Adele Sweetwood

2月 012017
 

Each day, the SAS Customer Contact Center participates in hundreds of interactions with customers, prospective customers, educators, students and the media. While the team responds to inbound calls, web forms, social media requests and emails, the live-chat sessions that occur on the corporate website make up the majority of these interactions.

The information contained in these chat transcripts can be a useful way to get feedback from customers and prospects. As a result, the contact center frequently asked by departments across the company what customers are saying about the company and its products – and what types of questions are asked.

The challenge

Chat transcripts are a source for measuring the relative happiness of those engaged with SAS. Using sentiment analysis, this information can help paint a more accurate picture of the health of customer relationships.

The live-chat feature includes an exit survey that provides some data including the visitor’s overall satisfaction with the chat agent and with SAS. While 13 percent of chat visitors complete the exit survey (which is above the industry average), that means thousands of chat sessions only have the transcript as a record of participant sentiment.

Analyzing chat transcripts often required the contact center to pore through the text to identify trends within the chat transcripts. With other, more pressing priorities, the manual review only provided some anecdotal information.

The approach

Performing more formal analytics using text information gets tricky due to the nature of text data. Text, unlike tabular data in databases or spreadsheets, is unstructured. There are no columns that dictate what bits of data go where. And, words can be assembled in nearly infinite combinations.

For the SAS team, however, the information contained within these transcripts were a valuable asset. Using text analytics, the team could start to uncover and understand trends and connections across thousands of chat sessions.

SAS turned to SAS Text Miner to conduct a more thorough analysis of the chat transcripts. The contact center worked with subject-matter experts across SAS to feed this text information into the analytics engine. The team used a variety of dimensions in the analysis:

  • Volume of the chat transcripts across different topics.
  • Web pages where the chat session originated.
  • Location of the customer.
  • Contact center agent who responded.
  • Duration of the chat session.
  • Products or initiatives mentioned within the text.

In addition, North Carolina State University’s Institute for Advanced Analytics began to use the chat data for a text analytics project focused on sentiment analysis. This partnership between the university and SAS helped students learn how to uncover trends in positive and negative sentiment across topics.

The results

After applying SAS text analytics to the chat data, the SAS contact center better understood the volume and type of inquiries and how they were being addressed. Often, the analysis could point areas on the corporate website that needed updates or improvements by tracking URLs for web pages that were the launch point for a chat.

Information from chat sessions also helped tune SAS’ strategy. After the announcement of Windows 10, the contact center received customer questions about the operating system, including some negative sentiment about a perceived lack of support. Based on this feedback, SAS released a statement to customers assuring them that Windows 10 was an integral part of the product roadmap.

The project with NC State University has also provided an opportunity for SAS and soon-to-be analytics professionals to continue and expand on the analysis of chat transcripts. They continue to look at the sentiment data and how it changes across different categories (products in use, duration of chat) to see if there are any trends to explore further.

Today, sentiment analysis feeds the training process for new chat agents and enables managers to highlight examples where an agent was able to turn a negative chat session into a positive resolution.

SAS Sentiment Analysis and SAS Text Analytics, combined with SAS Customer Intelligence solutions such as SAS Marketing Automation and SAS Real Time Decision Manager, allow marketing organizations like SAS to understand sentiment or emotion within text strings (chat, email, social, even voice to text) and use that information to inform sales, service, support and marketing efforts.

If you’d like to learn more about how to use SAS Sentiment Analysis to explore sentiment in electronic chat text, register for our SAS Sentiment Analysis course. And, the book, Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS, offers insights into SAS Text Miner capabilities and more.

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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, contact center, live chat, SAS Text Miner, sentiment analysis, text analytics, The Analytical Marketer

Using chat transcripts to understand customer sentiment was published on Customer Intelligence Blog.

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.

12月 052016
 

A common practice in traditional marketing is to first choose a target market to focus on. You then align your organization’s strategies and messaging to create a campaign in that target market. But what happens when it becomes clear that the campaign you created isn’t working? How agile are you in terms of adjusting on the fly and adapting to the needs of your prospective customers?

The challenge

A campaign we ran at SAS targeted small to medium-sized businesses, or SMBs. We needed to come up with tailor-made messaging that would be distinct from similar campaigns we were launching targeted at larger, enterprise-level companies. To do that, we highlighted what we thought wedata-analysisre business needs, language and case studies that would resonate with the SMBs.

But after the program launched and began, the results were disappointing. We saw lower-than-expected results for performance metrics including click-through rates and conversions. So we tweaked the messaging, offers and program structure to improve results. After crunching those numbers, the results came in – the campaign was still floundering.

We were now forced to take a fresh look. What had we done wrong? On reflection, we came upon an even more telling question: Did we actually need to separate SMBs from larger organizations? We started with an underlying assumption that the SMB market should be treated differently. Had that been a mistake?

The approach

To help guide us forward, we selected a roster of key performance metrics to analyze:

  • E-mails sent.
  • Open rates.
  • Click-through rates.
  • Opt-out rates.
  • Conversions (those who filled out registration forms to receive the promoted asset).
  • Lead-generated SSOs (an internal measure of conversions that we identify as leads that later progress to become sales opportunities).
  • Rate of completed leads to SSOs.

We then looked at how the SMBs responded to the SMB-specific campaign compared to how they responded when they received the enterprise-level messaging.

The results

To our surprise, SMBs responded more strongly to the enterprise-level campaign (see the table below). Our assumption had been proved wrong. So we adjusted by closing the SMB-specific campaign and retargeted the SMBs with our enterprise-level messaging.

adele-table

The takeaway for us was a reminder that we can’t afford to let our assumptions about the market hinder our ability to adjust to customers’ needs. In this situation, we relied on the power of analytics to provide the answers about what people wanted rather than continue in a losing cause.

You can best meet customers along their decision journey by relying on advanced analytics to increase the quality of a marketing campaign by using scoring, optimization and predictive capabilities. The standard spreadsheet-based reports that marketers used to rely on to see how their campaign performed have now shifted to interactive visualization dashboards to track the efficacy of their campaign, while making changes on the fly when necessary to ensure a campaign is reaching its potential. The biggest difference is that marketers now have these tools at their disposal. We no longer have to submit requests to the IT department to get this information.

==

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: Campaign Management, customer analytics, customer insights, customer journey, marketing campaigns, midmarket, smb

How analytics empowers campaign agility was published on Customer Intelligence.

12月 052016
 

A common practice in traditional marketing is to first choose a target market to focus on. You then align your organization’s strategies and messaging to create a campaign in that target market. But what happens when it becomes clear that the campaign you created isn’t working? How agile are you in terms of adjusting on the fly and adapting to the needs of your prospective customers?

The challenge

A campaign we ran at SAS targeted small to medium-sized businesses, or SMBs. We needed to come up with tailor-made messaging that would be distinct from similar campaigns we were launching targeted at larger, enterprise-level companies. To do that, we highlighted what we thought wedata-analysisre business needs, language and case studies that would resonate with the SMBs.

But after the program launched and began, the results were disappointing. We saw lower-than-expected results for performance metrics including click-through rates and conversions. So we tweaked the messaging, offers and program structure to improve results. After crunching those numbers, the results came in – the campaign was still floundering.

We were now forced to take a fresh look. What had we done wrong? On reflection, we came upon an even more telling question: Did we actually need to separate SMBs from larger organizations? We started with an underlying assumption that the SMB market should be treated differently. Had that been a mistake?

The approach

To help guide us forward, we selected a roster of key performance metrics to analyze:

  • E-mails sent.
  • Open rates.
  • Click-through rates.
  • Opt-out rates.
  • Conversions (those who filled out registration forms to receive the promoted asset).
  • Lead-generated SSOs (an internal measure of conversions that we identify as leads that later progress to become sales opportunities).
  • Rate of completed leads to SSOs.

We then looked at how the SMBs responded to the SMB-specific campaign compared to how they responded when they received the enterprise-level messaging.

The results

To our surprise, SMBs responded more strongly to the enterprise-level campaign (see the table below). Our assumption had been proved wrong. So we adjusted by closing the SMB-specific campaign and retargeted the SMBs with our enterprise-level messaging.

adele-table

The takeaway for us was a reminder that we can’t afford to let our assumptions about the market hinder our ability to adjust to customers’ needs. In this situation, we relied on the power of analytics to provide the answers about what people wanted rather than continue in a losing cause.

You can best meet customers along their decision journey by relying on advanced analytics to increase the quality of a marketing campaign by using scoring, optimization and predictive capabilities. The standard spreadsheet-based reports that marketers used to rely on to see how their campaign performed have now shifted to interactive visualization dashboards to track the efficacy of their campaign, while making changes on the fly when necessary to ensure a campaign is reaching its potential. The biggest difference is that marketers now have these tools at their disposal. We no longer have to submit requests to the IT department to get this information.

==

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: Campaign Management, customer analytics, customer insights, customer journey, marketing campaigns, midmarket, smb

How analytics empowers campaign agility was published on Customer Intelligence.

12月 052016
 

A common practice in traditional marketing is to first choose a target market to focus on. You then align your organization’s strategies and messaging to create a campaign in that target market. But what happens when it becomes clear that the campaign you created isn’t working? How agile are you in terms of adjusting on the fly and adapting to the needs of your prospective customers?

The challenge

A campaign we ran at SAS targeted small to medium-sized businesses, or SMBs. We needed to come up with tailor-made messaging that would be distinct from similar campaigns we were launching targeted at larger, enterprise-level companies. To do that, we highlighted what we thought wedata-analysisre business needs, language and case studies that would resonate with the SMBs.

But after the program launched and began, the results were disappointing. We saw lower-than-expected results for performance metrics including click-through rates and conversions. So we tweaked the messaging, offers and program structure to improve results. After crunching those numbers, the results came in – the campaign was still floundering.

We were now forced to take a fresh look. What had we done wrong? On reflection, we came upon an even more telling question: Did we actually need to separate SMBs from larger organizations? We started with an underlying assumption that the SMB market should be treated differently. Had that been a mistake?

The approach

To help guide us forward, we selected a roster of key performance metrics to analyze:

  • E-mails sent.
  • Open rates.
  • Click-through rates.
  • Opt-out rates.
  • Conversions (those who filled out registration forms to receive the promoted asset).
  • Lead-generated SSOs (an internal measure of conversions that we identify as leads that later progress to become sales opportunities).
  • Rate of completed leads to SSOs.

We then looked at how the SMBs responded to the SMB-specific campaign compared to how they responded when they received the enterprise-level messaging.

The results

To our surprise, SMBs responded more strongly to the enterprise-level campaign (see the table below). Our assumption had been proved wrong. So we adjusted by closing the SMB-specific campaign and retargeted the SMBs with our enterprise-level messaging.

adele-table

The takeaway for us was a reminder that we can’t afford to let our assumptions about the market hinder our ability to adjust to customers’ needs. In this situation, we relied on the power of analytics to provide the answers about what people wanted rather than continue in a losing cause.

You can best meet customers along their decision journey by relying on advanced analytics to increase the quality of a marketing campaign by using scoring, optimization and predictive capabilities. The standard spreadsheet-based reports that marketers used to rely on to see how their campaign performed have now shifted to interactive visualization dashboards to track the efficacy of their campaign, while making changes on the fly when necessary to ensure a campaign is reaching its potential. The biggest difference is that marketers now have these tools at their disposal. We no longer have to submit requests to the IT department to get this information.

==

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: Campaign Management, customer analytics, customer insights, customer journey, marketing campaigns, midmarket, smb

How analytics empowers campaign agility was published on Customer Intelligence.

11月 172016
 

At SAS, we've worked hard to transform ourselves into an analytical marketing organization. And it's an ongoing journey. As new tools and data sources appear, we'll continue to grow, change and improve. As the leader of this effort, I wish there had been a how-to guide available when we started […]

Three steps to modernizing your marketing organization was published on SAS Voices.

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

==

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.