Social media engagement is a trending way to reach customers and consumers in these times. In the present times, it is essential to use social media engagement as a marketing strategy. However, simply creating a page, regular posts, responding to questions, etc. are not the only activities. There should be metrics to check and analyze the performance of these activities. The details are usually dependent on platform and customer base. But there are few basic metrics that are common with every platform.
Lead generation is a vital activity for every organization—an on-going activity for the pre-sales and/or the sales department. But simply generating leads through various channels and running the same activities on all the leads don’t work. It’s like throwing all stones in the same way and hoping one will hit. Wastage of resources and time results in higher cost per sale. That’s why the lead scoring model has been practiced in the corporate workspace. Lead scoring is a methodology used to give ranks or scores to all the generated leads. The ranks or scores are usually given with 1-5 or 1-10 or 1-100. The model differs from business to business, as the lead base and target audience are different. But we will look into some of the basic pointers which are to be considered while doing a lead scoring.
Database Cleaning – Lead generation and database gathering is a tedious task. There are leads with all the information, incomplete information, or no information at all. It’s not easy to get information from the visitors. That’s why one of the first steps is to clean the database. Discard leads with no information or unusable incomplete information, try to get more information of leads who have given partial but useful information and sort leads with complete information. Further, divide the leads into 2 groups i.e., converted leads and dead leads. Check for common patterns among the converted leads which can help you to identify the strategy that works.
Lead demographics – Understanding lead demographics is crucial. Their location, professional information, personal information, social media details, etc. are some of the demographic information. Though old school, it is still useful to have this information. This data can help to understand behavioral and engagement analysis.
Scoring Threshold – While scoring leads to understanding the status of the leads, a threshold needs to be decided upon for scoring. The decided threshold will help to portray the position of leads i.e., whether a lead is an MQL or SQL, or none yet. The threshold can be given through points, i.e., giving points to every lead depending on the information and activities, and updating the score every time there is an action or activity by the lead. However, it is quite a tricky activity. If the points are too low, the sales team will waste hours on non-convertible leads, whereas if it’s too high, the pre-sales team might miss out on a good potential lead. One of the best ways to decide on the threshold factor is to understand and analyze common activities that are performed when a lead becomes qualified—activities like asking for specific quotations, demo, etc.
Explicit, Implicit & Negative Scoring – The data points for scoring leads can vary from business to business. But there are few basic ways of scoring.
Score Upgrading and Degrading – Leads go through the process to be filtered as qualified, unqualified and others. The filtering process differs for different businesses. When leads go through that funnel, some leads upgrade to become more qualified while some leads degrade. This happens when lead takes or stops certain actions like opening company emails, picking calls, etc. The process of upgrading or degrading will indicate whether leads are stagnant or not, the reasons behind degradation, common reasons behind upgradation, etc.
Regular update – Last but not the least, any model that is created becomes outdated in a few years. In these times where technological advancements happen every now and then, it is vital that the model is updated on a regular basis so that new leads are not missed out due to some new characteristics which the model failed to consider.
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