1月 112017

Omnichannel shoppers have been disrupting retailers for years, and its likely to top the industry’s agenda of challenges for years to come. But optimization, an omnichannel analytics technology, can help harness the positives of omnichannel retailing and minimize showrooming. Consider this everyday retail dilemma: E-commerce sales are growing, but in-store […]

Retailers use optimization to improve in-store fulfillment and keep customers satisfied was published on SAS Voices.

1月 112017

One of the most powerful sales tools is often something that you can’t foresee or control. Even though customers read papers, visit websites and talk with a salesperson, another factor can make all the difference – a referral from a friend or coworker.

Think about the way that sites like Google, Yelp and others have changed the way consumers make everyday decisions, such asadvocacy choosing restaurants. You can go to the restaurant nearest you or one you’ve visited before. Or, you can try something new by looking at your smartphone to see which dining spot has the highest ratings or the best reviews. Why? People show a preference for the personal experience of those in their networks.

For business-to-business software companies like SAS, the impact of customer advocacy is critical. These influencers can set the tone and provide a consistent positive influence throughout the customer journey. Unfortunately, this type of advocacy is tough to measure and hard to predict.

The challenge: Acquisition and retention

Although a customer may be a single record in your database, she doesn’t exist in a vacuum. Each contact has a connection to others within her business or the industry. Understanding and fostering good relationships can have a huge effect on your retention and loyalty efforts.

During our effort to map a modern customer journey, the SAS marketing team focused on different phases of this cycle. The customer journey contained these phases:

  • Acquisition – which includes need, research, decide and buy.
  • Retention – which includes adopt, use and recommend.

On the retention side, the team knew from anecdotal evidence that some SAS customers were advocates of the technology and for the company overall. In fact, several SAS regional offices and divisions had data confirming the idea that finding and rewarding high-value customers led to big returns. What was lacking was an overarching program for getting customers to advocate for SAS technology.

For a larger effort, the team assessed the customer behavior data, examining those who attended events, provided feedback on surveys, sent ideas to R&D, and generally stayed engaged with the company. From a revenue standpoint, those people were often the ones advocating for the use of new SAS technologies or the expansion of existing deployments.

What was less understood was the reach of these influencers and how their activities affected others. With that information, SAS could identify more advocates and nurture that behavior.

The approach: Identify advocates by scoring BFF behaviors

The SAS marketing team members started by digging into the data that they had on customers. They first identified a segment of the top accounts that contained more than 20,000 individual contacts and the team began to examine the behaviors exhibited by that group including:

  • Live event attendance.
  • Website traffic.
  • Technical support queries.
  • Customer satisfaction survey data.
  • Customer reference activity.
  • Webinar attendance.
  • White paper downloads.

This information provided a better understanding of the range of activities that customers undertake. However, simply cataloging the behaviors wasn’t enough. The team applied a scoring model for different types of interactions. This allowed the team to weight certain activities, helping to further identify which customers were the best advocates—“BFFs” (best friends forever) as the marketing team began to call them.

The results: Advocacy campaigns that matter

SAS marketing used the information to create a model that is the foundation for customer-focused data exploration. The initial effort helped shed light on how influential advocates can shape retention and additional sales. As a result, sales and marketing worked together to highlight BFFs within key accounts in an ongoing effort to foster better relationships with those key individuals.

Initiatives to locate and encourage advocates used the model to identify the likely candidates within customer organizations. The team then designed campaigns and outreach efforts to give these advocates the tools to foster and expand their influence.

The marketing team now focuses on advocacy campaigns that target potential BFFs. The goal is to build more SAS advocacy during the recommend phase of the customer journey.

Acquisition and retention campaigns begin by doing advanced segmentation in SAS Marketing Automation. Campaign workflows are created that are backed by analytics, ensuring that communications to customers are appropriate and relevant. Through the collection of both contact and response history data, attribution can be performed in SAS Visual Analytics that allows marketers to see correlations and cross-promotion opportunities.

Interested in learning how to leverage SAS Marketing Automation techniques for advanced segmentation? Explore our SAS Marketing Automation: Designing and Executing Outbound Marketing Campaigns and Customer Segmentation Using SAS Enterprise Miner course offerings.


Editor’s note: This post is part of a series excerpted from Adele Sweetwood’s book, The Analytical Marketer: How to Transform Your Marketing Organization. Each post is a real-world case study of how to improve your customers’ experience and optimize your marketing campaigns.

tags: Adele Sweetwood, customer advocacy, customer analytics, customer experience, customer journey, marketing automation, sas enterprise miner, sas marketing automation, segmentation, The Analytical Marketer

Customer advocates: Finding your customers’ BFFs was published on Customer Intelligence.

1月 112017


1. Don’t be afraid to launch a product without machine learning.


2. First, design and implement metrics.


3. Choose machine learning over a complex heuristic.


4. Keep the first model simple and get the infrastructure right.


5. Test the infrastructure independently from the machine learning.


6. Be careful about dropped data when copying pipelines.


7. Turn heuristics into features, or handle them externally.


8. Know the freshness requirements of your system.


9. Detect problems before exporting models.

10. Watch for silent failures.

11. Give feature column owners and documentation.


12. Don’t overthink which objective you choose to directly optimize.


13. Choose a simple, observable and attributable metric for your first objective.


14. Starting with an interpretable model makes debugging easier.

15. Separate Spam Filtering and Quality Ranking in a Policy Layer.

16. Plan to launch and iterate.


17. Start with directly observed and reported features as opposed to learned features.

18. Explore with features of content that generalize across contexts.

19. Use very specific features when you can.

20. Combine and modify existing features to create new features in human­ understandable ways.

21. The number of feature weights you can learn in a linear model is roughly proportional to the amount of data you have.

22. Clean up features you are no longer using.

23. You are not a typical end user.

24. Measure the delta between models.

25. When choosing models, utilitarian performance trumps predictive power.

26. Look for patterns in the measured errors, and create new features.

27.Try to quantify observed undesirable behavior.

28. Be aware that identical short­-term behavior does not imply identical long­-term behavior.

29. The best way to make sure that you train like you serve is to save the set of features used at serving time, and then pipe those features to a log to use them at training time.

30.Importance weight sampled data, don’t arbitrarily drop it!

31. Beware that if you join data from a table at training and serving time, the data in the table may change.

32. Re­use code between your training pipeline and your serving pipeline whenever possible.

33. If you produce a model based on the data until January 5th, test the model on the data from January 6th and after.

34. In binary classification for filtering (such as spam detection or determining interesting e­mails), make small short­ term sacrifices in performance for very clean data.

35. Beware of the inherent skew in ranking problems.

36.Avoid feedback loops with positional features.

37. Measure Training/Serving Skew.

38. Don’t waste time on new features if unaligned objectives have become the issue.

39. Launch decisions are a proxy for long­term product goals.

40. Keep ensembles simple.

41. When performance plateaus, look for qualitatively new sources of information to add rather than refining existing signals.

42. Don’t expect diversity, personalization, or relevance to be as correlated with popularity as you think they are.

43. Your friends tend to be the same across different products. Your interests tend not to be.

1月 112017

Last week I wrote about the 10 most popular articles from The DO Loop in 2016. The popular articles tend to be about elementary topics that appeal to a wide range of SAS programmers. Today I present an "editor's choice" list of technical articles that describe more advanced statistical methods in SAS.

I've grouped the articles into three categories: statistical graphics and visualization, statistical computations, and matrix computations. If you are a SAS statistical programmer, these articles deserve a second look.

Ten posts from The DO Loop that deserve a second look #SASTip
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Statistical graphics and visualization

An effect plot

SAS ODS graphics provides an easy way to create standard graphs for data analysis. The graphs in this list are more sophisticated:

Statistical computations

A nearest neighbor plot

These article show helpful statistical techniques that you should know about:

Matrix computations


The SAS DATA step is awesome. For many programming tasks, it is an efficient and effective tool. However, advanced analytical algorithms and multivariate statistics often require matrix-vector computations, which means programming in the SAS/IML language.

There you have it, 10 articles from The DO Loop in 2016 that I think are worth a second look. Did I omit your favorite article? Leave a comment.

tags: Data Analysis

The post Ten posts from 2016 that deserve a second look appeared first on The DO Loop.

1月 112017

Not too long ago (last month), in a theater not too far away (10 minutes from my house), I saw Rogue One: A Star Wars Story. According to CNN Money, the standalone Star Wars movie brought in $29 million at the domestic box office on opening night, making it the […]

The post 4 business lessons gleaned from Star Wars appeared first on SAS Analytics U Blog.

1月 102017

Streaming technologies have been around for years, but as Felix Liao recently blogged, the numbers and types of use cases that can take advantage of these technologies have now increased exponentially. I've blogged about why streaming is the most effective way to handle the volume, variety and velocity of big data. That's […]

The post Streaming to better data quality appeared first on The Data Roundtable.