How to Identify, Target, and Connect with Audiences —ECPA 2014
3 likes721 views
The document discusses the importance of using data to identify, target, and connect with audiences, emphasizing audience-centric strategies. It outlines the types of data that can be beneficial, including demographics, psychographics, and behavior patterns, and highlights the need for continuous optimization and measurement. The presentation also illustrates practical examples and tools for analyzing consumer data in the context of the publishing industry.
How to Identify, Target, and Connect with Audiences —ECPA 2014
- 1. Using Data to Identify, Target, and Connect with Audiences Peter McCarthy McCarthy Digital | The Logical Marketing Agency Presented April 30, 2014 Leadership Summit and ECPA Annual Member Meeting
- 2. Contents » Who am I and why am I here? » Audience-‐centricity is key » Data and tools, applied » What’s next? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 2
- 3. Contents » Who am I and why am I here? » Audience-‐centricity is key » Data and tools, applied » What’s next? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 3
- 4. Who am I? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 4
- 5. Who am I? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 5
- 6. Or did you mean this? » Demographics § My gender, age, income level, education level, marital status, etc. § Note: I include geographic region here » Psychographics § My beliefs, values, attitudes, opinions, favorite things, places, activities » Behaviors § What I have done, am doing (and, perhaps, most likely to do next) A person’s future behavior is impossible to predict. But is easier the nearer it is to happening and the more we know about the person. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 6
- 7. Why am I here? » United States Reading and eReading habits § 76% of the US population 18 and over read a book in the past year § 28% of that group do on either an eReader or a Tablet § Demographically diverse as could be 76% of US book readers age > 18 report they have read a book in the past 12 months?! = ~183,797,307 potential consumers (that we don’t know very well) Source: Pew Center for Internet Research April 30, 2014 Using Data to Identify, Target, Connect | ECPA 7
- 8. Contents » Who am I and why am I here? » Audience-‐centricity is key » Data and tools, applied » What’s next? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 8
- 9. Audience-‐centricity Consumer-‐first 1. Who 2. What 3. Why Results-‐oriented § Product § Placement 4. Where 5. When 6. How § Price § Promotion Measured Optimization § What’s working § What’s not § With nuance April 30, 2014 Using Data to Identify, Target, Connect | ECPA 9
- 10. The universe of potential readers Aware & Will Buy Aware & Will Not Unaware & Just Might! Unaware & Just Fine This is the gold mine of readers. It is the crossover hit. Especially true for niche and vertical publishers. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 10
- 11. So how do we find them? is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts. –-‐ Sherlock Holmes, A Scandal in Bohemia . It April 30, 2014 Using Data to Identify, Target, Connect | ECPA 11
- 12. Contents » Who am I and why am I here? » Audience-‐centricity » Data and tools, applied » What’s next? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 12
- 13. What I talk about when I talk about data “Universe” Publishing Industry (Truly) Consumer § Macro data § Facts, trends, projections § May be commercial in nature, may not § Likely Sources § Nonprofits like Pew Research § Trade groups § Corporate annual reports § Data Corporations (eg. ComScore, Gallup, Nielsen, etc.) § Consultancies (McKinsey, Forrester, Gartner) § News outlets § Macro and micro data § For industry as a whole § Or one publisher (our own) § Or one book/author (our own) § Facts, trends, projections § Generally commercial in nature § Likely Sources § Retailers to POS systems § Some CRM(ish) activity § Smaller set of Data Corporations (eg. Nielsen, BISG, Bowker) § Etc. § News outlets § Macro and micro data § Consumer behavior in aggregate § But also specific groups of consumers “in the wild” § Facts, trends, projections § Often not commercial in nature § Likely Sources § Bespoke surveys (Nielsen) § Real online usage § Search § Social § Commerce § Other § Analytic tools § Etc. I prefer talking about this April 30, 2014 Using Data to Identify, Target, Connect | ECPA 13
- 14. “Universe” All relevant and useful when pulling out the crystal ball April 30, 2014 Using Data to Identify, Target, Connect | ECPA 14
- 15. Example data: “universe” Global tablet market share by platform Source: Business Insider April 30, 2014 Using Data to Identify, Target, Connect | ECPA 15
- 16. Example: “smaller universe” Time per day Americans spend on different digital activities April 30, 2014 Using Data to Identify, Target, Connect | ECPA 16
- 17. Example: “smaller universe still” Demographic makeup of major social networks 2013 April 30, 2014 Using Data to Identify, Target, Connect | ECPA 17
- 18. Example: “universe not so far away” Amazon share price trailing 5 years Source: Google Finance April 30, 2014 Using Data to Identify, Target, Connect | ECPA 18
- 19. Industry Well-‐understood, (should be) easier, useful for running our businesses April 30, 2014 Using Data to Identify, Target, Connect | ECPA 19
- 20. Example: industry 6.93 Net Sales ($B) 2011 2012 Source: 2013 Bookstats US trade book sales Brick & Mortar 8.03 Net Sales ($B) 7.47 2011 2012 5.72 April 30, 2014 Using Data to Identify, Target, Connect | ECPA 20
- 21. Example: industry US eBook sales 2008 -‐ 2012 64 Net Sales ($M Logarithmic regression: R2=0.7853 291 869 2,109 3,042 +199% +143% +43% +355% 2008 2009 2010 2011 2012 Source: 2013 Bookstats April 30, 2014 Using Data to Identify, Target, Connect | ECPA 21
- 22. (Truly) Consumer Crazy and unbelievably useful April 30, 2014 Using Data to Identify, Target, Connect | ECPA 22
- 23. Some (really useful) sources of (US) consumer data The irrefutable ROI of social is in the analytics and BI on the back end § The Social Graph They know consumers. Now tying to offline sources. § Ad Platform Inventory, Open (APIs), demographics, beliefs, behaviors § Constant A/B testing Fail fast, fix § Result: Happy Users/Advertisers Despite incredible concerns over privacy. Relevance trumps it § Search, ads (& lots of data) Massive share of almost everything they do § Ad Platform Inventory, highly behavioral § Basically Building a Brain Yes. All products data-‐driven § Open APIs and tools § Smaller but… Wild adoption, solid usage § Ad Platform Targeting has arrived as has the front-‐end to allow for goals § Timely Basically “now” § Very Open (for now) Can get at the data April 30, 2014 Using Data to Identify, Target, Connect | ECPA 23
- 24. The graph is simply so rich and so aware Of course, the “currency” is personal privacy…which 73% of us (gladly?) pay April 30, 2014 Using Data to Identify, Target, Connect | ECPA 24
- 25. One can capture actual usage as it occurs “in the wild” A sampling of tools…mostly not huge, costly a la Adobe or Salesforce Social Analytics § Simply Measured § SproutSocial § Social Bakers § Followerwonk § Commmun.it § Bit.ly § Topy § Social Mention § Facebook Insights § Facebook Ad Interface § Facebook PowerEditor § EdgeRank Checker § SimplyMeasured § Twitter Ad Interface § Argyle § LinkedIn Analytics § Pinterest Analytics § Tumblr Analytics § Instagram…. Web/SEO Web/Email Analytics § Google Trends § Google AdWords § Moz § Soovle (autocompletes) § Raven § Compete § Quantcast § SEO Quake § Google universal analytics § WordTracker § WordStream § Amazon comp authors § Librarything tags/ comps § Etc. § Google Analytics § Omniture § Constant Contact § MailChimp § Optimizely – landing pages And many, many more to suit the myriad use-‐cases, but… April 30, 2014 Using Data to Identify, Target, Connect | ECPA 25
- 26. But it isn’t really about the tools Yes, one must have a toolkit. But it’s about triangulation and application Goals direct research Launch Hypothesis, test, hypothesis, test, hypothesis, test, hypothesis, test Measure & Optimize Communicate, Prune, change, apply apply April 30, 2014 Using Data to Identify, Target, Connect | ECPA 26
- 27. So let’s triangulate and see what happens I chose a title from the CBE Bestseller List, which seemed to be doing well but maybe had a little more in it. I tried to “work it” April 30, 2014 Using Data to Identify, Target, Connect | ECPA 27
- 28. LibraryThing, GoodReads, Amazon… Establish true comp authors and titles as well as reader and buyer verbiage April 30, 2014 Using Data to Identify, Target, Connect | ECPA 28
- 29. Multiple auto-‐fills (not logged in, cookies cleared) Hypothesis: no one searches for Terri Blackstock? Seems they do April 30, 2014 Using Data to Identify, Target, Connect | ECPA 29
- 30. And, indeed, it is definitely true Raven Tools (with assist from Moz, Majestic, SEM Rush assuage that concern That volume is on her name alone and only in Google. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 30
- 31. Google Trends: how does she stack up then? Waning but not bad. Interesting differences in regionality April 30, 2014 Using Data to Identify, Target, Connect | ECPA 31
- 32. Try a Google search (cached cleared, logged out) Tremendous results for author and title + author queries Fine on the “branded” terms. People who Seek shall find. But what about others? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 32
- 33. So, where are the fans? What are they saying? Google’s related searches, some “listening” in on Twitter… Again, seems okay. Now I want to know who is looking. Who are her fans? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 33
- 34. The graph Here rolled up under Twitter followers with data appended April 30, 2014 Using Data to Identify, Target, Connect | ECPA 34
- 35. Her Twitter map looks a lot like her search map Is she posting at the right time? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 35 Using Data to Identify, Target, Connect | ECPA
- 36. This is just fine. A deeper dive not worth it at this time Her Followers Her April 30, 2014 Using Data to Identify, Target, Connect | ECPA 36
- 37. So, Terri’s followers are mostly married white Christian women. 40-‐49 April 30, 2014 Using Data to Identify, Target, Connect | ECPA 37
- 38. Who else do they follow? Some patterns emerge but really not that much April 30, 2014 Using Data to Identify, Target, Connect | ECPA 38
- 39. New hypothesis: she’s undersized for some reason right now And I don’t need to know why (I always forget that!). Just remedy it… topsy author comp Start to look at two comp authors who seem “larger” or “hotter” right now. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 39
- 40. I decide to ride the Francine Rivers wave (so to type) Solely because of Dee Henderson’s lack of real online footprint People Talking About This 437 4,817 Terri Blackstock Francine Rivers A check on Facebook causes me a moment of pause April 30, 2014 Using Data to Identify, Target, Connect | ECPA 40
- 41. Grab a tool to compare the two authors in that space… Her fans are there but it feels like there’s a somewhat quiet echo chamber going on. § Especially with people who don’t already know her or her work. § Hypothesis: she isn’t garnering new demand from additional affinities § Hypothesis: she’s presenting as a devout Christian writer of suspenseful mysteries. Which is great for reaching the people who know and will buy. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 41
- 42. A quick stop at her site to check that is in working order First time we’ve turned away from the audience. Ran WooRank. Site needs some touch up but it will do. So, right into some narrow-‐casted, inexpensive Facebook ads April 30, 2014 Using Data to Identify, Target, Connect | ECPA 42
- 43. Let’s find new fans on Facebook! Our targeting: Women (no age as people don’t enter it) who live in the US, are married, enjoy family life, and as our first interest…Francine Rivers. Then we just take it from there April 30, 2014 Using Data to Identify, Target, Connect | ECPA 43
- 44. Then some Twitter followers of Francine Rivers Under 100 to be exact. Strong ones with the characteristics of Terri’s followers April 30, 2014 Using Data to Identify, Target, Connect | ECPA 44
- 45. Lastly we go looking for some keywords she can own Very dependent on her name. So, Christian suspense and variants jump out. We make sure she will be able to “own them.” There’s competition but she’ll get in the mix. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 45
- 46. A tool called Seorch gives us some good generic ones Christian suspense books Christian suspense fiction Christian suspense novels Christian suspense romance Christian suspense Christian suspense romance Christian suspense writers Christian suspense romance novels Christian suspense author Christian suspense movies Christian suspense series Christian suspense thrillers Christian suspense fiction books Christian suspense book reviews Christian suspense authors list Christian suspense audiobooks Christian suspense fiction authors Top Christian suspense authors Christian suspense publishers Top Christian suspense books Good Christian suspense books Best Christian suspense novels Christian suspense authors fiction Christian suspense romance books Christian fiction suspense series Popular Christian suspense authors And we knew the brand terms well already. Should be no issue to optimize her. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 46
- 47. Lastly, we’ll need to optimize for the incoming traffic Would have tightened the middle and bottom of the funnel first but the book is out » We’ll need to overhaul her Amazon presence to include some new terminology, without overdoing it » Speed up her site and get some of that same verbiage there » Add Google Analytics to her site so that we can set up a formal funnel to measure to our goal, which I would advocate should be a newsletter sign up… » It makes life easier when you’ve got a bigger group of people who know and will buy! April 30, 2014 Using Data to Identify, Target, Connect | ECPA 47
- 48. Contents » Who am I and why am I here? » Audience-‐centricity » Data and tools, applied » What’s next? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 48
- 49. Constant triangulation – before anything happens And, of course, more often than not it isn’t the chase I just described The smart business of the future will correlate and compute a mix of data including demographics, psychographics, web analytics, social analytics and business intelligence to create predictive scenarios that can be delivered in real time at the point of need. – Paul Simbeck-‐Hampson Marketing Consultant April 30, 2014 Using Data to Identify, Target, Connect | ECPA 49
- 50. These guys predicted the future! Opening weekend movie revenues. With greater than 90% accuracy. Source HP Labs: http://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdf April 30, 2014 Using Data to Identify, Target, Connect | ECPA 50
- 51. Thank you very much. pete@mccarthy-‐digital.com @petermccarthy April 30, 2014 Using Data to Identify, Target, Connect | ECPA 51