Updated November 3, 2021.
For decades, marketing research companies have used buyer intent to find ideal customers and test anything from product recommendations to new navigation elements on a landing page to a revised checkout process. In fact, there has been more than one marketing study proving the positive relationship between indicators of purchase intent as measured by the purchase intent scale and a significant uptick in active buyers.
Even though companies use purchase intent analysis for a variety of purposes, the process is still predominantly manual and proactive, which also means it’s slow and covers just a fraction of your audience purchase intent and ignores lots of potential customers.
With the development of decision-making tools powered by artificial intelligence, however, there are now lots of new possibilities for using purchase intent scales to identify a wider customer base and evaluate their buyer intent more precisely. To start, let’s recap a definition of a purchase intent scale and see what impact on purchase intent it actually has.
What Are Target Audience Purchase Intent Scales?
A purchase intent scale is essentially a way to measure how much market consumers are interested in buying your offering. Doing so accurately is not easy or straightforward. Since buyer intent data can be used in future modelling or forecasting, any discrepancy is likely to have exponential effects on your buying process and buying cycles in general.
For example, consider one of the most simple purchase intent scales used in a variety of marketing funnels:
- Will definitely buy
- Will probably buy
- Might or might not buy
- Will probably not buy
- Will definitely not buy
Each of the suggested responses must have a certain probability that expresses buyer intent, which can range from informational intent to transactional intent. Assigning a linear scale here (100%, 80%, 60%, 40%, 20% or similar) would not lead to accurate results — the difference between “definitely” and “probably” on the “buy” side is much larger than on the “not buy” side.
That’s why researchers use other variations for different segments, such as the powers of three, where responses are weighted by dividing them by three, so you get 81%, 27%, 9%, 3%, 1%. A similar technique assigns 75% to the “definitely buy” response, 25% to the “probably buy”, and 0% to all others (thus only counting those who see your offering in a positive light). Such techniques might vary from one test to another and depend greatly on the product or service in question, the price and the length of the purchase cycle — the longer the cycle the lower the probability for any action generally is.
Another, more involved scale, featuring 11 points, comes from Prof. Thomas Juster:
- Certain, practically certain (99%)
- Almost sure (90%)
- Very probable (80%)
- Probable (70%)
- Good possibility (60%)
- Fairly good possibility (50%)
- Fair possibility (40%)
- Some possibility (30%)
- Slight possibility (20%)
- Very slight possibility (10%)
- No chance, almost no chance (1%)
You can see how the Juster scale is almost linear because it offers better discrimination. Despite this, this scale is rarely used, as companies tend to default to questions with fewer suggested answers.
How Purchase Intent Scales Affect Price Elasticity and Buying Decisions
It’s easy to see how judicious use of a purchase intent scale can give researchers valuable information and display impressions that predict the general interest in the market. It’s simple and easy to act upon. But it also has a few issues that everyone should be aware of.
For example, buyer intent tends to be skewed when applied to innovative products (since people often don’t know if they want them yet) or significant product enhancements (with new features and use cases). Similarly, products that are expensive and have longer buying cycles tend to get fewer enthusiastic responses — that makes price elasticity really difficult to accurately measure.
You should also consider how you’re presenting your audience purchase intent survey. The quality of the presentation, along with which content types are being used (e.g. images, videos, copy), plays an important role in the product evaluation.
If you find the factors above intervening with your process, you could try switching your purchase intent scale to a rank-based system, or even something completely qualitative, such as gathering email addresses and sending personal emails to discover informational intent.
For research purposes, buyer intent is a diagnostic indicator and frequently not the only measure of evaluation. For sales teams, buyer intent is somewhat different — it allows you to easily pick leads and potential customers who are interested in your product the most and focus your human resources on converting them specifically. The problem is merging the best that purchase intent scales have to offer with an automated system that plugs into your buying process and works together with your sales team.
How AI Can Segment Content and Products for Your Customers
Since most sales today start with your ideal customer base doing online research (and many are completed digitally), you don’t have to send a team of researchers out to determine buyer intent offline. In fact, you can do so instantly, right on your website…
If you have the right tools.
Lift AI is the leading buyer intent solution that does this for your company’s website today. Relying on the power of its machine-learning model trained on more than one billion data points and 14 million live sales engagements, Lift AI doesn’t even need to ask your website visitors about their buyer intent — it simply already knows by reading their transactional intent through digital body language as they navigate your website.
Here’s how it works:
- A visitor comes to your website through one of your marketing funnels
- Lift AI predicts the visitor’s exact buyer intent with over 85% accuracy, based on matching their actions and decisions vs the modelled behavior
- If the visitor has a high score, Lift AI can segment them from the overall traffic and connect them directly to any available BDR through your chat platform for conversion
- If the visitor has a medium-to-low score, Lift AI can assign them to a nurturing bot or display various self-help content types
The process is repeated, regardless of how many market consumers visit your website. Lift AI is able to predict leads with high buyer intent, which constitute on average 9% of website visitors, with 85% accuracy. Those numbers also include completely anonymous visitors (about 98% of the total) who can’t be easily identified any other way.
You can even try Lift AI free for 30 days today. On average, companies see their chat conversions increase two to 10 times within the first 90 days, since their BDRs spend their days talking to leads who have the highest likelihood to buy.
It’s safe to say that the new era of using purchase intent scales is here, only that we don’t have to use them manually anymore to see their impact on purchase intent at all.