Fewer but Better Inquiries: Filters on Your Website

Why more inquiries are not always better

A business is pleased with 40 inquiries a month, twice as many as the previous year. After three months, sales draws a different conclusion: a third came from private individuals the business does not serve at all, another quarter sought a service it does not offer, and many expected a price far below any realistic proposal. The conversations cost time that was missing for the suitable prospects.

This guide therefore answers the question: How does a website filter out unsuitable inquiries before they arise, without deterring suitable prospects? We first clarify what a good inquiry is and introduce a model from information economics that explains how self-selection works. Then we look at findings on following up inquiries, weigh two views and go through four filters on the website.

The best filter for inquiries is honest information. Anyone who knows whom an offer is meant for sorts themselves.

What is a good inquiry? Terms and the 4P filter

An inquiry here is any contact via the website with a genuine request, as the article on measuring the website’s inquiry rate defines it. The inquiry rate is the number of inquiries divided by the number of visitors in a period. Inquiry quality describes how many of these inquiries fit the business and have a realistic prospect of an order.

How to recognize quality is described in the article on lead qualification with the 4P filter. It checks four things: fit, meaning whether the inquirer belongs to the customers the offer is built for, problem, person and plan. Fit and problem can be checked early and without awkward questions; person and plan are clarified in the first conversation. The website’s task follows from this: it can make fit and problem visible before the inquiry.

The thinking model: screening and self-selection

In 1975 the economist Joseph Stiglitz described screening theory. It deals with the same situation as Spence’s signaling theory, which the article on trust signals on your website explains: one side knows more than the other. In signaling theory, the side with the knowledge acts and sends signals. In screening, the side without the knowledge acts. It designs its offers so that the other side reveals, through its choice, which group it belongs to. This is called self-selection. An insurer, for example, offers a plan with a high deductible that mainly cautious customers choose.

For a website this means: the business does not know whether a visitor fits. The visitor often does not know either, because details are missing. If the website states openly whom the offer is meant for, roughly what it costs and what it requires, the visitor can check for themselves whether they belong. Suitable prospects feel confirmed and inquire more easily. Unsuitable ones leave without costing time.

For the visitor, a second logic comes in, which the article on the website inquiry rate describes with Fogg’s behavior model: an inquiry arises when motivation, ease and a trigger come together. A good filter lowers the motivation of the unsuitable and raises that of the suitable, without worsening ease for either.

What studies show about handling inquiries

Research on the so-called sales lead black hole shows how costly poor inquiries are. Sabnis, Chatterjee, Grewal and Lilien (2013) report from earlier studies that B2B sales reps never contact around 70 percent of the inquiries generated by marketing. They themselves point out that the figure also reflects volume: when marketing delivers more inquiries than anyone can handle, many inevitably go unanswered. Their own study draws on data from 461 sales reps at four companies. The more experienced the reps were, the less they responded to managerial tracking and to the sheer volume of inquiries, and the more strongly to the quality of prequalification.

For the website itself, a finding on landing pages is instructive and rather calls for caution. Dvir and Gafni (2018) found in two web experiments with 535 and 27,900 visitors that a shorter landing page with an identical form led to an inquiry considerably more often than a long one. Less content meant more willingness to provide one’s own details. For filters it follows: they must be short. A sentence on the target group or a price range barely lengthens a page; three paragraphs of conditions do.

The figures come from large US companies with their own sales teams. For a small business they show the direction: where time is scarce, the quality of inquiries decides whether they are handled at all.

Filter or open up? Two views

The first view warns against filters on the website. Every hurdle costs inquiries, good ones too. A price range deters before the business has been able to explain its value. A clear limit on the target group excludes customers who might have fit after all. Better to win as many inquiries as possible and sort them in conversation. This is supported by Dvir and Gafni’s finding that shorter pages bring more inquiries.

The second view considers filters necessary. Every unsuitable inquiry costs a conversation, a rejection and sometimes trouble. When conversation time is scarce, unsuitable inquiries crowd out suitable ones, as the finding by Sabnis and colleagues suggests. And anyone who withholds prices mainly attracts those hoping for a low price.

I consider one distinction to be the solution: filter through brief information, not through effort. Open details on target group, price range and prerequisites cost the visitor hardly anything if they fit into a few sentences. They help the suitable visitor and redirect the unsuitable one. Mandatory fields, registrations and long questionnaires, by contrast, cost everyone effort, including suitable visitors. A website should avoid these filters. The rule from the article on the inquiry path on the website remains: at most five fields, a second step optional.

The website’s four filters

Four filters follow from the model, which I call the website’s four filters. They follow the visitor’s path from the first screen to clicking send.

1. Target group with a limit

   The first screen and every service page state whom the offer is meant for, with a limit: size, industry, area. A sentence such as “We do not offer this service to private households” saves many conversations.

2. Price range

   A starting price, a range for typical cases or a price example. Anyone with a minimum order value states it. This filters out the hope for a low price.

3. Process and prerequisites

   What happens after the inquiry, how long does it take, what must the customer bring? Anyone who reads this knows whether they are ready now.

4. Two form questions

   One choice about the request and one about company size or area, as selection fields, not free text. That clarifies fit and problem before anyone calls.

For the second filter, the article on setting a minimum order value helps. What a price range on the service page looks like is described in the article on the service page, with two views on stating prices. And the fourth step follows the principle of small commitments: choosing the request is the first, easiest question at the start.

What the filters achieve: a worked example

In the article on lead qualification, 100 inquiries in a quarter pass through the 4P filter: 62 meet fit, 40 have a recognizable problem, 18 become A inquiries, 7 become orders. The following worked example shows the same quarter after the four filters were introduced.

The number of inquiries falls by more than a third, while the number of orders stays the same or rises slightly. What matters is the room gained: instead of 38 conversations with inquiries lacking fit, there are only 9. The time flows into the A inquiries, which are handled faster. How sales hours are distributed by classification is shown in the article on lead qualification.

What follows in general

Filters on the website are a form of openness. A business that says whom it works for, roughly what it costs and what it requires forgoes a few inquiries and gains trust among those who stay. It treats visitors as partners who can decide for themselves, not as contacts to be captured first. This stance relieves sales and strengthens alignment with marketing, which is no longer measured by volume but by conversations and orders.

The limits lie in the evidence. The follow-up figures come from large US companies, the findings on forms from experiments with consumers. The worked example is freely chosen. The model is robust: disclosing terms enables self-selection, and self-selection costs less than any conversation.

What remains and how to start

We set out to ask
a website filters out unsuitable inquiries without deterring suitable prospects. It filters through information, not effort: with a target group statement that names a limit, a price range, a process with prerequisites and two short selection questions in the form. According to screening theory, these open details enable self-selection. Suitable visitors recognize themselves and inquire more easily; unsuitable ones leave without costing time. Success is measured not by the number of inquiries but by A inquiries and orders.

• Count the inquiries of the last three months using the 4P filter: how many failed on fit or problem?
• Add a sentence on the target group with a limit to the home page and service pages.
• Publish a price range, a price example or the minimum order value.
• Add two selection questions on request and size or area to the form, without adding mandatory fields.
• After three months, compare A inquiries and orders with the previous quarter.