Turn Your Data into Action Arts & Business Council of Chicago: Marketing Matters Series
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1 Turn Your Data into Action Arts & Business Council of Chicago: Marketing Matters Series March 30, 2010
2 Maria Gray Development Director, Chicago Public Art Group Lara Goetsch Director of Marketing and Communications, TimeLine Theatre Company Anna Fitzloff Director of Marketing, Harris Theater for Music and Dance in Millennium Park Chloe Chittick Patton Senior Associate, Slover Linett Strategies Cheryl Slover Linett Partner, Slover Linett Strategies 1.
3 Goals of today s session Learn which data are most important to track Get tips on how best to track your data Consider specific strategies and tactics you can use to increase the success of your efforts Expand the tools in your toolkit 2.
4 What data is out there?
5 What information is out there? Interests Demographics Attendance activity Geography UNDERSTANDING YOUR PATRONS Length of history Donations Arts activity Spend Satisfaction 4.
6 Where does it come from? Ticket purchases Date, performance, price, seating area, # of tickets Donations Amount, date, level Surveys Satisfaction, demographics, other arts attendance, media usage Staff data entry Board activity, committees, volunteer event staffing Other ways of collecting data? 5.
7 Where and how is data best kept? Databases Typically used to house customer information Can be added to and analyzed Can range from basic (Excel) to sophisticated (Tessitura) It s more than a list! Survey files Contain individual survey participants responses to survey questions Also range from basic (Excel/SurveyMonkey) to sophisticated (SPSS) 6.
8 Database examples: CPAG/Total Info 7.
9 Database examples: CPAG/Total Info 8.
10 Database examples: TimeLine/Access 9.
11 Database examples: TimeLine/Access 10.
12 Database examples: TimeLine/Access 11.
13 Database examples: TimeLine/Access 12.
14 Database examples: TimeLine/Access 13.
15 Database examples: TimeLine/Access 14.
16 Database examples: Harris/Tessitura 15.
17 What can I do with my data?
18 Case study: Chicago Public Arts Group Fundraising Campaigns Contact and demographic information Donations and donor history Demographic information Volunteer information and hours Grants: records, calendar and tickler 17.
19 Case study: TimeLine Theatre Company MEASURE RETURN ON INVESTMENT 18.
20 Case study: TimeLine Theatre Company SEGMENT AUDIENCE/ACTIONS : 1,332 subscriptions = 811 households All mailed Backstory magazine $1,915 annually in postage Use database to save money Segment by subscriber preference 19.
21 Case study: TimeLine Theatre Company : Unexpected success Subscriptions skyrocket 50% 2,011 subscriptions = 1,206 households Potentially $2,850 annually in postage (49% increase) Actually $2,075 annually in postage (8% increase) Also savings in printing, time costs Insight into subscribers and their needs 20.
22 Case study: TimeLine Theatre Company SUPPORT MARKETING GOALS Example: Retain 1st Time Single Ticket Buyers 21.
23 Case study: TimeLine Theatre Company WHAT DO YOU WANT TO KNOW? Access database = Total customization Might take several steps, but if you know what you want and underlying structure is sound almost any question can be answered via data query TimeLine database currently holds more than 700 queries, created on demand 22.
24 Case study: Harris Theater Step 1: Patron History what they bought and for how much 23.
25 Case study: Harris Theater Step 2: List Segmentation grouping them based on affinity and price range sweet spot 24.
26 Case study: Harris Theater Step 3: Send a letter with targeted pitch and signature 25.
27 Case study: Harris Theater Step 4: Use the same method in other communications: , teleresources etc. 26.
28 Case study: Harris Theater Step 5: Transaction Processed Spoke to the customer s interest and price point 27.
29 Case study: Slover Linett/Music of the Baroque What is data mining? Data mining is broad: includes what we ve just heard about and more Means analyzing patterns within a database To find patterns and categories of behavior Based on actual/historic data and identifying characteristics To find relative proportions of segments/patterns 28.
30 Case study: Slover Linett/Music of the Baroque Goals: To identify key purchasing patterns and patron segments within the database knowledge Ultimately, so Music of the Baroque can apply distinct and motivating strategies to each segment In communications and/or ticket offers action 29.
31 Case study: Slover Linett/Music of the Baroque Process: Identify relevant data fields and time frame pull selected patron records for working file Create purchase categories for each year, based on possible individual level purchase activity Segment individuals into specific activity patterns across the given time period Identify size/relative proportions of each segment Follow with qualitative research 30.
32 Case study: Slover Linett/Music of the Baroque For example, each pattern could represent proportion of patrons who Have only purchased one ticket (ever) Have subscribed for at least 2 consecutive years Went from a single ticket buyer to a subscriber Had subscribed but don t any more Pattern A Pattern C Pattern B 21% 20% 9% 51% Pattern D 31.
33 Case study: Slover Linett/Music of the Baroque What can I do with this? Puts your patron base in perspective At least based on these particular factors Identify problem areas If a negative trajectory segment is a big proportion of your database, you know where to focus e.g., gauging relative size of patrons who only come once Targeting key segments and directing them towards a positive trajectory 32.
34 Common situations and how to make the most of them
35 Maximizing survey data Merging patron records with survey data can take two forms 1. Appending patron data to survey data Any data of relevance (donor history, attendance records, etc.) Reasons: To replace survey questions To provide actual data (vs. self reported) To compare survey respondents to patrons in database 34.
36 Maximizing survey data 2. Appending survey data to patron data Confidentiality is tricky be upfront in survey Attach attitudinal/behavioral data to patron records e.g., to tag patrons with why they didn t subscribe this year; why they donated; and so on 35.
37 What to do when you outgrow your system? Clean up your data Duplicate entries? Address corrections? Codes consistent? Make a database plan (Techsoup.org) Research databases Compare databases (Idealware.org review guide) Free demos Be patient with data conversion Training Don t skip or skimp 36.
38 Open Forum
39 Trouble shooting What are your thorny database issues? What gets in the way of your using your database more? What questions do you still have? 38.
40 Database Information Resources gworkbook.pdf SUMERS_GUIDE_low_cost_donor_mgmt.pdf AILED_REVIEWS_low_cost_donor_management.pdf abaseservices 39.
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