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The classic fallacy in B2B demand gen is that persona matters more than the intent. Persona matters when you're trying to determine the traffic quality but not as much when you're trying to determine the purchase intent.

Got it. So you are you saying that this might not work? 🥲 Bear with if I am asking a dumb question

I'm not saying that. Just sharing an observation.

Ok, but I take your point 👍

Very interesting 🙂

looking forward to hear your learnings, please do share

I was Thinking of a similar project on different notes, as @Aniruddh Jain said I beleive Intent is more important than persona when you think of conversions... So thought if we could use custom conversions through forms where, people belonging to a specific account tier and channel signing up from a specific form could be incentivised with conversion values than others, Maybe this way PM could be useful for B2B...but your point stands that we need good volume to do this

are you thinking of passing an event like VisitorScore ( score = {based_on_de_anonymized_date} )

@Niranjan Bala: I am calculating score based on 2 factors as of now-> {industry, revenue range, employee-size} and using historical data seeing users belonging to which segment convert better and contribute higher. Planning to score a web visitors based on this.

you have to be mindful that you might introduce a bias

Yes, 6Sense. hhmm, bias how?

you will be basing of your score based on past data, how large is that data set?

every week- we'll be looking at rolling last 3-4 months of data.

Great discussion guys! Here are my opinions and suggestions: 1. How about personalizing web pages based on the intent? It could be a great CRO 2. Let's say you have de-anonymized and you have a clear target profile, but Google search engine can't display your ads based on IP addressing matching (please correct me if I am wrong). It can only read some behaviours (which are in the dark). So I am unsure if we will be able to achieve anything out of de-anonymizing in the context of paid ads. 3. Rather a specific set of behaviours or activities on a website can be defined as conversions and Google ads can measure (CPA) against it. 4. Heat maps and scroll maps can also help in CRO.

Hey @Sai Krishna 1. Yes, I think there are tools who do that- personalisation using demographics and IP-deanon data. 2. I didn't get this second point 😬 Idea is not to serve ads based on IP address. Idea is to feed back the quality of the data into GAds as conversion value for smart campaigns to optimize on. 3. Yes, that is possible as well. But the again the volume of this data (e.g., CTA clicks) will be lower in volume. 4. Yes. But that is not the out of context in terms of this experiment I guess?

As you rightly said smart bidding works better if there is a large conversion volume. But this happens because the system studies the behaviour, the flow, and other data points. So my question is if you do this, won't it confuse the system and in fact resulting in negative consequences and backfire? Although I don't think Google smart bidding algorithm is that smart, such tweaks can work until the next time they update algorithms. However, its worth a try though.

Hey @Saheen: thanks for your inputs. I would be feeding back data in way that users from the "high converting segments" (based on past data) get more weightage.

Tempted to say "it depends" and call it a day. Haha I am pretty confident that if you can do this, it will result in lower CPAs and better assisted conversions. Intent- I don't think you need to be worrying as much about it. Apart from 1st party intent, the best kind of intent data is search intent. You already have that layered in as part of your performance marketing campaigns. So, if you can do this, I'm confident it'll be better than what we have right now. The problem is with the deanonamization stack. As much as I love it, it's very industry dependent where it shows enough useful data. We often use this data to optimise our search ads. I've seen it being less/more useful campaign to campaign as well.

Thanks for your input @Ishaan Shakunt. How did that LeadForensics thing work for the AgriTech co.?

Honestly, it showed us that that campaign sucked and we should shut it down. 😅 The reason we did it was because every platform level metric and user behaviour metrics pointed in the right direction but we weren't seeing conversions. So we got this tool (obviously also used it for other things), saw who was coming to the website and concluded that paid search as a channel wasn't the best for them.

Understood. Thanks for the input 🙂

Btw, we tried two other sources before that which just didn't have any meaningful data at all for this industry. Clearbit HubSpot's native data

interesting, that info helps. I'll reach out to you separately as well.