Intelligence versus Wisdom - The Single Customer View
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Marketing databases, data marts, and Single Customer Views have been talked about, built and rebuilt over time which means Marketers must work with data from everywhere. This can be challenging and allows for error when attempting to send campaigns and messages to a targeted audience. These slides were part of a webinar where we explore how simply gathering data to power intelligence isn't enough, the data must better inform the marketer to make better decisions - it must provide wisdom! In it we discuss how the fundamental facets of our personal relationships can be incorporated into a Single Customer View database providing memory, perspective, and a trustworthy foundation for analytics, segmentation and campaign management. The webinar is available on-demand here: https://youtu.be/2vaKFhzyn4g
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Intelligence versus Wisdom - The Single Customer View
- 1. Intelligence vs Wisdom Jim Kelly
- 6. Wisdom Good Judgement Wisdom Practical Contact with Facts & Events Knowledge Skills Facts Considered Iterative Learning Information Informed Experience Knowledge Gaps & Inaccuracies Bad ‘Facts’ Inaccurate Information Wisdom?
- 7. Wisdom Good Judgement Wisdom Practical Contact with Facts & Events Knowledge Skills Facts Considered Iterative Learning Information Informed Experience Misguided Judgement Knowledge Gaps & Inaccuracies Bad ‘Facts’ Inaccurate Information Mis- informed Wisdom?
- 8. Focus On The Foundation Good Judgement Wisdom Practical Contact with Facts & Events Knowledge Skills Facts Considered Iterative Learning Information Informed Experience
- 9. Focus On The Foundation Good Judgement Wisdom Practical Contact with Facts & Events Knowledge Skills Facts Considered Iterative Learning Information Informed Experience
- 10. DO I KNOW YOU? Human relationships need human memories
- 11. Personal vs Customer Relationships What do I know about them? What do others know about them? Has any money changed hands? How do they behave? Where and how do they live? What conversations have we had?
- 12. Personal vs Customer Relationships What do I know about them? What do others know about them? Has any money changed hands? How do they behave? Where and how do they live? What conversations have we had? Customer Data Behavioural Events
- 13. PEOPLE CHANGE Remember everything, forget no-one
- 14. The Ever Changing Human James Robert Smith
- 15. The Ever Changing Human James Robert Smith Mr J Smith Jim Smith James Smith JR Smith J Smith
- 16. The Ever Changing Human James Robert Smith Mr J Smith Jim Smith James Smith JR Smith J Smith 15 Oak Drive james@home.com 020 243 7666 07024 266722 MyBiz Co. jsmith@work.com 020 254 8777
- 17. The Ever Changing Human James Robert Smith Mr J Smith Jim Smith James Smith JR Smith J Smith 15 Oak Drive james@home.com 020 243 7666 07024 266722 MyBiz Co. jsmith@work.com 020 254 8777
- 18. The Ever Changing Human James Robert Smith Mr J Smith Jim Smith James Smith JR Smith J Smith 15 Oak Drive james@home.com 020 243 7666 07024 266722 MyBiz Co. jsmith@work.com 020 254 8777 253 Brook Rd 01432 657564
- 19. People Buy From People Who is your ‘Customer’? Household or Premise Account Person or Individual
- 20. MEMORIES FROM EVERYWHERE We’ve met before haven’t we?
- 21. Where Memories Come From Some Person Office Outside Work Gym Mutual Friends Office Socials
- 23. Origins of Facts Individuals Existing Web Registrations Sales Force Automation Complaints Database Finance System Customer Database
- 25. Origins of Facts Individuals … Finance Existing Electoral 3rd Party Households … Existing
- 26. Origins of Facts Purchases Refund/Return Shipping Individuals … Page Views … Link ClicksBasket Use Email Sent Website Sales Opens Click Through … … Events … Finance Existing Electoral 3rd Party Households … Existing
- 27. Origins of Facts Purchases Refund/Return Shipping Individuals … Page Views … Link ClicksBasket Use Email Sent Website Sales Opens Click Through … … Events … Finance Existing Electoral 3rd Party Households … Existing Is it legal? Is it trustworthy? Is it accurate and clean? Is it marketing ready? Is it structured?
- 28. Governance and Preparation Is it legal? Is it trustworthy? Is it accurate and clean? Is it marketing ready? Is it structured? Can I use it in analysis or queries & will it power targeting? Should it be from the customers perspective, or mine? Do I need to clean the data before it’s useable? Amend it perhaps? Should I trust this source over another source with same data? Am I legally able to store, maintain and use this data? Usable, Clean, Accurate & Trustworthy Data
- 29. THE IMPORTANCE OF ONE Matching and merging data to create a single individual
- 30. Improve Matching Through Better Hygiene Mr Jim Smith 15 Oal Dr. London, SW2 1FE Tel. 020 243 7666 Ordered: MX5624 BLACK Price: £60
- 31. Improve Matching Through Better Hygiene Mr Jim Smith 15 Oal Dr. London, SW2 1FE Tel. 020 243 7666 Ordered: MX5624 BLACK Price: £60 NO MATCH • New customer record created • New customer! Hurray! Acquisition metrics updated • “Welcome” conversation started • Can’t ship to “Oal Dr.”…oops • Customer complains
- 32. Improve Matching Through Better Hygiene Mr Jim Smith 15 Oal Dr. London, SW2 1FE Tel. 020 243 7666 Ordered: MX5624 BLACK Price: £60 CUSTOMER FOUND
- 33. Mr Jim or James or Jimmy Smith 15 Oak Drive London, SW2 1FE Tel. 020 243 7666 Data Hygiene Improve Matching Through Better Hygiene Mr Jim Smith 15 Oal Dr. London, SW2 1FE Tel. 020 243 7666 Ordered: MX5624 BLACK Price: £60 CUSTOER FOUND
- 34. Mr Jim or James or Jimmy Smith 15 Oak Drive London, SW2 1FE Tel. 020 243 7666 Data Hygiene Improve Matching Through Better Hygiene Mr Jim Smith 15 Oal Dr. London, SW2 1FE Tel. 020 243 7666 Ordered: MX5624 BLACK Price: £60 CUSTOMER FOUND • Existing customer • High value, worth over £1200 • Return customer! • Attributed to ‘New Suitcases!’ email • “Top customer” conversation started • Customer pleased with service
- 35. Badly Matched Data Creates Issues Acquisition •Duplicate individuals created •Acquisition metrics incorrect •Success over- represented •Existing customers treated as new ones Retention •Individuals not matched against known individuals •Retention offers badly targeted •‘Lost’ customers may actually not be lost at all Segmentation •Duplicate individuals •Can’t accurately understand buyer behaviour •Segment performance inaccurate Campaigns •Duplicated individuals •Cost wasted through duplicated comm’s •Customers cannot respond twice •Response rate greatly reduced
- 36. Duplicates vs False Positives Duplicates False Positives Bad Data Bad Metrics Bad Decisions Bad Data Bad Metrics Bad Decisions Duplicates False Positives • Reduces database • ‘Lose’ customers • Some customers receive someone else’s contact • Acting upon a false positive annoys the customer(s), damages metrics and may get you into trouble… • Enlarges database • ‘Gain’ customers • Some customers receive duplicate contact • Acting upon a single customer multiple times annoys the customer and damages metrics
- 37. PERSPECTIVE IS REALITY Memory defines relationship, perspective defines conversation
- 38. Data Needs Perspective (and it’s not yours) MX8732 BLK costs £125 I bought a black cocktail dress.
- 39. Data Needs Perspective (and it’s not yours) MX8732 BLK costs £125 I bought a black cocktail dress. Browsed on 2 May, bought on 3 May I browsed for 15 minutes then bought straight away. Spend £100 Default database date is 01/01/1900 I am not 114 years old! Umm no… product was £100, VAT £20, shipping £20 – I just spent £140 Returns Rate: 40% I order several sizes to try them on! I keep one of everything I buy!
- 40. BUILDING KNOWLEDGE Putting it all together :: The Single Customer View
- 41. What is a Single Customer View? Database Data Data Data Data Data PROCESS PRESENT
- 42. What is a Single Customer View? Database Data Data Data Data Data PROCESS Premises Customer Orders Order Lines Contact History Other… PRESENT
- 43. What should an SCV contain? Customer Detail Premises Detail Contact Channel Information Campaign Contact History Transactions Other Events and Responses
- 44. What should an SCV contain? Raw Data Clean Data Presented Data Derivation Rules Data Consolidation Events Subject Access/Usage Data
- 45. Turning Data Into Information Collect Prepare Merge Structure SCV • Extract, transform & load data from various sources • Format, clean & prepare data into a standard format • Match, de-duplicate & merge records into a single record • Apply structure, perspective, derivations & metrics • Clean, trustworthy & structured, single record per customer
- 46. Things To Think About • Data Sources – Data feed standards – Frequency of data feeds – Automated extraction/receipt – Ad-hoc sources • Data Standardisation – Shortened names/nicknames – Different field formats – International address formats – Vanity addresses • Data Validation – Name & address validation – Additional field validation – Salacious name screening • Data Hygiene – Postal location cleansing – Individual cleansing – Business premise cleansing • Matching – Tuned matching by source – Matching individuals – Matching households – Consolidating social groups – Matching commercial businesses – Consolidating business sites • Deduplication – Data survivorship rules – Data source trust differentials – Data merging & consolidation
- 47. Data Data Data Treat Each Data Source Differently Process Load Store Data SCV
- 48. The Logical Parts of an SCV Data Sources Structure & Schema Merge & Dedupe Rules Prepare Routines Collect Processes Enhancement Files Hygiene Files Single Customer View
- 49. The Logical Parts of an SCV Data Sources Structure & Schema Merge & Dedupe Rules Prepare Routines Collect Processes Enhancement Files Hygiene Files Single Customer View Third-party data hygiene reference files e.g. address, phone etc. Third-party data enhancement files e.g. suppression, geo-dems The processes that extract and load data from multiple sources The data model that joins all the data together and stores it The business rules that optimise matching, cleaning & deduplication The routines that clean and process the data into usable information For example, customer data, transactions and communication history
- 50. Yours Licensed from 3rd Parties Build, Buy or Rent The Logical Parts of an SCV Data Sources Structure & Schema Merge & Dedupe Rules Prepare Routines Collect Processes Enhancement Files Hygiene Files Single Customer View
- 51. KEY THOUGHTS Something to leave you with
- 52. Key Thoughts Memories define relationships. Perspective defines conversation. Have clear marketing goals in mind. Start small, think big. Operational data is for operations. Don’t confuse Big Data with Lots of Data. If you outsource your SCV, be clear what you own. Avoid duplicates & false positives. Clean data wins. Gathering data is not enough. It must inform better decisions. It must provide wisdom. Data comes from everywhere. Only your date of birth will stay the same. Great new wisdom? Personalise without being personal.
- 53. Q & A Thank you for listening Intelligence :: ability to acquire and apply knowledge and skills Wisdom :: informed decision powered by good intelligence