MARKETING DASHBOARDS. Why they fail to deliver value and how we overcame it. Frank Moreno VP Worldwide Marketing Datawatch

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1 Frank Moreno VP Worldwide Marketing Datawatch Ellen Wilson Marketing Operations Analyst Datawatch MARKETING DASHBOARDS Why they fail to deliver value and how we overcame it

2 There s a better way to build marketing dashboards 2

3 There are now 5,381 martech solutions up 39% from 2016 Marketing Technology Landscape ( Martech 5000 ) May 2017 MORE DATA TO MEASURE THAN EVER BEFORE 3 Sources: CabinetM, Capterra, G2 Crowd, Google, LUMA Partners, Siftery, TrustRadius see for details. Produced by Scott Brinker (@chiefmartec) and Anand Thaker (@anandthaker).

4 ? A tool is missing 4

5 The Problem With MOST Dashboards Drag & Drop Dashboards? KPI s in Seconds? Total Leads Demos Impressions Downloads Clicks Views Shares 1000 s of connectors? All just single datasets No context You can t ask why? 5

6 CEOs, your board don t care about 99% of the metrics that you are tracking. They do care about revenue, profit, cash flow and growth. Vanity Metrics, too often marketers rely on feel good measurement that justify their marketing spend. These only sound good. Do these really mean something in terms of revenue. Activity Metrics, measure what you do vs what results and impact you have on the business Former Marketo VP, co-founder Jon Miller 2011 webinar The Right Metrics Cost Metrics, versus results & outcomes Revenue Metrics Marketing Program Performance 6

7 AdWords ROI Clicks Conversions Landing page Marketing Pipeline History from SFDC MQL Opportunity Monthly reports 7

8 59% have a dedicated Marketing Ops person or team 8

9 59% have a dedicated Marketing Ops person or team THIS IS THE PERSON SPENDING ALL THIS TIME IN SPREADSHEETS 9

10 Many different systems Manual data entry Landing pages List purchases Event leads Unstructured sources (PDF, web) Dupes Blank cells Name formatting Text vs Numeric Pivots Vlookups Macros Multiple worksheets Often cleaning before putting in CRM and then again when generating reports Splitting Columns Removing characters Joining Data 10

11 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

12 IT REQUEST Roadblocks & Pitfalls of Preparing Data ACCESSIBLE DATA IT-PROVIDED DATA Manual data prep in spreadsheets Access Wrangle Blend Transform Govern Cleanse Share Manipulate Enrich Missed deadlines Inaccurate Incomplete Costly 12

13 Marketing Analytics Maturity Model TACTIC-DRIVEN CAMPAIGN DRIVEN INTEGRATED PREDICTIVE ANALYTICS TOOLS Google Analytics dashboards ESP dashboards shopping cart dashboards ad dashboards Spreadsheets w/ multiple data sources integrated Regular campaign performance reports Data warehouse (single source of truth) auto-updating dashboards with key metrics Predictive models Team of data scientists/analysts New leads Cost-per-click New subscribers Customer acquisition cost Revenue by campaign CAC to LTV Gross margin Goal-vs-actual Custom metrics METRICS Cost per lead Marketing ROI KEY CHALLENGES Checking multiple sources for insights Drowning in Drowning in spreadsheets, need need dev time dev to time SOL to queries SQL queries Developing people with data skills (to analyze and ask good questions) Need high technical skills (data scientists, senior analysts) 13

14 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) 14

15 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 15 June 14, 2017

16 About Datawatch PUBLICLY TRADED: NASDAQ - DWCH INNOVATION: Pioneer in self-service data extraction & blending and realtime visual data discovery FOUNDED: 1985 CUSTOMERS: Over 40,000 global customers of every size, including 431 of The Fortune 500 EMPLOYEES: 150+ Worldwide OUR USE CASE: Solution used by finance & accounting from operational processes to data prep for analytics changes led to us using Monarch in marketing Datawatch enables ordinary business users to do extraordinary things with their data Spend less time preparing data and more time analyzing Access all of the important and trusted data you need 16

17 Our Need for Analytics Incomplete dashboards Messy data Added Marketing Analyst Spend too much time in Excel Need to prove contribution to revenue Re-establish credibility New management & budget cuts prompted a change 17

18 Datawatch Monarch self-service data preparation for building better and faster marketing dashboards

19 Marketing CMO-Level Dashboard 19 June 14, 2017

20 DATA PREP FOR CMO DASHBOARDS Ellen Wilson 20 June 14, 2017

21 Marketing CMO-Level Demonstration ##,### ### ##,### ### ##,### ### ##,### ### #,### ### ##,### ### #,### ## #,### ## #,### ## #,### ## #,### ## #,### ## #,### ## #,### ## #,### ## #,### # #,### ## #,### ## ### # 21 June 14, 2017

22 Demo: MQLs Connecting Monarch directly to Salesforce to have automated report updates and simple report joins 22 June 14, 2017

23 Demo: LinkedIn Organic Interactions Dragging and dropping organic LinkedIn post information into Monarch for analysis 23 June 14, 2017

24 Demo: Top AdWords Keywords Removing non-alphanumeric characters from Google AdWords Keyword reports 24 June 14, 2017

25 Demo: Web Traffic from Social Media Consolidating Referral Traffic in Google Analytics reporting for accurate social media tracking 25 June 14, 2017

26 Demo: Capturing Organic LinkedIn Data Appending Influitive CSV reports and Salesforce data for holistic Customer Success Metrics 26 June 14, 2017

27 Demo: PDF Data Extraction Extracting data trapped in PDF reports for further data prep and analysis, without any manual data entry 27 June 14, 2017

28 ROI on Automating Dashboard Creation Automation Yields Annual Benefit of $121,000 *Based on $100,000 Annual Salary & 7 hours per week on Dashboard Creation Annual Cost of Marketing Analyst Cost of Dashboard Creation Marketing Contribution to Business Marketing Contribution to Business Calculated Annual Benefit Average Salary: $100,000/year 40 hrs x 52 weeks = 2,080 working hours/year $100,000 2,080 hours = $48/hour 7 Hours x 52 weeks x $48 per hour = $17,000 allotted to dashboard creation If a 4-person Marketing Department contributes $2.4 M per year to Closed-Won Business Marketing Contribution to Business $2,400,000 (2080 hours x 4 people) = $288/hour of benefit from work $288 x 7 hours x 52 weeks = $105,000 $105,000 + $17,000 extra productivity = $121,000 per analyst per year 28