Synchronization of marketing and sales

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For example, our practice has shown that Group A leads (the most ready to buy) do not need to be warmed up with special offers and discounts. But less decisive Group B leads already need more attention from sales.


Transparent and clear lead evaluation criteria help to establish interaction and build more coordinated work between teams.

Both teams also have a common understanding of which leads are considered list of albania phone number quality for the transition from marketing to sales, and can assess the quality of the pipeline without waiting until the close of the deal cycle.

Types of Lead Scoring
Pardot scoring (used by Salesforce) is based on behavioral indicators. You assign points for certain actions (Pardot assets). And if filling out a form is more valuable to you than visiting a pricing page, then you assign more points to the form. And so, the more points a user gets, the more engaged they are considered. The hottest ones are transferred to sales.

Predictive scoring captures signals at the device level, page speed, etc. This is scoring based on big data: people who behaved in a certain way on certain pages are more likely to buy with a certain conversion.

Here, you use machine learning to analyze huge amounts of data and identify hidden patterns of behavior. This method allows you to predict which leads have a high chance of winning a deal, even if their actions do not seem to stand out at first glance.

Scoring based on powerful questions - we use exactly this kind of scoring at Dashly:). By receiving answers to questions, we collect data on what conversion is shown by different groups of leads that gave certain answers.

For example:

Lead Tiers
And our task is to find groups that differ significantly in conversion to sales based on the answers in our quiz. And then build marketing and sales work based on these groups. Roughly speaking, divide leads into three priorities: group A - the hottest, group B - average, group C - cold.

There are two ways to identify user groups and strong questions:

Historical data: questionnaires that the client had (NPS), data from sales (managers enter deals into the CRM and this data can help formulate strong questions);
Collect data from scratch.
What's good about scoring based on responses: it doesn't require any complex technological solution to implement, and it's more accurate because we get the lead's characteristics from the lead himself, rather than guessing based on behavior.

In my opinion, the best data is the one that is easier to interpret. Yes, these are straightforward questions, but they give a good prediction of conversion without any complicated calculations.