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All Things Intent Signals (with Monish Munshi!)

Join Factors.ai co-founder Praveen Das and Freshworks Marketing Leader Monish Munshi as they discuss how to leverage intent signals to bolster your marketing strategy. A few neat tools tips as well!

January 31, 2022

Hello and Welcome to the 6th episode of The Factors Podcast! In the previous episode we talked about building an account intelligence foundation. In this one, we build on that by discussing all things intent signals!

A brief history and overview of Intent signals

Intent signals are a relatively recent phenomena which date back to around 4-5 years ago. 

It started when people in B2B who were practising ABM realised that they need to go beyond simply getting leads in organisations to sell your product to. What is more valuable is if you can know that, at an account level, there is interest within that organisation in what you’re offering.

In simpler terms, if people belonging to certain accounts are talking about you, engaging with you/your product online, searching for your brand/product on the internet and if such engagement is relevant for you — that signals a higher intent from these potential customers and therefore, a higher chance of conversions. 

This involves looking directly at people searching and engaging with your brand and category and even looking at the competition in your category. 

Intent signals are also based on the fact  that the B2B buying decision is more of an educational process rather than a discussion process that just involves calls with the sales and marketing teams. There are a lot of conversations that happen completely off-topic which involve consumers educating themselves on the different things in the market, the different use cases that people are finding solutions for and then they relate these solutions to the brands that are catering to those use cases. 

Finally, there is the practice of understanding the intent of the people when they are very early in their buying stage. Marketing and sales teams start engaging with them at the time when they are educating themselves around their problem and use that opportunity to start educating them about their brand. This is also what is known as the dark funnel: having large quantities of information of what buyers are looking for and what organisations they belong to but there is no information as to who these consumers are. 

In short, this is where the conversation on intent data started.

Is intent a way to drive more efficiency to existing channels? 

Earlier, the main source of such data was Google. But identity resolution is very hard through Google Search. Search does not give you impression level information — that is, it won’t tell you how many people searched for brand X and they don’t share user level information for every search query. With Google, de-anonymizing the impressions level data requires a lot of integrations which makes the process complex and cumbersome.

Then vendors like Bombora and Demandbase came onto the scene. They work with third party intent data to give impressions level data. Initially you started getting information about who all searched for brand X. Now, it is even more granular by also telling you what the intent of the people was behind the search. For example, it will tell you if they searched for X’s product or if they searched for job openings at X and so on. Other vendors like TrustRadius and G2 who were doing reviews also  realised that they have data on who all came to search and learn about which kind of products and they started monetising this by giving third party intent, particularly on people who were visiting these sites. 

There’s also another category of vendors like TechTarget. These vendors who do not go for third party intent but create their own closed door, vault gardens of publishers and publishing websites. TechTarget essentially created the category of second party intent. Their website focuses on hosting B2B technology persons. These users look for technologies, rate technologies and look for learning about technologies —all on these websites. Resultantly, most of their visitors are qualified leads who have better buying ability and capability and they are being served content which is more relevant for them. Their account level data is, however, limited to their ecosystem.

There are multiple types of intent data. First party intent data: this from one’s own properties and one’s own ads. Second, data from website de-anonymisation (looking at IP addresses and other signals you can identify the place of employment of the user). There are multiple vendors in this space like Clearbit, KickFire, Demandbase, 6sense. Some of these use just IP based reverse lookups and others are layering in signals like cookies to de-anonymize users.

With work from home, now that IP addresses from offices won’t work, how has that affected the data collection?

Once IP addresses from offices stopped working and people started working from their homes/home offices, these vendors went to sources where they could get device IDs. They figured out how to connect the cookies of the CRM and the marketing automation tools with the overall technology with the IP addresses (reverse IP). This started being used to get a more accurate picture of the user and their organisation.

Additional first party intent data sources, at an impressions level, are very hard to implement. It takes a lot of effort. Will it be supported in the future as we see more and more restrictions coming up?

We are moving towards a cookie-less world. Users are starting to depend on device IDs and visitor de-anonymisation and creating data-partnerships across the globe so that they become the primary source of connection between anonymity and the de-anonymised world.

So in addition to de-anonymizing intent in the early stages of the user journey, another option is to work with a partner like LiveRamp, who is leading the charge on a cookie-less world. 

Then there are second party intent data sources which work around creating content syndication networks. TechTarget and others have created their property and have highly qualified technology buyers visiting their property. They are also able to figure out what kinds of content people are most interested in. That also gives you intent. By using a logged in experience, they are able to figure out whether the interest levels for a particular category are spiking in a certain company or not. These sources give you PII level data and intent at an email ID level, post which you’ll have to do the account matching. This data helps you figure which category is spiking within a company and who, in particular, within the company are looking for solutions.

You can see more and more of these content syndication partners who launch similar offerings for second party intent. Most people also classify review sites as second party, although there is no logged in experience. So you de-anonymise at an account level and which users from what accounts are looking for reviews in your category or on your competitor or on your own page.

Between general content consumption related signals from content syndication partners & reviews signals — which one works better?

It is a case of either/or. Marketers need to mine all of this information. It depends on the budget and the intelligence being gathered and how marketers plan to activate this information. If they activate this information just by running ads, then they can go with either of these two signals. They can start from the first party and the second party and build on that.

But if you want to activate both sales and marketing, it essentially means getting account level intent on one end and contact level intent on the other end. Outbound activities can be started along with that. Most of these things are complementary to each other. The aim is to gauge intent and understand the consumer at their learning stage. Marketers can use either of these data signals to this purpose but efficiency comes through when this data is utilised properly. You get only intelligence from these sources. How the intelligence is activated is what matters.

We also discussed the general third party intent data from sources like Bombora where the general web browsing behaviour is categorised into topics. This behaviour is at a certain level of granularity that enables identification of the accounts which are spiking for interest in certain topics. So apart from these, are there any other kinds of data sources to unearth signals about where a certain company is in the sales cycle?

A lot of CDP/ABL vendors in the market are coming up with their own third party intent signals. This is happening because they are mining a lot of data and they are using this data to build this information around the overall intent. From the social media perspective, there are companies which connect your CRM and marketing automation platforms with the engagement happening on social media platforms. 

Another thing to keep in mind is being able to figure out from analyst disclosures and corporate plans and recent sales about indications that say, companies are embarking on a digital transformation project and if that should include services or areas that you can deliver as well. So that’s another kind of a signal that marketers could leverage. While this is not intent, it does play out the corporate objectives. So combining the account intelligence datasets with web browsing and intent datasets of this nature would probably give the best of both worlds and help narrow down on accounts that are the right fit— either in the market or have strategic objectives that fit with what you’re offering.

In conclusion, intent data is a good indicator for reaching out to the right customers at the right time. But it is important to ensure that you understand that intent data, at the end of the data, is just data. Efficiency comes in with how that data is used. It is very easy to get potential customers annoyed by reaching out to them when they are not in the market for your product. On the other hand, engaging with them when they are realising that they have a problem and are learning about the problem by aiding with the learning process can make them feel like you are part of their journey. They may start looking at you as a possible solution to that problem.