Pulling together your cross channel marketing pieces
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Taking your marketing performance to the next level requires pulling all the pieces together to get greater clarity on performance, within and across channels. This is where an advanced attribution solution can be of help. Learn how the UK’s leading price comparison website, Moneysupermarket.com, plans to leverage these capabilities to drive big results.
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Pulling together your cross channel marketing pieces
- 1. Click to edit Master title style • Pulling Togetherstyles Click to edit Master text Your – Cross-Channel Second level Marketing Pieces Via Advanced • Third level Attribution – Fourth level » Fifth level Casey K Carey Chief Marketing Officer – Adometry
- 2. Click to edit Master title style pulling together your • Click to edit Master text styles CROSS-CHANNEL MARKETING – Second level • Third level – Fourth level » Fifth level via advanced attribution 2 #Engage13
- 3. a bit about us Click to edit Master title style • Click to edit Master text styles – Second level • Third level – Fourth level » Fifth level Casey Carey Chief Marketing Officer @caseycarey Ben Sidebottom Head of Media Systems #Engage13
- 4. to succeed, modern marketers must increasingly… Click to edit Master title style • Click to edit Master text styles –Become level Second data-driven, • Third level Understand the customer journey, Be multi-channel in approach, but it has become more complex. but organize and measure in silos. – Fourth level » Fifth level but there is a data deluge. #Engage13
- 5. Click to edit Master title style • Click to edit Master text styles – Second level • Third level – Fourth level » Fifth level #Engage13
- 6. Click to edit Master title style • Click to edit Master text styles – Second level • Third level – Fourth level » Fifth level last-click, the attribution zombie
- 7. contrary to popular belief, last-click is the living dead Click to edit Master title style • Click to edit Master text styles – Second level Digital makes it possible to measure everylevel • Third touchpoint – Fourth level Attribution makes » Fifth level the connection to ROMI 54% 14% 42% of marketers use last click attribution… consider last-click to be “very effective”… are unsure which attribution approach they should use… Source: eConsultancy, Marketing Attribution, Valuing the Customer Journey, 2012 #Engage13
- 8. Click to edit Master title style LAST-CLICK ATTRIBUTION • Click to edit Master text styles • • • • – Second level • Third MYOPIA CHANNEL level – Fourth level INEFFICIENT SPEND » Fifth level LOWER PERFORMANCE UNREALIZED POTENTIAL
- 9. Using advanced attribution to… Click to edit Master title style • Click to edit Master text styles – Second level • Third level – Fourth level » Fifth level …understand the true connection between marketing and results. 9
- 10. Click to edit Master title style • Click to edit Master text styles – Second level • Third level – Fourth level » Fifth level #Engage13 #Engage13
- 11. descriptive model approach to advanced attribution Click to edit Master title style “Data Driven A/B • Click to edit Master text styles Testing” – Second level • Third level – Fourth level » Fifth level 10.0% Conversion Rate 6.5% Conversion Rate #Engage13
- 12. Click to edit Master title style 10.0% • Click to edit Master text styles Conversion Rate – Second level Channel – Campaign Fourth level + Recency » + Frequency Fifth level + Recency Site + Frequency + Recency Placement + Frequency + Recency Creative + Frequency + Recency Specificity Confidence • Third level + Frequency Hierarchical Bayesian Shrinkage • A principled statistical approach to data sparsity • Each weight is derived from confidence weighted average of weights at different levels of granularity #Engage13
- 13. providing insights and clarity to crosschannel performance Click to edit Master title style • Click to edit Master text styles – Second level • Third level Attribution – Fourth level » Fifth level • • • • • • • KPIs Conversion Events Channel Source Campaign Placement, keyword, etc. Creative, content, format Reach & Frequency Cross-Channel Dashboard
- 14. using attribution to fuel predictive optimization Click to edit Master title style • Click to edit Master text styles – Second level • Third level Past – Fourth level » Fifth level Future Present Attribution Optimization How did my marketing perform? How can we improve? #Engage13
- 15. accounting for diminishing returns nature title style Click to edit Masterof marketing Maximum Marginal Return Maximum Average Return • Click to edit Master text styles – Second level Point of Saturation KPI Performance • Third level – Fourth level » Fifth level Ad Volume #Engage13
- 16. optimal investment across and within channels Click to edit Master title style Evaluating Scenarios • Overall Optimization Master text styles Click to edit For Each Scenario – Specific Recommendations – Second level • Third level – Fourth level » Fifth level View optimization opportunities across channels Reallocate spend across and within channels Granular line item changes – “I/O ready” • Modify default recommendations • Rerun prediction • Evaluate revised KPIs #Engage13
- 17. driving greater ROMI Click to edit Master title style Value Drivers • Click to edit Master text styles Display eCPA – Second level • Third level Optimization Within Channels – Fourth level Areas of Impact 20% - 30% decrease in effective CPA for display and retargeting 10% - 20% improvement by optimizing channel performance including PPC, Affiliate, Email, and Social » Fifth level Optimization Across Channels 20% - 40% improvement in performance by optimizing spend across various channels Reduction in Analysis and Reporting Costs 25% - 50% decrease in effort required to pull, aggregate, and report on marketing performance across channels Overall Marketing ROI 20% - 40% improvement in overall performance of marketing investments #Engage13
- 18. Click to edit Master title style • Click to edit Master text styles – Second level • Third level – Fourth level » Fifth level Adometry and MSM Nov 2013
- 19. Overview of MSM Click to edit Master title style • One of the UK’s largest • price comparison websitesstyles Click to edit Master text – Second level • £204m Third level • annual revenue – Fourth level » Fifth level • In-house digital team of 50+ • Digital transformation programme 19
- 20. Why change our view? Click to edit Master title style Display Impression Organic Click • Click to edit Master text styles Paid Search Click – Second level • Third level – Fourth level » Fifth level Direct To Site Happy Customer! 20 #Engage13
- 21. How we changed our view? Arbitrary Models Click to edit Master title style • Click to edit Master text styles 50% – Second level Last Click First Click Even Click Last/First Click U Shape Model Time Decay Model Programmatic (Self Defining Model) • Third level Order of complexity – Fourth level » Fifth level 21 #Engage13
- 22. Challenges in Changing Our Approach Click to edit Master title style Business Acceptance Tagging Calibration and data integration • Click to edit Master text styles – Second level • Lack of understanding • Replacing•current trusted Third level model – Fourth level • Digital marketing optimisation • Financial reporting • Change management • » Fifth level Code implementation / Resource • Iterative process • • Defining click and impression based touch points Standardising reporting parameters • Rule testing • Linking data sources • Process changes • UI reporting and data feeds 22 #Engage13
- 23. Opportunities Today Click to edit Master title style Future • Click to edit Master text styles – Second level TV / • Third level Econometric Paid Search Modelling – Fourth level » Fifth level Audience+ Partner Portal 23 #Engage13
- 24. Click to edit Master title style QUESTIONS? • Click to edit Master text styles – Second level • Third level – Fourth level » Fifth level
- 25. Click to edit Master title style • Click to edit Master text styles – Second level • Third level – Fourth level » Fifth level Casey Carey casey.carey@adometry.com Ben Sidebottom ben.sidebottom@moneysupermarket.com #Engage13
Editor's Notes
- To understand the weight or credit due to an event in a particular sequence, we first find all examples of that sequence – both converting and non-converting. From this we can calculate the conversion rate for that sequence.Next, we find a similar sequence of events that excludes one of the events from the prior sequence – for example, the first event. Again, we find both converting and non-converting examples. We then calculate the conversion rate for this (second) sequence.Now, by comparing the conversion rates of the sequences we can determine the weight or impact of the the missing event. If the rates are identical or similar then the missing event deserves little or no credit whereas if the rates are different then that missing event had an impact and deserves credit.There are some points that you can make after this – the death is in the details stuff:Obviously simplified explanation.Number of sequence and length of sequences will varyWe normalize the weights such that they add up to one (1)Tons of events - "big data problem"If there is only one event in the sequence it will get 100% of the creditAn event is actually a complicated thing – has lots of attributes – site, campaign, placement, creative concept, tactic, etc. we calculate at the lowest level and roll the results upAlso frequency and recency.But this is the stuff that the engineers have all worked out in a principled manner. Machine learning, etc.Data Driven Algorithm ConfigurationScientific approach to determining Look back & Look ahead periodsDealing with Data SparsityBayesian Hierarchical Shrinkage Absolute vs. Relative AttributionAccounting for baseline behaviorCausality vs. CorrelationInverse propensity weighting
- Lays the foundation for our benefits.Introduction to how we approach the problem of looking at historical data and making changes to improve performance.
- Introduction and agenda - caseyThe business case for better attribution - caseyHow advanced attribution works - casey Data requiredModeling methodologyTypes of reportsOptimizationOverview of MSM - benWhy an advanced attribution initiative (objectives of initiative) - benExpected opportunities to leverage new insights to improve performance across channels - benOpportunities for integrating marketing systems and data sources to provide more accurate attribution and leverage the result in marketing channels –ben Q&A – casey & ben
- - Overview of MSM - simple
- Take a typical path of a user with 4 different touch points; user searches for car insurance and clicks on a MSM PPC ad then bookmarks and decides to come back later. They then return via that bookmark and we pick up a DTS click. The user fillss in the form and tells us he is due for a renewal and gets some quotes but doesn’t complete the purchase. MSM then find him later on another site and deliver a customised ad about his car insurance renewal. He sees the ad and types Moneysupermarket into his browser search and clicks on an organic click. The user then completes the quote and purchases a car insurance policy.You can see a last click model would simply say organic gets all of the revenue. If 100% of our users did this exact touch point journey, last click would tell us to switch off all PPC and Display as all revenue would go to organicWhen we look at this we know other medias took part in getting the user to convert and it wasn’t all of the work of organic. We know that is not an efficient way to do things and that the PPC click and Display impression had an influence on the users final purchase
- MSM used a last click model for 100% of the revenue share in both digital media and financial reporting. Now, MSM have adopted a new model and we have started applying it across the channels to gain insights and efficiencies. 18 months ago, we started the process of reviewing our attribution model and looked for ways we could gain efficiencies from our marketing spends and also how we can diversify our marketing mix by bringing in new medias which were previously stopped due to poor performance on a last click basis.We reviewed the process and started by looking at the available models (start with arbitrary models)Explain each model briefly but end with all models are arbitrary. Also these models do not take into account non-converting paths – no down weightingShow the meaning of ‘arbitrary’ and explain that picking any of the first models is simply picking one by random choice with no proof that it works for their businessShow programmatic model at the end then outline it is more complex the further up the ordering you go
- MSM faced many challenges and still do when it comes to remodelling the data. The 3 main areas of challenge are getting business acceptance, tagging the site correctly and calibration and data integration.Business acceptance – biggest challenge here is getting people to understand the need for a new approach and how the new approach works. This also relates to replacing a trusted process. What people do not understand, they tend to not trust; which makes sense, we are humans. So getting people involved early and explaining the modelling systems throughout the process helped get people on-board early and once you have a few on-board it helps get others on too. Digital marketing optimisation was always done on last click and it worked. Channels perform and the optimisation efforts to date were all based off last click data so changing this is also a challenge as it effects a large portion of our digital marketing team. Financial reporting is the same, processes in place and it is what budgets and marketing efforts are reported on.Tagging – To implement such a complex tool correctly, you need to have resource available and committed to the project. At MSM, we did have a dedicate development resource which made things easier but there are challenges around getting the dev team to understand the benefits or what the tag is doing. Standardising the reporting parameters was probably our biggest challenge. We had to go into each channel and understand what parameters we currently use in our URLs or pages and create a mapping table. We use a common source code in our internal tracking, but we didn’t find that granular enough to do what we needed, so a new parameter structure was constructed and deployed across the medias. Testing as well needs to be robust and thorough. Even though the tag itself was pretty simple, the different channels, combinations, entry points and user behaviours (like clicking back button or going from secure to non-secure which doesn’t pass through the referring URL) all proved to be trickier than first thought and we had to iteratively test and review the implementation to make sure it passed all tagging requirements.Calibration – so calibration was done on the reporting side to make sure that the data was being reported on correctly and that the collection of data was as accurate as possible. This was an iterative process with many tweaks to the data points. Examples of this is where we didn’t capture the RTB bid ID in the creative tag so we had to add this and let the data collect. Defining click and impression based touch points was something we have customised with our instance of attribution. So paid search is obvious with click based but when it comes to display, we have customised some bits there. Some activity we run is paid on a CPC so providing data based on impression touch points didn’t make sense and we found it was putting the algorithms off a bit as they were optimising to clicks, not impressions. Data linking was an important part of our process. We have a lot of data feeds going in and out of adometry to achieve the goals we have. So going into Adometry we have the adometry tag data, our conversion feed, marin ppc feed, mediamath impression feed, media partners click feed – for our external partners we work with, shopping feeds as they come from a different source, 3 different lookup tables for campaigns-conversion types and audience+ campaigns to pixels and a channel conversion aggregation lookup. Then UI reporting was going through the different reporting dimensions and combinations and making sure we had all of the reports that we needed to be able to do our insights and optimisation.
- - Will go through this off the cuff. Will talk through what we hope to gain from each area and how it will work. Will be quite a lot of talking in each area but will keep it within the time limits.