Search and Discovery writer
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What does a typical online shopper’s digital experience consist of before making a purchase decision and heading to the relevant product page?
Let’s say you’re a gourmet barbequing aficionado, and you see an ad for a highly rated pellet grill on your favorite social media site. Hmm; that looks fantastic, you think, but right now you’re tracking down a vacation photo of friends.
The next day, thanks to your web activity being subtly tracked (hah; it’s so obvious), another ad for this great-looking grill pops up just as you’re having your double latte and reading the latest foodie news. After yesterday’s apparent starting point, you can’t help but be intrigued by these new barbeque notifications. And this time, as you see the photo of smoking, juicy ribs on the grill, you can’t resist a click.
Lots of people are going through this same sort of process before they shell out the bucks for a coveted item on their to-buy list. People shopping online spend 50% of their time seeking out information from third-party intent data sources (like review sites such as Yelp, which contain backlinks), according to Gartner. One reason is probably because third-party information, positive or negative, seems more neutral or trustworthy, as opposed to that glowing marketing copy that website visitors might fall prey to on a product detail page.
Back to your new search for the grill. You’ve decided to look up this gorgeous item by entering your target keywords “pellet grill” on Amazon and reading everything that its thrilled (and disgruntled) buyers have to say about it.
How do they rate their purchase? An average of at least 4.5 stars is your requirement. Do any of those reviews look like they were solicited by the manufacturer in exchange for a free grill? What are the most convincing new customers’ raves saying? What are the one-star-rating buyers incensed about (like did a thief make off with the grill before they could open their front door and haul the box inside)? Is there any other notable customer behavior?
If you’re OK with the mix of ecommerce review commentary, you head to the manufacturer’s site to check the detailed specs and retail price, and further mull over your possible purchase. Is there any reason (free shipping or simple returns?) to buy this beast on Amazon vs. ordering it from the seller?
You decide to sleep on it. Customer data aside, maybe tomorrow you’ll get some opinions from actual humans you know who enjoy barbequing.
If you’re a B2B buyer looking to make a purchase, the process could unfold in a similar way, only on a more business-level scale. With B2B, you might alert a couple of your company team members to check out the product and see if you all confer that it’s the right move. The three of you might spend hours logging search queries, reading blog posts and related information, perhaps even downloading an eBook put out by the manufacturer. Finally, you make the business’s day by hitting the Purchase button.
All of this detailed shopping data figures into our hot topic for today, buyer intent.
Now let’s switch your hats and say you’re a marketer with visibility into the grill-hunter’s omnichannel customer journey.
As someone interested in using customer intent data, you take note of all of these relevant content consumption moves and signs of prospective buyer interest. You count the number of times your target accounts have opened your promotional email, how many minutes they’ve spent on different pages on other organizations’ websites, how many clicks they’ve registered on your own site, anything that seems to indicate an intention to buy, and if not buy right away, then purchase eventually. You’ve made a dent in your user intent analysis.
So for the record, buying intent, also known as purchase intent, is the probability of how close a consumer or B2B buyer is to completing a purchase of a product or service from a company.
It’s the person’s buying intent as deduced by their browsing activity and entire online journey, which is deduced from data collected through caches or their online content-consumption footprints.
What’s so great about accurately inferring someone’s buyer intent insights, as compared with, for instance, collecting just demographics segmentation data (which might be used in classic personalization techniques)?
Artificial intelligence (AI) and machine learning are substantially impacting how content marketing efforts work. Basically, indicated buyer intent supplies the means to predict who’s truly ready to buy based on their shopping journey touchpoints. It allows for behavior prediction. And if you can intelligently predict a buyer’s moment of forking over their dough, plus address any of their pain points standing in the way, well, there are all sorts of things you can do to capitalize on that crystal-ball knowledge.
When you can get your hands on valuable buyer intent data, you may be able to proactively provide enough of the right information to encourage your qualified leads to take the plunge.
As you can see, equipped with real-time insight on buyer intent, marketers and sales teams can come out way ahead.
Online marketers have coined the terms search intent, searcher intent, user intent, navigational intent, informational intent, transactional intent, buying intent, buyer intent, and customer intent. Intent is clearly a hot commodity. What’s the difference between all of these types?
With the goal of fully understanding consumer intent, types of user intent can be parsed in even more subtle ways, say those in the know, including:
This blog post is geared toward bumping up your business’s bottom line, so shall we agree that all of these intent-oriented variations are basically just good-old buyer intent? If someone’s interested in buying and they’re progressing through the buying cycle, whether they’re people on computers or mobile shoppers having micro-moments, they possess buyer intent.
Speaking of consumer behavior, let’s not forget the importance of the subset of buying signals from B2B buyers.
Buyer intent also applies to the potentially more-lucrative gains possible from rounding up and accurately analyzing B2B buyer intent data. B2B marketers could hit the jackpot if they’re able to use B2B intent data to approach a shopper at the optimal point in their customer journey, such as right before the buying decision. Plus, B2B sales industry rewards appear to be rather ripe for the picking: the sales reps in this sector are utilizing only 46% of the available intent data and monitoring tools, according to Demand Gen Report. So there’s lots of room for opportunity in the world of B2B intent.
Deciphering buyer intent signals lets you:
You collect buyer intent data, you analyze it, and you let your marketing gang loose to update your marketing campaigns. You stop spinning your wheels trying to sweet-talk ambivalent shoppers based on sketchy metrics. You narrow your focus to hook the big fish, lower your bounce rate, and improve your ROI.
With a solid understanding of customer needs and potential buyers’ intent, Marketing can:
How difficult is it to correctly identify high intent on the buying journey of your potential customers and use buyer intent data to improve your customer experience, as well as your bottom line?
If you’re not an analytics company, intent analysis can be a bit, well, involved.
But there’s a solution: you can team up with an expert partner, a SaaS provider like Algolia, which can help you expertly illuminate the buyer intent data points of your target audience like a pro and optimize your website marketing to boot.
If your intent is to leverage your user search intent and breathe new life into your digital marketing for better customer retention and a prospective jump in your conversion rate, there’s no time to waste. Jot down your contact information on this landing page and let’s get rolling.