“HP Boosts Win Rate By 40% With Predictive” – Now that’s newsworthy!
This headline is testimony to the success of Predictive Lead Scoring – the technology that is powering marketers today. Converting leads into a win by nurturing them down the sales funnel is invaluable to businesses; and lead scoring has been playing a major role in ensuring this. Add the power of AI and Big Data to it, and what you get is a model that enables the kind of results that HP has seen.
In this article, I will be talking about the advancement of lead scoring through predictive analytics. The difference that it can make for businesses is colossal and I hope I can help show its relevance and importance to your business.
Lead Scoring
Lead Scoring is the process by which businesses have been able to score the quality of leads and convert them into customers successfully. Values are assigned to leads on the basis of their demographic profile, engagement patterns, and their responses to marketing efforts. Based on the score, the Marketing Team decides whether the lead is worth pursuing, the most effective ways to nurture the lead, and the action required to persuade them to a sale, and retain their loyalty.
Traditional lead scoring relies on a set of explicit and implicit behavior patterns to assign values.
Criteria that are “Explicit” are mostly demographic patterns and information like client background, company size etc., which helps the team determine if they are a good fit for the company.
“Implicit” criteria delve deeper into the behavior patterns of the lead. The lead’s response to marketing efforts is evaluated here. Do they open emails sent by the company? Did they sign up for more information? Are they registering for webinars? These and other variables show their level of interest in the company and its services. These factors help put together a score that determines which leads need nurturing, and the ways in which to do it.
Related: How will IoT influence marketing?
The Need for Big Data and AI in Lead Scoring
The more we get to know a person, the more familiar we become with their thinking patterns. This simple premise is what makes AI backed lead scoring such a formidable force – the more data assimilated about a lead, the more possible outcomes that can be derived; and the more fine-tuned the analysis, the better the quality of lead scoring.
Lead scoring relies on data processed through the complete marketing life cycle; and there are multiple touch points through this life cycle – the lead’s behavioral patterns at different stages in the sales funnel, the multiple channels used by marketing, etc. Analysis must be done for multiple leads on different channels in different stages of the life cycle. Now that’s a lot of information to process! Also to be factored in is the fact that lead scoring by traditional means is prone to human error. This often leads to over-scored and under-qualified leads being passed on to the sales team, which is disastrous in terms of time and resources spent on these leads.
With AI and Big Data coming into the picture, these problems are eliminated to a large extent.
AI Powered Predictive Lead Scoring
Each stage of the lead scoring process can benefit from an AI-powered fine-tuning. AI-driven lead conversion success insights could give an idea on the sales-readiness of the lead. Engagement pattern analysis by AI-backed technology gives you specific details like the minimum level of engagements that drive successful conversions for a lead through multiple channels. Through analysis of data collected from implicit factors, AI enables a near-perfect engagement scoring, which helps determine the sales readiness of the lead.
The ability of AI to process large amounts of data and derive actionable outcomes out of them is the crux of the success of predictive analytics. Deriving patterns and systematic relationships out of data is what AI does best. Using predictive analytics backed by AI gives you a mathematical and scientifically derived analysis, which forecasts how your marketing efforts will be received by leads. This helps in knowing exactly what to do at each stage of the cycle.
There is a lot more that predictive analysis could do for marketers. Here are some of the possibilities:
- Improves business efficiency and fine-tunes the marketing funnel at a low cost.
- Predicts consumer behavior effectively, providing insight to their purchase leanings, probabilities of cancellation, inclination to switch brands, etc.
- Provides actionable outcomes and predicts the impact of marketing efforts.
- Enables customer retention by detecting when and why the customer might leave the sales process.
- Accumulates data about the lead’s behavior patterns and continuously improves the sales process.
At Suyati, we’re working on enabling the predictive analytics capabilities for one of our clients. Being in an industry that has many users from multiple locations all over the world, the client finds it difficult to classify the right prospects and assign it to the right Customer Relationship Executives, which must eventually convert as a business-generating lead to the company. What we are developing is a Machine Learning (ML) model which will learn from their existing data and predict a score according to the probability of the lead getting converted. Using predictive scoring, we aim to assign ranks (as A, B, C, D, E and F) to every lead, and the leads are then assigned to the Customer Relationship Executives according to their seniority/profile.
Through this, we hope to give our clients a combination of Big Data, AI, and lead scoring – a highly optimized lead generation funnel that will be very beneficial for businesses. Sounds interesting? Get in touch to know more!
