Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to Data Quality Bootcamp
Similar to Data Quality Bootcamp (20)
Recently uploaded
Recently uploaded (20)
Data Quality Bootcamp
- 1. April 14, 2015 Elliott Lowe Dir, Marketing Ops @elliottlowe Institute for Integrative Nutrition Inga Romanoff President/CEO @ingaroma Romanoff Consulting Data Quality Bootcamp Why Dirty Data = Low ROI, & What You Can Do About It
- 2. Page 2 Inga Romanoff President, Romanoff Consulting Elliott Lowe Director, Marketing Operations, Institute for Integrative Nutrition With over 15 years of marketing experience in the U.S., Russia, and EMEA, Inga is no stranger to Marketing Automation. Inga is a Principal of a boutique marketing automation consultancy. She is passionate about helping clients implement and optimize Marketo, recruit talent, and get exceptional results. She is an award-winning Certified Marketo Expert & a multi-year Marketo Champion, and leads Marketo User Group in New York. With over 30 years of experience at startups and large public companies, Elliott Lowe specializes in building solid operations foundations for rapidly growing companies. Presently, Elliott heads up Marketing Operations at the Institute for Integrative Nutrition, the world's largest nutrition school. He is a Marketo-Certified Expert, a multi- year Marketo Champion and a co-leader of the Marketo New York User Group.
- 3. Page 3 Agenda #DataQuality Why Should I Care About Data Quality? Where Dirty Data Comes From Your 6-Step Program to Clean Data Data Quality Saves Tips and Tricks
- 4. Page 4 Why Should I Care About Data Quality? “25 percent of the average B2B marketer’s database is inaccurate and 60 percent of companies have an overall data health of ‘unreliable’.” - SiriusDecisions study COMPANIES DO NOT HAVE A SOPHISTICATED APPROACH TO DATA QUALITY1 74% MARKETERS SAY DATA QUALITY IS THE BIGGEST OBSTACLE TO MARKETING AUTOMATION SUCCESS3 36% COMPANIES WITH CENTRAL DATA MGTMT HAD A SIGNIFICANT INCREASE IN PROFITS1 53% RECORDS ANALYZED WERE LACKING FIRMOGRAPHIC DATA2 88% 1 2015 Experian The data quality benchmark report 2 2014 Netprospex Annual Marketing Data Benchmark Report 3 Ascend2 Marketing Automation Benchmark Survey, July 2014 #DataQuality Facts
- 5. Page 5 Data Quality Horror Stories
- 6. Page 6 “Quality is never an accident…” ~Will A. Foster
- 7. Page 7 6-Step Program to Clean Data 1. Perform data audit 2. Perform systems audit 3. Revise data capture processes 4. Correct data errors 5. Implement email alerts and reports 6. Manage data quality across the organization
- 8. Page 8 Where Dirty Data Comes From Systems • Flawed setup • Poorly designed integrations People and Process • Manual input • Lack of a data quality strategy 51% 48% 44% 32% 0% 10% 20% 30% 40% 50% 60% Most common data errors1 1 2015 Experian The data quality benchmark report
- 9. Page 9 Data Quality Saves
- 10. Page 10 Progressive Profiling - Almost • Successful launch of progressive profiling • High fillout rate except Business Name • Field: Company Name • New custom field and smart campaign with logic to substitute “N/A” values
- 11. Page 11 Duplication Prevention - Not • Dupe app matched on First Name + Last Name + Email Address • Most new leads from forms have only a First Name and Email Address • [Not Provided] added if Last Name field is empty (for sync with SFDC) • Dupe app almost never detected a match, yet we had thousands of email duplicates
- 12. Page 12 Tips and Tricks
- 13. Page 13 External Data Capture • Marketo forms, Server-Side Form Post, SOAP, REST • Restricting form field inputs • Form field pre-population • Form field validation • List import
- 14. Page 14 External Data Capture • Marketo forms, Server-Side Form Post, SOAP, REST • Restricting form field inputs • Form field pre-population • Form field validation • List import
- 15. Page 15 External Data Capture • Marketo forms, Server-Side Form Post, SOAP, REST • Restricting form field inputs • Form field pre-population • Form field validation • List import
- 16. Page 16 Sales Inputs • Capture fields needed for marketing • Prevent duplicate creation • Marketing attribution for Sales leads • [mktUnknown] created from Outlook/Google MSI plug-in
- 17. Page 17 Sales Inputs • Capture fields needed for marketing • Prevent duplicate creation • Marketing attribution for Sales leads • [mktUnknown] created from Outlook/Google MSI plug-in
- 18. Page 18 CRM Sync • Deleted Leads / Contacts • Leads without email address • Sync performance • Field visibility • Syncing to SFDC campaigns
- 19. Page 19 CRM Sync – Error Reports
- 20. Page 20 CRM Sync - Failure Alert Email
- 21. Page 21 Duplicate Records • Email notifications and weekly reports • Merging is evil • Retain values during merge based on priority • Mass merging with Marketo Easy Merge
- 22. Page 22 Duplicate Records - Alerts • Email notifications and weekly reports • Merging is evil • Retain values during merge based on priority • Mass merging with Marketo Easy Merge
- 23. Page 23 Duplicate Records • Email notifications and weekly reports • Merging is evil • Retain values during merge based on priority • Mass merging with Marketo Easy Merge
- 24. Page 24 Data Normalization • Normalization smart campaigns • Phone and email validation • Capitalization of the First and Last Name in SFDC
- 26. Page 26 Go to Your Resources Page VISIT: summit.ingaroma.com (this page will be live for 1 week)
- 27. Thank You!
Editor's Notes
- Update with new cover from visual brand deck.
- Today we’re going to cover several major points including [rephrase points above] At end we will provide a URL to a library of useful tools.
- According to a SiriusDecision study, up to 25 percent of the data in the average B2B company include critical data errors ranging from incorrect demographic data to a lack of current disposition. And in my experience, in B2C companies even a larger percentage (as high as 50%) may be bad, especially, if e-commerce primary revenue channel. Consider the impact on your acquisition cost: Database 250,000 Cost per lead $50 / lead Total cost of acquisition = $12.5mm With 25% of your leads having bad data, your actual cost of acquisition is 33% higher and you basically wasted over $3mm! In addition there is a significant impact on revenue. According to the SiriusDecision study, organizations that actively manage their data quality have only 10 percent bad data and are realizing nearly 70 percent more revenue purely based on data quality. So what’s the reality? [discuss each fact] Finally, 36% of marketers say that data quality is the biggest obstacle to MA success, which for this audience can be a career limiting event. I’m sure everyone here has a story or two about data quality issues at their organizations – here are a couple of ours. Sirius Decisions - The Impact of Bad Data on Demand Creation (11/25/08)
- The first has to do with internal data quality issues. At Monster.com, I led a project to send their first personalized email to hundreds of thousands of leads. However since this was the first time to do this, I wanted to ensure that the data was clean, so I had several team members manually review the list for errors. What we found was that the sales team of over 1K over the years had added comments like “Idiot”, “No Longer with Company”, etc. in the First Name field of thousands of records and there was no field to indicate Lead Status. The second has to do with external data quality issues. At a recent NYMUG meeting one of our members shared that they had a lead capture form with two fields – email address and zip code, but when the form was submitted, many leads had entered their password, not their zip code in the second field. We’d love to hear your stories and perhaps include some of them in a future blog article (anonymously of course), so please tweet yours to #DataQuality. Promo not running – all emails delayed (1.75mm API calls)
- To sum up what we’ve illustrated previously, “Quality is never an accident; it is always the result of high intention, sincere effort, intelligent direction and skillful execution...” However, it turns out most companies discover issues randomly. According to the 2015 Experian benchmark report, Most organizations say data quality issues are detected when reported by employees, customers or prospects, and Fewer than one in two companies conduct proactive data audits to discover data quality issues. You have to be the catalyst for making this happen by making sure your executive leadership and stakeholders understand the cost of bad data and they allocate personnel tools and training accordingly. Just remember, it takes time and it’s not an accident – it takes a plan, analysis and careful thought to make it happen.
- So here’s your first takeaway from our presentation today. Most of you probably have many of these 6 steps, but perhaps not all and perhaps not extensively across all departments in your organization. Only when these steps are comprehensive and accepted throughout will you be on your way to becoming a strong Data Quality organization! So let’s discuss a few key things to look for in your data and system audits and then we’ll get to some actual examples, tips and tricks for cleaning up and stopping the proliferation of data errors. Perform Data Audit 2. Perform Systems Audit 3. Revise data capture processes including training 4. Correct data errors 5. Implement email alerts and reports (tips on that later) 6. Manage data quality across the organization
- As you think about managing data across the organization, consider where dirty data comes from. The sources of bad data are manifold – some you can control and some you can’t. However, you can mitigate data errors that you can control – more on that later. These are the most common sources of data errors from the Experian study, but a big cause that was not an option on the survey was CRM sync.
- Both of these examples were situations where a workaround was employed to overcome CRM sync validation rules, but these workarounds actually caused data errors.
- Here’s an example of a very successful launch of progressive profiling capability. Collecting this additional data, allowed this client to increase data completeness to about 88% - higher than any other previously employed data sources. However, we noticed that for one of the fields, the fillout was rate was extremely low. Specifically, only 3% completed the form field. Upon further investigation, we noticed that this is the field that had nearly 100% existing data – an unlikely percentage. Turns out, the values in the field were mostly “N/A” as we utilized the standard “Company Name” field on the form. “Company Name” is a field required to sync a lead to Salesforce and therefore, when empty, we add a placeholder value. Well, that would prevent Progressive Profiling from showing this to the prospect. As a solution, we created a workaround by showing a different field (e.g. Business Name) and re-populating “Company Name” if a valid value was entered. To do this, we set up a processing campaign that was triggered when a form was filled out.
- In SFDC, Dupe Blocker was configured to only match if First Name, Last Name and Email Address were the same. Most of Leads created via form submissions only had a First Name and Email Address and a smart campaign would add [Not Provided] in the last name field if empty, so it would sync to SFDC. Sales almost always enters the First Name and Last Name, so Dupe Blocker almost never detected a match, yet we had thousands of email duplicates. If Sales happened to identify duplicate records, they would merge them, but most of the existing Leads were older and they almost always converted the new duplicate Lead to a Contact prior to realizing there was a duplicate. So when the records were merged, the Contact record won and valuable attribution data including the original creation date and source was lost. To add insult to injury, if the stars aligned and Dupe Blocker detected the duplicate, it was configured only to flag a record as a duplicate, so that it could be merged rather than preventing the duplicate from being created. We worked with the SFDC team and Data Governance Director to change the configuration of DupeBlocker, which eliminated the duplicates and consequent loss of attribution data.
- Here on the next few slides we will share some hands-on tips and tricks, and secrets that you can take with you to improve data quality in your organization.
- Ideally use Marketo forms (embedded or native), but server-side form post is acceptable substitute. Don’t use SOAP or ReST API if possible for new lead creation. Restricting form field inputs: use Select Fields, Input Masking and Required Fields when possible to ensure consistent data. Field pre-population – Allows leads to revalidate and update their info. Don’t allow email address field change – instead use a second field to capture the new email address and use smart campaign to update the Email field on the existing record Field validation –Javascript must be on. Examples: Strikeiron for email / phone, Reachforce for company name. Be sure that you have the same field validations in your forms as you do in SFDC. Restrict List Import role permission to only trained staff. Use an import template. Use field aliases if necessary.
- Ideally use Marketo forms (embedded or native), but server-side form post is acceptable substitute. Don’t use SOAP or ReST API if possible for new lead creation. Restricting form field inputs: use Select Fields, Input Masking and Required Fields when possible to ensure consistent data. Field pre-population – Allows leads to revalidate and update their info. Don’t allow email address field change – instead use a second field to capture the new email address and use smart campaign to update the Email field on the existing record Field validation –Javascript must be on. Examples: Strikeiron for email / phone, Reachforce for company name. Be sure that you have the same field validations in your forms as you do in SFDC. Restrict List Import role permission to only trained staff. Use an import template. Use field aliases if necessary.
- Ideally use Marketo forms (embedded or native), but server-side form post is acceptable substitute. Don’t use SOAP or ReST API if possible for new lead creation. Restricting form field inputs: use Select Fields, Input Masking and Required Fields when possible to ensure consistent data. Field pre-population – Allows leads to revalidate and update their info. Don’t allow email address field change – instead use a second field to capture the new email address and use smart campaign to update the Email field on the existing record Field validation –Javascript must be on. Examples: Strikeiron for email / phone, Reachforce for company name. Be sure that you have the same field validations in your forms as you do in SFDC. Restrict List Import role permission to only trained staff. Use an import template. Use field aliases if necessary.
- Capture needed fields and Require and/or train Sales to capture data such as email and/or phone, Lead Status, location, interest/persona Ensure accuracy/consistency Avoid comments in CRM fields Picklists and dependent lists Field validation Properly configrued Workflow rule updates Cross object formula fields – not recommended Prevent duplicates via properly configured app / process / training [mktUnknown] created from Outlook/Google MSI plug-in – alert sender and resolve
- Capture needed fields and ensure accuracy/consistency Require and/or train Sales to capture data such as email and/or phone, Lead Status, location, interest/persona Avoid comments in CRM fields Picklists and dependent lists Field validation Properly configrued Workflow rule updates Cross object formula fields – not recommended Prevent duplicates via properly configured app / process / training Marketing attribution for Sales leads [mktUnknown] created from Outlook/Google MSI plug-in – alert sender and resolve Avoid comments in CRM fields (training and validation)
- Prevent Lead / Contact deletion due to metrics and data loss! If you do allow deletion, have a Marketo smart campaign with the ‘Lead is Deleted from SFDC’ trigger that deletes the Marketo lead. Otherwise you will have an orphaned lead that will create a new SFDC Lead the next time it is synced Leads without email address or deleted email address Prevent merging if only one has an email address Prevent deleting email addresses Prevent and track lead deletion Sync performance Top syncing fields – get list from Marketo tech support. Identify unneeded / redundant syncs to SFDC Average Sync Time from SFDC – compare SFDC Created Date to Marketo Created Date. Long delays can result in duplicates. Field visibility Syncing to SFDC campaigns – if sync fails, your leads will not be in SFDC campaigns and your execs and stakeholders using SFDC-based reporting will get an incomplete and innacurate picture of your program performance.
- Alert emails Is Deleted – Sync Failures – add Sync Test field to Marketo lead that is read only to CRM, update value to ‘True’ and run CRM report for those records that are false
- Audit checks Total Marketo leads vs. SFDC Leads / Contacts (with email address) Alert emails Is Deleted – Sync Failures – add Sync Test field to Marketo lead that is read only to CRM, update value to ‘True’ and run CRM report for those records that are false
- Email notifications when they occur and weekly reports of existing duplicates Email duplicate alert Name duplicate alert Merging is evil If not done correctly, valuable source attribution and cost data can be lost It’s better to delete a manually-created new duplicate than merge with an existing lead if caught early and there isn’t any valuable activity associated with the duplicate Retain values during merge based on priority You may want to have multiple fields for key data such as email, address, revenue, number of employees, etc. Then you preserve and use those field values from the most reliable source If you have a massive number of duplicates, you can perform a mass merge with Marketo Easy Merge
- Email notifications when they occur and weekly reports of existing duplicates Email duplicate alert Name duplicate alert Merging is evil If not done correctly, valuable source attribution and cost data can be lost It’s better to delete a manually-created new duplicate than merge with an existing lead if caught early and there isn’t any valuable activity associated with the duplicate Retain values during merge based on priority You may want to have multiple fields for key data such as email, address, revenue, number of employees, etc. Then you preserve and use those field values from the most reliable source If you have a massive number of duplicates, you can perform a mass merge with Marketo Easy Merge
- Email notifications when they occur and weekly reports of existing duplicates Email duplicate alert Name duplicate alert Merging is evil If not done correctly, valuable source attribution and cost data can be lost It’s better to delete a manually-created new duplicate than merge with an existing lead if caught early and there isn’t any valuable activity associated with the duplicate Retain values during merge based on priority You may want to have multiple fields for key data such as email, address, revenue, number of employees, etc. Then you preserve and use those field values from the most reliable source If you have a massive number of duplicates, you can perform a mass merge with Marketo Easy Merge
- Normalization smart campaigns Phone and email validation Salesforce capitalization of the First and Last Name
- Audit It’s not an accident, you are the catalyst for this Get buy-in within the organization Clean-Up One-time data clean up Process Create campaigns Make your organization part of the process, create credibility Sustain Create data quality strategy across the organization
- Update with new cover from visual brand deck.