raphic data: information such as age, income and position held. Basically it is the lead profile. The closer to the description of your person, the better; behavioral data: how much does the lead interact with your company? Have you downloaded any important content? Are you subscribed to the newsletter? This data says more about the lead's interest. By putting demographic and behavioral data together, your score will be more balanced and accurate. In the end, it is useless to have the lead with the best profile and zero interest, nor one who is totally interested, but who does not fit the ideal profile . 3. Determine t
e activation score The activation score is nothing more than Telegram Number Data the cut-off line between an MQL and an SQL, that is, when the lead must go from the marketing team to the sales team . Again, it is important that this decision be made together by the marketing and sales teams, to avoid problems later. By determining the activation limit, it will be easier to move on to the next step, which is to decide which grades will be given for each factor that counts in favor of the lead on the purchase journey. 4. Create a scoring pattern Defining a scoring pattern is the next logical step to take on
this mean? For each lead action, or for each positive demographic characteristic they present, a score must be assigned. But the points should not be defined randomly, much less distributed equally for all actions and characteristics. Whatever has more weight to push the lead further down the funnel should receive a higher score, while less important activities receive lower scores. This will prevent candidates from being classified as smart only for the number of positive actions they perform and will prioritize more realistic evaluation criteria. 5. Make good use of negative pu