Predictive Lead Scoring: approaches and strategies to maximize results

In the world of performance marketing, one of the most powerful tools at our disposal is the lead scoring algorithm. But what exactly does that mean? Simply put, a lead scoring algorithm assigns each potential customer a score, indicative of the quality of the lead, that is, the likelihood that that customer will take a desired action, such as buying a product or signing up for a newsletter. Before we can make the most of a lead scoring algorithm, it is critical to clarify what we mean by “lead quality.” This concept can vary depending on the specific objectives: are we trying to predict the purchase of a product? Or subscription to a newsletter? Precisely defining these goals is the first step in creating an effective scoring system.

Once the goals have been established, we can proceed to develop the algorithm. These predictive tools are trained on a historical data set and use machine learning techniques, such as regression, decision trees, and neural networks. By analyzing past data, the algorithm identifies patterns and correlations that help estimate the likelihood of new leads converting.

The patterns are then constantly refined through feedback and new data collected so that their accuracy can improve over time. This continuous process of improvement increases the accuracy of the predictions, making lead scoring a tool that can not only.

What data to use to feed predictive scoring algorithms

To build effective predictive models, it is essential to collect detailed data on lead behavior and preferences. This data is divided into two sets: a training set and a test set. The algorithm is trained on the training set and then tested on the test set to compare predictions with actual results. Once satisfactory accuracy is achieved, the algorithm can be applied to potential customers to estimate the probability that they will become actual customers or take specific actions, such as buying a product.

In order to have a lead scoring algorithm, it is obviously critical to have the target variable, which indicates whether or not a specific action has occurred, such as whether a prospect has become an actual customer, signed up for a newsletter, or purchased a product. This variable is typically binary (Yes/No) and requires complete data, including both successes and failures. Often we are given only the data on leads that converted, but to train a lead scoring algorithm we need all the data, regardless of the outcome.

In addition to the target variable, the other variables that can be used fall mainly into two categories:

• CRM data: These can include individual variables (such as age, job role, city, gender, educational qualification) or company variables (such as turnover, number of employees).
• Behavioral data: Collected from website interactions, these include the number of pages visited, number of sessions, acquisition channels, events recorded, and documents downloaded.

The most important variables in explaining a lead’s likelihood of taking a certain action, however, are often those that are calculated. That is, variables that are extrapolated from information in CRM and/or behavioral data through artificial intelligence and machine learning methods. These variables provide in-depth insights that go beyond basic tracking data. For example, information about a user’s interests can be obtained from a site’s browsing data and then to associate specific interests related to particular products or topics with each lead, or complex variables can be extracted that take into account not only the actions performed, but also the time at which they are performed, creating a sort of historical series of activities. Thus, the most complex step in the modeling process is not so much the construction of the algorithm, but the selection and calculation of the variables to be included. The quality and completeness of the data are critical to the successful operation of any predictive model. Making sure you have accurate and relevant data is the key to reliable results.

Which algorithms to choose to achieve predictive scoring

One of the most frequently asked questions concerns the choice of algorithms to use, but the answer is often unsatisfactory: it depends on the data we have. In general, there are at least three major families of algorithms that can be employed:

• Data-driven models: These models are extremely flexible and allow complex relationships between data to be captured without requiring overly restrictive statistical assumptions. The algorithm thus has the freedom to discover connections between variables independently, making these models particularly powerful in scenarios with nonlinear or complex data.
• Shrinkage models: A typical example is ridge-regression. These models operate by reducing the number of predictors, i.e., the variables included in the model. This approach is useful to avoid the problem of overfitting, which occurs when too many variables compromise the model’s ability to generalize. By reducing the set of variables and focusing only on those that are truly relevant, the accuracy of the predictions is improved.
• Ensemble models: These are the most complex models, as they combine the predictions of several models to produce a more accurate final result. They use techniques such as bagging, boosting, or stacking to improve performance.

The choice of algorithm depends on the quality, quantity, and type of data available, making a thorough evaluation of the dataset necessary.There is no universal algorithm that works in every situation. Some experience is required to identify the optimal model, while also considering computational efficiency and speed of execution. The answer, therefore, often lies in preliminary analysis of the data. It is important to remember the “Garbage in, Garbage out” principle: if the input data is poor, even the most sophisticated algorithm will produce unsatisfactory results. The quality of the source information is crucial to obtaining accurate and useful predictions.

Common problems in implementing an automatic and predictive scoring system

Implementing a scoring system presents several significant challenges, including:

• Limited number of leads: one of the main problems we often encounter is the scarcity of leads and potential customers on which to train and test models. This situation is particularly critical in the early stages of the project, when the volume of leads is small and models must be continually retrained as available data increases. The solution is not simple: one approach may be to use synthetic data, which can supplement real data and improve model performance in the early stages of development.
• Data in silos: Another frequent problem is the segregation of data into silos, with CRM on one side and behavioral data on the other. Companies often fail to effectively integrate data from different sources, leading to fragmentation of information. This prevents them from getting a complete and consistent view of the customer, which is critical for a customer-centric strategy. The solution comes through the implementation of data integration systems to unify information and make it consistently accessible.
• Limited variables: the complexity of machine learning algorithms requires a large amount of data and variables. Having few useful variables can limit the model’s ability to generate accurate predictions. To overcome this problem, it is necessary to enrich the datasets with additional variables that can improve the predictive ability of the model.
• Data quality: low data quality is another significant obstacle. Data that are unreliable or unavailable for all potential customers can compromise the accuracy of models. For example, if a potential customer’s turnover is a critical variable but is self-reported and found to be inconsistent, alternative methods must be found to enrich this information. The use of external datasets and data enrichment techniques can significantly improve the quality of the data, making it more useful for training models.

Addressing these problems requires a strategic approach that includes using synthetic data, integrating data from different sources, enriching datasets, and improving data quality. This is the only way to build robust and reliable machine learning models that can effectively support business decisions.

What information can be gained from a predictive lead scoring model

Lead scoring is not only a method of assigning a probability to the quality of our prospects, but it offers a range of useful information for optimizing our marketing strategies. In addition to determining the probability of conversion, lead scoring allows us to rank leads qualitatively by establishing a probability threshold commonly set at 0.5. This means that we can divide our prospects into two categories: those with a greater than 50 percent probability of becoming customers, and those with a lower probability.

For example, if one lead has a score of 0.70 and another has a score of 0.98, a salesperson will probably contact the second one first. Both leads have a high probability of conversion, but the second one has a higher probability, making it a priority.

Another crucial piece of information provided by these algorithms is the importance of the variables that influence the likelihood of conversion. For example, the job title of the potential customer can have a significant impact on the probability of becoming a customer. How he or she is contacted-for example, by phone call versus email-and the type of interest expressed, as well as the type of company, can also play a determining role.

Knowing these variables allows you to better target marketing efforts. For example, if you find that decision-makers with a certain job title respond better to phone calls than to emails, you can optimize your contact strategy accordingly. Similarly, if a particular industry shows higher conversion rates, you can focus marketing efforts on that segment.