Tds north america strategy summit (nyc) personalization 2014-09-11b
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Digital marketing personalization: recommendations about how to do it on the cheap, discussion of challenges, and a few examples of how it's been done well.
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Tds north america strategy summit (nyc) personalization 2014-09-11b
- 1. Cracking the Code of Personalization Travel Distribution Summit 2014, NYC Jonathan Isernhagen September 11, 2014
- 2. Session Agenda 1) Gain insights into how you can collect data more intelligently to effectively re-market and boost conversion 2) Maximize the benefits of the mobile paradigm: Now that it is possible to know your customers’ every move, learn how to capitalize on this information 3) Geo and hyper-locality: Understand how travel brands can best reap the benefits from knowing exactly where the customer is 4) Hear insightful case studies on the most effective ways to personalize your offers, deals, loyalty discounts and more 2014 Budget Review jonathan.isernhagen@wyn.com @jon_isernhagen
- 3. 2014 Budget Review Discussion Agenda 1) Definitions 2) Decisions 3) Data collection a) From internal systems b) From external vendors 4) Analysis 5) Personalization a) Email b) On website jonathan.isernhagen@wyn.com @jon_isernhagen
- 4. Definitions • Customization: changing the characteristics of a product to meet individual customer needs (e.g. Dell PCs) • Optimization: rigorously A/B testing all aspects of your marketing presence to find the highest value combination • Segmentation: a marketing strategy that involves: – dividing a broad target market into subsets of consumers who 2014 Budget Review have common needs and priorities, and then; – designing and implementing strategies to target them. • Personalization: presenting potential consumers the most relevant products, offers, content and services jonathan.isernhagen@wyn.com @jon_isernhagen
- 5. 2014 Budget Review Discussion Agenda 1) Definitions 2) Decisions 3) Data collection a) From internal systems b) From external vendors 4) Analysis 5) Personalization a) Email b) On website jonathan.isernhagen@wyn.com @jon_isernhagen
- 6. Strategic Focus: Customer, Product or Cost There are 3 value propositions: • Operational Excellence • Product Leadership • Customer Intimacy Choose any one (1). 2014 Budget Review jonathan.isernhagen@wyn.com @jon_isernhagen
- 7. Wal-Mart’s innovations: • Cross dock • Satellite ordering • Aggregating consumer demand to squeeze suppliers 2014 Budget Review Strategic Focus: Operational Excellence jonathan.isernhagen@wyn.com @jon_isernhagen
- 8. (Jobs pull quote about showing customers what they’ve never seen before) 2014 Budget Review Strategic Focus: Product Leadership jonathan.isernhagen@wyn.com @jon_isernhagen
- 9. “Be everywhere, do everything, to astonish the customer.” 2014 Budget Review Strategic Focus: Customer Intimacy and never fail jonathan.isernhagen@wyn.com @jon_isernhagen
- 10. 2014 Budget Review Discussion Agenda 1) Definitions 2) Decisions 3) Data collection a) Direct b) Indirect 4) Analysis 5) Personalization a) Email b) On website jonathan.isernhagen@wyn.com @jon_isernhagen
- 11. Source: “The Power of One” 2014 Budget Review Personalization Purposes 1) Adapting navigation 2) Helping consumers find information 3) Personalizing the presentation of information 4) Recommending products or experiences 5) Providing help and tutoring/education 6) Identifying relevant communities 7) Supporting collaboration jonathan.isernhagen@wyn.com @jon_isernhagen
- 12. Sources: http://www.evergage.com/blog/3-personalization-recommendations-travel-websites/ http://www.monetate.com/2013/04/3-ways-travel-needs-to-personalize-now/ 2014 Budget Review Personalization….a request of travel sites 1) Remember my previous search inputs a) Keywords (if applicable) b) Where and when I’ve traveled (from and to) c) What kinds of accommodations I’ve booked 2) Condition your offerings/content on past behavior 3) Make suggestions which go beyond my basic request. jonathan.isernhagen@wyn.com @jon_isernhagen
- 13. 2014 Budget Review Approaches to gathering data Approach Direct Indirect Description Posing questions to candidates/customers Inferring customer wants/needs from behavior Works when They give complete and honest answers. The remaining 98% of the time. Gathered via Surveys/forms Server logs, accounting systems, vendor purchases How you’ll use it Programming Machine learning* Logical programming Decision-theory inference Requires Some reason for customers to answer. Massive amounts of data Knowledge of user beliefs Knowledge of probabilities jonathan.isernhagen@wyn.com @jon_isernhagen
- 14. Pulling profile data together: back office transactions Transaction data: • Customer #1: 3/18/12, Ramada Yonkers, $119.00 • Customer #1: 11/22/13, Best Western Inn Ramsey, $551.18 • Customer #1: 2/14/14, Days Inn Nanuet, $93.81 • Customer #2: • . Transaction summarized data: • Customer #1: 209 days ago, 3 stays, $763.99 total spend • Customer #2: • . 2014 Budget Review Customer/Visitor Records • Customer #1, Mike Johnson, ... • Customer #2, Amy Morris,… • Customer #3, Frieda Zimmerman… • . • . jonathan.isernhagen@wyn.com @jon_isernhagen
- 15. SQL: Visual QuickStart Guide = easy SQL onramp • Simple, English-like language • Enables you to play with the data and understand its possibilities e.g. Select Name_first, Name_last From tblCustomers Where State = “AK” 2014 Budget Review jonathan.isernhagen@wyn.com @jon_isernhagen
- 16. Pulling profile data together: web site behavior Transaction data: • Customer #1: 3/18/12, Ramada Yonkers, $119.00 • Customer #1: 11/22/13, Best Western Inn Ramsey, $551.18 • Customer #1: 2/14/14, Days Inn Nanuet, $93.81 • Customer #2: • . Transaction summarized data: • Customer #1: 209 days ago, 3 stays, $763.99 total spend • Customer #2: • . Site data: • Customer #1: 225 days ago, 12 page viewed, 5 minutes on site • Customer #2: • . Site visit data: • Customer #1: 2/1/14 13:40:00 Days Inn Home Page • Customer #1: 2/1/14 13:40:10 Days Inn Results Page • Customer #1: 2/1/14 13:40:25 Days Inn Property Detail Page • . 2014 Budget Review Customer/Visitor Records • Customer #1, Mike Johnson, ... • Customer #2, Amy Morris,… • Customer #3, Frieda Zimmerman… • . • . jonathan.isernhagen@wyn.com @jon_isernhagen
- 17. Extracting web data from Google/Adobe Analytics Adobe Analytics Premium 2014 Budget Review Google Analytics Premium Adobe Analytics jonathan.isernhagen@wyn.com @jon_isernhagen Google Analytics BigQuery Your database Live Stream Your database Data feeds Your database
- 18. Transaction data: • Customer #1: 3/18/12, Ramada Yonkers, $119.00 • Customer #1: 11/22/13, Best Western Inn Ramsey, $551.18 • Customer #1: 2/14/14, Days Inn Nanuet, $93.81 • Customer #2: • . Transaction summarized data: • Customer #1: 209 days ago, 3 stays, $763.99 total spend • Customer #2: • . Site data: • Customer #1: 225 days ago, 12 page viewed, 5 minutes on site • Customer #2: • . Site visit data: • Customer #1: 2/1/14 13:40:00 Days Inn Home Page • Customer #1: 2/1/14 13:40:10 Days Inn Results Page • Customer #1: 2/1/14 13:40:25 Days Inn Property Detail Page • . 2014 Budget Review Pulling profile data together: email data Email records (Sends, bounces, opens, clicks, bookings) Customer/Visitor Records • Customer #1, Mike Johnson, ... • Customer #2, Amy Morris,… • Customer #3, Frieda Zimmerman… • . • . jonathan.isernhagen@wyn.com @jon_isernhagen
- 19. Transaction data: • Customer #1: 3/18/12, Ramada Yonkers, $119.00 • Customer #1: 11/22/13, Best Western Inn Ramsey, $551.18 • Customer #1: 2/14/14, Days Inn Nanuet, $93.81 • Customer #2: • . Transaction summarized data: • Customer #1: 209 days ago, 3 stays, $763.99 total spend • Customer #2: • . Site data: • Customer #1: 225 days ago, 12 page viewed, 5 minutes on site • Customer #2: Email records (Sends, bounces, opens, clicks, bookings) Vendor-provided • . demographics/psychographics • Customer #1, retired construction Site visit data: • Customer #1: 2/1/14 13:40:00 Days Inn Home Page • Customer #1: 2/1/14 13:40:10 Days Inn Results Page • Customer #1: 2/1/14 13:40:25 Days Inn Property Detail Page • . 2014 Budget Review Pulling profile data together: vendor data Customer/Visitor Records • Customer #1, Mike Johnson, ... • Customer #2, Amy Morris,… • Customer #3, Frieda Zimmerman… • . • . foreman, $485K net worth, 3 children, 13 grandchildren, 2 Pomeranians…. jonathan.isernhagen@wyn.com @jon_isernhagen
- 20. Demographic/Psychographic data appends 1) Age/Sex/Race/Marital status/# and age of kids/Life stage 2) House value/type/residency length 3) Income/net worth/affluence/financial stress 4) Consumer-saver type/Coupon user 5) Web consumer type/ISP domain 6) Category bucket/Portrait 7) Politics/Religion/Environmental concern/Veteran status 8) Auto Make/Type/Fuel 9) Hobbies/Interests/Fashion segment/Pets 10) Medical interests 2014 Budget Review jonathan.isernhagen@wyn.com @jon_isernhagen
- 21. 2014 Budget Review Discussion Agenda 1) Definitions 2) Decisions 3) Data collection a) Direct b) Indirect 4) Analysis 5) Personalization a) Email b) On website jonathan.isernhagen@wyn.com @jon_isernhagen
- 22. Definitions: Data Mining “The computational process of discovering patterns in large data sets … the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as: • groups of data records (cluster analysis), and; • dependencies (association rule mining). 2014 Budget Review http://en.wikipedia.org/wiki/Data_mining jonathan.isernhagen@wyn.com @jon_isernhagen
- 23. Data mining by Clustering: flower categorization 2014 Budget Review http://www.mathworks.com/help/stats/examples/cluster-analysis.html Fisher’s iris data
- 24. Practical uses for clustering 1) Predicting whether a site visitor belongs to a high-value segment based on data available during by the time the first search is executed. 2) Examining a new purchased list of potential consumers for characteristics which predict high lifetime value. 2014 Budget Review jonathan.isernhagen@wyn.com @jon_isernhagen
- 25. Data mining by Association Rules: politics v. beers 2014 Budget Review http://www.marketplace.org/topics/life/final-note/what-your-beer-says-about-your-politics
- 26. Data Science on the cheap: Coursera and R 2014 Budget Review jonathan.isernhagen@wyn.com @jon_isernhagen
- 27. 2014 Budget Review Discussion Agenda 1) Definitions 2) Decisions 3) Data collection a) Direct b) Indirect 4) Personalization a) Email b) On website jonathan.isernhagen@wyn.com @jon_isernhagen
- 28. Source: TheEmailGuide.com 2014 Budget Review Advantages to personalizing e-mail 1) Technically simple and cheap 1) No architectural changes needed 2) No A/B test tool required 2) Asynchronous: time to analyze results instead of responding real-time 3) Email address is ready-made primary key for combination with other data sources jonathan.isernhagen@wyn.com @jon_isernhagen
- 29. Personalized email best practice: Slingshot • Not highly subdivided • Softened #Fname# • Top-of-funnel offer (for re-engagement campaign) • Sent only to people who hadn’t already downloaded this ap. Source: http://blog.hubspot.com/blog/tabid/6307/bid/341 46/7-Excellent-Examples-of-Email-Personalization-in- 2014 Budget Review Action.aspx
- 30. Personalized email best practice: Dropbox • Behaviorally triggered • Provides education on how best to use their product. • Increases “stickiness” Source: http://blog.hubspot.com/blog/tabid/ 6307/bid/34146/7-Excellent- Examples-of-Email-Personalization-in- Action.aspx jonathan.isernhagen@wyn.com @2j0o1n4 _Buisdegertn Rhevaiegwen
- 31. 2014 Budget Review Personalized email best practice: Twitter • Association mining • Favorite restaurants and people of other washsquaretavern followers turn out to be good recommendations. Source: http://blog.hubspot.com/blog/tabid/ 6307/bid/34146/7-Excellent- Examples-of-Email-Personalization-in- Action.aspx
- 32. 2014 Budget Review Discussion Agenda 1) Definitions 2) Decisions 3) Data collection a) Direct b) Indirect 4) Personalization a) Email b) On website jonathan.isernhagen@wyn.com @jon_isernhagen
- 33. Source: “The Power of One” 2014 Budget Review N-tier website architecture jonathan.isernhagen@wyn.com @jon_isernhagen
- 34. Personalization using SiteSpect A/B testing tool Browser Web server / Application server Personalization engine / A/B testing tool Algorithm engine 2014 Budget Review jonathan.isernhagen@wyn.com @jon_isernhagen Cookie data Cookie Page request w/cookie data Personalized Page response Request and Cookie data Recommended Content Recommendation request Recommendation Response
- 35. Site personalization: Guardian Royal Baby toggle 2014 Budget Review jonathan.isernhagen@wyn.com @jon_isernhagen
- 36. 2014 Budget Review Site personalization: Netflix
- 37. 2014 Budget Review Site personalization: Orbitz jonathan.isernhagen@wyn.com @jon_isernhagen
- 38. Recommended Reading: The Power of One 2014 Budget Review This one Not This one jonathan.isernhagen@wyn.com @jon_isernhagen
- 39. Summary take-aways 1) Know the main value you provide your customers 2014 Budget Review a) Is Customer Intimacy your main differentiator? b) Prioritize personalization accordingly 2) Identify the low-hanging fruit a) Learn your data b) Do the simple stuff (e.g. email) at least 3) Adopt a customer-centric point of view a) Subscribe to your own email distributions b) Understand your customer’s goals c) Manage your customer relationships as a scarce resource jonathan.isernhagen@wyn.com @jon_isernhagen
Notas del editor
- Hi, my name is Jonathan Isernhagen and I do web analytics for Wyndham Hotels.
- Our agenda for this session includes four topics, I will address myself to #1 and #4: 1) Gain insights into how you can collect data more intelligently to effectively re-market and boost conversion, and; 2) Hear case studies on the most effective ways to personalize your offers, deals, loyalty discounts and more
- For my part of the discusion, I’d like to: Start with some definitions, then; Get you to ask yourself how seriously you want to pursue personalization, then; Explore how to get consumer data in usable form, and analyze it, then finally; Discuss some personalization examples.
- There are terms in this space whose meanings overlap, so rightly or wrongly, this is what I mean when I’m using them: “Customization” has to do with changing characteristics of an actual product to meet unique customer needs; “Optimization” refers to A/B testing all aspects of your marketing to come up with the most profitable configuration; “Segmentation” involves using one of more consumer characteristics to separate your market into buckets for differential treatment, and; “Personalization” is segmentation taken to an extreme, to the point that you wrap your entire company around the customer to better serve his or her needs as seen through the consumer’s eyes. Each consumer becomes a “segment of one.”
- Personalization is obviously a hot topic, but should you make it a major resource focus?
- If you happened to be in business school 17 years ago….as a very young boy….your strategy professors hit you pretty hard with “The Discipline of Market Leaders” by Treacy and Wiersema. Their thesis was that there are three axes on which market-leading companies differentiate themselves: Operational Excellence, which is another term for cost leadership Product Leadership, which is when you tell the accountants and focus groups to buzz off while you chase the product vision that came to you last night in a dream, and; Customer Intimacy, which is the G-rated version of exactly what it sounds like. According to their research, you can’t pursue more than one of these as your main focus or you’ll fritter away resources and be “stuck in the middle.” So before you start thinking about personalization, take a few moments to decide whether you primarily want to pursue….
- …a cost leadership focus, like Wal-Mart so that at your quality level, in the places you’ve chosen to compete, are you able to run your competitors into the red while remaining profitable, or….
- Steve jobs and Apple are the classic Product examplars, since Jobs basically told focus groups and shareholders to buzz off while he pursued product visions which for the most part worked out really nicely.
- ….Macy’s, which is a lot like Nieman-Marcus except that they seem to be more popular here in the northeast and their mission statement underscores my main point that personalization is exhausting, like trying to entertain a toddler for a long period of time…..which….
- But let’s say that customer intimacy is your strategic differentiator and you’re up for the challenge…. or that your boss really, really likes personalization, and wants a whole bunch of it. You’re going to need some data
- As we dive into “how,” let’s consider the “why.” The data we gather should help us to accomplish one or more of these tasks on behalf of the consumer….
- As we dive into “how,” let’s consider the “why.” The data we gather should help us to accomplish one or more of these tasks on behalf of the consumer….
- There are two main ways to gather consumer data: directly and indirectly. Direct data gathering requires straight-up point-blank interrogation via forms or surveys. Consumers are usually only willing to: submit to this when they receive an immediate benefit, and; answer honestly when they have no incentive to lie. If you can get honest answers to your targeting questions, direct data gathering all but eliminates the need for analysis. Most of the time, at least on the Web, you’ll be using data gathered indirectly from back office accounting systems, server logs, and/or helpful vendors.
- The data you have in-house are usually the easiest and cheapest to get to. Even if you don’t have a CRM system or web tracking tool, if you have the right access and a little bit of SQL, you can: summarize transaction data and then; append it to each customer profile to get measures of transaction and recency, frequency and monetary value, and; maybe guess at lifetime value.
- If you don’t already know it, SQL, which used to stand for “Structured Query Language” before it suddenly didn’t, is easy to learn and; gives you the ability to dive in and really understand and think creatively about how to use your data to accomplish your business goals. This is a book I give to every analyst on my team.
- Once you’ve appended summarized transaction data, if you have a web monitoring tool you can often export your click data and then roll it up into sessions. You can then bolt the summarized data to your customer records to give you a richer understanding of your consumers’ behavior. People shop a lot more than they book, and the content and timing of their searches are very revealing.
- Your ability to access raw site click, visit and visitor data is a function of the tool you use: If you’re using straight vanilla GA, you’re out of luck. Google owns the data and provides no means of exporting it at the record level. If you’re using GA Premium, you can now export up to X million records once per day; If you’re using Adobe Analytics or AA Premium you can set up a recurring FTP process for exporting your records; If you’re using Adobe Analytics Premium specifically, you also have the option of using their live stream product …..
- Another valuable data source is your email service provider Responsys, ExactTarget and Strongmail are all capable of exporting tables that contain all of your sends, bounces, opens and clicks These can also be consolidated as a measure of customer engagement.
- To round out your consumer picture, and give you more hooks to hang your model on, data vendors have an amazing variety of data for sale.
- Two vendors we’re evaluating offer between 550 and 650 individual fields that they can to your consumer data records with an 85% or greater success rate. I tried to summarize them into general categories but can barely do them justice here. Infocommand and Mosaic (and probably all the others) are happy to provide you with a detailed catalogue that shows every field they offer.
- If you have the good fortune to have consumers willing to honestly tell you their interests and preferences in questionnaire form, you may be able to skip the analysis step altogether. Otherwise, some data mining may be in order.
- Wikipedia defines data mining as the computational process of discovering patterns in large data. You can use algorithms to: Bucket things together, and/or Find associations among them.
- For example: These data show the sepal length and width of three species of iris flower, represented by red, green and black circles. In this example of what’s called “Clustering,” there are three types of Iris and the algorithm tries to bucket the species according to their leaf dimensions. This can be done by measuring the distance from center points in the middle of each mass, or it can be done by drawing boundary lines. Once you’ve done this with a known set of specimens, you get a set of rules you can apply to new ones. You do this when you have categories already in mind.
- Two practical personalization uses for clustering might be: Finding leading indicators of high site visitor value (and deciding whether to display an ad which could take her offsite) using data available before the first search. Looking at a new purchased list to see which of its potential consumers have characteristics common to high-value consumers.
- Another type of data mining is determining association rules. The guys at National Review probably didn’t go in thinking that drinking Sam Adams turns you into a committed Republican, or that drinking Corona makes you liberal and unwilling to show up to vote. These just happen to be characteristics which move together. We don’t have to know causality to find them useful.
- If you’re interested in learning more about this stuff on the cheap, the online learning center Coursera offers excellent online courses in data mining and other data disciplines for free
- I’d first like to define what I mean by certain terms, then discuss the important decisions every company has to make about personalization, and finally
- Personalizing a dynamic website is an order of magnitude more difficult than personalizing email. Requires mapping content assets to customer personnas.
- Your ability to access raw site click, visit and visitor data is a function of the web analytics tool you use: If you’re using straight vanilla Google Analytics, you’re out of luck. Google owns the data and provides no means of exporting it at the record level. If you’re using GA Premium, you can now export up to X million records once per day; If you’re using Adobe Analytics, you will need to contact their ClientCare group to set up a recurring FTP process for exporting your records; If you’re using Adobe Analytics Advanced…..