Updated on July 26, 2023: We’ve simplified the article’s structure and added up-to-date metrics and examples.
For decades, marketing research companies have used buyer intent to find ideal customers and test new assumptions, anything from product recommendations to website navigation to a new checkout process.
Buyer intent works. More than one marketing study has detected a positive correlation between indicators of purchase intent and the number of active buyers.
While companies use the purchase intent analysis for various purposes, the process remains manual and proactive. As a result, purchase intent detection takes a long time and covers a fraction of the potential audience.
Everything is changing, however, with the development of decision-making tools powered by artificial intelligence. These tools present new possibilities for using purchase intent scales to identify a wider customer base and evaluate buyer intent with more precision.
How should we define a purchase intent scale? And how can we measure its impact on improving your conversions?
Tip: Automate the use of purchase intent scales with Lift AI. This tool instantly identifies buyer intent and connects your team to the best leads using your marketing tools.
What Are Purchase Intent Scales for Your Target Audience?
A purchase intent scale is a way to measure the interest in your product offering by the market.
However, companies using purchase intent scales have to be careful. Since buyer intent data is used in future modelling and forecasting, any discrepancy will have an exponential effect on your buying process.
Here’s one of the most commonly 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 above responses has a certain probability that expresses buyer intent, ranging from informational intent to transactional intent.
Assigning a linear scale to purchase intent (e.g. 100%, 80%, 60%, 40%, 20%) doesn’t lead to accurate results, since 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 of the purchase intent scale. For example, the power of three, where the responses are weighted by dividing them by three. Thus you get 81%, 27%, 9%, 3%, 1%.
Another technique assigns 75% to the “definitely buy” response, 25% to the “probably buy,” and 0% to all others. As a result, it only counts those who see your offering in a positive light.
Picking the best scale for your company depends greatly on the product or service your offer, and the price and the length of the average purchase cycle. The longer the cycle the lower the probability for any positive action.
A more detailed 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, it’s rarely used, as companies tend to default to questions with fewer suggested answers.
How Purchase Intent Scales Affect Price Elasticity and Buying Decisions
A judicious use of purchase intent scales can give researchers valuable information that predicts the general interest in the market. The results are simple and easy to act on. At the same time, there are a few issues with purchase intent scales that everyone should be aware of.
For example, buyer intent tends to be skewed when applied to innovative products (since people 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 difficult to measure accurately.
You should consider the way you’re presenting your audience with a purchase intent survey. The quality of the presentation, along with the content types being used (e.g. text, images, videos), plays an important role in the product evaluation.
If the factors above intervene with your process, you could try switching your purchase intent scale to a rank-based system, or even a qualitative one, such as sending personal emails to discover informational intent.
For research purposes, buyer intent is a diagnostic indicator and is rarely the only measure of evaluation.
For sales teams, buyer intent is somewhat different. It allows you to pick leads and potential customers who are interested in your product the most and focus your sales efforts on converting them.
Until recently, a difficult problem was 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 Purchase Intent Automatically
Since most sales today start with your potential customers searching for solutions online, you don’t have to send a team of researchers to determine buyer intent online. You can now do that instantly, right on your website.
That is if you have the right tools.
Lift AI is the leading buyer intent solution that uses purchase intent scales to evaluate every website visitor.
Relying on the power of its machine-learning model trained on more than one billion data points and 15 million live sales engagements, Lift AI doesn’t ask your website visitors about their buyer intent — it knows it by reading their transactional intent signals (actions) 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 by matching their actions and decisions to the modelled behavior
- If the visitor has a high score, Lift AI can segment them from the general traffic and connect them directly to any available BDR through chat (or another marketing tool) 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 content guide
The process is repeated, regardless of how many customers visit your website. Lift AI is able to accurately predict leads with high buyer intent, which constitute on average 9% of website visitors. That includes anonymous visitors too (about 98% of the total traffic) who can’t be easily identified any other way.
The best part — Lift AI works as soon as you paste its JavaScript snippet to your website and easily integrates with your marketing stack (e.g. LivePerson or Drift chat).
Companies that use Lift AI see their chat conversions increase two to 10 times within the first 90 days. For example, PointClickCare increased its conversions by 400%, adding $1M of extra revenue thanks to the Lift AI integration. Formstack grew its conversions by 420%.
Try Lift AI free for 30 days. See what’s possible when your BDRs spend their days talking to leads that have the highest likelihood to buy.
The new era of using purchase intent scales is here. We don’t have to deploy them manually anymore to see the full impact on identifying purchase intent.
