With Analytics in Your Rear View, You’ll Never See Your Future

Given the tectonic shifts in the marketing landscape, a siloed ROI approach that is only rear-view mirror focused can lead to poor decision making. Marketers cannot afford to stifle their innovation by employing an “it worked in the past” mindset.


Marketing professionals need, more than ever, to showcase the value of their expenditures.  Even though we are seeing hints of a fragile economic recovery accountability will still be a driving factor in decision making.  With ROI often the ultimate metric determining what stays and goes in the marketing department, marketers need to be sure that their ROI methodology is sound not only in its ability to evaluate the past, but also in its power to predict the future.


Given the tectonic shifts in the marketing landscape an ROI approach that is siloed and rear-facing can lead to poor decision making.   An inability to compare ROI’s across the marketing enterprise or ROI methodologies that can’t be used to forecast  typically provide no actionable data on which to base future plans. We need to evolve our ROI practices so marketers can understand the tradeoffs between investments in different channels and can use these models as a guiding light to war game various plans to predict future results and returns.
In 2000, our ROI measurement practices were limited by technology and marketers’ varied understanding of analytics. Oftentimes, marketers would use different ROI methodologies for each of their channels.  This prevented them from accurately evaluating and comparing “returns” from one channel to another in an apples-to-apples fashion.

Another problem with ROI in the early part of this decade was that digital marketers  gave too much credit to the last online activity a customer performed before they purchased, a strategy known as the “last click wins” model. While deeply flawed, there was not a readily available toolset that allowed for simultaneous, measures of multiple touch points in both the online and offline space. For instance, here’s a typical consumer’s purchase path, ending with the opening of a new account – a very tangible metric.  

1. Sees TV Spot
2. Sees TV Spot Again
3. Sees Online Banner Ad
4. Clicks on Natural Search
5. Clicks on Paid Search
6. Sees Print Ad
7. Receives Brand Email
8. Visits Brand Site
9. Visits Brand Site
10. Receives Brand Email
11. Clicks on Banner Ad
12. Brand Website New Account

In 2000, here’s an oversimplification of how many marketers would have applied ROI values:

1. Sees TV Spot – $0
2. Sees TV Spot Again – $0
3. Sees Online Banner Ad – $0
4. Clicks on Natural Search – $0
5. Clicks on Paid Search – $0
6. Sees Print Ad – $0
7. Receives Brand Email – $0
8. Visits Brand Site – $0
9. Visits Brand Site – $0
10. Receives Brand Email – $0
11. Clicks on Banner Ad – $500
12. Opens New Account – $500


It’s easy to ask, “What were we thinking?” in retrospect. But the fact of the matter was that marketers had a whole new digital world thrown at them and they grasped onto whatever metrics they could that made sense given their limited knowledge.  At least they were measuring something and taking the first baby step in trying to be more accountable for their digital investments.  The fundamental error can be found in the basic ROI calculation formula that was used at the time:

ROI = Value of Sales from Ads/Ad Costs
= (Impressions * Click Rate * Sales Conversion Rate * Value of sales)/Ad Costs

Our measurement approach and attribution values did not remotely reflect the communication ecosystem that the consumer experienced. It gave all the credit to the last touch point and only allowed us to measure the performance of this single touch point against itself over time.  Applying this information to future campaigns often lead to flawed decision making.

Our analytics capabilities have long surpassed this humble beginning.  Now, however, marketers have a much surer footing in analytics which dispels many of our earlier challenges and officially renders the last click, rear-view mirror mindset irrelevant.

Going forward, analysts need to adopt new holistic analytics models that accurately compare investments, returns and credit across multiple channels and touch points. Analysts also need to become data forecasters — not historians. Committing to an ROI before a campaign goes live adds teeth to the optimization process and empowers companies with the ability to change the initial strategy in mid-campaign to alter the outcomes. This fosters better integration between the analytics team and the rest of the marketing organization, giving the analysts skin in the game and moving them from “observers” to “active participants.”

Marketers can do this by applying the following methodology to their ROI calculations:


Probability of purchase as a function of the Baseline  + Exposure to TV + Exposure to Print +Exposure to Online Advertising + Exposure to CRM + Exposure to Paid Search + Interaction Between Media Exposures)

Amount Purchased as a function of Baseline Sales Level + Exposure to TV + Exposure to Print +Exposure to Online Advertising + Exposure to CRM + Exposure to Paid Search + Interaction Between Media Exposures

ROI = (Increase in Sales Due to the Increases Probability of Purchase with Online Advertising Exposure + Increase in Sales Due to the Increase in Amount Purchased with Online Advertising Exposure)/Cost of Exposures

Note:  The analytic treatment above is intentionally straight forward.  In practice the relationship between media and sales is often quite complex and concepts of nonlinearity, advertizing decay, saturation points, volume decomposition, and high order media interactions have kept (and will keep) industry analysts gainfully employed for years.

This ROI evolution is a much closer approximation for how consumers interact with your brands and more accurately shares the credit among multiple touch points. While it is no small undertaking to build this type of analytics eco-system, it is the foundation for a surprisingly accurate forecasting tool. Let’s see how it affects the ROI valuations of the consumer purchase path we saw earlier:


1. Sees TV Spot – $50
2. Sees TV Spot Again – $30
3. Sees Online Banner Ad – $60
4. Clicks on Natural Search – $40
5. Clicks on Paid Search – $20
6. Sees Print Ad – $50
7. Receives Brand Email – $45
8. Visits Brand Site – $50
9. Visits Brand Site – $75
10. Receives Brand Email – $35
11. Clicks on Banner Ad – $45
12. Opens New Account – $500

As you can see, this model gives us a much different view of our multichannel campaign’s touch points. Marketers can look for changes throughout the ecosystem when they ramp up or down a channel to optimize their campaigns.  They can also build out sophisticated attribution models that shed light on consumers’ use of push versus pull media. Over time, benchmarks for an overall campaign and each discrete element needs to be established and forecasts for overall performance committed to before the campaign is launched.

At Organic, we recently completed a comprehensive attribute model study of the relationship between display and search media for a cross section of durable goods brands and products.  We analyzed data from a 75-day period that included Search, Display and Brand site performance metrics resulting in a database of over 1 billion records including as many as 30 different touch points for a single consumer.

Some of our findings included:
* More than 50% of success activities driven from paid search occur when a user sees a display ad prior to paid search.  We saw lifts in key brand site activities ranging from 13% to 42% when a user saw an ad prior to clicking on paid search. Success activities are defined as the subset of brand site activities that have the highest correlation with retail sales.

* Using a “last click wins” approach would have significantly undervalued the display media ROI  and overstated the paid search ROI.


Media Type         Last Click              Shared Values                 ROI
Display                    Yes                                No                          $15.31
Paid Search               Yes                                No                          $19.72

Display                     No                                 Yes                         $23.51
Paid Search                No                                 Yes                         $8.78

* 50% of the highest value brand site conversations occurred within a range of 1 – 4 display impressions while the other 50% occurred at 5+ impressions with conversion significantly falling off after 10 impressions.  This provides a baseline for quantifying effective reach and frequency levels for different messaging – but that is a discussion for another time.

* Over 95% of brand site leads from View Thru are occurring within 6-7 days of an impression.  However, a consumer who has clicked on the ad, and appears more engaged,  may actually have the mindset to shop and research for a longer duration of time, between 12 – 14 days.

* Compared to a baseline of “no prior display exposure,” paid search clicks are 1.05 times more likely to drive leads when a display ad is viewed the same day and are 1.75 times more likely to drive a lead when the display ad is viewed the previous day.


Building the analytics ecosystem necessary to support these calculations typically requires a rich behavioral data set combined with custom survey data.  The model can also be layered with more sophisticated measures such as CLTV (customer lifetime value), net present value of campaign investments and the inclusion of predictive macroeconomic variables such as Housing Starts or Unemployment.

While it is a significant undertaking, attribute modeling is clearly the preferred method for calculating an ROI for both digital-only and cross-channel campaigns; otherwise, resources allocation will be flawed and results will suffer.  This is the foundation of a good ROI model.  Adding macro economic factors to the model is the next step in moving from rear view looking to forecasting future results and war gaming various scenarios.  Shifting analytics from rear-facing to predictive is an important part of being a digitally-savvy marketer. In my next post, I’ll begin providing you with the framework you need to build your own ROI crystal ball to accurately predict results, deliver successful campaigns and and salvage a failing one.

Steve Kerho is the SVP, Analytics, Marketing Optimization at Organic (

About the author

Steve has over 24 years of agency and client side experience leading CRM, interactive marketing, sales and media practices for brands including Nissan, Bank of America, Visa and Procter & Gamble, to name a few. In 2011, he was named an Adweek Media-All Star for his innovative work measuring earned and owned media content and developing predictive analytics models to optimize digital ecosystems