In today’s competitive landscape, companies must constantly refine their marketing strategies to maximize efficiency and achieve tangible results. Lead generation, the process of attracting and converting potential customers, is at the heart of these strategies in many industries. To optimize lead generation, it is essential to be clear about the entire sales cycle and to use advanced tools to identify the most promising leads quickly and effectively.
In highly transactional settings, such as the lending industry, speed is essential. A loan application evaluated with non-predictive scoring can take months, an unsustainable amount of time since the customer will surely look for faster alternatives.
Non-predictive lead scoring is a simple operation: you assign a score to each interaction, such as 5 points for an email left, 30 points for signing up for a newsletter, and so on. However, this methodology merely adds up scores, offering no predictive elements.
In contrast, the adoption of advanced technologies, such as predictive lead scoring, is optimal for improving acquisition and conversion strategies. This technique uses machine learning algorithms to analyze historical data and identify leads with the highest conversion potential. In this way, companies can allocate resources more efficiently and significantly improve the performance of advertising campaigns.
A great potential for predictive lead scoring lies in leveraging data obtained from combined customer experiences. Imagine a car dealership: a customer requests information online, takes an onsite test drive, gathers additional digital information, and finally concludes the purchase at a physical store. It is essential to collect data from all touchpoints and ensure that the reconciliation key, such as email, is captured everywhere. In this way, predictive scoring can update dynamically as the user does or does not interact with the various touchpoints, based on the weight the algorithm has given to each of the interactions.
The value given will then trigger a series of actions: retargeting campaigns, chats, automated email streams, etc.
Below, we will explore three use cases of predictive lead scoring: managing advertising campaigns, optimizing user experience, and improving reporting capabilities. Each section will illustrate how this technology can be implemented to achieve tangible and measurable results, improving the overall effectiveness of marketing initiatives.
Use Case: Predictive Lead Scoring and Adv Campaigns
Suppose the user is in the search phase on Google and YouTube and views some ads. How do you feed these campaigns in the early stages? The traditional approach is to set a target Cost Per Acquisition, stating a willingness to pay, for example, €100 per lead. When conversion occurs on the site, the campaign reports success, but this is really an estimate, a bet, rather than a true assessment of the quality of leads generated.
An evolution to this approach is Value Bidding, where, through offline conversions, a more accurate value is reassigned to the conversion. However, this method requires waiting until the end of the sales cycle, limiting the ability to optimize in real time.
Predictive lead scoring, on the other hand, based on robust historical data, allows leads to be assigned a value as early as their first interactions in the funnel. In this way, campaigns can be optimized right away, reducing costs and improving the efficiency of automated campaigns.
Use Case: Predictive Lead Scoring and User Experience
Imagine implementing a predictive lead scoring system to create a dynamic customer experience that is personalized based on user value. The predictive value is passed to the data layer and then to the site frontend. When a user, recognized through a unique ID, browses or returns to the site, we can show them personalized content such as pop-ups with exclusive discounts. This strategy is particularly effective because it is activated on users that the predictive model identifies as having high potential.
Use Case: Predictive Lead Scoring and Reporting
A predictive lead scoring system also significantly improves reporting capabilities, as it allows campaign performance to be analyzed in a more layered manner, offering detailed insights into the quality of leads generated by different channels.
Importing the scoring into Google Ads as a metric allows the average lead scoring of the channels used to be evaluated, helping to optimize the marketing channel mix. In addition, the scoring can be converted into dimensions, i.e., bands (high, medium, low) and used as a segment for further analysis and optimization. This approach allows for a more comprehensive and integrated view of marketing performance, improving decision-making and strategic capability.
For those engaged intensively in lead generation, it is critical to collect high-quality data and develop differentiated strategies based on lead type. Predictive lead scoring facilitates the automation of this process, both in the form of metrics and dimensions.
Conclusion
Predictive lead scoring systems often fail because prepackaged solutions fail to adequately adapt to the specifics of individual businesses. Although models provide a solid foundation, in our experience it is fine-tuning that makes the difference, making predictions truly reliable.
At Bytek, we rely on our marketing platform, Bytek Prediction Platform, and we believe that marketers, to make the most of artificial intelligence, must follow three basic principles:
- Seamless Integrations: It is essential to effectively integrate scoring signals into all business systems. If these signals remain confined to reports, they bring no strategic value to our initiatives.
- Composability on the Company’s Cloud Data Warehouse: It is not sustainable to have a lead scoring system with its own separate database, which becomes just yet another fragmented tool. The company must have a unified, holistic database that brings together all relevant information. The predictive marketing data hub one chooses must natively integrate with this database, thus ensuring that all crucial data is collected and providing the best possible scoring.
- Internal Machine Learning Models: The marketing data hub must offer adequate configurability to fit specific business needs. A system that is too rigid would not respond effectively to each company’s unique needs.
These principles underpin our strategy and we believe are critical for any organization that intends to maximize the effectiveness of lead generation through artificial intelligence.
