With the implementation of inbound marketing techniques, your company may collect more data about your prospects than it is able to process. A relevant strategy for qualifying them is to implement a marketing scoring method which, by assigning a value to each prospect, will allow you to quickly identify the most interesting targets.
Our advice for optimizing the settings of your marketing scoring model
Cross-reference demographic data and behavioral data
Combining these two types of data will help you understand more precisely what your marketing scoring model is supposed to assess.
To determine first which behavioral data matter to you, look for all the answers to the question: “how can a potential customer interact with us?”.
Here are several scenarios to consider:
> A visitor comes to your website
- Through which page did the visitor arrive on your site?
- Was it a landing page?
- Did they view several of them?
- Did they come back to the site after X or Y amount of time?
- Did they use your chatbot?
> A person signed up for a webinar that you are organizing
- Did they attend it in full?
- Did they ask one or more questions? Which ones?
- Did they complete your form at the end?
- Did they read the follow-up emails sent afterward?
> A visitor seems interested in a downloadable white paper on your company’s website
- Did they request to receive it (in exchange for an email address, for example)?
- Once the request was made, did they download it?
- Was this download followed by them sharing it?
For their part, demographic data make it possible to answer the question: “what common points can be established between our target customers and our best customers?”
After a certain period of activity, you should normally be able to identify who your best customers are. If we turn back to your prospects, it therefore seems wise to carefully monitor those who appear to have a profile similar to the best customers your company already has. From there, it may be relevant to look among the latter for cross-cutting characteristics that will enrich your scoring model.
You may in particular think about these elements:
- Industry sector;
- Company size;
- Job title;
- Number of years in this position;
- Areas of interest…
Focusing your analysis solely on one of these two data dimensions may expose you to the following risk: overestimating the potential of a prospect who in reality is only moderately interested in your offer.
Integrate the notion of a “threshold” into your model
Setting a threshold value and integrating it into your scoring ensures that leads will actually be handled only on the condition that they reach/exceed a certain qualification threshold previously defined by your sales team. You thus optimize lead management by prioritizing those with the best potential, while at the same time simplifying the work of your marketing team, which can rely on this value system to qualify and then pass on incoming prospects more effectively (see the article on setting up stages here).
With experience, play the personalization card
When it comes to lead scoring, not all actions are equal. To explain: two users each visit a page on your site. Will you assign the same level of qualification to the first one who views your pricing page as to the second who merely looked up your legal notices? That would show a lack of discernment.
It is up to you to generalize this reasoning by deciding to give more value in your lead scoring model to certain pages, certain actions, or even the combination of both. For example: a prospect opens an email, clicks on the link leading to one of your articles, and ends up reading at least half of it. This sequence of steps may be worth a certain number of points in your model.
Giving is giving; taking back is NOT stealing!
If your model focuses only on adding points without including the possibility of taking some away, the score of some of your prospects can become excessively flattering. The rule is as follows: with no interaction with your company for a set period, a prospect’s score must be revised downward, even if it means falling below the threshold value mentioned earlier. This may be an opportunity to launch a follow-up campaign aimed at this kind of profile; in any case, it will save you valuable time by significantly reducing interpretation biases regarding the potential of your prospects.
Our selection of lead scoring tools
ListFlow : ListFlow presents itself as a Lead Data Platform focused on generating highly qualified leads. After a preliminary phase of cleaning/enriching the data collected on your prospects, the platform centralizes contacts in a unified marketing database in order to score each prospect according to a range of criteria (age, influence, company size, revenue potential, etc.). These qualified leads can then be distributed by segment and across different campaign scenarios.
Leadboxer : Leadboxer is a lead generation and scoring platform for B2B companies of all sizes. On the scoring side, the software automatically generates a “visitor profile” for each internet user visiting the site. It then collects data relating to their behavior and interactions during browsing to assign them a score based on criteria defined and editable by the account manager.
Lead Manager by Velocify : developed by the Californian company Velocify, this solution is based on the principle that rapid response and the right level of follow-up with a prospect are essential to stand out from the competition. Its main features include lead capture and deduplication, flexible lead distribution, and configurable lead scoring to optimize distribution according to the user’s needs.
To conclude, we will leave you with one final piece of advice, both simple and sound, for assessing the quality of your scoring. To know whether your method and your scoring tools are being used effectively, you must be able to provide an informed answer to the question “how do I know when I need to do ___?”, whatever type of action you are considering.
Article written by Thomas Roudet, from ListFlow.
