Best Practices in Lead Mgt & Analytics_The Big Analytics Book

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20. Best Practices in Lead Management and Use of Analytics by Marianne Seiler

Pick up any article on the critical

issues facing B2B marketing and

sales executives today and lead

management will be listed. This

critical process of identifying,

capturing, qualifying, nurturing and

closing sales opportunities is the

fuel for fiscal growth. Despite its

importance, most executives admit

their firms do a miserable job at

managing sales opportunities.

In recent years, with advances in

CRM, sales and lead nurturing

software and access to new data sources, firms have begun applying analytics to

improve lead management. Sadly, for many companies, their efforts have not been

successful.

Based on client engagements and student conversations, it seems when lead

management analytics goes wrong, it frequently starts at lead scoring. As lead scoring

is the second stage of this highly interconnected process, its weaknesses are carried

forward corrupting the analytics in lead nurturing, distribution and pipeline

optimization.

Lead management is the closed-loop

process which accelerates sales growth

through demand generation, lead

scoring, lead nurturing, lead

distribution and sales pipeline

optimization of opportunities among

new, existing and former customers.

Marianne Seiler, Ph.D.

Faculty, Predictive Analytics

at Northwestern University

LinkedIn Contact

Marianne specializes in helping companies drive

growth through the application of analytics. She

focuses on restructuring organizations, re-

engineering processes, implementing enabling

technologies and establishing performance metrics

necessary to convert analytic insights into market

outcomes.

At Northwestern, Marianne teaches in the Masters in

Predictive Analytics program. Her courses focus on

the application of analytics to business and

customer strategy. Prior to joining Northwestern,

Marianne worked for Accenture where she led the

firm’s work in distribution, marketing and sales

analytics for Financial Services firms. Additionally,

she created Accenture’s perspective, methodology

and tools on analytics-driven lead management and

worked with clients across industries to optimize

their demand generation process.

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Lead scoring problems often arise from either: (1) failure to apply analytics

comprehensively in lead scoring; (2) failure to create a holistic lead analytic record; (3)

or failure to apply data thoughtfully throughout the lead scoring process.

First, to be effective, analytics needs to be integrated into each of the five key steps in

lead scoring. When analytics is not fully present, scoring of leads is based largely on

business rules/judgement. The interpretation of these rules can be very loosely applied,

resulting in Sales having little faith in the process. Many times, sales re-qualifies the

lead when it is received from the lead nurturing process costing the company time and

money.

Second, firms need to develop a lead analytic record as the basis for their scoring

activities. Lead analytic records provide a rich set of information on all aspects of a

lead. This includes lead:

1. Opportunity: lead source, date, campaign, product, line of business, etc.

2. Contact: person generating the lead, their role/responsibility, education, number

of years with their firm, etc.

3. Company: company the lead contact works for, history as a customer, industry,

financial status, size, geography, etc.

4. Account Relationship: years as a customer, share of wallet, product held,

profitability of the account, etc.

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Finally, companies need to bring external and internal data to the modeling process in a

thoughtful manner. Data has associated costs in collection, cleansing, matching and

potentially licensing. Methodically considering data elements and adding them when

they bring the most value improves model quality and lead management analytics ROI.

Attention to these key challenges will not only improve lead scoring effectiveness but

also enhance the performance of lead nurturing, distribution and pipeline optimization.

Lead Scoring Steps Sample data . . . .

Aggregation & Data Hygiene DUNS #; company name; address; business phone; etc.

Qualification Contact title; responsibility; purchase history; company budget; decision

timeframe; etc.

Scoring & Ranking Company revenue; # employees; years in business; account sales, margin, cost

to serve, customer profitability; account quote / purchase history; etc.

Enrichment Contact buyer preferences; account buyer preferences; industry / segment

needs / value propositions; account competitor data; etc.

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