Right, so I was chewing the fat with Morgan the other day, a token founder who’s been through the social media wringer more than once. We were talking about my deep-dive articles aimed at token founders about social influence – the kind that goes beyond the usual ‘influencer marketing is good’ fluff. I’m aiming for proper, informative pieces that help founders avoid the pitfalls and actually get a decent ROI.
“The biggest headache?” Morgan groaned, swirling the dregs of his coffee. “Proving anything actually worked. We threw money at ‘influencers’, saw some engagement, maybe a small price bump… but was it because of them? Who knows!”
That’s the million-dollar question, isn’t it? And it’s precisely what my articles are tackling, specifically the monster that is Measuring the ROI of Social Influence: Developing a Robust Attribution Model for Token Campaigns.
Forget just slapping ads on a tweet and hoping for the best. To truly understand if social influence is working, you need a robust attribution model. This isn’t just about tracking clicks; it’s about understanding how different touchpoints with influencers ultimately lead to someone buying your token.
The first hurdle is the ‘Influencer Taxonomy’. It’s more than just follower count, as I was saying in one of the articles. It’s about segmenting creators based on audience demographics (are their followers actually interested in crypto, and your crypto?), engagement rates (are they genuinely engaging with their audience, or just shouting into the void?), content style (is it authentic and relatable, or a blatant sales pitch?), credibility within the crypto community (are they known and respected?), and past performance in similar campaigns (do they have a track record of driving results?). Spending all your budget on just the people with the largest follower numbers may not be the best choice if their audience have no interest in your token
Then we get to the attribution models themselves. There are a few key options, each with pros and cons:
- First-Touch Attribution: This gives all the credit to the first influencer a potential buyer interacted with. Simple, yes, but pretty flawed. It undervalues later interactions that might have been the tipping point.
- Last-Touch Attribution: The opposite, giving credit only to the last influencer before the purchase. Again, easy to implement, but ignores the initial awareness and nurturing.
- Multi-Touch Attribution: This is where things get interesting. It attempts to distribute credit across all touchpoints. Think of it like dividing a pie: some slices go to the first influencer, others to those in the middle, and the biggest slice to the one right before the purchase. There are various ways to do this, from equally weighted models to more sophisticated algorithms that weigh each touchpoint based on its impact. This is where things become much more complicated, but is the most appropriate. It gives credit to the influencers that got the potential buyer over the line.
Choosing the right model depends on your campaign goals and resources. A simpler model like ‘last-touch’ might be sufficient for a short, highly targeted campaign. However, for a longer-term awareness campaign, a multi-touch model is almost essential.
Crucially, setting up tracking mechanisms is absolutely essential. You need to be able to track who saw what content, when, and whether they eventually purchased your token. This means using things like:
- UTM parameters: Adding these to links shared by influencers allows you to track traffic from specific sources in Google Analytics (or similar tools).
- Referral codes: Giving each influencer a unique referral code allows you to directly attribute purchases to them. This is best applied when your token launch has a pre-sale, or an affiliate program.
- Pixel tracking: Implementing a tracking pixel on your website allows you to retarget users who interacted with influencer content. Google, for example, use this method to track who goes to your site after seeing an ad. This will allow you to ensure that they are not simply landing on your site from organic search.
Finally, data analysis is essential. Once you’ve collected the data, you need to analyse it. Look for patterns. Which influencers are driving the most valuable traffic? Which content resonates best with your target audience? Use these insights to optimise your future campaigns. Don’t be afraid to axe influencers who aren’t delivering and double down on those who are.
As an example, imagine you see that an influencer with a smaller following but high engagement, consistently drives more qualified leads than one with millions of followers. That’s a signal to re-evaluate your influencer strategy. And, if you aren’t receiving the ROI you expected, then re-evaluate your initial goals to see if they were realistic.
For Morgan, it was a lightbulb moment. “So, less ‘spray and pray’ and more… surgical precision?”, he grinned.
That’s exactly it. The key to accurately measuring social influence ROI lies in understanding and segmenting your influencers, choosing the right attribution model, setting up proper tracking, and analysing the data. This allows you to optimize campaigns, and ultimately get a return on your investment.