Spotting The Golden Goose via Lead Scoring 

Behavioural insights can feed opportunity scoring models so sales and marketing teams can track potential customers as they move from interest to intent and act to improve closing rates.

Not all leads are created equal.

Unless you have a stack of handpicked super leads, it’s time-consuming to follow up on each case with the same tenacity. It can also prove counterproductive to nudge a lead who is still at the research stage for fear of them seeing it as pestering rather than a value-added call. Sales teams in the admit that there exists a sweet spot where a potential lead reaches out to seek a demo, initial pricing information or more details. This is an ideal opportunity that typically leads to a conversion.

Qualifying a lead is dependent on many factors, lead scoring models condense demographic and behavioural data to set a score to determine how ripe the lead is. It helps sales teams take specific actions based on where the prospect has reached in their decision-making process and whether they are ready to take the plunge.

This seems like a sales problem

The lines between advertising, marketing and sales have been blurred for a while now. The buyer journey, especially in a B2B scenario, doubles back several times, oscillating between solution exploration, requirement building and vendor selection. The competition between solution providers is too tough to leave sales reps to pursue under-qualified leads while missing the boat on those that are low-hanging fruit.

Moreover, lead scoring presents an ideal view to identify which elements are effective in guiding prospects through the funnel. For example, if the data discovers that a customer who downloads a price sheet is more likely to close, or feature comparisons work to eliminate the competition, then marketing teams must focus on creating more such content with a call-to-action that is more fitting. Content scoring is a subset of lead scoring that can be effective if sales and marketing teams work in sync.

A lead scoring model will assign a point system based on how much interest they have shown in the product, ascertain whether they have the budget for the buy and if it presents a ticket size that justifies a senior executive getting involved. Think of it as allocating your best resources towards big spenders and giving fresh prospects time to stew.

Another subset of lead scoring is consumer scoring which determines the customer lifetime value and informs teams so they can act in a way that will nurture the relationship. There is a legitimate danger in closing unqualified leads. While it may seem like a quick win at the time, it can result in a higher rate of cancelled orders and drop retention numbers in the long run.

How it works

Companies need a basic history of purchases so the model can study patterns in the factors that may have contributed to a conversion. It’s about establishing what successful deals look like. The model is then run on open opportunities that have the most in common with these successful deals to predict the chances of them converting. This is visible through a dashboard embedded directly in the CRM tool.

Most lead scoring models work much in the same way. For example, SAP’s opportunity scoring model is called Deal Intelligence. It uses sales attributes like sales area, priority, status, sales cycle, sales reps performance, deal lifecycles, deal size, etc. Oracle’s Eloqua begins by defining lead qualification criteria as well. Here, marketing and sales teams work together to define both the ideal profile, and the qualities of a good lead

It then establishes a scoring criteria. A qualified lead has two dimensions namely; a profile score and engagement score. The profile score is based on the job role, industry and revenue of the company to determine whether the prospect is the ideal decision-maker. In the Eloqua system, the prospect is allocated A, B, C or D. ‘A’ being the best fit. The engagement score depends on the implicit data about a prospect’s activities such as website visits and email opens, which determine their level of interest. This is linked to a score between 1 and 4 where 1 is the highest.

These two scores combine to offer a lead score represented in a graph. In this case, A1 is the most qualified lead and a D4 is the least qualified and offers a visual guide. As users move along the sales funnel, their lead scores also change accordingly.

Hubspot’s predictive lead scoring software allows users to multiple personas. For example, if you are a software company that sells two different types of software, via different sales teams, to different types of buyers – you could create two different lead scores – one for a buyer’s fit and the other for their interest in each tool. Then, these respective scores can be routed to the right sales teams.

Salesforce’s Einstein Opportunity Scoring uses data science and machine learning to score opportunities so teams can prioritise their actions. It also follows a rule-based scoring approach. Interestingly, it studies lost opportunities as well to give marketers added insight into what may have gone wrong. Choosing which factors to consider needs to be a collaborative effort between sales and business teams to customise deal-makers based on the specific industry.

Every ten days as a default or sooner if the admin chooses to do so, the model will reanalyse the opportunity data and refresh the findings. This is also a great way to test if new marketing efforts are paying off.

Time to take action

Experts suggest putting in place a service-level agreement (SLA) to automate certain tasks based on triggers informed by the lead scoring model. For example, strong leads need a follow-up with a 24-hour window, those that score lower may need a reminder after 48 hours. Adobe’s automated campaigns lets you sync workflows so some of these tasks can be run without human intervention. There is a further opportunity to cross-sell or view which other products the prospect is considering.

Online behaviour, social engagement and email engagement can tell you a lot about a prospects intentions. Marketers would do well to use this as a feedback loop on the kind of content that performs better in sync with an attribution model. Use the information from lead scoring as a chance to flag spam users and build a negative scoring system to filter and remove bad players who may be uninterested and misleading to the model.

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