Leverage Contact Washing Machine to Drive Sales Ready Leads

Webinar Details: 29th Jan, 2015 - 11am PDT / 2pm EDT

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Quick Intro

Rahul SachdevaData Analytics Manager

Isha VohraWeb Marketer

You’ll learn about

• Insights on how bad data impacts your ROMI (return-on-marketing-investment)

• Ways to categorize your bad data

• Metrics to measure bad data impact

• Types of Contact Washing Machine (CWM)

• CWM Success Stories

@Grazitti

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Let’s Start

66%

80%

30%

54%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

MARKETING LEAD GENERATION FINANCE CUSTOMER RELATIONSHIPS

% OF COMPANIES IMPACTED

Bad Data – Function-wise

Impact on Sales & Marketing

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Sales• Lost revenue • Wasted resources• Lower productivity• Damage to your credibility• Failure of your marketing

automation initiatives

Marketing• Delivering wrong message to

wrong customer/prospect• Annoying customers/ prospects

with wrong and redundant messages

• Failing to leverage multi-modal marketing capabilities

• Distorting campaign success metrics

Lead Sources• PPC Ads• Organic Web Traffic• Social Media• Partner Referrals• List Purchase• Tradeshows & Events• Sales Cold Leads• Web Listings & portals• Content Syndication• Inbound Calls

• Qualify• Create

Opportunity• Follow-up

Close Opportunity

WON

LOST

Common Lead Flow

Lead Sources• PPC Ads• Organic Web Traffic• Social Media• Partner Referrals• List Purchase• Tradeshows & Events• Sales Cold Leads• Web Listings & portals• Content Syndication• Inbound Calls

• Qualify• Create

Opportunity• Follow-up

Close Opportunity

WON

LOST

Here’s what brings Bad Data

1. Missing Data: Empty fields that should contain data. Example: A lead can not be assigned to a Sales rep because country and state field are missing.

Types of Bad Data in your Marketing System

2. Wrong or inaccurate data: Information that has not been entered correctly or maintained. Example: A junk or a 10-minute email ID, captured from a web form.

Types of Bad Data in your Marketing System

3. Inappropriate data: Data that’s been entered in the wrong field. Example: A promising lead doesn’t appear in regional reports because the city name is in the State field.

Types of Bad Data in your Marketing System

4. Non-conforming data: Data that hasn’t been normalized as per the system of records. Example: A marketing segment of all U.S. leads doesn’t include clients with “USA” or “United States of America” in the Country field.

Types of Bad Data in your Marketing System

5. Duplicate data: A single Account, Contact, Lead, etc. that occupies more than one record in the database. Example: Sales reps in different regions unknowingly create three distinct IBM Accounts, wasting valuable time, sowing confusion, and causing missed opportunities.

Types of Bad Data in your Marketing System

Do you have bad data in your system?

1. Duplicate Data %Total Number of Duplicate Leads/ Total number on Known Leads

2. Lead Quality ScoreAssign weightage to fields like Job Title, Country, City, State, Postal Code, Phone Number. Assess existing lead database to measure quality score for each lead.

3. Email Deliverability %Measure average email deliverability for last 180 days. Segment for new data and existing data in lead database.

4. Unsubscribe %Measure average email un-subscription for last 180 days. Segment for new data and existing data in lead database.

5. Email Bounce ReasonsMonitor email soft bounces and hard bounces. Extract a report to see top reasons for bounced emails.

6. MQLs marked as Bad Data by Sales TeamMonitor leads which are marked by Sales Reps in Salesforce as Bad Data and return to marketing system for clean-up or nurturing.

Measuring Impact of Bad Data

Cleaning & Normalization Only

• Series of Smart Campaigns for passing leads through data filters

• Trigger when a lead is created and push it through each campaign in a sequence, before sending it for lead scoring

• Batch run old data through CWM campaigns for better results

Contact Washing Machine (Semi-automatic)

inside your Marketing Automation System

Contact Washing Machine (Semi-automatic)

inside your Marketing Automation System

Filter Company Name

Filter Phone Number

Filter Lead Name

Contact Washing Machine (fully-automatic)

using External Data Application

Cleaning, Normalization & Enrichment

Built on the Alteryx platform, the Contact Washing Machine works in a three step process-

• Cleansing – In the first step, the raw data is scrubbed against a set of predefined bad values for the fields, e.g. Name, Company Name, Email etc. The app flags bad data and separates it from the good data stream. The bad data records are then separated from the good data ones.

• Normalization – This is the result from previous step, i.e. good data is normalized for standardization of values such as country, state, city, job title etc.

• Enrichment – In the last step, the normalized data is enriched with third party business database to help sales executives get extra information for qualifying and prioritizing their leads.

Working of The Contact Washing Machine

CLEAN

CONSISTENT

CORRECT

COMPLETE

The Contact Washing Machine delivers DATA that is

Which CWM do you need?

Feature Semi-automatic Automatic

Setup in Marketing Automation Platform ✓ -

Integration with Marketing Automation Platform ✓ ✓

Data Cleaning ✓ ✓

Data Normalization ✓ ✓

Data Quality Assessment ✓ ✓

Data Enrichment X ✓

Contact Washing MachineSemi-automatic vs Automatic

CWM Success Stories

CWM Impact

Leveraging the Contact Washing Machine, Alteryx got -

• More than 200K clean records processed per minute

• Over 40% improved lead quality with enriched information from external database

• Up to 37% Increase in the Sales Productivity

• 25% Improved Sales Qualification and Prioritization

With a seamless data cleaning process, Cloudwords got -

• 95%+ Delivery Rate on cold leads

• Clean lead segments for better customer engagement with targeted messaging

• Detailed account information by leveraging 3rd party sites like Zoom Info and Hoovers

• Increased campaign conversions by sifting the existing data to remove duplicates/junk data

• Functional lead qualification and lead scoring systems

Final Takeaways

Final Takeaways

Integrate Data

Sources

Audit Legacy Data

Get Sales Feedback

Integrate CWM

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