segmentation

4月 272018
 

Analyzing ticket sales and customer data for large sports and entertainment events is a complex endeavor. But SAS Visual Analytics makes it easy, with location analytics, customer segmentation, predictive artificial intelligence (AI) capabilities – and more. This blog post covers a brief overview of these features by using a fictitious event company [...]

Analyze ticket sales using location analytics and customer segmentation in SAS Visual Analytics was published on SAS Voices by Falko Schulz

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.

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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.

11月 022016
 

Leads are the lifeblood of any sales effort. But not all leads are created equal. Some have a high value for an organization and represent a realistic opportunity to win business. Others are early-stage engagements that take months or years of development.

Because of this disparity, the question “What is a lead?” puzzles many organizations. Sales and marketing groups have worked for years to formalize the definition of a lead and what it means within an existing business model. Regardless of your definition, one thing is consistent – marketing has to adapt its strategies to bring in more, better, or just different mixes of leads. The key question is: “How do you get there?”

The challenge

Over the years, the SAS marketing organization built a complex method of passing leads from marketing to sales. The process was similar to what other companies have in place, that is, leads that met a set of rules were qualified and then sent to a salesperson to follow up. The system was effective but difficult to manage, leadsespecially when business needs changed.

To build a new model to score and qualify leads, the marketing team looked at existing data and then conferred with their counterparts in sales to reorient the lead management process to accomplish two main goals:

  • Increase the number and percentage of leads that convert to opportunities. This meant identifying the best leads and finding a faster way to pass more high-qualified leads to sales.
  • Improve the outcomes from the lead conversion process. Obviously, high-quality leads are essential to creating a larger pipeline of deals. The team needed a better way to score, and then prioritize, leads.

An added wrinkle was that the project had to be global. For example, a lead in Australia would have the same meaning as a lead in Germany. That way, the company could compare lead performance across geographies and fuel global decisions about what strategies would be more effective.

The approach

While the previous rules-based model was geared more toward quantity, the team opted for a model-based approach to lead scoring that emphasized quality based on likely outcomes. The team developed an analytics-driven model that could evaluate the range of customer behaviors (registrations, website page views, e-mail clicks, and so on) to identify the best leads.

Beyond the quality-versus-quantity discussion, the sales and marketing teams agreed that the timing of the lead handoff to sales was also important. To accomplish this effectively, the model evaluated many behaviors, and once certain criteria were met, the information was added to the customer relationship management (CRM) system. To improve the lead conversion process, the team also focused on converting more sales-ready leads. Not only did the new scoring model evaluate more behavioral data, but that information was passed on as a “digital footprint” for each lead. The salesperson can see interactions for the lead from within the CRM system, giving her important information to guide her initial outreach.

Additionally, the team decided not to send all leads to the CRM system. Because the model does a better job of classifying better leads, those that aren’t routed to sales go to a lead-nurturing pro- gram, where the contact receives a cadence of relevant e-mails. The contact’s behavior when receiving those e-mails (click-thrus, registrations, website visits, etc.) are all fed into the model.

The results

When the lead-scoring model was still in the early stages, the initial feedback was positive. Salespeople appreciated that the leads were more qualified and reliable. Rather than sifting through dozens of contacts, they know that leads indicate an interest in SAS and its solutions. That was once a luxury for a salesperson. Now, it’s an everyday reality.

To fine tune the model, analysts track the total number of leads passed to sales and the number of leads that convert to opportunities. The marketing team wants to make sure rates continue to rise for both numbers. If there is a plateau or a decline, the analysts receive rapid feedback and can adjust programs as necessary.

SAS marketing analysts can also fine tune the model as sales requirements change or the market evolves. The model is more flexible than the rules-based approach, allowing the team to rapidly adjust strategies. The team can adjust the lead conversion rate if there is a shift in internal focus or if a sales group an increase or decrease in capacity.

How SAS can help

We've created a practical ebook to modernizing a marketing organization with marketing analytics: Your guide to modernizing the marketing organization.

SAS Customer Intelligence 360 enables the delivery of contextually relevant emails, ensuring their content is personalized and timely.  Emails sent with SAS Customer Intelligence 360 are backed by segmentation, analytics and scoring behind the scenes to help ensure messaging matches the customer journey.

Whether you're just getting started or want to add new skills, we offer a variety of free tutorials and other training options: Learn SAS Customer Intelligence 360

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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: customer journey, email marketing, lead scoring, marketing analytics, marketing campaigns, sales leads, SAS Customer Intelligence 360, segmentation, The Analytical Marketer

Scoring leads to drive more effective sales was published on Customer Intelligence.

9月 272016
 

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.

When in doubt, one of the easiest things marketers can do is send an email blast. The approach is predicated on a strength-in-numbers mentality. If you send out enough messages, somebody, somewhere, will receive it and take the desired action.

While marketers still use blast messages, their value is waning. Why? You are competing for attention with your emails, website, advertisements, collateral, events and any other initiative. People are using their phones, computers, tablets and TVs to consume information. It’s harder than ever to reach, much less sway, a customer.

The challenge

By 2010, SAS marketing efforts included a blend of blasts and more personalized emails. The marketing team’s goal was to find the right mix of messages and communications methods that would anticipate customers’ needs and turn emails into a conversation with them on their journeys.

The advent of a new customer-journey approach at SAS gave us an opportunity to rethink our email strategy and see what approaches worked best at different phases of the journey.

The marketing team looked at historical data and asked some questions. For example, where along the path is thought leadership more effective than something conversationproduct-specific? And where is third-party content more compelling than internal content?

The approach

The marketing team members began assembling data on the customer journey and behavior across each phase. They found examples of customers receiving messages that were out of sync with their actual buying stage. For instance, a contact would receive messages designed for the early stages of a journey even after the deal was won (or lost).

Marketing analysts also evaluated and identified content gaps across the customer journey. Looking at the totality of interactions, it was clear that building a conversation with the customer would require an overhaul of the email marketing strategy. Here are some key takeaways from the analysis:

  • Scoring allowed the team to assign a value to all actions, not just registrations. Each interaction with SAS was tracked and added to the score. With more pervasive – and more realistic – scoring of these behaviors, the team could further analyze the relative value of different messages and offers.
  • Segmentation identified the stage of the customer journey. Once scoring was complete and applied to contacts, the team could choose which message to send based on the stage.
  • Automation provided the foundation for faster, analytics-driven communications. With segments in place, the team created targeted and relevant email communications to provide the right message at the right stage of the customer journey.
  • Analytics delivered the right business strategy based on the desired outcome. Marketing analysts could evaluate how the entire marketing mix was working to move customers through different stages.

The results

After this analysis, the team created and refined email campaigns to fit the stages of the customer journey. The content for the phases included:

  • Need. High-level messaging, including industry-specific content and thought leadership strategies. Blogs and articles at this phase explain the problem and provide a path forward.
  • Research. Content that validates the customer’s need to solve the problem. Material here focuses on specific business issues and includes third-party resources like analyst reviews and research reports.
  • Decide. Deeper content that provides more product-specific information. This material validates the proposed solution through customer success stories, research reports, product fact sheets and so on.
  • Adopt. On-board and self-service content. This stage focuses on introducing customers to support resources and online communities, as well as do-it-yourself material that introduces the customer to the solution.
  • Use. Adoption content, such as advanced educational information, user conferences, and product-specific webinars. At this stage, users turn to more technical resources to expand their knowledge.
  • Recommend. Content specific to extending the relationship with the customer. This includes speaking opportunities, focus group participation and sales references.

When customers reach the buy phase, interactions occur primarily between sales and the customer. As a result, customers are typically excluded from email communications.

Eventually, our entire online experience will be personalized as a way to best engage our customers and prospects and to help ensure we are communicating with them in a way that they prefer. How do we do this? By using customer experience analytics to track, analyze and then take action when appropriate based on behavior, instead of simply when we want to promote something. In other words, we have adopted an analytical mindset.

How SAS can help

We've created a practical ebook to modernizing a marketing organization with marketing analytics: Your guide to modernizing the marketing organization.

SAS Customer Intelligence 360 enables the delivery of contextually relevant emails, ensuring their content is personalized and timely.  Emails sent with SAS Customer Intelligence 360 are backed by segmentation, analytics and scoring behind the scenes to help ensure messaging matches the customer journey.

Whether you're just getting started or want to add new skills, we offer a variety of free tutorials and other training options: Learn SAS Customer Intelligence 360

 

tags: customer journey, email marketing, marketing analytics, marketing campaigns, SAS Customer Intelligence 360, segmentation, The Analytical Marketer

Moving from blasts to conversations was published on Customer Intelligence.

8月 272016
 

For the uninitiated, SAS 360 Engage enables organizations to interact with consumers by allowing them to create, manage and deliver digital content over web and mobile channels.  Wait a minute. SAS does more than the analytics? That is correct. SAS 360 Engage is a marketing super force serving as a one-stop shop for data capture all the way through delivering highly-targeted, personalized digital experiences.

360 Engage 1

Being able to dynamically place content and offers into digital channels – across devices and points in time – is nothing new for savvy marketing brands focused on optimization. As customer journeys spread across fragmented touch points while customers are demanding seamless and relevant experiences, content-oriented marketers have been forced to reevaluate their strategies for engagement. But the complexity, pace and volume of modern marketing easily overwhelms traditional planning and design approaches that rely on historical conventions, myopic single-channel perspectives and sequential act-and-learn iteration.

Presently, marketers primarily use a variety of content optimization approaches that include A/B testing and multivariate testing. A/B testing, at its simplest, is a method of website or mobile optimization in which the conversion rates of two versions of a page are compared using visitor response rates. By tracking the way visitors interact with the content– the videos they watch, the buttons they click, or whether they sign up for a newsletter – you can infer which version of the content is most effective.

Due to the popularity of this technique, SAS 360 Engage supports A/B/n testing.  A/B/n testing is an extension of A/B testing, where “N” refers to the number of versions being tested, anywhere from two versions to the “nth” version. For example, when a brand has more than one idea for what the ideal digital experience should be, A/B/n can be used to compare each hypothesis and produce an optimized decision based on data, not subjectivity.

360 Engage 2

Testing is attractive because it is efficient, measurable and serves as a machete cutting through the noise and assumptions associated with delivering effective experiences. In parallel, the evolving marketing landscape is driving a greater mandate for testing: to operate in more channels, handle more data and support more users. Testing must mature beyond traditional on-site experimentation to fully optimize a multifaceted customer journey.

360 Engage 3

The majority of today’s technologies for personalization have generally failed to effectively use data science to offer consumers a contextualized digital experience. Many of today’s offerings are based on simple rules-based segmentation to drive recommendations. Building off the benefits of multi-channel A/B/n testing, this is where SAS 360 Engage injects its analytical muscle to differentiate from other personalization technologies.  Let's break this down:

  • At the conclusion of an A/B/n test, there is usually a winner and one or more losers.
  • Is there really one superior experience for your entire marketable audience? Is it possible that experiences should vary by segment?

Performing algorithmic segmentation sounds awesome, but who really has the time to do it? We have so many tests to run.360 Engage 4

360 Engage 5

360 Engage 6

The time has arrived for predictive marketing to have its moment in the sun, and with Forrester recently naming SAS the leader in customer analytics, it's official - the 800-pound gorilla in advanced analytics is locked in on solving complex issues facing the space of data-driven marketing. Making digital personalization more relevant for target audiences is just like preparing a delicious meal; it all comes down to the ingredients and preparation process to rise to the occasion!

A beautiful and interpretable visualization is generated highlighting what is unique about this segment, as compared to everyone else who was exposed to the test. If the brand wants to target this audience in future campaigns, a single click populates this segment in the platform for future journey orchestration.

If you look closely at the image, you will note in the upper half of the report that the winner of the A/B/n test is variant A. However, the lower half of the report showcases a newly discovered segment. It turns out that when a specific customer segment with recent purchase, stay and amenity activity interacts with this hospitality brand, variant B produces better results. How did SAS 360 Engage do this? By applying automated firepower (i.e. algorithmic clustering) to produce this prescriptive and actionable insight. To learn more about this segment, marketers can profile the audience:

SAS 360 Engage was built with the recognition that some marketing teams don't have data scientists available, and have real needs for analytical automation. To improve upon the concept of A/B/n testing, augmenting this capability with automated, algorithmic segmentation with prescriptive results addresses an important need. Let's assume you've run an A/B/n test with four versions of a page, and variant A was crowned the champion. Wouldn't it be nice to know that if a specific segment arrived at your website, an alternative experience would facilitate a better result?

tags: A/B Testing, Campaign Management, customer journey, data science, digital marketing, Digital Personalization, marketing analytics, predictive analytics, Predictive Marketing, Prescriptive Analytics, SAS 360 Engage, SAS Customer Intelligence 360, segmentation

SAS 360 Engage: A/B testing and algorithmic segmentation was published on Customer Intelligence.

4月 152016
 

As promised a couple of weeks ago, I am very happy to share Part 2 of a webcast series highlighting how SAS participates in the space of digital analytics for data-driven marketing with applications for personalization and attribution. Before launching the video, let me set some context for what you are about to see.

Why do we care about the intersection of digital analytics and personalization? Honestly, it is increasingly important to predict how customers will behave so you can personalize experiences with relevance. The deeper your understanding of customer behavior and lifestyle preferences, the more impactful personalization can be. However, digital personalization at the individual level remains elusive for most enterprises who face challenges in data management, analytics, measurement, and execution. As customer interactions spread across fragmented touch points and consumers demand seamless and relevant experiences, content-oriented marketers have been forced to re-evaluate their strategies for engagement. But the complexity, pace and volume of modern marketing easily overwhelms traditional planning and design approaches that rely on historical conventions, myopic single-channel perspectives and sequential act-and-learn iteration.

The majority of technologies in use today for digital personalization have generally failed to effectively use predictive analytics to offer customers a contextualized digital experience. Most are based on simple rules-based recommendations, segmentation and targeting that are usually limited to a single customer touch point. Predictive MarketingDespite some use of predictive techniques, digital experience delivery platforms are behind in incorporating predictive analytics to contextualize experiences using 1st-, 2nd- and 3rd-party customer data. In my opinion, I believe the usage of digital data mining and predictive analytics to prioritize and inform the marketing teams on what to test, and to analytically define segment audiences prior to assigning test cells, is a massive opportunity. Marketers are very creative, and can imagine hundreds of different testing ideas – which tests do we prioritize if we cannot run them all? This is where advanced analytics can help inform our strategies in support of content optimization, as it allows the data to prioritize our strategy, and help us focus on what is important.

Moving on to our second subject of interest, we transition to the wonderful world of marketing attribution. At the very core of this topic, modern marketers recognize that customers expect brands to deliver relevant conversations across all channels at any given moment. The challenge is to uncover the interactions that drive conversions through integrated measurement and insights. However, organizations struggle to employ a holistic measurement approach because:

  1. It's confusing to distinguish among the measurement approaches available.
  2. Marketers bombard customers with extraneous content.
  3. Today's misaligned data makes customer level measurement a very difficult task.

It seems like attribution has been a problem for marketers for a very long time. According to a popular quote by Avinash Kaushik of Google:

“There are few things more complicated in analytics (all analytics, big data and huge data!) than multichannel attribution modeling."

The question is: Why is it challenging? SAS strongly believes three years later that we are living in a game-changing moment within digital analytics. Marketers are being enabled 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. The time has arrived for algorithmic attribution . . . Attribution

 

Did I whet your appetite? I hope so...please enjoy episode two of our two-part webcast series, now available for on demand viewing:

 

SAS for Digital Analytics: Personalization and Attribution [Part 2]

 

SAS Customer Intelligence offers a one-stop modern marketing platform to comprehensively support the objectives of predictive personalization and algorithmic attribution - from digital data collection, management, predictive analytics, omnichannel journey orchestration, delivery across online and offline channels, and measurement. On April 19 at SAS Global Forum 2016, SAS Customer Intelligence 360 will make its debut, and subjects like digital intelligence and predictive personalization will be primary topics. This new offering will drive unprecedented innovation in customer analytics and data-driven marketing, putting predictive analytical intelligence directly in the hands of digital and integrated marketers responsible for the customer experience.

If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

tags: Advanced Analytics, customer intelligence, Data Mining, data science, Digital Analytics, Digital Attribution, Digital Intelligence, digital marketing, Digital Personalization, marketing analytics, Predictive Marketing, Predictive Personalization, segmentation

Introduction to SAS for digital personalization and attribution was published on Customer Intelligence.

3月 312016
 

Digital analytics primarily supports functions of customer and prospect marketing. When it comes to the goals of digital analysis, it literally mirrors the mission of modern marketing. But what exactly is today's version of marketing all about?Modern Marketing

Honestly, we've been talking about this for years. And years. We ALL know it's what we should be doing and conceptually it's very simple, but practically, it has been very hard to achieve. Why?

Even with great web analytics, there have always been critical missing insights, which meant we didn't know for certain what the next-best-interaction for each customer was at any point in time. In addition, the development of insights and the use of analytics to define high-propensity audience segments has been distinctly slow and batch-driven in nature, delaying relevant delivery of targeted interactions. So we may get the message right, but we probably don't deliver it in a timely, consistent way, which has a dramatic impact on customer responsiveness and marketing effectiveness.

So in today's connected, always-on, highly opinionated world, we need to be a little sharper in meeting our customer's basic expectations, never mind surprising, delighting, and impressing them. While the concept of customer-centricity continues to increase in importance, improving our analytical approach to support this premise is vital.

SAS recognizes today's modern marketing challenges with digital and customer analytics. It is our mission to enable marketers to benefit from approachable and actionable advanced analytics to make more powerful decisions within today’s complex and interconnected business environments. That sounds great, right? I sense some of you reading this are raising an eyebrow of suspicion at this very moment.

Practically speaking, we want to show you exactly what that means. On March 29th, 2016, we aired episode one of a two-part webcast series, and it is now available for on demand viewing:

SAS for Digital Analytics: Introduction & Advancing Segmentation [Part 1]

We genuinely hope the webcast provided a proper introduction to how SAS participates in the space of digital analytics for data-driven marketing, and please come back in a couple of weeks when we will post Part 2 in this series entitled: SAS for Digital Analytics: Personalization & Attribution [Part 2]

If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

tags: Advanced Analytics, customer analytics, customer intelligence, data integration, data management, Data Mining, data science, Digital Analytics, Digital Intelligence, digital marketing, Integrated Marketing, marketing analytics, predictive analytics, Predictive Marketing, segmentation, web analytics, webcast

Introduction to SAS for digital analytics and segmentation was published on Customer Intelligence.

1月 262016
 

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:

Image 2

 

 

 

 

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:

  1. 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?
  2. 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:

Image 3

 

 

 

 

 

 

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.

Image 4

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:

  1. Personalization
  2. Attribution
  3. Segmentation
  4. Acquisition
  5. Optimization

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.

If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

 

tags: Business Analytics, business intelligence, customer analytics, customer intelligence, Customer Lifecycle Management, Data Mining, Digital Intelligence, marketing analytics, Marketing Attribution, personalization, Predictive Marketing, segmentation

The analytics of customer intelligence and why it matters was published on Customer Analytics.

1月 192016
 

Marketers have used segmentation as a technique to target customers for communications, products, and services since the introduction of  customer relationship management (i.e., CRM) and database marketing. Within the context of segmentation, there are a variety of applications, ranging from consumer demographics, geography, behavior, psychographics, events and cultural backgrounds. Over time, segmentation has proven its value, and brands continue to use this strategy across every stage of the customer journey:

  • Acquisition
  • Upsell/cross-sell
  • Retention
  • Winback

Let's provide a proper definition for this marketing technique. As my SAS peer and friend Randy Collica stated in his influential book on this subject:

"Segmentation is in essence the process by which items or subjects are categorized or classified into groups that share similar characteristics. These techniques can be beneficial in classifying customer groups. Typical marketing activities seek to improve their relationships with prospective and current customers. The better you know about your customer's needs, desires, and their purchasing behaviors, the better you can construct marketing programs designed to fit their needs, desires, and behaviors."

Moving beyond the academic interpretation, in today's integrated marketing ecosystem, SAS Global Customer Intelligence director Wilson Raj provides a modern viewpoint:

"In an era of big data, hyperconnected digital customers and hyper-personalization, segmentation is the cornerstone of customer insight and understanding across the modern digital business. The question is: Is your segmentation approach antiquated or advanced?"

This provides a nice transition to review the types of segmentation methods I observe with clients. It ultimately boils down to two categories:

  1. Business rules for segmentation (i.e., non-quantitative)
  2. Analytical segmentation (i.e., quantitative)

Let's dive deeper into each of these...

Business Rules For Segmentation

This technique centers on a qualitative, or non-quantitative, approach leveraging various customer attributes conceptualized through conversations with business stakeholders and customer focus groups to gather pointed data. This information represents consumer experiential behavior, and analysts will assign subjective segments for targeted campaign treatments. Although directionally useful, in this day and age of data-driven marketing, it is my opinion that this approach will have suboptimal results.

Analytical Segmentation

Within this category, there are two approaches marketing analysts can select from:

  1. Supervised (i.e., classification)
  2. Unsupervised (i.e., clustering)

Supervised segmentation is typically referred to as a family of pattern analysis approaches. Supporters of this method stress that the actionable deliverable from the analysis classifies homogeneous segments that can be profiled, and informs targeting strategies across the customer lifecycle. The use of the term supervised refers to specific data mining (or data science) techniques, such as decision trees, random forests, gradient boosting or neural networks.  One key difference in supervised approaches is that the analysis requires a dependent (or target) variable, whereas no dependent variable is designated in unsupervised models. The dependent variable is usually a 1-0 (or yes/no) flag-type variable that matches the objective of the segmentation. Examples of this include:

  • Product purchase to identify segments with higher probabilities to convert on what you offer.
  • Upsell/cross-sell to identify segments who are likely to deepen their relationship with your brand.
  • Retention to identify segments most likely to unsubscribe, attrite, or defect.
  • Click behavior to identify segments of anonymous web traffic likely to click on your served display media.

After applying these techniques, analysts can deliver a visual representation of the segments to help explain the results to nontechnical stakeholders. Here is a video demonstration example of SAS Visual Analytics within the context of supervised segmentation being applied to a brand's digital traffic through the use of analytical decision trees:

 

Critics of this approach argue that the resulting model is actually a predictive model rather than a segmentation model because of the probability prediction output. The distinction lies in the use of the model. Segmentation is classifying customer bases into distinct groups based on multidimensional data and is used to suggest an actionable roadmap to design relevant marketing, product and customer service strategies to drive desired business outcomes.  As long as we stay focused on this premise, there is nothing to debate.

On the other hand, unsupervised approaches, such as clustering, association/apriori, principal components or factor analysis point to a subset of multivariate segmentation techniques that group consumers based on similar characteristics. The goal is to explore the data to find intrinsic structures. K-means cluster analysis is the most popular technique I view with clients for interdependent segmentation, in which all applicable data attributes are simultaneously considered, and there is no splitting of dependent (or target) and independent (or predictor) variables. Here is a video demonstration example of SAS Visual Statistics for unsupervised segmentation being applied to a brand's digital traffic (including inferred attributes sourced from a digital data management platform) through the use of K-means clustering:

 

Keep in mind that unsupervised applications are not provided training examples (i.e., there isn't a 1-0 or yes/no flag type variable to bias the formation of the segments). Subsequently, it is fair to make the interpretation that the results of a K-means clustering analysis is more data driven, hence more natural and better suited to the underlying structure of the data. This advantage is also its major drawback: it can be difficult to judge the quality of clustering results in a conclusive way without running live campaigns.

Naturally, the question is which technique is better to use in practice – supervised or unsupervised approaches for segmentation? In my opinion, the answer is both (assuming you have access to data that can be used as the dependent or target variable). When you think about it, I can use an unsupervised technique to find natural segments in my marketable universe, and then use a supervised technique (or more than one via champion-challenger applications) to build unique models on how to treat each cluster segment based on goals defined by internal business stakeholders.

Now, let me pose a question I have been receiving more frequently from clients over the past couple of years.

"Our desired segmentation strategies are outpacing our ability to build supporting analytic models. How can we overcome this?"

Does this sound familiar? For many of my clients, this is a painful reality limiting their potential. That's why I'm personally excited about new SAS technology to address this challenge. SAS Factory Miner allows marketers to dream bigger when it comes to analytical segmentation. It fosters an interactive, approachable environment to support working relationships between strategic visionaries and analysts/data scientists. The benefit for the marketer campaign manager is the ability to expand your segmentation strategies from 5 or 10 segments to 100's or 1000's, while remaining actionable within the demands of today's modern marketing ecosystem. The advantage for the supporting analyst team is the ability to be more efficient, and exploit modern analytical methods and processing power, without the need for incremental resources.

Here is a video demonstration example of SAS Factory Miner for supersizing your data-driven segmentation capabilities:

 

I'll end this posting by revisiting a question we shared in the beginning:

Is your segmentation approach antiquated or advanced?

Dream bigger my friends. The possibilities are inspiring!

If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

 

tags: Clustering, CRM, Data Driven Marketing, Data Mining, data science, Decision Trees, marketing analytics, personalization, segmentation

Analytical segmentation for data-driven marketing was published on Customer Analytics.

10月 212015
 

The Rule of Three is a writing principle that suggests that things that come in threes are inherently funnier, more satisfying, or more effective than other numbers of things – Wikipedia.

3 Ps of success, Blind Mice, Little Pigs, Stooges, Musketeers, The Matrix, The Lord of the Rings, rings, pairs of shoes, 3 year memberships… Everything is better in 3s – including this shopaholic series!

  1. A Shopaholic’s Guide to Analytics
  2. A Shopaholic’s Guide to Analytics II.A: Half a shopping bag of useful techniques in Analytics
  3. In this last hoorah on the topic of Retail (therapy) Analytics we’ll empty our bag of some the most useful analytics techniques for keeping our customers happy and loyal. From the customer’s perspective, these are “A feeling that I got a good deal” and “Convenience”.

Today I am referring to any entity that transacts as a customer – individual, household, business etc. and any item for sale or service as a product.

A feeling that I got a good deal – what offers, when and how often?

Whether it’s the word SALE or finding a rare collectable, we all want to feel that we had a fair transaction. But as a retailer or service provider, we need a balance between being competitive and fair to our customers and staying in business.

What is the impact of price on demand?
How long should a promotion run before it’s unprofitable?

shopaholics-guide-to-analytics-1Price does not affect demand of all products the same way – electricity versus floor cushions, burgers versus Porsches. The economics 101 method to understand this impact is price elasticity / sensitivity – the ratio of the percentage change in demand over the percentage change in price. “Elastic” products (ratio greater than 1) are sensitive to price changes.

However, price and demand change over time, sometimes seasonally but not always consistently – affected by economic and other environmental factors. In this case, time series forecasting “causal models” (described in “The right product”, II.A) can be used to model the relationship between price and demand directly. From this model, price elasticity can be calculated, or what-if scenarios can be run to measure the direct impact of price, taking other factors into account, at points in the future.

These techniques can also be used to quantify expected impacts of promotional activity, length and frequency and avoid over promotion.

Which creative is more appealing to customers?
Which product offer is more profitable?

Predictive models and optimisation techniques (described in “Good service”, II.A) can be used to best allocate competing offers or where similar offers have been given in the past. If there is no history, we need to test the effectiveness of our offers through experiments on small samples of customers and extrapolate these to what is likely in real-life. This is known as a choice experiment. To derive statistically viable decisions, we use experimental design to make sure we are capturing sufficient information across the different choices. A simplistic form of this is an A/B test.

Convenience – what is relevant and where?

If shopping was a sport, then as an elite athlete, I expect towels to be stocked in the locker room and the showers to be functioning. Basically, there’s enough going on in our lives – and often too many other competitive options – for customers to deal with difficult or restrictive processes.

Yes, I realise I sound like a brat. But as the e-tailer market grows, people continue to work longer hours and globalisation is a reality, it is even more important for retailers and service providers to make transacting easy. There are operational considerations – integrated systems, web design, accessibility, etc. – but there is also the need for detailed profiling to understand the viability of the target market.

What products are the most relevant?
What is the best store layout and window dressing?
What are the most effective channels?

Demographic – a profile of the different types or segments of customers and how they are likely to behave under various circumstances e.g. during lunch breaks, with young children, in retirement, etc. Using statistical segmentation techniques such as clustering or self-organising maps are useful for creating segments but profiling is the process of differentiating these segments and is done through slicing and dicing and visual exploration[1].shopaholics-guide-to-analytics-2

Where should we build the next store?
Where should we locate the distribution centre?

Geospatial and location – a profile of the geography and terrain overlayed with hotspots of activity e.g. industrial, commercial, residential, thoroughfares etc. and, to optimise decision making, demographics and economics. Geospatial visualisations and network maps are helpful to highlight and differentiate between these areas of interest.

BUT convenience is underpinned by how well we understand our customers’ needs.

Do we have enough of the right products?
Are we proving exceptional customer service?
Is there sufficient value and choice?

I hope that you have picked up a pair or two of comfy shoes to help you on your analytics journey. If you ever feel lost in the sale crowds, as with the sport of shopping, focus on one thing at a time – an “aha” moment you can make a reality. Set your well-articulated goals and invest in the right-fitting solution of people, process and technology for the relationship you want to have with your customers.

Learn more about how you can quickly get started with exploring your data in the cloud with this on-demand video, Insights in Seconds.

Happy shopping!

[1] SAS Enterprise Miner has the out-of-the-box ability to profile segments statistically using comparative graphs.

tags: analytics, price, retail, segmentation, visualization

A Shopaholic’s Guide to Analytics II.B was published on Left of the Date Line.