artificial intelligence

11月 212017
 

I recently spent two days with an innovative communications customer explaining exactly what SAS analytics can do to help them take their advertising platform to a whole new level. Media meets data resulting in addressable advertising. SAS would essentially be the brain behind all their advertising decisions, helping them ingest [...]

Analytics = brilliance was published on SAS Voices by Suzanne Clayton

10月 042017
 

Stories have been flooding the market lately about artificial intelligence (AI) causing the next world war. Based originally on comments made by Elon Musk (see below), many others are jumping in to share similar fears. China, Russia, soon all countries w strong computer science. Competition for AI superiority at national level [...]

Will AI cause the next major war? was published on SAS Voices by Mary Beth Ainsworth

9月 262017
 

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 [...]

Algorithmic marketing attribution and conversion journey analysis [Part 3] was published on Customer Intelligence Blog.

9月 192017
 

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 [...]

Algorithmic marketing attribution and conversion journey analysis [Part 2] was published on Customer Intelligence Blog.

9月 152017
 

Editor's note: Tiffany Carpenter, Head of Customer Intelligence, SAS UK & Ireland sizes up the benefits of the two technologies and offers up a solution to businesses wanting the best of both. With constant pressure on profit margins, organisations need to strike a balance between improving cost efficiencies and customer [...]

Measuring up: robotic process automation versus real-time decision making was published on Customer Intelligence Blog.

9月 142017
 

Editor's note: This blog post was authored by Malcolm Lightbody (SAS Customer Intelligence Product Management) and Suneel Grover (SAS Principal Solutions Architect).

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


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.

Algorithmic attribution

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:

  1. The elasticities of a single touch point.
  2. The interdependencies between different touch points.
  3. Cause and effect and timing dependencies.
  4. 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.

Algorithmic marketing attribution and conversion journey analysis [Part 1] was published on Customer Intelligence Blog.

7月 252017
 

In the first half of 2017 and in my only domain – which is marketing – an announcement set the tone for a major change. How not to be stunned when “Coca-Cola ditches global CMO role in leadership shake-up”? If there is only one product you can find anywhere on [...]

With AI, marketing is needed but marketers might not be was published on SAS Voices by Christine Coudert

7月 132017
 

Artificial intelligence promises to transform society on the scale of the industrial, technical, and digital revolutions before it. Machines that can sense, reason and act will accelerate solutions to large-scale problems in myriad of fields, including science, finance, medicine and education, augmenting human capability and helping us to go further, [...]

5 questions about artificial intelligence with Intel's Pat Richards was published on SAS Voices by Scott Batchelor

7月 112017
 

Everyone is talking about artificial intelligence (AI). In fact, many SAS customers who've been using our analytics capabilities for years or even decades are asking: What can we do with AI? What exactly is AI from a software perspective? How can we infuse cognitive computing into our customer interactions and on the customer [...]

Diary of an AI webinar was published on SAS Voices by Suzanne Clayton

7月 012017
 

We live in exciting times. Our relationships with machines, objects and things are quickly changing. Since mankind lived in caves, we have pushed our will into passive tools with our hands and our voices. Our mice and our keyboards do exactly as we tell them to, and devices like the [...]

Artificial intelligence: Separating the reality from the hype was published on SAS Voices by Oliver Schabenberger