Media vs Data: Why the Double Standard?
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The document discusses the discrepancies in data accuracy within mobile advertising, highlighting that significant portions of demographic and location data can be inaccurate, leading to wasted ad impressions. It introduces the concept of 'acpm' to adjust costs based on data accuracy and emphasizes the importance of prioritizing data verification alongside media quality. Ultimately, it calls for deeper scrutiny of data vendors to address and mitigate these inaccuracies.
Media vs Data: Why the Double Standard?
- 1. 1 Media vs. Data: Why the Double Standard? Jake Moskowitz Head of the Emodo Institute
- 2. 2 Let’s Get to Know Each Other The Emodo Institute • Research, education & resolution of data concerns that challenge mobile advertising. • Helps media planners, buyers & service providers sharpen the efficacy of mobile data, so campaigns have a greater impact.
- 3. 3 VS WHERE − Segment designations − Bid request metadata that triggers bidder decisions − Attribution studies that lead to reallocation Media Data We focussomuchon verifyingwhere our adsrun…. − Whitelists/blacklists − Brand Safety − Viewability − Fraud HOW … andsolittleon verifying howwe decidewhat to buy
- 4. 4 Data: $1 - $2 CPM Media: $1 - $2 CPM Balancing Out Our Focus
- 5. 5 We’ve Done So Much Work to Eliminate Media Waste
- 6. 6 Case Study: Demographic Data • Nielsen DAR benchmarks show 50-60% of data is wrong for even medium- level specificity of demo targeting • And it’s not improving in any substantive way Demographic, Age Span Nielsen DAR Mobile Demo Benchmarks – Q1’18
- 7. 7 This Lat/Long location data is extremely precise, right? 40.759463° N, 73.984597° W
- 8. 8 Right. But it points (very precisely) to the wrong place. Not just 10 feet to the left or right. Often, the data is 10s, 100s, 1000s of miles off.
- 9. 9 Only 39% of device location data points were within one mile of where they were claimed to be. Case Study: Location Data
- 10. 10 Across all raw data tested, vendors only eliminate 28.4% of data inaccuracies Pattern recognition techniques used by vendors don’t seem to work. Case Study: Pattern Recognition
- 11. 11 CASE STUDY: QUESTION ASSUMPTIONS • SDK scores are extremely consistent • All accuracy scores are between 60 and 75% Emodo has tested a wide range of 3rd party SDKs Case Study: SDK Data
- 12. 12 Audience scores for a single vendor: • For the same use case, • For two separate campaigns, • From two separate brands, • Via two separate agencies, • Less than 60 days apart. Campaign #1 88.78% accurate Campaign #2 11.65% accurate (audience A) 25.44% accurate (audience B) Case Study: Vendor Consistency
- 13. We care about accuracy. So why do we prioritize other factors? Source: Factual Survey, May 2019
- 14. 14 So, what do we end up with? 67M devices that have visited a Hyundai dealership in the last 30 days?
- 15. 15 More segment stuffing... Step 2: Understand Why is the Data Inaccurate? 102M devices that are active members of the BP Motor Club?
- 16. 16 And another... 128M likely Millstone coffee drinkers?
- 18. 18 Transparency
- 19. 19 Accurate
- 21. 21 A Day in the Life of a Data Point Occurrenc e Categorize Define ExpandMatch Cross- platform Use A Day in the Life of a Data Point
- 22. 22 Introducing the “aCPM” Inaccurate data is a primary cause of wasted impressions. • The aCPM adjusts cost for lost value from data inaccuracy • Applies the accuracy rate to the original CPM to calculate cost of only the accurate impressions. • Example: If CPM is $3.00 and data is 50% accurate, the aCPM would actually be $6.00 • Taking steps to improve accuracy can significantly reduce aCPM
- 23. 23 SEGMENT BMW INTENDER PRICE $0.80 DEFINITION VISITED THE BMW PART OF A 3RD PARTY AUTO SITE IN THE LAST 90 DAYS LOOK-A-LIKE MODELING YES (70% modeled) DETERMINISTIC DATA USED YES Calculate an aCPM x 2 x4 ________ = $ 6.40 aCPM x 2 x1.5 ________ = $ 4.50 aCPM SUPPLY QSR LUNCH PRICE $1.50 GEOFENCE CRITERIA WITHIN 1 MILE OF A STORE POI DATABASE UPDATED QUARTERLY LAT/LONG FILTERING NO
- 24. 24 What Could Go Wrong? 1. Tech problems: Data captured isn’t correct because technology failed, such as “last known location” Step 3: Ask Your Data Vendors Revealing Questions Bad Data Sources: 2. Low quality data: Data that isn’t persistently collected, honestly provided, adequately scalable, etc. 3. Categorizations are wrong: store definitions are wrong or too liberal; irresponsible assumptions about meaning Questions to Ask Your Data Vendor: What % of your data do you throw out? How do you verify accuracy? How do you verify your POI? What restrictions on use of deterministic? 4. Privacy restrictions: no use of deterministic data due to privacy concerns (only modeled data used)
- 25. 25 What Could Go Wrong? 1. Optimizing scale at the cost of accuracy: Lookalike model extrapolation ratio is high Step 3: Ask Your Data Vendors Revealing Questions Data Trade-Offs: 2. Data loss due to low match rates 3. Inaccurate cross-device due to probabilistic methods, or incorrect assignment of a person within a household Questions to Ask Your Data Vendor: What % are modeled? What’s your match rate to x? What % are running on source platform? 4. Use of wrong segments (accidental or purposeful) such as to increase scale or deliver in full Exactly which segments were used?
- 26. 26 1. Prioritize: Establish data accuracy as a top priority – equal to media quality 2. Recognize: Remember that price and scale are negatively correlated with quality 3. Calculate: Assess the value of data options by doing a simple aCPM calculation 4. Ask: Seek deeper answers to revealing vendor questions Crucial Steps to Better Data Summary
- 27. 27 Q & A Q&A
- 29. 29 Any follow-up questions, feel free to reach out: THANK YOU Jake Moskowitz Jake.moskowitz@emodoinc.com Emodo Institute blurb here…
- 30. 30 Why Data Science is Veiled Science 1 Competition 2 Sales Cycle 3 Accountability 4 Evolution 5 Weakness 6 Perception
Editor's Notes
- #4: What’s new in this slide? This is basically a Verizon vs. ATT commercial for the past 10 years
- #6: What’s new in this slide? This is basically a Verizon vs. ATT commercial for the past 10 years
- #8: Here’s another example. If I told you that this Lat / Long location data was extremely precise you’d probably agree, right? - It looks very precise. Those are certainly very precise numbers. - Each one of those digits to the right of the decimal point increases the location resolution ten-fold. Pretty exact.
- #9: Now, what if I told you that it points (albeit very precisely) to the wrong place? That may be confusing. It should be. Those Lat/Long coordinates can be precise. But often, those precise numbers simply point to the wrong place. It’s kind of like a musician playing a piece of music perfectly, but playing the wrong song. Only, the musician’s faux pas doesn’t chip away at your return on investment.
- #12: What’s new in this slide? This is basically a Verizon vs. ATT commercial for the past 10 years
- #15: So, when you go to select segments for a campaign, how does all of this data sourcing and processing come into play? As sophisticated as you’ve become with data, when you buy programmatically, your opportunities to use that expertise become intentionally limited. Data stores and vendors have designed a marketplace that games your expertise and takes advantage of your need for scale. Here are two ways they do this: Data stores encourage over-buying and discourage informed decisions by revealing no distinguishing details about segments and validating that buying more and more segments = more unique qualified reach. You’ll notice that the only attributes exposed are price, reach and name – nothing about quality, like source, accuracy or % look alike audience. That leads us to #2. Segment Stuffing: Data vendors often over-inflate the segments they sell because they know you’re looking for scale Data stores are set up to encourage a “spray and pray” approach. They make it very easy to add as many segments as you’d like to your campaign and somehow each new segment adds significant “unique” targeted reach. But if you look closely, the numbers start to call attention to themselves and reveal their flaws. Somehow, without a lot of effort, the world of Hyundai intenders, for example, can end up including just about everybody in the country – if those numbers were actually uniques and accurate. It’s definitely scale, but at some point targeting becomes pointless if you’re targeting everybody. And you have the honor of paying the vendor a $1.25 CPM for the pleasure. Segment Stuffing: Data vendors often over-inflate the segments they sell because they know you’re looking for scale. The biggest segment often wins. Segment stuffing is common. It’s difficult to know how broad reach is shaped in large segments. Often, the segment is expanded with “look-alike” audiences. This PlaceIQ example targets 60M IOS and Android devices that have visited a US Hyundai dealership in the last 30 days. Do you believe that’s accurate? It’s definitely a surprising number, but it begs the question, what is it based on? Can you imagine the extrapolations that go into a segment like that? What would happen if you targeted that segment? You’d likely pay for a lot of wasted impressions.
- #16: Here’s another one. 102M devices that are active members of the BP Motor Club. Have you ever heard of the BP Motor Club? I hadn't. But I can assure you their membership is far lower than that. Not just that, what is that based on? Can you imagine the extrapolations that go into a segment like that? And this is from Acxiom, one of the most respected names in data.
- #17: And another... 128M likely Millstone coffee drinkers. I'm sure that's really high quality data there. I'll bet their regression model threshold is 0.15. Okay, these segments are pretty obvious. But, that’s the point. Most segment stuffing isn’t obvious. Every segment you choose could easily have the same problem and produce a significant amount of waste. You don’t know where the data came from, how its been processed or how it’s been inflated for scale. How would you know? That’s next. Let’s talk about how you can be more certain.
- #31: Competition: Protect the vendor’s secret sauce Sales Cycle: Reduce technical concerns / additional questions Revenue: Keep marketers from gaming the system Evolution: Data science is nascent & continues to change Weakness: Conceal flaws and lagging capabilities Perception: Some vendors, at best, use “pseudo” science