In Part 1 and Part 2 of this blog posting series, we discussed: Our current viewpoints on marketing attribution and conversion journey analysis in 2017. The selection criteria of the best measurement approach. Introduced our vision on handling marketing attribution and conversion journey analysis. We would like to conclude this [...]
In Part 1 of this blog posting series, we discussed our current viewpoints on marketing attribution and conversion journey analysis in 2017. We concluded on a cliffhanger, and would like to return to our question of which attribution measurement method should we ultimately focus on. As with all difficult questions [...]
Everyone has a marketing attribution problem, and all attribution measurement methods are wrong. We hear that all the time. Like many urban myths, it is founded in truth. Most organizations believe they can do better on attribution. They all understand that there are gaps, for example, missing touchpoint data, multiple identities across devices, arbitrary decisions on weightings for rules, and uncertainty about what actions arise from the results.
Broadly speaking, the holy grail of media measurement is to analyze the impact and business value of all company-generated marketing interactions across the complex customer journey. In this post, our goal is to take a transparent approach in discussing how SAS is building data-driven marketing technology to help customers progress beyond typical attribution methods to make the business case for customer journey optimization.
Being SAS, we advocate an analytic approach to addressing the operational and process-related obstacles that we commonly hear from customers. We want to treat them as two sides of the same coin. The output of attribution analytics informs marketers about what touch points and sequence of activities drive conversions. This leads marketers to make strategic decisions about future investment levels, as well as more tactical decisions about what activities to run. In an ideal world, the results of subsequent actions are fed back into the attribution model to increase not only its explanatory power, but also its predictive abilities, as shown below:
The diagram above shows the main parts of an attribution project. The actual analysis is just part of the process, with upstream and downstream dependencies. But this doesn’t always happen as it should. Consider a standard attribution report. Let us for the moment ignore what technique was used to generate the result and place ourselves in the shoes of the marketer trying to figure out what to do next.
In the graph above, we see the results of an attribution analysis based on a variety of measurement methods. Before answering the question of which method should we focus on, let's do a quick review of rules-based and algorithmic measurement techniques.
Last-touch and first-touch attribution
This type of attribution allocates 100 percent of the credit to either the last or first touch of the customer journey. This approach has genuine weaknesses, and ignores all other interactions with your brand across a multi-touch journey.
Linear attribution arbitrarily allocates an equal credit weight to every interaction along the customer journey. Although slightly better than the last- and first-touch approaches, linear attribution will undercredit and overcredit specific interactions.
Time-decay and position-based attribution
Time-decay attribution arbitrarily biases the channel weighting based on the recency of the channel touches across the customer journey. If you support the concept of recency within RFM analysis, there is some merit to approach. Position-based attribution places more weight on the first and last touches, while providing less value to the interactions in between.
In contrast, algorithmic attribution (sometimes referred to as custom models) assigns data-driven conversion credit across all touch points preceding the conversion, and uses math typically associated with predictive analytics or machine learning to identify where credit is due. It analyzes both converting and non-converting consumer paths across all channels. Most importantly, it uses data to uncover the correlations and success factors within marketing efforts. Here is a video summarizing a customer case study example to help demystify what we mean.
Why doesn’t everyone use algorithmic attribution?
Although many marketers recognize the value and importance of algorithmic attribution, adopting it hasn’t been easy. There are several reasons:
- Much-needed modernization. The volume of data that you can collect is massive and may overwhelm outdated data management and analytical platforms. Especially when you’ll need to integrate multiple data sources. Organizations have a decision to make regarding modernization.
- Scarcity of expertise. Some believe the talent required to unlock the marketing value in data is scarce. However, there are more than 150 universities offering business analytic and data science programs. Talent is flooding into industry. The synergy between analysts and strategically minded marketers is the key to unlock this door.
- Effective use of data. Organizations are rethinking how they collect, analyze and act on important data sources. Are you using all your crucial marketing data? How do you merge website and mobile app visitor data with email and display campaign data? If you accomplish all of this, how do you take prescriptive action between data, analytics and your media delivery end points?
- Getting business buy-in. Algorithmic attribution is often perceived as a black box, which vested interest groups can use as a reason to maintain the status quo.
Returning to our question of which method should we ultimately focus on, the answer is it depends. An attribution report on its own cannot decide this. And it doesn’t even matter if the attribution report is generated using the most sophisticated algorithmic techniques. There are four things that the report won't tell you:
- The elasticities of a single touch point.
- The interdependencies between different touch points.
- Cause and effect and timing dependencies.
- Differences between different groups of customers.
In Part 2 of this blog posting series, we will dive into specific detail within these areas, as well as introduce our vision within SAS Customer Intelligence 360 on handling algorithmic marketing attribution and conversion journey analysis.
I begin this blog post with one goal in mind. I want to raise awareness on the subject of customer and marketing analytics, and why this field is exploding in interest and popularity. Let's begin with a primer for the uninitiated, and lay down some definitions:
Customer Analytics: The processes, technologies, and enablement that give brands the customer insight necessary to provide offers that are anticipated, relevant and timely.
Marketing Analytics: The processes and technologies that enable brands to assess the success of their marketing initiatives by evaluating performance using important business metrics, such as ROI, channel attribution, and overall marketing effectiveness.
If you aren't a fan of textbook definitions, here is a creative alternative:
Still not on board? Here's my perspective on the subject:
Customers are more empowered and connected than ever before, with access to information anywhere, any time – where to shop, what to buy and how much to pay. Brands realize it is increasingly important to predict how customers will behave to respond accordingly. Simply put, the deeper your understanding of customer buying habits and lifestyle preferences, the more accurate your predictions of future buying behaviors will be.
Marketers need to be enabled to benefit from approachable and actionable advanced analytics to make more powerful decisions within today’s complex and interconnected business environments. In my mind, the big picture boils down to one, two or three core enablers, based on your organization's goals and preferences:
Marketing analysts tasked with making sense of customer data, big or small, have to migrate through a complex maze of myths and realities about technology platforms, advanced analytics solutions and, most importantly, the magnitude of customer analytics efforts. On the surface, it appears that customer analytics is a well-entrenched discipline in many organizations, but under the hood, old problems persist around data integration and data quality while new ones emerge around the real-time application of insights and the ability to rein in digital data for customer-based analysis.When I speak with clients, there are two key themes that I continually hear:
- Data is a big challenge. As customer interactions with brands increase and diversify, brands need to integrate data effectively in order to provide the contextual and real-time insights their customers are growing to expect. Haven't you grown tired of saying we spend 80 percent of our time on data management related tasks, and 20 percent on analysis?
- Analytic talent is hard to find. Brands struggle to find individuals with the right analytic skills to meet the challenges they are facing today. Without the talent to unlock actionable insights, modern customer analytics cannot meet its potential. (Given my public affiliation with The George Washington University's M.S. in Business Analytics program, I'd recommend checking it out if you are hunting for quality talent.)
To me, these themes point to a workflow entitled the marketing analytics lifecycle:
With the growing importance of customer analytics in organizations, the ability to extract insight and embed it back into organizational processes is at the forefront of business transformation. However, this requires considerations for where relevant data resides, the ability to reshape it for downstream analytic tasks (predictive modeling vs. reporting), and how to take action on the derived insights. Furthermore, there are the roles of different people within the organization that need to be considered:
- Marketing Analyst/Technologist
- Data Scientist/Statistician
- Marketing Manager
- Supporting IT Team
Customer analysis touches all of these roles, and to enable this audience comprehensively, all aspects of the marketing analytics lifecycle must be supported. To directly address this, I want to to highlight what SAS is doing to help our clients meet these challenges.
Marketing Analytics Lifecycle Stage #1: Integrate and Prepare Data
Customer analytics is highly dependent on the quality of the ingredients we feed into analysis. Now, the digital marketing industry has been taken by storm by the emergence of Digital DMPs, like Oracle BlueKai, Neustar, and Krux, who aim to provide marketers support in programmatic ad buying and selling. Marketers and publishers are learning that harnessing their first-party data; developing single and consistent identities for their consumers across devices and systems, like email and site optimization; and gaining access to second-party data are mission critical. However, the subject of data mining and predictive analytics has largely been ignored by the Digital DMP space. Brands who want to exploit the benefits of advanced analytics have additional considerations to support their data management challenges. The following video highlights how SAS helps manage and prepare data of all sizes, from 1st party customer data to clickstream and IoT, specifically for analytics:
Some of you might be questioning the value of this, so let me offer a different perspective. Over the past few years, I have developed a personal frustration of attending various marketing conferences and repeatedly observing high-level presentations about the potential of analytics. Even more challenging has been the recent trend of companies presenting magical (i.e., "easy-button") black-box marketing cloud solutions that address every imaginable analytical problem; in my opinion, high-quality advanced analytics has not reached a point of commoditization, and remains a point of competitive differentiation. Do not be mislead by sleight-of-hand magic!
Marketing Analytics Lifecycle Stage #2 & #4: Explore Data, Develop Models, and Deploy
What types of marketing challenges are you attempting to solve with customer analytics? Srividya Sridharan and Brandon Purcell are two leading researchers in the space of customer insights, and recently released a report entitled How Analytics Drives Customer Life-Cycle Management recommending the deployment of various analytical techniques across the customer life cycle to grow existing customer relationships and provide insight into future behavior. Highly recommended reading! Let's review some of the most common problems (or opportunities) we view at SAS with our clients.
Within each of the categories, a myriad of analytic techniques can be executed to assist and improve your brand's abilities to address them. The following video is a demonstration of how I used SAS Visual Statistics and Logistic Regression analysis to understand drivers by marketing channel of business conversions on a website or mobile app. The benefit of understanding these data-driven drivers is to influence downstream marketing personalization and acquisition campaigns. In addition, capabilities related to group-by modeling, deployment scoring and model comparison with other algorithmic approaches are highlighted.
Big digital data, scalable predictive analytics, visualization, approachability, and actionability. Stay thirsty my friends, because it is our clients who are expressing their needs, and SAS is stepping up to meet their challenges!
If you would like to learn more on how we address other marketing and customer analytic problems, please click on any of the following topics:
With that said, we have one final stage of the lifecycle to review.
Marketing Analytics Lifecycle Stage #3: Explain Results and Share Insights
An individual's ability to communicate clearly, succinctly and in the appropriate vernacular when presenting analytical recommendations to a marketing organization is extremely important when focused on driving change with data-driven methods. I recently wrote a blog post on this topic entitled Translating Predictive Marketing Analytics, and if you're tired of reading, here's another video - this time focused on explaining the results of analytical exercises in easy-to-consume business language.
As I close this blog post, I want to leave you with a few thoughts. For your brand's customers, technology is transparent, user-enabling, and disintermediating. The journey they embark with you on is fractured and takes place across channels, devices, and points in time. The question becomes – are you prepared for moments of truth as they occur across these channels over time? Customer analytics represents the opportunity to optimize every consumer experience, and revisiting a point I made earlier, the deeper your understanding of customer buying habits and lifestyle preferences, the more accurate your predictions of future buying behaviors will be.
The analytics of customer intelligence and why it matters was published on Customer Analytics.
Broadly speaking, the holy grail of digital media measurement is to analyze the impact and business value of all company-generated marketing interactions across the complex customer journey. In this blog post, my goal is to take a transparent approach in discussing how data-driven marketers can progress past rules-based attribution methods, and make the business case for leveraging algorithmic applications.
Let's begin with a video example that pokes humor at the common problems related to multi-channel marketing attribution. The business challenge is that everybody believes they should have more marketing budget because their tactics are supposedly responsible for driving sales revenue. The video suggests that challenges arise rapidly when supporting analysis to justify these claims isn't sound. While the video is fictional, the problems are very real. With that said, there are three main drivers to getting digital attribution analysis right:
- Allocating credit across marketing channels more accurately
- Providing invaluable insights to channel interactions
- Empowering marketers to spend more wisely in future media activity
Have you ever given thought to the many ways that a customer can find your brand's digital properties? Organic results on a search engine, display media campaigns, social media links, re-targeting on external sites, and the list goes on in today's fragmented digital ecosystem. One thing is for certain - consumer digital journeys are far from linear. They can occur across multiple platforms, devices and sessions, and organizations are challenged with gaining an accurate understanding of how:
- External referral clicks (or hits) are mapped to channels and visits
- Visits are mapped to anonymous visitors
- Anonymous multi-channel visitor journeys are mapped to identifiable individuals across different browsers and devices
With careful consideration towards the areas of data management, data integration, and data quality, analyzing customer-centric (or visitor-centric) channel activity on their journeys to making a purchase with your brand can have immense benefits. Ultimately, marketers desire to apply a percentage value that can be attached to each channel's contribution to the purchasing event (or revenue). This is critical, as it allows the organization to determine how important each channel was in the customer journey, and subsequently, influence how future media spend should be allocated.
Sounds fairly easy, right? Well, as Avinash Kaushik (digital analytics thought leader at Google) stated in his influential blog post on multi-channel attribution modeling:
"There are few things more complicated in analytics
(all analytics, big data and huge data!)
than multi-channel attribution modeling."
The question is...why is it challenging? Avinash's blog post was written in the summer of 2013, and I strongly believe 2.5 years later we are living in a game-changing moment within digital analytics. Marketers are being enabled by technology companies with approachable and self-service analytic capabilities, and this trend directly impacts our ability to improve our approaches to problems like attribution analysis. However, rules-based methods of attribution channel weighting continue to be far more popular in the industry to date, which contradicts the recent analytic approachability trend. Before we dive into algorithmic attribution, let's review the family of approaches commonly applied in rules-based attribution:
Last Touch & First Touch Attribution
Allocates 100% of the credit to either the last or first touch of the customer journey. This approach has genuine weaknesses, and ignores all other interactions with your brand across a multi-touch journey. It is stunning, in my opinion, that web/digital analytic technologies have traditionally defaulted to this approach in enabling their users to perform attribution analysis. The reason for this was last/first touch attribution was easy, and could claim ownership of the converting visit (although that is only partially true). Thankfully, times are changing for the better, and this rudimentary approach has proven ineffective, guiding marketers (for the sake of job security) to try more intelligent methods.
Arbitrarily allocates an equal credit weighting to every interaction along the customer journey. Although slightly better than the last and first touch approaches, linear attribution will under-credit and over-credit specific interactions. In a nutshell, it over-simplifies the complex customer journey with your brand.
Time Decay & Position Based Attribution
Time decay attribution arbitrarily biases the channel weighting based on the recency of the channel touches across the customer journey. If you are bought into the concept of recency within RFM analysis, there is some merit to approach, but only when comparing with other rules-based methods. Position based attribution is another example of arbitrary biasing, but this time we place higher weights on the first and last touches, and provide less value to the interactions in-between. As Gary Angel (partner & principal of the digital analytics center of excellence at Ernst & Young) stated in his recent blog posting:
"There’s really no reason to believe that any single weighting system somehow captures accurately the right credit for any given sequence of campaigns and there’s every reason to think that the credit should vary depending on the order, time and nature of the individual campaigns."
Although there are some other minor variants to the rules-based method approaches, highlighted above are the majority of approaches that the digital marketing industry commonly uses. As a principal solutions architect at SAS, I have the opportunity to meet with clients across multiple industries to discuss and assist in solving their marketing challenges. When it comes to attribution, here is a summary of what I have seen clients doing in 2015:
Buying Web/Digital Analytics Software That Includes Rules-Based Attribution Measurement
This is typically when an organization invests in a premium (or more expensive) software package from their web/digital analytics technology partner, which includes out-of-the-box attribution capabilities. Here is a video example discussing how one of the most popular web analytic platforms in the world includes capabilities for various methods of rules-based attribution.
Two takeaways from this video that I love are:
- Comparing the attribution problem to soccer (or futbol), and accepting that we cannot give 100% credit to the goal scorer. There is a build up of passes to set up the goal (i.e. purchase), and each of these events (i.e. marketing channel touches) contribute value. Even though names like Messi, Ronaldo, and Neymar are commonly known in soccer, ignoring names like Iniesta, James Rodríguez, or Schweinsteiger would be a travesty.
- Focus on the journey, and performing visitor-centric analysis as compared to visit-centric analysis
The difficulty I possess with the video is leveraging the term "data-driven attribution" when rules-based methods are the only approaches highlighted. In my opinion, we are only grazing the surface of what is possible. Algorithmic attribution, on the other hand, assigns data-driven conversion credit across all touch points preceding the conversion, using data science to dictate where credit is due. It begins at the event level and analyzes both converting and non-converting paths across all channels. Most importantly, it allows the data to point out the hidden correlations and insights within marketing efforts.
Have you ever wondered why web/digital analytic software doesn't include data mining and predictive analytic capabilities? It has to do with how digital data is collected, aggregated, and prepared for the downstream analysis use case.
Web/digital analytics has always had a BIG data challenge to cope with since it's inception in the mid 1990's, and when the use case for users is to run historical summary reports and visual dashboards, clickstream data is collected and normalized in a structured format as shown in the schematic to the right.
This format does a very nice job of organizing clickstream data in such a way that we go from big data to small, relevant data for reporting. However, this approach has limited analytical value when it comes to attribution analysis, and digital marketers are only offered rules-based methods and capabilities.
Data mining and predictive analytics for algorithmic attribution require a different digital data collection methodology that summarizes clickstream data to look like this:
Ultimately, the data is collected and prepared to summarize all click activity across a customer's digital journey in one table row. The data table view shifts from being tall and thin, to short and wide. The more attributes or predictors an analyst adds, the wider the table gets. This concept is referred to as preparing data for the analytic base table (or input modeling table). This is the best practice for advanced algorithms to be used to fit the data. Shhhhhhhh! We'll keep that insightful secret between us.
More importantly, don't let this intimidate you if you're new to these concepts. It boils down to the ability to reshape granular, HIT-level digital data for the best practices associated with data mining. Can it be done? Absolutely, and algorithmic digital attribution is a prime example of big data analytics for modern marketing. The question I challenge my clients with is to consider the arbitrary (or subjective) nature of rules-based methods, and associated limitations. Although they are easy to apply and understand, how do you know you aren't leaving opportunity on the table? This leads me to the next recent trend of what I observe clients doing.
Buying Algorithmic Attribution Consultative Services
The best way to kick this section off is to share how 3rd party marketing attribution vendors introduce themselves. Here are two video examples to consider:
How do you feel after watching these videos? If you are raising your hands to the sky thanking the higher forces of the marketing universe, I completely understand. Many of my clients describe their marketing organization's culture as unprepared for algorithmic attribution, ranging from lack of subject-matter expertise, big data hurdles, or employee analytical skills. There can be tremendous value in selecting an external partner to handle analysis and actionability, and accelerate your ability to make better digital media investment decisions. 3rd party attribution vendors have the domain knowledge, technology, and a track record, right?
In addition, this segment of my clientbase seem less concerned with transparently understanding how to analytically arrive to their decision strategy, as long as the financial results of their attribution vendor's services look good compared to baseline KPIs. Although these vendors will never reveal their analytical secret sauce (i.e. intellectual property), digital marketing is an overwhelming ecosystem, and who has the time to discuss analytical model diagnostics, misclassification rates, ROC plots, lift curves, and that silly confusion matrix...
That's one trend I see. The other trend is when a marketing organization is analytically mature, and this leads me to the next section.
Building Algorithmic Attribution Models In-House
Do you want to perform algorithmic attribution analysis yourself and maintain a transparent (white-box) understanding of how your analytic approaches are influencing your digital media strategies? If you answered yes, I believe the best way to take you on this journey is through a case study. I'm a strong advocate of this approach, and believe this is a cutting edge application of marketing analytics:
Case Study: Hospitality Industry
Unable to scale digital analytics for algorithmic attribution to measure drivers of conversion and advertising effectiveness.
Business needs to understand:
- Drivers of resort hotel bookings online,
- Marketing channel attribution to bookings with statistical validation,
- Insights to allocate future digital media ad spend.
- Clickstream and display ad-serving data very large in size,
- Rules-based attribution methods largely inaccurate.
- 90 day est. file size for extracted Adobe HIT data: 3.0 TB,
- 90 day est. file size for extracted Google DoubleClick (display media) data: 4.0 TB,
- Analytical data prep, modeling, and scoring workflow must be capable of processing on Hadoop platform (i.e. big data lake).
Digital Data Preparation Summary:
In this exercise, the hospitality brand was extracting raw data from their relationships with these digital marketing technologies into an internal Hadoop data landing zone. Their goal is to start stitching various digital marketing data sources together to gain a more complete view of how consumers interact with their brand. Analytically speaking, this is very exciting because we can gain a better understanding of the value of channel touches, onsite click activity, media impressions, viewability, creative content, ad formats, and other factors that we do not have comprehensive visibility into with traditional web/digital analytics.
One valuable insight I would like to share is if you have never worked with raw clickstream data or display media data before, it would be advantageous to obtain a data dictionary and channel processing documentation from your digital marketing solution vendor(s). For example, every website that has installed web analytic tracking has an array of unique goals, interactions, segments, and other attributes that were configured for that specific business model. Analysts will not understand what eVar 47 is without a translation document. Guess what? eVar 47 is going to have a completely different definition from Brand #1 to Brand #2 to Brand #3. Sorry - there is no easy button for this.
Your analysts will thank you sincerely for taking these steps, and it will improve their ability to succeed. Since this is a SAS Blog, I imagine many of you will want to understand how we worked with the raw digital data in this case study.
1. Data access: SAS Data Loader for Hadoop
2. Visual data exploration to assess data quality issues: SAS Visual Analytics
3. Reshaping the data for analytic modeling (i.e. recoding, transformations, joins, summarization, transpositions): SAS Enterprise Guide
Analytic Model Development Summary:
Now we move on to the fun and sexy step of the process...
Our methodology of approach was to address the digital attribution challenge as a predictive modeling problem. This involves three key goals:
- Produce a predictive model that computes the probability of conversion given a set of visitor journey predictors.
- Determine the incremental lift in probability of conversion for each channel in a visitor journey, and use this to compute attribution.
- Provide insight into the relationship between conversion and the predictor variables (marketing channels, onsite click activity, digital demographics, etc.).
To drill into the details a bit further, I'll break this down in three steps through a hypothetical example:
Step 1 - Create Predictors and Target
- Convert visitor journeys into a table with rows of channel impression counts and conversion information.
- This data is used to train (and validate) the predictive model.
Step 2 - Compute Incremental Lift
- Use the predictive model to compute the incremental lift in probability of conversion by adding one channel at a time in each visitor journey.
- Example for one visitor journey: Display > Email > Search > Display > $
Step 3 - Compute Attribution
- Process all conversion journeys & accumulate channel credit to compute channel attribution.
Analytic Modeling Results:
Now we can get to the fun and sexy stuff...
This analysis included 17 marketing channels, over 1,000 predictors, ~24,000,000 digital visitor journeys, and a rare conversion event occurrence of less than 1%. Oh my!
Now let me ask you a question - do you believe there is one piece of math that will solve all of our attribution challenges?
Absolutely not. The game of digital media investment is all about precision, precision, PRECISION! To maximize precision, in the field of data mining, we employ the use of champion-challenger modeling. Simply put, we throw a bunch of math at the data, and the algorithm that does the best job of fitting the data (i.e. minimizing error) is selected.
Scaling to large digital data with champion-challenger modeling is not trivial, but through the modernization of analytical processing in recent years, the time has arrived to dream bigger. Random forests, neural networks, regressions, decision trees, support vector machines, and more are all fair game, which means we can produce accurate assessments of marketing channel importance using the power of advanced analytics. Here is a snapshot of our modeling results within this project:
For those of you unfamiliar with misclassification rates, it's nothing more than a metric to summarize how many mistakes our analytical model is making. The lower the value, the better, and in this exercise, the random forest algorithm did the best job in analyzing and fitting our attribution data. There's your champion!
Next, let's share a lift chart visualization to help us get our heads around what we've accomplished here:
The beautiful takeaway in this example is we have identified an attractive segment (top decile with highest probability scores) that is 8.5 times more likely to convert as compared to randomly targeting the entire marketable population. Secondly, if we alter that segment view to the top two deciles, they are 4.7 times more likely to convert.
BOOM! This is awesome because we can now profile these segments, and proactively hunt for look-a-likes. In addition, be imaginative in how you might use these segments in other forms of digital marketing activities. For example, A/B testing in web personalization efforts.
But what about the marketing channels themselves? Which ones ended up being more (or less) important)? Well, here is a great visualization for channel weighting interpretation:
The odds ratio plot clearly highlights these insights in a non-technical manner. Channels above the horizontal line have a positive impact in increasing the probability of a visitor conversion, and channels below the line have a negative impact. For those of you who are unfamiliar with odds ratio plots, they serve as an ingredient to feed into a marketing dashboard that can explain market channel attribution performance.
So how accurate were we? Was this model any good?
True positive rate simply means how accurate was our ability to correctly predict conversions. True negative rate summarizes our ability to accurately predict non-conversions. Given that our original event of conversion behavior was below 1% across a three month time window, our ability to predict conversions based on the modeling insights is a MASSIVE improvement (86.67 times more accurate) versus the mass marketing approach (or pure random targeting). Even though there is still room for improvement, these are very promising results.
To deploy or activate on these insights, this will vary based on your organization's approach to taking action. It may be the scoring of an internal database, or it might be passing the model score code to your digital data management platform to improve their ability to deliver media more intelligently. There are a number of use cases for marketing activation, but by doing this analysis in-house, you will have flexibility to conform to a variety of downstream process options.
Again, I suspect many of you will want to understand how we analytically modeled the digital data in this case study.
- Algorithmic modeling: SAS Enterprise Miner (High Performance Data Mining)
- Analytic scoring: SAS Scoring Accelerator for Hadoop
- Marketing channel performance dashboarding: SAS Visual Analytics
Why Aren’t More Organizations Doing This?
From my experiences in 2015, I believe there are three reasons:
- Large data volumes require the use of modern big data platforms
- The talent required to unlock the marketing value in that data is scarce, but the climate is improving - if you're searching for talent, please consider the future analysts, data miners, and data scientists we are training at the GWU MSBA program in Washington DC
- Organizations are rethinking how they collect, analyze, and take action on important digital data sources
If you made it this far in the blog posting, I applaud your commitment, desire, and time sacrifice to go on this written journey with me. We discussed the current landscape of digital marketing attribution, from methods of approach to providing a real case study in support of making the justification for algorithmic attribution (i.e. it's not a mythical creature from another universe). Digital data mining is on the rise, becoming more approachable, and will provide organizations competitive advantage within their industries for years to come.
Marketing analytics matter!
Making the case for algorithmic digital attribution was published on Customer Analytics.