TO MARKETING ANALYTICS

Transcription

1 AN EASY APPROACH TO MARKETING ANALYTICS Data access, quality & automation for improving trust Frank Moreno V P W O R L D W I D E M A R K E T I N G, Datawatch Kory Himmer S R. M A N A G E R, M A R K E T I N G O P E R A T I O N S, BitSight Technologies

2 Data and Analytics Landscape GARTNER S FOUR BIG CHALLENGES Establish TRUST in the data Manage DIVERSITY of data, users & outputs Build the data LITERACY of your workforce Master the COMPLEXITY of data BY 2022, THE MAJORITY OF INDIVIDUALS IN MATURE ECONOMIES WILL CONSUME MORE FALSE INFORMATION THAN TRUE INFORMATION BY 2020, 80% OF ORGANIZATIONS WILL INITIATE DELIBERATE COMPETENCY DEVELOPMENT IN THE FIELD OF DATA LITERACY

3 Misguided Conclusions Know what to ask Access to the data Lousy data INSIGHT REQUIREMENTS Information Handcuffs Know what to ask Great data Limited access to the data NIRVANA Common Challenges on the Path to an Effective Marketing Data Strategy TEAM ENABLEMENT Fact Bubble Access to the data Great data Don t know what to ask DATA MANAGEMENT *SiriusDecisions webinar, Three Keys to Unlocking the Data-Driven Organization, March 2018

4 There are now 6,829 MARTECH SOLUTIONS up 27% from 2017 Marketing Technology Landscape ( Martech 5000 ) April 2018 MORE DATA TO MEASURE THAN EVER BEFORE Copyright 2018 Marketing Technology Media, LLC. See for details and sources. Produced by Scott Brinker (@chiefmartec), Anand Thaker (@anandthaker), and Blue Green Brands.

5 Marketing Technology Landscape ( Martech 5000 )? MORE DATA TO MEASURE THAN EVER BEFORE April 2018 A TOOL IS MISSING Copyright 2018 Marketing Technology Media, LLC. See for details and sources. Produced by Scott Brinker (@chiefmartec), Anand Thaker (@anandthaker), and Blue Green Brands.

6 The Problem with MOST Dashboards Total Leads Demos Drag & Drop Dashboards? KPI s in Seconds? 1000 s of connectors? Impressions Downloads Clicks All just single datasets No context Views Shares You can t ask Why?

7 59% have a dedicated Marketing Ops person or team

8 59% have a dedicated Marketing Ops person or team THIS IS THE PERSON SPENDING ALL THIS TIME IN SPREADSHEETS

9 Dupes Many different systems Manual data entry Landing pages Blank cells Name formatting Text vs Numeric List purchases Event leads Unstructured sources (PDF, web) Pivots Vlookups Macros Multiple worksheets Often cleaning before putting in CRM and then again when generating reports Splitting Columns Removing characters Joining Data

10 Spreadsheets are not built to Clean data Transform data Combine data Clean up leading or trailing characters & spaces Split columns into parts (ie. Name, Date, address, fiscal qtr) Calculated fields w/out functions Reusability no macros Fill blanks w/out if/then/else Replace string values Convert case for all data in a column (i.e. UPPER, Proper, lower) Pivot/Unpivot columns Remove/show dedupes Extract Nulls Join and normalize disparate data Fuzzy matching Join analysis for recommended format (outer/inner join)

11 Results of Lack of Trust in our Data? Finger-pointing about incorrect data Wasted time producing duplicate work Accessing trusted data is challenging & frustrating Contributions go unrewarded TOP-LINE ANALYTICS GOALS ARE NOT MET: Monetize data Agile enterprise data analytics Decisions made based on trusted high quality data $3.1 Trillion Yearly cost of poor data quality in US 50% Analyst time wasted finding & correcting data

12 Analysts waste up to 80% of their time preparing data versus analyzing Wasted time preparing data $22,000 per year, per analyst wasted Blue Hill Research, March 2016

13 What is Data Preparation? Parsing PDF, Text to Data Sort, Filter, Calculations Transform Data - Pivot, Unpivot, Aggregate ACCESS MANIPULATE ENRICH COMBINE LOAD Cleanse Individual Fields Merge with External Data Repeatable Process

14 Kory Himmer Sr. Marketing Operations BitSight Technologies 5+ years in Marketing B2B Technology SaaS companies Experience in: Founded in 2011 Headquartered in Cambridge, MA 300 Employees CRM Database Management Marketing Automation Reporting & Analytics Prior Companies

15 Primary Technologies Used CRM Data Sources Events Web Forms Webinars Sales Prospecting Partner Portal Data Augmentation Web Analytics List Imports Account Engagement

16 Why Monarch We have bad data!!! More sophisticated analysis required - SFDC Reporting not enough Minimal IT/Enterprise Apps Resources Single Source of Truth for Reporting

17 Use Case #1: Data Normalization 1. Select Lead/Contact Tables to Join 2. Select Field Mappings 3. Columns with normalized data Bad Data Good Data

18 Use Case #2: Single Lead/Contact View 1. Select Lead/Contact Tables to Join 3. Columns with normalized data 2. Select Field Mappings

19 Use Case #3: Formula Builder 1. Default lead ownership to account owner 3. Custom Owner Field built 2. Write formula

20 The Results Cohort Analysis, leveraging 5 Salesforce objects used: Lead, Contact, Campaign Member, Opportunity, Opportunity Contact Role Consolidate into a single report using Monarch platform Currently pushing to Excel, but exact same process would be followed to push to a BI platform (Qlik, Tableau, Power BI, etc)

21 DEFENSE OFFENSE Marketing s Data Strategy 1. Lead quality score 2. Lead funnel conversion rates 3. Customer profiling (geography, industry, persona, size) 4. ROI on campaigns and content 5. Net promoter score 6. Messaging effectiveness (A/B testing) 7. Market share 1. Predictors-to-purchase and predictors-to-churn 2. Forecasted campaign performance 3. Performance-based budget allocation 4. Total Addressable Market 5. Predictive analytics Example: Web design predicted impact on site performance 6. Forecasted marketing contribution to pipeline/revenue 7. Text analytics from customer feedback 8. Customer Lifetime Value 1. Website traffic and performance metrics Example: Click-through rate, bounce rate, site conversion rate 2. SEO / SEM Example: Conversion rate, top keywords, cost per lead 3. Social media performance metrics 4. Content downloads 5. Campaign performance monitoring 6. Marketing budgeting 7. Contact database growth 1. Marketing attribution modeling 2. Revenue contribution by campaign/source 3. Content effectiveness 4. Social sentiment and brand awareness measurement Analysis of likes, follows, shares, AI-based language analysis 5. Web traffic analysis By device, source, industry, account, etc. OPERATIONAL EFFICIENCY ANALYTIC INSIGHT *Concept derived from Harvard Business Review article What s your Data Strategy?

22 About Datawatch Datawatch is the data intelligence solutions partner that fuels your marketing analytics. MORE DATA MORE TRUST MORE MINDS Collaborative No Coding Easy to Use Excel-like Interface Highly Agile Repeatable / Reusable Socialization Data Market Place Machine Learning All Data Types Data Stewardship Smart Recommendation PUBLICLY TRADED INNOVATION BRANDS CUSTOMERS NASDAQ - DWCH FOUNDED 1985 Pioneer in self-service data extraction & blending and real-time visual data discovery Monarch Swarm Panopticon Angoss Over 14,000 global customers of every size, including 431 of The Fortune 500

23 QUESTIONS? Booth #250 Frank Moreno V P W O R L D W I D E M A R K E T I N G, Datawatch Kory Himmer S R. M A N A G E R, M A R K E T I N G O P E R A T I O N S, BitSight Technologies