What is lead scoring: which prospect to call back first

When enquiries are few there is no need for a score; everyone gets a call. Once the web form, WhatsApp, a trade fair list, and the phone all bring enquiries in the same week, the salesperson builds the order by instinct. Instinct usually puts the latest or the loudest first.

Scoring ties that order to a visible rule. When the rule is written down, two salespeople give the same enquiry the same priority, and a manager can ask why a company is waiting. The term itself, the definition of a prospect or lead, comes before scoring: if it is unclear what counts as a lead, it is unclear what is being scored.

Fit information is usually written at first contact: sector, city, team size, the role of the person who got in touch. Behaviour information builds up over time: visiting the pricing page, asking for a demo, asking about the quote again. If the two are not kept apart, a very interested enquiry that does not suit you overtakes a suitable company that has not spoken up yet.

A good start is looking back at closed work. What did the customers you won have in common, and which sign was there from the start in the quotes you lost? The answers give you the list of criteria. Keep the list short; the reason each criterion is there should fit in one sentence.

• Split closed work into won and lost, and write down what each group has in common.
• List fit and interest criteria separately.
• Give each criterion a high, medium, or low weight; the team sets the figures itself.
• Write down the hand-over threshold and what happens to enquiries below it.
• Compare the model with won deals at regular intervals and correct it.

Example scenario: picture a company in Bursa selling industrial kitchen equipment. If a look at past work showed that most won quotes came from hotel and hospital kitchens, while enquiries from cafés did not close after long exchanges, it would give the sector criterion a high weight. The same company could put every demo request at the top of the salesperson's list.

Once the model is built, the first job is to test it. Score last quarter's enquiries with the new model after the fact and check whether the deals you won gather in the top half of the list. If they do not, the weights are wrong or the chosen criteria do not explain winning. Repeat this test whenever the product or the target market changes.

If a company that asked about prices months ago and has not written since still sits at the top of the list with a high score, the model has gone stale. The interest score should fade over time. In the same way, giving a low score to someone who left the city field blank makes the enquirer pay for your failure to collect the data.

The most expensive mistake is a split definition. If marketing calls everyone who fills in a form a lead while sales counts only companies whose budget has been discussed, the score becomes an argument between two teams and nobody trusts it. Before scoring starts, both teams put one definition in writing.

An enquiry that passes the threshold is handed over to sales and assigned an owner. The reason goes with the score: which criterion came out high and which channel the enquiry came through. The salesperson starts the first call from that reason and reaches the customer with a sentence that returns to what they wrote, rather than a question from someone who does not know them.

An enquiry below the threshold is not thrown away. For a company not yet at the decision stage, marketing sends useful content; as interest grows the score rises and the enquiry comes back to sales. For this loop to work, the record should also say who holds the enquiry below the threshold; an enquiry with no owner reaches nobody, however high its score climbs.

Article 11(1)(g) of Law No. 6698 gives a data subject the right to object to a result against them that arises from the analysis of their data exclusively by automated systems. If a score kept about a company contact leads to that person never being called, you are close to the scope of that provision. The Personal Data Protection Authority's recommendations on artificial intelligence also ask that the role of human intervention be established in decision-making.

In practice this means the score stays a suggestion. Closing a low-scoring enquiry or not calling it at all happens by a salesperson's decision, with a reason visible on the record. This article is not legal advice; as the data controller, discuss your own purposes of processing with your legal adviser.

A record opens at the Prospect stage and moves to In contact, Opportunity, Customer, and Loyal customer as the work progresses; the current stage is marked. The source of the opportunity is kept on the record: WhatsApp, the website, email, phone, an office visit, a referral, Instagram, or a site visit, for example. The source criterion of a scoring table can be read from that field. On the customer list an AI Insight card can flag a company as a high-potential opportunity; that flag is a suggestion, and the representative decides whom to call back first. You can see all the tabs of the record on the Corex360 CRM module page.