In the world of KPIs and executive summaries, where everyone is hard pressed for time, making sense out of data deluge can be tedious. In this context, significance of “Lead Scoring” in prioritizing leads & measuring campaign success has been widely agreed upon. However, same cannot be said about consensus on “Lead Scoring” practices and norms. While, the old and beloved “BANT” is still the most used scoring norm, there are many views that passionately point out the limitations of “BANT”. But they haven’t yet concurred into a single, comprehensive yet flexible standard.
In addition to Lead Scoring Norms, accuracy or effectiveness of Lead Score is also a function of data collection techniques. Since realization of a “Lead Scoring Algorithm” is affected by limitations in data collection techniques, it is pertinent to quickly examine them for arriving at the best possible scenario.
Data collection is possible through either information sought explicitly or tracked implicitly.
Explicit Data Collection: The data points are obtained from a prospect or lead by asking them questions explicitly and then tabulating them. Such data collection is generally done through web forms or during conversations (on call/at event etc). Some of the short comings of explicit data collection are:
Explicit Data Collection: The data points are obtained from a prospect or lead by asking them questions explicitly and then tabulating them. Such data collection is generally done through web forms or during conversations (on call/at event etc). Some of the short comings of explicit data collection are:
- The number of data points that can be solicited, is likely to be limited by responder fatigue.
- Accuracy of data can’t be taken for granted as
a) Respondents may deliberately provide in-accurate data to avoid contact by sales.
b) In case of pending decisions, the data provided may differ from the actual, once these decisions are made.
Implicit Data Collection: The behavioral data points (& psychographic attributes that manifest as behavior) are obtained by trailing the lead or prospect. The demographic data points in this case are obtained from relevant databases that have already accumulated information. The biggest advantage of implicit data collection is minimal (if not “zero”) gap between intent and actual behavior. Some of the short comings of this method of collecting data are:
- The number of data points that can be collected just by trailing the lead/prospect.
- Possible privacy infringement
Before web-analytics, reverse-ip technology and cookies have gained prominence and acceptance, it would have been very difficult to trail B2B prospects (either online or offline) and analyze their behavior. Hence the dependency on explicit data collection would have been indispensible. But then, given the possible respondent fatigue, data collection instruments would have been limited to collecting data points that were considered most important. BANT, in my opinion may have suffered from this limitation as it restricts itself to very few parameters, while every “B2B Marketing Resource” emphasizes on the complexity involved in engaging a prospect and converting them into a customer.
But with advancements in web-tracking and web-analytics, the scope for collecting different data points (& types) has increased tremendously. In this context, it would be in the best interest of a company to trail the web behavior of prospect, to collect all the data points necessary for a scoring algorithm and resort to explicit data collection only a) to avoid privacy infringement and b) in cases where collecting specific data points implicitly is not possible.
Such an effort helps in identifying scoring parameters based on the best practices and guidelines defined by different “B2B Marketing Resources and Research” and the below scoring algorithm is an effort in that direction.
In addition to recommendations from different marketing resources & thought processes, other aspects considered while arriving at these parameters are:
- Avoiding double counting (i.e. giving credit to a single activity manifested in multiple forms, multiple times)
- Defining these parameters against the backdrop of “Marketing Automation” platforms.
The parameters thus identified were:
- Purchase Readiness (or) Decision Making Stage (P)
- Degree of Intent (of prospects) revealed (I)
- Engagement or Interest (Time Spent, Recency) (E)
- Demographic factors and any purchase barriers defined by Sales (eg: Revenue & other demographic data like Title etc) (D)
- Identity of individual contact or prospect established (being able to attribute behavior to an individual decision maker) (I)
- Off-site (& Off-line) Activities (O)
Purchase Readiness (or) Decision Stage
Significance & Implications
Research1 suggests that, since the time a need or problem is felt, prospects (individual consumer or an organization), especially in case of high-involvement products, go through below decision making stages before actual transaction
i. information gathering
ii. evaluation (or trial) of alternatives
iii. purchase ready
Identifying the right “Purchase Decision Stage”, will help in:
- Identifying activities and campaigns that connect with the prospect most
- Identifying activities and campaigns that propel prospects to the next stage of decision making
- Prioritizing based on immediacy exhibited
How to obtain data points
“Purchase Decision Stage” of a prospect can be obtained from the pages visited on the website.
“Information Gathering Pages” are the ones which establish your industry/sector, as the key to solving prospects’ need or problem. They don’t speak about the superiority of your brand or the specific solution you offer. They instead provide all the information that a prospect should be aware of, to take a decision that serves him (or the organization) the best.
“Evaluation (or trial)” pages are those which position your brand (or your solution) as the best suited to serve the needs of prospect. Whether it is explaining your solution in depth or the industry feedback your company has received, the objective is to establish the superiority of your brand (vis-à-vis competitors or bench marks) and inspire trust in your brand and solution.
“Purchase Readiness” pages are the contact-us, RFx and similar pages that a prospect visits to make the contact when he is more or less ready for the final transaction.
Cheat Sheet
- Examples of Information Gathering pages: Whitepapers, Blogs, Webinars etc
- Examples of Evaluation/Trial pages: Case Studies, Testimonials, Demo, Free Trial, Pricing etc
- Examples of Purchase Ready pages: Contact Us, Request for Quote etc
- If a prospect visits pages pertaining to multiple “Decision Making Stages”, the most advanced of these “Decision Making Stages” should be attributed to him
- It is possible that some of the white papers or blogs are intended to establish the superiority of your specific brand or solution. If the content is clearly captured in the anchor text or title, such white papers or articles should be considered as Evaluation/Trial pages
Degree of “Intent” Revealed
Significance & Implications:
Understanding the intent of a prospect (his “use-cases”, “purchase barriers”, "opinions" etc), will help in identifying (or developing) the right communication that captures their attention, establishes the most appropriate associations, helps in overcoming any purchase barriers and closing the deals faster.
The more we know about the “intent(s)” of prospect, the more it can be used for our advantage in making the connection with the prospect. Hence capturing as much intent as possible is desired. So prospects or leads have to be scored based on how much of their intent is “known”.
How to obtain data points
Through web analytics, the information about intent of a prospect can be identified from:
- The pages (related to specific solutions/products/utilities/use-cases/dispositions etc) visited by the prospect
- The “search queries” used by the prospect in your website internal search.
- The “search queries” used in different search engines to arrive at your website
- The referral sources specific to the prospect (if they indicate any specific context)
- Any related comments by the prospect on different social networks, blogs etc
Cheat Sheet
It is possible that some pages provide data points for both “intent” of the prospect and also “purchase readiness” of the prospect. For example, consider this webinar “Learn How To Turn More of Your Web Traffic Into Qualified Sales Leads” by a marketing automation vendor. A visit from a prospect to this page suggests that the prospect is in “Information Gathering” stage of decision making. It also indicates the use-case/utility that is relevant to him. In such cases, a visit to this page should be counted in both the sections of scoring. That is because; this single act has provided multiple insights.
Engagement (or) Interest
Significance & Implications
Engagement impacts brand recall, strength of brand associations and thus response of prospects to brand communication or sales pitch. The higher the engagement, the more likely they are to warm-up to your communication. Engagement can be captured in:
- Total Time Spent
- Recency of Last Visit or Last Few Visits
Information pertaining to a) time spent and b) date and time of last visit is captured by Marketing Automation or Web Analytics vendors. This information will help in determining the score for this particular section.
Cheat Sheet
Page views & total repeat visits can also act as metrics for engagement. But they are not considered since each additional page view or repeat visit contributes towards time spent and thus results in “double counting” when “Time Spent” is taken into account already. Also, “Total Time Spent” is a better metric than the other two as it is sum of products of page views and stickiness of each page or sum of products of repeat visits and stickiness of each visit. Hence only “total times spent was considered” to ensure single counting.
Demographic & Others Parameters (mostly purchase barriers) Defined by Sales
Significance & Implications
Inspite of all the interest evinced by prospects, there could be some purchase barriers that can’t be overcome. They could be either external, like revenue of the prospect (not sufficient to allocate budget for your solution), laws of land in geography of prospects etc or internal like unavailability of solution in the geography of prospect.
So, such demographic (or other similar factors) factors that result in strong purchase barriers should be considered for scoring. Since it is “Sales Team” that is more likely to be aware of such factors and barriers, from their personal interaction with prospects during closure, it is recommended that the parameters for this section of scoring are obtained from them.
Obtaining these metrics or parameters from Sales Team will also ensure higher acceptance for marketing qualified leads among Sales Team.
How to obtain data points
Some of the demographic details would be captured in your web analytics platform (for eg: geography). Rest of the details can be obtained from various databases integrated with your marketing automation platform. If the information is still not available, it should be researched or sought explicitly.
Cheat Sheet
The most important (and compulsory in most cases) parameter in this section is “Annual Revenue” of the prospect. This is because, annual revenue of recent times determines current and future budgets in most cases. It can also determine the potential for up-sell and cross-sell opportunities.
Identity of Individual Contact or Lead Established
Significance & Implications
All the leads or contacts (specific individuals) who have identified themselves on your website or through email campaigns:
- Allow you to track their personal web-behavior (only on your website) and thus let you customize website experience and nurturing content for them (at individual decision maker level).
- Reduce your overheads in researching about the prospect and identifying contact details of decision makers who are more than happy to be contacted.
Hence such leads should be scored for these advantages they offer.
How to obtain data points
The necessary data points will be captured automatically by your marketing automation (or web-analytics) vendor.
Off-Site (& Off-Line) Activities
Significance & Implications
Marketing Automation Platforms or web-analytics tools will only be able to track online prospect behavior or engagement on your website (limited tracking on other portals like social networks maybe available). Some of the key parameters that web-analytics tools will not be able to capture “automatically”, but still are relevant are:
- Offline engagement (at events, exhibitions etc) and insights developed during these interactions.
- Milestones (like a “Meeting” set-up) which are influenced by different offline, offsite, online interactions and confirmed offline (eg: on phone).
- Psychographic factors that don't translate into website behavior.
How to obtain data points
The data points have to be exported into "Scoring Algorithm" from offline data collection instruments to ensure that output from the scoring algorithm is comprehensive.
The data points have to be exported into "Scoring Algorithm" from offline data collection instruments to ensure that output from the scoring algorithm is comprehensive.
Summary
In the new scoring algorithm, “Budget” from “BANT” translates as “Revenue” of the prospect (a Demographic parameter defined by sales), which indicates the scope for budget allocation either immediately or in near future. This also addresses one of the major criticisms laid against “BANT” that, it is responsibility of marketers and sales team to get a prospect allocate budget by effectively communicating the need & benefits even if there was no prior budget allocation and hence considering “Budget” as a parameter is short sighted.
In the times of marketing automation and contact database integration, nurturing activities are targeted at engaging only the decision makers with right authority. Thus the parameter “Authority” is implicitly addressed in most of the present day marketing efforts. In case it is not addressed or if there is a need for additional emphasis on decision makers’ titles and seniority, necessary inclusion in “Demographic parameters defined by sales” will serve the purpose.
“Need” can be established based on the level of “Engagement” & degree of “Intent” revealed by the prospect.
“Timeline” can be established based on “Purchase Readiness” (or Decision Stage).
So the new scoring algorithm (“PIEDIO”) addresses all the aspects of “BANT” and also a host of other very relevant aspects.
Scoring based on “Social Media” activity in “PIEDIO”
If your web-analytics or marketing automation solution is able to track all necessary social interactions of your prospects, they can be scored appropriately under “Degree of Intent Revealed” or “Engagement”.
If all the interactions of your leads (or prospects) on social platform are not tracked automatically, they may have to be captured using other instruments and uploaded into your scoring system under relevant sections or under “Off-Site” activities as appropriate.
Thus this scoring algorithm is capable of bridging the gaps in scoring your leads or prospects based on their “social” activities.
Score Depreciation in “PIEDIO”
This scoring algorithm also allows for score depreciation based on inactivity. All data points pertaining to “Recency” (within “Engagement”) that are updated in real-time (or periodically) affect score depreciation along with other score changes automatically.
Scoring – Other Points of Note
- Scoring is not just for prioritizing or quarantining leads. It can also be used2 to identify objectives and processes for future lead nurturing efforts (like AB testing landing pages for a prospect scoring low on “Time Spent” or trying new channels to reach a prospect scoring low on “Recency” or trying new link baits for a prospect scoring low on “Degree of Intent Revealed” etc).
- Increase in score can be considered as the common denominator (objective) for all lead-nurturing campaigns. Hence, it can be regarded as common KPI and can be used for normalizing performance measurement across campaigns.
- In case, implicit data collection falls short in identifying necessary data points, your marketing automation solution can provide you custom dynamic form fields (based on past behaviour & form fills) to capture necessary information (explicitly).
- Company level score is not a simple addition of individual “scores” of all the visitors/decision makers from the company, but is an outcome of scoring aggregated activity of all individuals from the company.
- Having maximum score limits across various scoring parameters will help in normalizing and ensures ease of comparison between leads.
- Weightage distribution across different scoring parameters may be influenced by various internal factors (like resource availability etc), external factors (like economic conditions etc) and hence may have to be changed with time as appropriate. Weightage distribution can also be used to drive specific desired behaviour across organization.
Note
- In recent times, some of the marketing folks3 have even tried to expand the traditional stages of decision making1. For this particular scoring algorithm, only traditional stages of decision making are considered for ease of application and wider acceptance.
- The name “PIEDIO” (Purchase Readiness, Intent Revealed, Engagement, Demographics & Purchase Barriers, Identity of Lead (or Contact) Established, Off-site (& Off-line) Activities) was used only for ease of recall. It doesn’t have any other significance and hence is dispensible.
References
