How first party intent data usage eliminates waste and drives faster sales.
According to a recent survey by Digital Commerce 360 B2B buyers tend not to be impulse purchasers (as we know!) and they do a lot of research in making purchase decisions for their companies. Over 62% visit up to 3 websites before entering a purchasing cycle while 37% can search 4 or more in the process. They found that on average a B2B purchaser will conduct 12 searches before engaging on a particular site. This means the digital trace left in those searches and visits provides a lot of data about a buyer’s intent.
However the Aberdeen Group states that most B2B companies have ‘no idea’ when it comes to identifying companies currently in-market to buy what they sell. Indeed, the majority of B2B marketing budgets are spent trying to make up for this lack of visibility with highly inefficient and non-scalable tactics. Much marketing automation has been centred around marketing dollars and sales time wasted on companies that will not purchase.
One of the biggest problems that marketers face is that despite the copious amount of content generated for marketing campaigns, they find it difficult to personalise at scale. Traditional marketing automation is providing too much waste in that whilst many of the activities to coordinate triggers and responses to campaigns are covered, the underlying understanding of what potential customers want or need are not. Content marketers will need to have access to superior, advanced tools and technologies, enabling them to better analyze the content they create. Deeper insights will allow them to effectively predict content performance and patterns in audience engagement.
The most recent approaches to solve this issue is to use intent data from predictive analysis, to predict the next needed content for the buyer journey.
When you search for something online or visit a website, you are expressing an interest in particular topics. If you were to read more posts which contained the topic then it would be reasonable to infer that you are highly interested in that topic.
In the case of B2B intent data, it can take into account factors such as the sites a buyer is visiting, products or services that they are researching and the level of purchase intent they are exhibiting. Since most B2B purchase journeys require lots of research, these content interactions can be very predictive of buyer intent.
Aberdeen group states, by using first party Intent data It is possible to determine, well ahead of a buying decision, which companies are in-market to buy, with up to a 91% accuracy.
Companies such as Idio can help eliminate the waste from marketing campaigns, leveraging intent data by tracking content consumption, tagging topic metadata and then linking to individuals. In this way a company is able to build an interest profile for any individual purchaser and visualize intent. By identifying the buyer and intent, the most relevant pieces of content can be placed into marketing campaigns automatically to suit the next best step in the buying journey.
The activity can remove the manual segmentation that Marketers are spending fruitless hours on. To be able to provide a true 1:1 engagement process at scale, in the buying cycle is key to greater personalisation and winning more sales.
In addition using intent data can personalise your website experience to identify anonymous users. Marketers can use the personalisation to build brand loyalty, understand the needs of visitors, speed registrations and upsell/cross sell products. and help nurturing through personalised emails.
For Jon Ewing, CMO at Fitch Group has talked of the value it has given him, “Using an integrated user intent engine for website personalization and content personalization. Suddenly, on your first-ever visit to the site, we could show you something that would resonate with you. That was the Holy Grail and it actually worked. This is a huge competitive advantage for us.”
The more a company can predict the behaviour and ‘next best’ piece of content needed through predictive analysis, so marketers are able to predict purchase trends and patterns.
By integrating into the marketing automation process there is less likelihood of waste and more possibility of sales.