For decades, marketing research companies have used buyer intent to test new products, packaging, advertising, brand awareness, brand loyalty, and much more. In fact, buyer intent expressed through a purchase intent scale is one of the most time-tested and popular measures employed by researchers.
Even though companies use purchase intent scales 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 real customer interactions.
With the development of artificial intelligence, however, there are now lots of new possibilities for using purchase intent scales to identify and evaluate buyer intent. To start, let’s recap what a purchase intent scale actually is.
What Are Purchase Intent Scales?
A purchase intent scale is essentially a way to measure how much someone is interested in any given 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.
For example, consider one of the most simple and widely used purchase intent scales:
- 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. 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, 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. 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 its purchase cycle — the longer the cycle is the lower the probabilities generally are.
Another, more involved scale comes from Prof. Thomas Juster and has 11 points:
- 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.
The Disadvantages of Purchase Intent Scales
It’s easy to see how judicious use of a purchase intent scale can give researchers valuable information, predicting 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 new, innovative products (since people often don’t know if they want them yet). Similarly, products that are expensive tend to get fewer enthusiastic responses.
You should also consider how you’re presenting your survey. The quality of the presentation, along with images and 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.
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 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 can work together with your sales team.
Automate Purchase Intent Scales with AI
Since most sales today start with the buyer 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 digital body language they navigate your website.
Here’s how it works:
- A visitor comes to your website
- Lift AI predicts the visitor’s exact buyer intent with over 85% accuracy, based on matching their behaviour vs the modelled behaviour
- If the visitor has a high score, Lift AI can connect them directly to your 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 a self-help guide
The process is repeated, regardless of how many website visitors you get. 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 at all.