial awareness to final purchase. By using the correct data tracking methods, and combining it with common sense and a good understanding of your product, it is possible to get a good overview of where you stand.