What is lead scoring?
According to Wikipedia, lead scoring is to hierarchically ranking prospects against certain objectives. The resulting score determines which one beto engage, and in what order of priority.
In Sales and Marketing, isthe score’s criteria is ofbased on achieving the optimum outcome for theorganization. The outcome may be the willingness to buy, customer engagement, profit margin optimization, better loyalty focusing etc. What lead scoring actually does is to rank qualified prospects or leads against a threshold or barrier. Those leads withscores above this threshold are “qualified” and forwarded from marketing to sales in order to convert the lead to customer.
Regardless of if it is traditional or predictive , lead scoring is an essential marketing tool leads, especially when the market is massive and you have limited sales resources to handle such number of leads.
Traditional lead scoring
Traditional lead scoring makes use of predefined criteria or attributes, multiplied by weighting factors criterion in order to deduce the total score. Criteria definition and weighting factor values depend on the target outcome with the right choice them being the critical points needed to become successful.
Traditional lead scoring attributes
When scoring a lead, the criteria may be distinguished to two categories: explicit or implicit.
Often called demographics, explicit criteriaare based on characteristics of the lead, and they are intentionally provided by the lead. Explicit criteria may include among others:
- Industry that the lead belongs to
- Financial size
- Employee size
- Contact person job title
Since such characteristics are not volatile, they are more accurate and reliable
Your implicit creteria are based on behavioral attributes such as the actions that the lead may take when e.g. visiting a B2B website. Contrary to explicit attributes, this information is not deliberately provided by the lead, and it is either extracted or inferred. Implicit attributes may include among others:
- Online activity such as website pages visited, links clicked etc
- Content interaction such as content (brochures, ebooks etc.) downloads, brochures attended etc
- Subscriptions in newsletters, RSS feeds
A combination of both attribute categories may give the overall score
Features and challenges
Consider the following simplified lead scoring system
At first, you should define the attributes. This may be something custom based on some common logic (such as negative score for Gmail based email accounts or high score to big size enterprises) or may follow more standardized tools (such as BANT). Once completed,you set the corresponding weighting coefficients and threshold or “qualified”barrier You then test the model against historical data, distributing it to your sales people if successful.
After each completed sales cycle, you reevaluate the model redistributed it back to your sales team until you achieve satisfactory results .You can evaluate model against successful or failed scorings using procedures be based on the simplified rule: A failure is a qualified lead that did not end to customer or a non-qualified lead that finally ended to customer
As it easily derived, traditional lead scoring systems are more or less manual systems, in terms of rules definition (attributes and weight factors) and methodology reevaluation improvement and adjustment.
Predictive lead scoring
Despite the fact that traditional lead scoring has been successful for most organizations, 47% of marketers indicate that quality of leads need improvement need improvementandwith 43% of them saying that it does not provide sufficient insight into buying attributes, Because of this, Ppredictive lead scoring, has continuously gain acceptance over the last few years:
- Organizations with predictive lead scoring increased 14 times in period 2011-2014.
- 98% of organizations ausing predictive lead scoring, say they would purchase lead scoring again
Other studies indicate that predictive lead scoring is one of the key factors for increasing qualified leads.
Instead of using rigid rules and scoring a lead on them, predictive lead scoring uses existing sales data, along with data mining and analytics techniques to build the “ideal” lead. Subsequent leads are compared with this lead and correspondingly labeled qualified or not.
A major innovation in predictive lead scoring, is that the marketer does not have to define attributes and their corresponding weight factors. Additionally, there is no need for a periodical “run and check” process . In predictive lead scoring, the marketer defines the KPIs used in the analytics so the model algorithm can create a formula for automatically ranking leads so the marketer can easily identify the most qualified ones. The model is continuously fed with data onleads that successful converted to customers orand those that failed eliminating the need for “run and check” processes.
The following table summarizes predictive lead scoring features compared to traditional one:
The role of Machine learning and Predictive analysis
Predictive lead scoring uses big data techniques, along with analytics, to build a model to predict the probability a lead converting to a customer. Perspective analytics may use these probabilities to suggest actions as well as , the deliverables and implications thesefor them. Leveraging predictive and perspective analytics gives the marketers a solid, robust scientific approach on how to effectively increase conversion rates.
Artificial intelligence, in the form of machine learning, is used in predictive lead scoring in several ways:
- In predictive lead scoring analytics data are not limited to behavior or demographic. You can consolidate and use your own proprietary customer data with public data from providers, social media and other sources, both structured and unstructured as well. Such massive data quantities cannot be analyzed and processed by human intelligence, in an acceptable time frame.
- Predictive analysis model feed and review require complicated mathematical and arithmetic computations applied in stochastic and multifactorial systems to operate effectively.
Some may expect that moving from traditional lead scoring or from scratch to predictive lead scoring,may require investments that mid andsmall size organizations may not be able to afford .Because of this, ,83% of organizations using predictive lead scoring are 250M and below, as studies confirm However, cloud based solutions and vendor partnerships can help organizations to overcome this obstacle.
Another major challenges to predictive lead scoring adoption -compared to traditional- has to do with data:
Not enough data
Predictive analytics require large volumes of data not only to run the model but also to build and test it. While predictive scoring models are designed to be auto fed with new data in both conversion and no-conversion cases, someone may thatnot have enough sufficient data available to run the model speciallyespecially in the early stages. This is often called acold startand you cannot reliably make predictions in this situation.
Not consistent data or missing data
You might have large volumes of data but it could be inconsistent and it missing critical information, especially wherewhen marketing and sales teams are not aligned.
Typical examples of this situation include
- - Upon a successful lead conversion, sales representatives do not provide necessary -relative to predictive lead scoring model- information in your organization’s CRM and/or ERP systems
- Failures to convert information is not posted either inadvertently or on-purpose.
Setting up right qualification criteria for the model.
Provided granted that the data are available and of good quality, the model can set up basic qualification criteria by itself. The marketer should not consider predictive lead scoring as a “black box”, but should continuously evaluated and adjust its criteria.
Sales teams are not on board
Predictive lead scoring helps align the marketing and sales teams, since it produces qualified leads of good quality and large volumes. Thus, you need to educate your sales team on how predictive lead scoring works, what goes into it, and why it’s been scored that way. If this is not the case, they will continue to use the old way of possible leads customerconversion, ignoring the predictive model. Credibility of the model and education of sales is key.
Although a relatively new methodology, predictive lead scoring has been proven effective and reliable for turning properly qualifying lead into customers. Based on artificial intelligence capabilities, it has been one of key factors for marketing automation.