A 10 Step Framework to Accurate Lead Scores
Lead scoring is the process of ranking a leads fit, interest and propensity to buy by assigning or removing point values to the lead based on explicit (demographics and profile fit) and implicit (behaviors and activities) attributes.
Successful lead scoring enables marketing to prioritize leads, send (only) sales-ready leads to the sales team, nurture the not-yet-sales ready leads until they become qualified and reduce the perennial problem of lead leakage. From a payback perspective, Marketing Sherpa reports that "On average, organizations that use lead scoring experience a 77% lift in lead generation ROI, over organizations that do not use lead scoring."
But scoring leads is easier said than done. Interpreting a lead’s digital footprints in order to gauge their persona, preferences, position in the purchase cycle and propensity to buy is a sophisticated undertaking. Marketing Sherpa also reports that 79% of marketers have not yet implemented lead scoring, which contributes to another recent report from Gartner which shares that 70% of sales leads are either not properly leveraged or completely ignored.
Many marketers simply throw every lead over the fence to sales. But sending leads to the sales team that are not qualified contributes to lead leakage (as sales people ignore leads) and inefficient sales win rates (as sales people invest time and money in unqualified deals they won’t win) and further contributes to the cultural divide between sales and marketing.
Instead, it’s marketing’s job to score leads in order to send sales-ready buyers to the sales force, nurture the not-yet-ready-to-buy prospects until they’re ready and disqualify the leads that are really not leads at all.
If you’ve not yet implemented lead scoring model, consider this 10 step approach.
- Team with sales. Marketers sometimes make the mistake of trying to define a qualified sales lead without collaborating with the sales team. Defining a lead is a concerted and on-going process mutually determined by both sales and marketing. You want to identify the traits most often associated with serious buyers. You may want to identify buy signals by persona or buy cycle phase. You also want to use lead scoring to optimize your entire revenue funnel, so it’s wise to define lead cycle stages. There’s varying nomenclature, but sales and marketing alignment industry recognized terms include Suspect, Prospect, Marketing Qualified Lead (MQL), Sales Accepted Lead (SAL), Sales Qualified Lead (SQL) and so on. Also perform some database profiling to look for the common criteria among your best customers, as well as patterns for both won and lost sale opportunities.
- Start with explicit data. When determining lead scoring criteria, most marketers score leads based on target market demographic criteria (company size, location, industry, contact title, etc.) This criterion needs to be included, but it’s important to recognize that demographic data only shows how interested you are in the prospect and not how interested the prospect is in you.
- Then include implicit data. Behavioral data, often called implicit criteria, measures behaviors and activities which suggest buyer interest or a propensity to buy. Implicit data will include behavioral activities such as email click-throughs, the volume of visits to the website, the keywords typed into a search field, the specific pages read, and the type and number of collateral downloads. A lead scoring best practice is to make explicit criteria a go/no go gate, and then weight the implicit criteria more heavily than the explicit criteria once the gate has been passed (i.e. once it’s determined the lead falls within your target market).
- Enrich your data. Your quest for lead conversions stands in contrast to your desire for rich customer information. You need to ask for as little information as possible to get good lead conversions, but you want as much customer information as possible to get accurate lead scores. Here’s where data append services can fill the gap. There are simple data augmentation services from companies such as Salesforce.com (via their Data.com) or Hoovers which add data from their knowledgebases to the lead records. Or stepping it up a level, there are more sophisticated services from companies such as Demandbase, Reachforce and Marketo (with its acquisition of Insightera) which capture IP addresses or other unique identifiers to retrieve additional information about the prospect as well as personalize their digital experience using that information. For example, the anonymous lead’s IP address may be correlated to a company name, industry and location and any or all of that data may dynamically display what website content they see.
- Capture multiple lead scores. Lead scoring is a process under continuous examination and improvement. It’s a good idea to create multiple lead scoring models to aid your learning and find the most accurate indicator. Start by calculating both a numeric score and a relative score such as a letter grade of A/B/C or a readiness indicator such as Hot/Warm/Cold. Once these two basic scores are in play, create an Account (aka Company) lead score. Account scores consider how many contacts from the same company are consuming your content and digitally engaging. More often than not Account lead scores are either a composite (sum) score or an average of each of the Contact scores for that entity. Also recognize that if your products or solutions target different target markets or your company locations serve different types of customers you will need to create lead scores by product, line of business or even geography. Each lead score model should determine at what score or level the lead should be forwarded to inside sales or the sales force.
- Use progressive profiling. When engaging prospects, recognize your lead scoring data requirements are best served over multiple visits and interactions. Don’t try to capture too much lead data in a single visit. In fact, your landing page, form or other prospect conversions will increase significantly when you ask for less information. So first collect the basics such as contact name, title and email address, and then implement a strategy such as progressive profiling to capture successive data points over multiple interactions.
- Begin with simple scoring techniques. After you’ve assigned point values to web pages and digital content, consider some additional tactics such as using negative scores in order to weed out unqualified prospects. Negative scores may be applied to leads that spend a lot of time on the website Careers page or simply fall outside your target market. On the flip side, you’ll want to bump up point scores for pages such as online videos, customer reviews, competitor comparisons, demo requests, event registrations, product specifications, case study downloads, pricing pages and pages related to support, warranty and terms information as this type of content shows much more serious buying intent.
- Include advanced scoring techniques. Here’s some advanced methods that can improve lead score accuracy:
- Depreciate lead scores based on periods of inactivity (i.e. reduce the score if the lead goes silent for 30 days)
- Put caps on data scoring elements (so that repeated activities occurring over a short period of time don't inflate and distort the score) or limit how often a data scoring element is counted (for example, only count a maximum of three website visits per day)
- If you create an Account lead score, place a premium weighting when multiple contacts from the same company are checking out your website
- Score based on the referring website (especially if it’s a competitor website)
- Over many years I’ve discovered search terms such as your company name in the referring search engine or keywords such as ‘price’, ‘support’, ‘warranty’ and ‘versus’ in your own website search engine can be the strongest tell-tales of buyer intent
- Consider weighting duplicate content consumption favorably. For instance, if a website visitor watches your webinar or product demo video from beginning to end more than once you may want to increase their score
- In B2B markets I’ve found that blogs should be uniquely weighted. Serious B2B buyers often navigate from your product pages to your blog in order to get past the marketing propaganda and learn about the human side of a potential vendor relationship. Give special scoring if a lead subscribes to your RSS or blog notification method
- Use rolling lead scores which display historical point-in-time values. This time-based scoring allows marketers or sales staff to see the month to month lead score values to understand progress and trending. The upward or downward lead score trajectory is a strong indicator of buyer readiness
- Allow calculated lead scores to be manually appended
- Suffice it to say there’s many more valuable advanced scoring techniques than can be shared in this limited blog post, so make sure you really think through these types of capabilities as they can significantly accelerate your journey to calculating accurate lead scores
- Demonstrate payback. I highly recommend focusing on measures that show Top of the Funnel (TOFU) impact (lead acquisition (quantity and quality) growth), lead velocity (lead conversions per revenue cycle stage, sales cycle durations) and bottom line earnings (sales win rates, ROI/ROMI). It’s also very helpful to display these metrics in a central dashboard. Once historical measures get solidified it’s time to begin predictive analytics. For B2B businesses with lengthy sales cycles, once you know your revenue cycle stages, conversions and durations, you’ve got the data to predict the company’s revenue beyond the current quarter’s sales forecast. This is hugely powerful for businesses needing to provide market guidance.
- Continuously improve. Lead score models should be reviewed and updated periodically. If customers are reaching a sales-ready score but not buying, you need to change your point values or raise the threshold score. The SAL rate is a leading indicator as to whether your lead scoring is working. And while all revenue cycle stage conversions should be measued it's the MQL to closed deal conversion rate and velocity that will be a primary measure of success. Also perform an after the fact comparison of lead scores with actual won and lost sales. For example, do leads with high scores result in higher win rates? If not, you need to revisit the scoring model. Lead scoring is an iterative process that seeks to identify a leads propensity to buy as well as the exact point in time when a lead becomes sales-ready. These are challenging goals and lead score models will continuously evolve with sales feedback, new learning and review of events such as won and lost sale opportunities.
Lead Scoring Summary
Lead scoring brings measurability, consistency, process control and clarity to lead qualification.
With accurate scoring models, the bulk of leads that would otherwise be prematurely forwarded to sales are instead nurtured by marketing until they demonstrate buying signals. This results in passing fewer, but higher quality leads to the sales team. Increases in lead quality clearly contribute to improvements in sales productivity, shorter sales cycles, fewer sale opportunities that end in no decision, higher sales win rates and stronger ties between sales and marketing.