Big Data and Marketing Attribution
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Today’s organizations are challenged to gain insight into most productive marketing and sales actions across multiple channels they use. Doing this requires multi-channel marketing attribution approach. Facing this topic I have made a personal research, and realize a synthesis, which has helped me to clarify some ideas. The attached presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights
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Big Data and Marketing Attribution
- 1. Marketing Attribution Problematic michel.bruley@teradata.co m Extract from various presentations: RPM Direct, Kevin Hillstrom, Teradata Aster, … September 2012 www.decideo.fr/bruley
- 2. Marketing Channels and Attribution Mail TV Print www.decideo.fr/bruley TM Quote TM Sale
- 3. Marketing Channels and Attribution Mail TV TM Quote TM Sale Web Quote Web Sale Print Search Aggregator Banner email www.decideo.fr/bruley
- 4. Marketing Channels and Attribution Mail TV TM Quote TM Sale Web Quote Web Sale Print Search Aggregator Banner email www.decideo.fr/bruley
- 5. Marketing Channels and Attribution Mail TV TM Quote TM Sale Web Quote Web Sale Print Search Aggregator Banner email www.decideo.fr/bruley
- 6. Marketing Channels and Attribution Putting Response in Correct “Bucket” Putting Sales in Correct “Bucket” Calculating Media Cost per Sale Spending Next Tactical Marketing Dollar Building Media Specific Targeting Models Making Strategic Business Decisions www.decideo.fr/bruley
- 7. Channel Attribution Methodology • Individual Customer Identification Numbers • Marketing Source Codes • Name and Address Match • Factor Analysis • Dynamic Time Series Regression • Proportionately “Factor” Leftovers into Marketing Channels www.decideo.fr/bruley
- 8. What is a latent conversion www.decideo.fr/bruley
- 9. What’s wrong with Google Analytics? ROI is attributed to the last (latent) referrer (Most recent keyword, ad, email, blog etc.) What’s wrong with most recent? – Shoppers initiate search using broad categories – Later narrow down to product names/IDs – Perhaps then narrow to store brand Broad categories don’t get the credit they (may) deserve The only exception is direct type-in Google Analytics takes the blame, though many web analytics tools work the same in a default implementation www.decideo.fr/bruley
- 10. Attribution examples Quiz: An existing customer receives a catalog on September 1. On September 2, the customer uses Google to search for merchandise, visits your website, and purchases an item. What % of the order do you allocate to catalogs, to search, and to organic brand loyalty? Quiz: An existing customer receives a catalog on September 1, receives e-mail marketing campaigns on September 7 and September 9, and purchases on your website on September 10, buying merchandise featured in your catalog and merchandise available only online. What % of the order do you allocate to catalogs, email, and to organic brand loyalty? Quiz: An existing customer receives a catalog on August 1, and receives 17 e-mail marketing messages between 8/1 and 10/1. On 10/4, the customer uses Google to search for merchandise, visits, and buys an item. What % of the order do you allocate to catalogs, to e-mail, to search, and to organic brand loyalty? www.decideo.fr/bruley
- 11. Test + Results = Attribution Rules The beautiful thing about catalog marketing and e-mail marketing is that you can test, you can see what happens to other channels when you do not mail a catalog, when you do not send an e-mail campaign! Sample 80,000 twelve-month buyers with a valid e-mail address. Group 1 = 20,000: Catalogs = Yes, E-Mail = Yes Group 2 = 20,000: Catalogs = Yes, E-Mail = No Group 3 = 20,000: Catalogs = No, E-Mail = Yes Group 4 = 20,000: Catalogs = No, E-Mail = No Execute for a month, quarter, season, or year! In a controlled experiment, the results of your test tell you what impact catalog marketing and e-mail marketing have on other channels (search, mobile, social, display ads, affiliates), so you can set up reasonable attribution rules! www.decideo.fr/bruley
- 12. Some Results We have four test panels in this test. We sent one catalog and nine e-mail campaigns during a one-month timeframe www.decideo.fr/bruley
- 13. What Is The Organic Percentage? The organic percentage is possibly the most important metric a direct marketer / catalog brand can track. It is the percentage of demand that will be generated if no marketing exists. What about our example? Take the $5.80 generated in the no catalogs / no e-mail test panel, and divide it by the $11.37 generated in the catalogs + e-mail test panel. The result is 51%. www.decideo.fr/bruley 13
- 14. A 51% Organic Percentage We must execute catalog and e-mail mail/holdout test panels, in order to properly estimate what our organic percentage is. •When the organic percentage is < 20%, your matchback/allocation process is generally accurate. •When the organic percentage is > 40%, matchbacks and allocation programs become increasingly inaccurate. Does The Organic Percentage Vary? •Some customers are “highly organic”, while other customers require “large amounts of marketing”. There are HUGE profit opportunities in knowing this difference! •Customers who mail orders to a company or use the telephone to order require advertising. •Customers who combine catalogs and online channels are a “hybrid”, requiring much less advertising. •Customers who order online or in stores are highly organic, you can reduce advertising! www.decideo.fr/bruley
- 15. Key Takeaways Have the courage to execute both catalog mail/holdout tests and e-mail mail/holdout tests. Test four panels, test for a quarter or season or year if you can. The results are going to be breathtaking!. Catalogs: In many cases, orders that would have happened anyway are attributed to catalogs, causing us to spend way too much money mailing catalogs. E-Mail: E-Mail is frequently cannibalized by catalogs. E-Mail frequently causes Search/Mobile orders to happen. Search: Search is often the outcome of catalog marketing or e-mail marketing. Social/Mobile: In the early stages of a channel, sales are frequently cannibalized from existing channels, or the existing channels cause the sale to happen in the new channel. Over time, new channels become “organic”, and do not require oldschool channels in order to create sales on their own. Tests can validate this. www.decideo.fr/bruley
- 16. Multi-Channel Customer Analysis Business Question(s): •Prior to new product additions? •Is there any identifiable pattern of behavior prior to account closure? •If so, what does this pattern look like? www.decideo.fr/bruley
- 17. Value of Aster Data for Digital Marketing Aster Data Analysis •Click-stream • # of visitors, visitor location, browser type • Last click analysis •Online behavior • • •A/B Common interaction behaviors Optimal paths through website + Teradata Adds… •Multi-channel (online & offline) campaign analysis testing • •Search • • - Complete customer interaction history Where to place this button, link, etc. •Marketing Optimization Mix of paid per click and organic investment Which search terms drive traffic, behaviors- •Advertising Optimization Attribution + cost of conversion •Conversion • How to optimize advertising placement • Where are shopping carts abandoned & why •Marketing • Attribution What % credit to give each referring channel or campaign www.decideo.fr/bruley return on + Aprimo Adds… •Campaign management - Take action to influence behaviors •Marketing Resource Management - Take action to optimize marketing spend
- 19. Aster nPath Identifies the “Last Mile” All interaction patterns evaluated in a single pass userID 10001 Prepares multi-structured data 20001 •Stitches rows together by customer in a timeordered view Aster MapReduce Platform event time userID event time 10001 10001 20001 20001 10001 20001 Scans all records to produce a complete set of paths •No need to define patterns in advance Step 1: Pivot data via nPath SQL-MapReduce parallelized for top performance using MapReduce where SQL falls down channel1 … channeln time1 … timen 10001 Online Retail … Research products 12:00 PM 1/1/2010 … 3:00 PM 2/15/2010 20001 •Fully custID Store Purchase … BankX Credit Card 1:45 PM 1/1/2010 … 12:20 PM 2/22/2010 Summarize output for business exploration order the most popular paths and yet represent the long tail too Step 2: Run nPath SQLMapReduce Java Logic •Rank channel1 … channeln 35 Online Retail … Research products 26 www.decideo.fr/bruley Total # of Customers Store Purchase … BankX Credit Card
- 20. Aster nPath example: Account Closure www.decideo.fr/bruley
- 21. Marketing Strategy for Success Where should I increase my Marketing Spend to drive higher ROI? Multi-Touch Attribution Go beyond “last click” and identify which ads and channels perform the best Quantify which ads lead (attribute) to conversion Calculate true ROI on a per ad basis Run time-sensitive promotions by knowing which ads convert the fastest. Customer Journey Leading to Purchase on Online Store www.decideo.fr/bruley
Hinweis der Redaktion
- Here is aPOC that we did for a financial institution and I’d like to point a couple of things out first. Many times “Big Data” is focused on unstructured or new data that isn’t being analyzed today. Here is an example of Big Data Analytics on existing structured or relational data. All of the data was pulled from existing database systems, Teradata and Oracle, and loaded directly into Aster. Some of the data was unstructured in its original form but preprocessing was done on the data and then modeled into a relational database. So Big Data Analytics or MapReduce analytics can also analyze existing data as well as new data in the enterprise. The business questions that customer wanted to solve that they can’t solve easily today in SQL is “What events led up to a product purchase?” or “What events led up to a customer defection”. So we combined all these data subject areas and interactions and used our pattern and path analytics to answer these questions.
- So the analysis is around finding the path of interactions that lead up to an event, with the event being a product purchase, a customer defection, or an appointment with a loan officer. The event analyzed in this example was an “Account Closure” and displayed using visualization tool like Tableau reading our Aster database tables. What you see are the list of interactions on the left that include branch activity, call center and web activity. I won’t go into detail but these interactions can be across multiple channels. On the bottom going right to left are the number of interactions that lead up to the event of “Account Closure”. One of the challenges of Big Data is that it does generate a lot of data and often it is hard to see the signal that lead to the event in all the noise of the data. So this is why Big Data Analytics is a very iterative process with the Data Scientist applying part science and part art while understanding their companies data and business problems.
- & Abandoned shopping cart analysis