UNVEILING VALUE-BASED BIDDING SECRETS FOR MAXIMUM EFFICIENCY GAIN.pptx
•Download as PPTX, PDF•
0 likes•160 views
Regarding Value Based Bidding (VBB): - What is it? - What THEY don’t tell you - 4 Revealing case studies to inform: - Data perspective - Business perspective - Google Ads operations - Campaign strategy
Recommended
More Related Content
What's hot
What's hot (20)
Similar to UNVEILING VALUE-BASED BIDDING SECRETS FOR MAXIMUM EFFICIENCY GAIN.pptx
Similar to UNVEILING VALUE-BASED BIDDING SECRETS FOR MAXIMUM EFFICIENCY GAIN.pptx (20)
Recently uploaded
Recently uploaded (20)
UNVEILING VALUE-BASED BIDDING SECRETS FOR MAXIMUM EFFICIENCY GAIN.pptx
- 1. UNLOCK GOOGLE MARKETING PLATFORM'S POWER: UNVEILING VALUE-BASED BIDDING SECRETS FOR MAXIMUM EFFICIENCY GAIN Inside scoop on the non-obvious success tactics without clickbait
- 3. What’s coming up in the next half hour? ● What is it? ● What they don’t tell you ● 4 Revealing case studies to inform: ○ Data perspective ○ Business perspective ○ Google Ads operations ○ Campaign strategy
- 5. Maturity growth, optimise for value
- 6. Value Based Bidding for campaign optimisation https://www.thinkwithgoogle.com/intl/en-emea/marketing-strategies/automation/bidding-for-value-automation/
- 7. Data Choices
- 8. Do this first The need for clean, high quality data is obvious and cannot be overstated ● Privacy first ● Regulatory compliant ● Lighter client side ● Cleaner ● First party ● Consented ● Modelled #TAKEAWAY
- 9. Data Quality Considerations ● Volume ○ Minima ● Completeness ○ Conversion rate ● Consistency ○ Data types ○ Outliers ○ Volatility ● Range ○ Values ○ Skewness ● Timeliness ○ Reliable ○ Available
- 10. ● Collect ● Visualise ● Analyse ○ Score ● Clean ● Enrichen ● Deploy ● Monitor ● Repeat regularly Pre Engagement Analysis
- 11. Why is this important? What goes on under the hood isn’t visible. Control the input to inform the output. #TAKEAWAY
- 12. Uncomfortable truth VBB is not guaranteed to work for you. Not necessarily a plug and play success story. Success or failure of VBB contingent on YOUR choices, not Google’s model. #TAKEAWAY
- 13. Pre engagement analysis output Variability in values offers more opportunity to find efficiencies. #TAKEAWAY
- 14. Pre engagement analysis output Volatility in values is a risk. Aim for a consistent weekly promotion cadence. #TAKEAWAY
- 18. Pre engagement analysis output ● Check distributions ○ Where is the value? ○ Where will VBB optimise? ○ What signal are you sending to VBB? ○ Does this align with strategic goals? #TAKEAWAY
- 19. Value optimisation Where will VBB assign budget?
- 20. Value optimisation Where does your strategy want VBB assign budget?
- 21. Value optimisation Where does your strategy want VBB assign budget?
- 22. Value vs Propensity distributions What if the high value is already nailed on?
- 23. Value vs Propensity distributions What if the high value is already nailed on?
- 24. Value vs Propensity distributions What if the high value is already nailed on?
- 25. Value vs Propensity distributions What if the high value is already nailed on?
- 26. Value vs Propensity distributions What if the high value is already nailed on?
- 27. Value vs Propensity distributions What if the high value is already nailed on?
- 28. Value vs Propensity distributions What if the high value is already nailed on?
- 29. Key Choices for Success
- 30. Value Based Bidding for campaign optimisation …optimised toward individual customer business outcomes and transactions
- 31. Value Based Bidding for campaign optimisation …optimised toward individual customer business outcomes and transactions What are they? How do I get and use this data?
- 32. Value Based Bidding for campaign optimisation CPC CPA ROAS Cost of sale Transaction value Profit LTV
- 33. Google Marketing Platform Value Based Bidding for campaign optimisation CPC CPA ROAS Cost of sale Transaction value Profit LTV
- 34. Ecommerce Value Based Bidding for campaign optimisation CPC CPA ROAS Cost of sale Transaction value Profit LTV
- 35. ERP / CRM Value Based Bidding for campaign optimisation CPC CPA ROAS Cost of sale Transaction value Profit LTV
- 36. ERP / CRM Value Based Bidding for campaign optimisation CPC CPA ROAS Cost of sale Transaction value Profit LTV
- 37. Value Based Bidding for campaign optimisation
- 38. Ad platform alignment ● Not all accounts are primed for VBB ● Not all business strategies go well with VBB ● Google Ads strategy != Business strategy ● Margins are pretty difficult to obtain ● VBB is a process and not a one stop tech exercise #LEARNINGS
- 39. Ad platform alignment ● Get to know all of the system features (1): ○ Enhanced conversions and consent ○ OCI (Enhanced Conversions for Leads) ○ Conversion Adjustments, Seasonal adjustments, Data exclusions ○ Cart data reporting ○ Phoebe, Soteria, LTV modelling ○ Feeds (Page, customer, GMC, dynamic) #LEARNINGS
- 40. Ad platform alignment ● Get to know all of the account structure options: ○ Shared budget ○ Portfolio strategies (tROAS with max CPC) ○ Ad group ROAS levers ■ Increase ROAS to reduce spend ■ Decrease ROAS to increase spend ○ The new match type considerations - full broad ○ Multiplacement black boxes (pMax, DemandGen) #LEARNINGS
- 41. Level 1 - mapping margin to ROAS
- 42. Level 1 - mapping margin to ROAS Plain and simple the business needs and wants more ● Determine how (that’s on you) ● Assortment (availability, stock state, competitiveness (price, delivery, payment …) ) The how - is not that simple and requires meetings and a lot of QA ● Value distribution (rev. vs profit ) ● Vendor deals and intricacies ● Account restructure ● Find opportunity
- 43. Lorem Ipsum Lorem Ipsum Lorem Ipsum Lorem Ipsum Lorem Ipsum Lorem Ipsum Lorem Ipsum Account Category Y Category X N Ad Groups ROAS X N Ad Groups ROAS X DSA N Ad Group ROAS Y N Ad Group ROAS Y DSA Ad Account structure based on search logic (Category - budget holder, Ad group - context holder, DSA - ideas)
- 44. Determine focus areas - vendor, category, product based on the retailer ability to perform! Winner(s)!
- 45. ABC - Understand the assortment distribution - value, count and segment as much as you can to understand value.
- 46. ABC - Understand the assortment distribution - value, count and segment as much as you can to understand value.
- 47. Determine focus areas - vendor, category, product based on the retailer ability to perform! Stages: 0 Old approach (CPC control) 1 ROAS Mapping / tROAS SBS 2 Corona (crazy expansion) 3 Continuation (+ new products) Stage 0 Stage 1 Stage 2 Stage N Revenue - Q1 2018 - Q2 2022
- 48. Level 2 - POAS - the Perfect solution?
- 49. Level 2 - POAS (sGTM, Soteria, I hate Firestore) Plain and simple the business needs to understand ● Technical aspect is solved (high degree of automation (400k skus)) ● Assortment (availability, stock state, competitiveness (price, delivery, payment …) ) POAS allows you to understand the ‘profitability’ with a twist ● What if profit happens at the EoY ● What if you are positioning ● What if POAS is 1 ● High profitability could mean lower volume ● What if dynamic pricing
- 50. Always put the profit value conversion as secondary!!! (As the initial point)
- 51. What to do now - shift budget? Ask additional budget? As always it depends! Ad Group Cost (EUR) Value (EUR) Profit (EUR) POAS Vendor 1 2 800 168 000 20 000 714% Vendor 2 1 050 75 000 18 000 1714% CLient says I understand Vendor 2 efforts bring more yet I have a full store of Vendor 1 …
- 52. What if? - shift budget? Remove group? - As always it depends! Ad Group Cost (EUR) Value (EUR) Profit (EUR) POAS Vendor 1 1 000 10 000 1 000 100% Usually stay with tROAS but be mindful of the minimum meaningful ROAS or in some cases revert to max conv value or tCPA (or some other channel?)!
- 53. Level 3 - leads and predictions
- 54. Level 3 - leads and predictions (EC4L, Conv. adj.) Plain and simple the business has issues with lead / conv. quality ● Technical aspect is solved (high degree of skill (OCI, eLTV, CRM) ● Everything on the call center side is superb:) VBB relies on importing the QL / prediction as fast as possible (3-7d) ● How accurate are we in this short span ● Sacrifice accuracy for efficiency ● Spot the outliers (definite -) ● Validate subsequently
- 55. Period 0 - pMax and Search are cool. Cannot attribute QL to Lead.
- 56. P1 - pMax sucks. After quality can be attributed to lead.
- 57. P2 - pMax almost gone. Discovery > DemandGen made a mess though.
- 58. Save budget, obtain more QL, earn more.
- 59. Leads are not easy ● Time to upload is limited (3-7d) ● OCI lowers the conversion count (can VBB work with that?) ● Make sure you validate the model quality (continuously) ● In some industries this kind of data upload is perhaps the only option to make the algo smarter as the audiences are not allowed #LEARNINGS
- 60. Level 3b - leads and predictions (EC4L, Conv. adj.) Plain and simple the business has issues with lead / conv. quality ● Technical aspect is solved (high degree of skill (OCI, eLTV, CRM) ● Everything on the product side is superb:) VBB relies on importing the QL / prediction as fast as possible (3-7d) ● How accurate are we in this short span ● Sacrifice accuracy for efficiency ● Spot the outliers (definite -) ● Validate subsequently
- 61. The reward system - which needs to happen - inform on success. Conversion Value 1080.41 936.29 727.12 517.78 296.85 175.32 152.90 77.06 67.70 50.02 44.97 38.00 34.15 30.45 19.96 8.75 8.59 -15.76 -21.41 -65.25 Conversion Value Bucket 1080.41 Highly positive (>2x above avg CPA) 300 EUR 936.29 727.12 517.78 296.85 Positive (above avg CPA) 150 EUR 175.32 152.90 77.06 Negative (bellow avg CPA) 0 or? 67.70 50.02 44.97 38.00 34.15 30.45 19.96 8.75 8.59 -15.76 Highly Negative 0 or? -21.41 -65.25 Do not upload!
- 62. Estimated value Conversion Value Bucket 1080.41 Highly positive (>2x above avg CPA) 300 EUR 936.29 727.12 517.78 296.85 Positive (above avg CPA) 150 EUR 175.32 152.90 77.06 Negative (bellow avg CPA) 0 or? 67.70 50.02 44.97 38.00 34.15 30.45 19.96 8.75 8.59 -15.76 Highly Negative 0 or? -21.41 -65.25 Conversion Value 1145.66 1001.54 792.37 583.03 362.1 240.57 218.15 142.31 132.95 115.27 110.22 103.25 99.4 95.7 85.21 74 73.84 49.49 43.84 0 Conversion Value 2291.32 2003.08 1584.74 1166.06 543.15 360.855 327.225 142.31 132.95 115.27 110.22 103.25 99.4 95.7 85.21 74 73.84 0 0 0 Aligned to the new 0 Aligned and weighted
- 63. ● What happens when you give the same value? (Or no value?) ● What is the range from top to bottom? ● Distribution shape - width and height? ● Can you use negative values? ● Does it matter to use only integers, no floats? Case study follow up questions
- 64. ● High cardinality data - is it better to normalise to a discrete range? ○ Speed of model curation and update ● What are the effects of sparse values? Is imputation a solution? ○ Synthetic data for data gaps? ○ What if this introduces inadvertent bias? ○ Can you use deliberate bias to direct optimisation? ● Is a curated VBB payload better than sending raw data? Case study follow up questions
- 65. Case study follow up questions ● Can you feed the VBB a contrived/weighted value to influence the model? ● If a user scores 4 (low ish - floating voter) and you add a weight to floating voters, do they convert better and move into the high propensity group?
- 67. Soteria Phoebe
- 68. Measure and score prediction against outcome #TAKEAWAY Most common missing component
- 69. Analytical Choices: Testing & Proving Incrementality
- 70. Proof ● Measurement against control group baseline ● Does the control group performance change? ○ Give it time - >6 weeks ● Compared to those given actual values? ○ Non converters vs ‘anyway converters’ vs converters due to optimisation ● Net new customers ● Existing customer growth
- 71. Change Management ● Transition from CPA to tROAS ○ Revenue to profit to CLV to propensity ■ Short term drop for long term gain? ● What happens to the performance? ● How do you manage change? ○ Test and learn ○ Campaign planning
- 72. Conclusion
- 73. Takeaways ● Privacy first ● Data quality ○ Value variability is good ○ Volatility can kill you ● Data choices ○ Pre engagement analysis ● Campaign planning ● Account configuration
- 74. Takeaways ● Incrementality ○ Existing customer growth ○ Net new customer acquisition ● Change management ○ Expect initial downturn ○ Plan VBB deployment ■ Promotions ■ Affiliate relationships
- 75. Takeaways ● Success is not guaranteed ● Choose success metrics ○ Short or long term? ○ What exactly does profit mean? ○ Campaign ROAS? ○ Net new customers? ○ 0% ROAS is not bad ■ Market position ■ New vertical established
Editor's Notes
- Why is this different to other VBB presentations? We’re going deeper than you’ve seen previously. We’re asking questions not even Google has the answer to.
- Let’s showcase VBB
- How Google sees the values mapped to efficiency - this is not a critique of Google’s intro to VBB - it’s a necessary and useful step. But don’t let this be the totality of your VBB understanding.
- This is the top level Think With Google explanation - the enquiring mind will ask how this happens in specific contexts. What about commercial pressures. Market trends. Affiliate influences. Partnership demands. Strategic goals. Merchandising. And WHAT EXACTLY IS Value?
- Get the basics out of the way. Embrace the reality of modern analytics. This is a foundation that is unavoidable. Why would you want to avoid this part? Demonstrate excellence in the basics.
- In terms of data quality, the better the signal you can send to Google, the better. A strong signal returns reliable and repeatable output. Output you can understand and trust. Volume - meet and exceed the minima. 15 conversions in the last 30 days Completeness - no point having huge gaps in your week. That’ll affect the choice of metric. Online proxy actions happen more frequently and reliably than offline lead conversion for example. Consistent data you can rely on is preferable for obvious reasons. We’ll come on to how range affects VBB performance but you will certainly include this in your quality scoring. And timeliness is deffo a favourite. Prefer near real time to week lags in sales data.
- Scoring for data quality requires a level of governance. You can compare the performance of two data sets form the same source but with different levels of quality - values missing, outliers unaddressed and so on and you will see differing returns and performance. Ideally, you will have data quality automated as a mature setup
- See more in the architecture section. Record the values being sent to VBB, report and align on the output and performance. This is essential instrumentation for your test and learn framework. Establish data quality as a performance metric - maybe even your North Star if nascent/emerging?
- Time for home truths. Yes, you can set it and forget it but that isn’t going to be 100% certain of success. Put in the hard yards and you’ll make a difference. Fully understand the ask and you’ll be more likely to see the returns you’d expect. Let’s start with data quality
- Variability is a signal of what’s good and what’s bad as far as the machine is concerned. That means there’s more scope to apply bid adjustments. If a linear bid is adjusted up and down according to the value distribution, the arbitrage effect will be more apparent. But is this always what you want?
- Variability is a signal of what’s good and what’s bad as far as the machine is concerned. That means there’s more scope to apply bid adjustments. If a linear bid is adjusted up and down according to the value distribution, the arbitrage effect will be more apparent. But is this always what you want?
- Consider these illustrative distributions. Where is VBB going to optimise for? What’s signalled as ‘good’?
- See where we flag ‘value’? That’s where VBB is going to optimise bids. Away from low value to high value. Less of what you don’t want, more of what you do. Automagically!
- But what about the other, less clear distributions. Less clear signal. Imagine a subscription business selling one product for $x - no variability in value makes good/bad prospects hard to identify. Maybe quantity rather than price is a better choice and has more variability?
- In scoring the data, ask these questions.
- But this is a tricky scenario. Seldom do we have such clean distributions. Something messier, and noisier is more likely. So where is VBB going to go here? We need to be cautious so as not to ruin existing value.
- If we’ve got a winning formula here already, we need to be careful not to undo this good work.
- Protect this. But it’s not that simple….
- We have data that shows low LTV customers are more likely to buy once…..but we don’t retain their custom.
- We’ve got solid value, at a retention rate that works.
- This predicted behaviour is in the wrong place on our LTV scale though
- Needs to move over there….
- Can we get VBB to ignore that signal and focus on what is strategically useful for us?
- Hide the data. Don’t score it.
- We need to tell VBB ONLYO what it needs to know. Leave the high LTV campaigns alone.
- Let’s showcase VBB
- Notice the language here
- Individual customer business outcomes. Right. But exactly what are we talking about? And how do we get and use these values?
- Examples and where we might get them - consider technicalities of data pipelines, data timeliness, governance, customer consent
- Consider how we integrate across GMP for data accessibility and activation
- Real time values are available as the transaction happens on the site and in the app
- CRM integration may require more technical work. The higher utility of the data may come at a cost in terms of investment required
- CRM integration may require more technical work. The higher utility of the data may come at a cost in terms of investment required
- A lot deeper. There’s quite a bit to unpack here
- In scoring the data, ask these questions.
- In scoring the data, ask these questions.
- In scoring the data, ask these questions.
- In scoring the data, ask these questions.
- Search demand is not the only factor when deciding on what to do next.
- In scoring the data, ask these questions.
- In scoring the data, ask these questions.
- In scoring the data, ask these questions.
- In scoring the data, ask these questions.
- Before you try these tactics, make sure you have tested the approach on your campaigns, with your data, and your customers. Results DO vary. As well as considering the range, the values, the data types - it’s very context dependant.
- These are thoughts, questions, unanswered brain farts. I don’t expect to provide answers yet. One day. At the bar? For sparse data - 30-50 conversion per month is normally needed. Is it okay to impute values? Do we leave data sparse and let the Google AI figure it out?
- So quality is actually a huge topic in it’s own right. Availability and signal strength are more easily quantified but the testing aspect cannot be avoided.
- So quality is actually a huge topic in it’s own right. Availability and signal strength are more easily quantified but the testing aspect cannot be avoided.
- Let’s put it together. Some code!?
- Google projects Phoebe and Soteria follow these architecture patterns - choosing either Firestore or VertexAI as the data enrichment sources, The Firestore API in sGTM is well known - a Firestore variable is straightforward to set up. The VertexAI integration is achieved via an HTTP request - like talking to a Cloud Function perhaps?
- In these documented VBB architectures the feedback loop does not exist. The model values being fed to GMP come from an existing predictive model. GMP uses these to refine bidding. Were the predictions any good? Did the user buy? Feed the predictions and outcomes back into the model for retraining. VBB performance is dependant on the predictions being fed to it.
- Then we compare performance over time. Typically over 6 weeks to be able to train the model. What comparison do we need to perform to establish incrementality. Consider testing different control group parameters.
- Then we compare performance over time. Typically over 6 weeks to be able to train the model. What comparison do we need to perform to establish incrementality. Consider testing different control group parameters.
- Then we compare performance over time. Typically over 6 weeks to be able to train the model. What comparison do we need to perform to establish incrementality. Consider testing different control group parameters.
- Some examples of values we might consider in building VBB for non-retail See how the utility might vary but the timeliness, and variability will be satisfied.