“Intent data” is a little-known, but very powerful customer intelligence tool that your business can use to better understand and serve its customers. Intent data is information about what a user was trying to do before they came across your website or app. Read this blog post for helpful tips on how you can use Intent Data in your business!
The goal for any good sales team is to generate targeted leads for certain deals. In order to do this, you need to have the right intent data with the sales team. This will give them the insight into what their lead is interested in buying and when they will be prepared to buy it—while simultaneously having a good understanding of what value the organization can add before the deal goes through.
This process takes time, but can ultimately pay off with high-quality deals and huge revenue gains for companies that know how to make it work.
Intent data sells. Whether you are an individual looking to purchase a flat-screen television, or a large corporation trying to do some market research on possible new vendors, as it has a massive value. Simply put, these answers the fundamental question of ultimate relevance: what does your customer want?
Marketing teams can gain great insight into what customers desire by understanding their unencrypted search terms and viewing the URLs that they have visited.
The collection and analysis of this information can tell sales teams exactly how much revenue they could be generating from a sale. With the right marketing message—but only if they know how to recognize precise buying terminology and efficiently interpret that into relevant business opportunities.
The most effective way to collect the data of buyer’s intent is by analyzing the unencrypted search terms and URLs of potential customers. When a user enters a text query into a search engine, such as Google or Bing, that query is sent through the internet to be processed by any number of sites before finally ending up on your website (or "destination URL").
As the encrypted information passes servers and hops from one step to another, it can leave an expansive trail of valuable insight into what that user was searching for—which could potentially lead to additional revenue for your business.
Buyer data is most commonly gathered from search engines like Google and Bing via programmatic surveillance tools such as ad networks and affiliate marketing networks (AMNs). These programs monitor all clicks made on web pages by end-users and then compile that information into a form that can be easily and immediately analyzed.
This data is then sold to marketing teams who make use of the information in their customer acquisition and retention efforts. Popular AMNs for this type of data include Google Affiliate Network (GAN) and LinkShare.
Traditionally, marketing teams have relied on web analytics data to gain insight into the performance of their marketing campaigns. This type of data is limited in what it can tell you by the URL that it traces back to—meaning that if a user opens up two different pages from your website in their browser, they will both be traced through analytics with just one unique identifier.
This data works differently by offering much more granularity when it comes to recognizing browsing behavior and user intent. This data differs from web tracking as it does not focus on recording metadata such as what end-users click, but rather on what they search for, find interesting, and ultimately purchase through those searches.
Machine learning technology has enabled search engines to gather massive amounts of user data with ease and accuracy—this lets savvy marketers know exactly how much revenue could be gained by putting a product in front of potential buyers at various stages of the buying cycle.
The most effective way to use buyer data is by using it as a complementary set of keywords that boost the relevancy of search ads on a given platform or within a certain industry.
For example, if a company sells industrial robotics and uses ad campaigns with Google's advertising service, they will want to utilize specific terms that people would likely type into a search engine when looking for industrial robots in order to boost their exposure in searches where those words are used. If successful, this can lead to an increase in click-through rate (CTR) which directly impacts the amount of revenue brought in overtime—and here is how you can do it.
Using such data is incredibly straightforward, but requires an acute understanding of your potential customer base and their general preferences in terms of products and services.
By studying this methodically you can discover trends in terms of how much revenue could be generated by focusing on certain keywords at different stages of the buying process. Once these have been identified it becomes much easier to target high-value customers with well-placed marketing campaigns that will lead directly to increased sales—if done correctly!
When using this data it is important not only to note when a customer enters the market but also how long they are in there before taking them out again. This will inform marketers about whether or not they are fast followers, trendsetters, or habitual late adopters for a certain product type.
Predictive data is a powerful marketing tool that changes the way companies approach their customers from a marketing perspective. This type of data allows marketers to build out buyer personas at a much more granular level by understanding how potential customers might behave based on what they have done in the past.
In other words, this predictive data can be thought of as intent-driven demographic and psychographic information that gives you insight into which products specific types of people are going to buy, when they plan on buying it, and how much they are willing to pay for it.
It is essentially the opposite of user’s data—this method looks deeper into metadata to identify trends in different types of business activity.
For example, if your company sells accounting software you might be interested in knowing how many firms are actually making full use of their solution for invoicing and core financial functions versus just using it for easy management reporting functionality. This type of information can help you target high-value customers with tailored messaging that suggests they improve their core business processes to better leverage your technology—they may become keener on the prospect of doing this than they would have been without your knowledge.
Targeted data of buyer’s intent can also allow sales teams to better understand who their customers are and leverage that information for personalized outreach. While it is important to keep an eye on where your prospect stands in the buying process you should be even more concerned with being able to identify if they have become a very viable customer.
This means understanding how quickly they tend to move from research into early consideration, which will give you more insight into whether or not they could buy very soon but just aren't quite ready yet or if they are a slow mover that still needs convincing before being willing to spend money.
In addition, this type of predictive data allows marketers and salespeople alike not only to look at where someone is in the buying process but also to see what factors tend to push them from one stage into the next. This is incredibly useful for making more accurate projections about when a lead will be ripe for conversion and if it even makes sense to proceed with further outreach at that time.
While machine learning has enabled search engines to gather massive amounts of data, this information also presents a challenge for marketers. It can be difficult to figure out how best to target potential customers at each stage of the buying process. By having access to this data it is possible for sales teams and marketing ops folks alike to not only pinpoint entry points into the sales cycle.
But it also helps identify high-value opportunities that could be lost if targeted too late or not at all. Creating promotional campaigns that include well-defined messaging and an offer that provides true value (e.g., special end-user pricing) will help capture these deals before they slip through your fingers.
Buyer’s data of intent can be used in conjunction with other types of B2B data but it would also make sense to use all of this information in concert with external demographic and psychographic data, which can be purchased from third-party vendors offering "big data" solutions.
In fact, these types of datasets are quite popular among businesses because they allow marketers to get a more comprehensive view of their prospects from across the web—this entails not only seeing what products or services have been discussed by prospects but also who is responsible for talking about them.
Another tactic that may be employed with data is to compare it against actual buyer behavior. You can uncover which factors tend to motivate people the most when they are in the process of determining whether or not to buy something—this will show you what aspects of your product are really moving the needle and which ones don't seem to matter as much, which will make crafting more effective sales pitches somewhat easier.
In addition to this data data from search engines, marketers can also leverage account-level information that is collected from CRM systems. This type of information may include revenue, number of employees, and similar metrics. Marketers should understand these criteria so they can see competing firms as a whole rather than just a list of names and titles.
If your potential customer base fits within a certain industry or vertical then using this combined "fit" data will be extremely valuable in helping you focus on the right leads at the right time.
In addition, to fit data, predictive assessments are also widely used for targeting—if you know how likely it is that a lead may purchase your product then it becomes much easier to focus marketing efforts on those deals that will be the most profitable.
When evaluating intent platforms for purchase there are a number of characteristics that should be carefully considered prior to make a final decision. These criteria are more important than others, depending on your needs, but they all deserve some degree of scrutiny during the due diligence process.
First, you should determine how much historical data is available within the platform. The more information it has access to, the more effective it should be at making future predictions—this should be one of your top considerations when considering potential solutions because if you can't use an intent platform then there's no point in having one installed in the first place.
Second, look at what kinds of actions are included within each dataset (e.g., linking multiple social media accounts), as this will affect overall accuracy and may reveal insight into other aspects of your business that could help increase revenue or reduce expenses.
You should also take a close look at its ability to aggregate insights from multiple web properties—this functionality is important because many businesses have an online presence that spans multiple websites and social media networks.
Finally, evaluate whether or not the intent platform you're looking at is adaptable to other systems—many businesses have a custom-built marketing automation solution already in place which may need to be integrated with the formula provided by any new technology they decide upon.
In addition, it should be scalable both in terms of account size and the amount of history available for analysis—the more people there are using an intent platform, the more accurate its predictions will become because it will be learning from a larger pool of insights. Furthermore, it would make sense to look at how much historical data can be stored if this plays a major role within your business model.
While these methods of buyer intent data collection and analysis may seem like something straight out of a science fiction novel, many companies are already using them today to better understand their buyers and generate more qualified leads; they simply don't know it yet!
We've seen major tech firms such as Google, Amazon, and Microsoft all dive into this technology in an effort to:
(i) gather search intent data from users who may not even know what they want;
(ii) determine how best to target these prospects; and
(iii) create custom content via machine learning algorithms.
These will only continue to get better with time—so if you think about it like strategic warfare, you clearly want to be on the right side of The Iron Curtain.
