SAS Customer Intelligence 360

5月 152018
 

Editor's note: Learn more about SAS 360 Plan, the latest addition to the SAS Customer Intelligence 360 solution. We all know that digital platforms have transformed the role of marketing in the last few years; however, in many organizations, marketing operations hasn’t kept pace with that innovation. Marketers need to be [...]

Purpose-driven marketing planning with SAS Customer Intelligence 360 was published on Customer Intelligence Blog.

4月 052018
 

SAS Global Forum 2018 takes place April 8-11 in Denver. The following post is from Sebastian Dziadkowiec and Piotr Czetwertynski, presenters at the event. You can join Sebastian and Piotr for their talk: “An Agile Approach to Building an Omni-Channel Customer Experience” on April 9 at 2 p.m. in Meeting Room 302. We'll also post their presentation here after the event has concluded.

Keys to building a successful and future-proof omni-channel customer experience

Most organizations acknowledge that building a seamless and consistent customer experience is critical to long-term success. The big question is: Now what? With all of the channels to stitch together – from brick and mortar experiences to online clicks – how do you track and make sense of all that customer data? And, more importantly, how do you use that data to create the very best customer experience?

Over many years of implementing SAS Customer Intelligence and helping our clients give their customers exactly what they want and when they want it, our team has identified some characteristics that make for successful projects. Here are some of the key components that most often make or break a Customer Intelligence project.

Time to market

Everyone likes to see value generated quickly and reaching the break-even point for project within weeks of project launch is critical. In case of campaign management, it is possible. Instead of following the traditional waterfall path, with all the IT-heavy components like requirements gathering and analysis, solution design, many streams of implementation and testing, it is worth considering releasing a minimum viable product as soon as possible. Such approach allows us to focus on delivering business value and field-testing all the creative ideas, rather than building an IT system in perfect accordance to requirements, and one that may no longer be relevant at the day of release.

Applying analytics in the decisioning process

Go beyond traditional, rule-based approach to get the most out of the data you have. Nowadays, everyone speaks about machine learning, big data, NBA, artificial intelligence and so on. It is up to each organization and CI project to forge those fancy buzz words into real value, by embedding advanced analytics techniques in the decisioning process. There are many ways to boost various use cases by the advanced methods; make sure you will be able to use all you need and integrate their results seamlessly, regardless of when and how you engage with your customers.

While working on a CI project you should also keep in mind other areas: project organization, building a future-proof solution that will stay relevant for years, and constant search for additional opportunities to use available data and solutions to generate incremental value beyond the core scope of customer intelligence project.

There isn’t a one-size fits all approach to implementing a CI project, but these lessons learned can greatly increase your chances for project success – successful delivery generating a high ROI in a short timeframe while staying relevant in the long run - through the very best possible customer experience.

Find out more at the SAS Global User Forum 2018

Join Sebastian and Piotr for their “An Agile Approach to Building an Omni-Channel Customer Experience” Breakout Session at SAS Global Forum April 9 at 2 p.m. in Meeting Room 302.

About the Authors

Piotr Czetwertyński

Piotr is Customer Analytics Manager in Accenture. He has 11 years of experience in Campaign Management and Analytics. Currently he is one of the people responsible for launching of Accenture Center of Excellence for SAS CI in Warsaw, Poland.

Piotr recently focuses on solutioning & strategy in the areas of campaign management, BI & Analytics.

Sebastian Dziadkowiec

Sebastian has 8 years of experience in technology and management consulting, mostly in communications industry. He went through the entire project lifecycle on numerous engagements, starting from programmer, through business and technical analyst, up to solution architect and team manager on large-scale analytics projects.

Sebastian specializes in analytics solutions technology architecture, particularly focusing on customer intelligence and big data. He serves as technology lead in Accenture Center of Excellence for SAS CI in Warsaw, Poland.

 

 

Keys to building a successful and future-proof omni-channel customer experience was published on SAS Users.

3月 082018
 

On any given month, several million visitors come to the SAS web – whether it’s www.sas.com, support.sas.com, blogs.sas.com or communities.sas.com. The one thing that these millions of visitors have in common is that they came to the SAS web with a task. Those tasks are varied, but they’re all looking [...]

Using SAS at SAS: The power of web optimization with SAS Customer Intelligence 360 was published on Customer Intelligence Blog.

9月 062017
 

When it comes to the SAS web experience on sas.com, support.sas.com (and more), we have a vision and mission to create experiences that connect people to the things that matter – quickly, easily and enjoyably. You could say that we’re a user-task-focused bunch of web people here, and so whether you’re a web visitor to SAS researching a solution area or evaluating a product, looking for how-to content or applying for a job, our goal is to make sure you complete that task easily.

Using tools like SAS Customer Intelligence 360 helps us do this by allowing us to take the guesswork out creating the most optimized web experiences possible through it's IA, machine learning, omnichannel marketing and analytics and more is all the rage – and for good reason – don’t lose sight of the power and impact of good old fashioned a/b and multivariant testing.

The power of small in a big customer journey

If you think of your website as a product, and think of that product as being comprised of dozens, maybe hundreds of small interactions that users engage with – imagery, video, buttons, content, forms, etc. – then the ability to refine and improve those small interactions for users can have big impact and investment return for the product as a whole. Herein lies the beauty of web testing – the ability (really art and science) of taking these small interactions and testing them to refine and improve the user experience.

So what does this look like in real life, and how to do it with SAS Customer Intelligence 360?

On the top right corner of the sas.com homepage we have a small “sticky” orange call-to-action button for requesting a SAS demo. We like to test this button.

A button test? Yes, I know – it doesn’t get much smaller than this, which is why I affectionately refer to this particular button as “the little button that could.” It’s small but mighty, and by the end of this year, this little button will have helped to generate several hundred demo and pricing requests for sales. That’s good for SAS, but better for our site visitors because we’re helping to easily connect them with a high-value task they’re looking to accomplish during their customer journey.

How do we know this button is mighty? We’ve tested a ton of variations with this little guy measuring CTR and CVR. It started off as a “Contact Us” button, and went through a/b test variations as “Connect with SAS” “How to Buy” “Request Pricing” as we came to realize what users were actually contacting us for. So here we are today with our control as “Request a SAS Demo>"

Setting up a simple a/b test like this literally takes no longer than five minutes in SAS Customer Intelligence 360. Here's how:

  • First, you set up a message or creative.
  • Finally, you create your

    Easy breezy. Activate it, let it run, get statistical significance, declare a winner, optimize, rinse and repeat.

    Now, add segmentation targeting to the mix

    So now let’s take our testing a step further. We’ve tested our button to where we have a strong control, but what if we now refine our testing and run a test targeted to a particular segment, such as a “return user” segment to our website - and test button variations of Request a SAS Demo vs. Explore SAS Services.

    Why do this? The hypothesis is that for new users to our site, requesting a SAS demo is a top task, and our control button is helping users accomplish that. For repeat visitors, who know more about SAS, our solutions and products – maybe they are deeper in the customer journey and doing more repeat research and validation on www.sas.com. If so, what might be more appropriate content for that audience? Maybe it’s our services content. SAS has great services available to SAS users - such as Training, Certification, Consulting, Technical Support, and more. Would this content be more relevant for a return user on the website that's possibly deeper in the research and evaluate phase, or maybe already a customer? Let's find out.

    Setting up this segmentation a/b test is just like I noted above – you create a spot, build the creative, and set up your task. After you have set up the task, you have the option to select a “Target” as part of this task, and for this test, we select "New or Return User" as the criteria from the drop down, and then "Returning" as the value. Then just activate and see what optimization goodness takes place.

    So, how did our test perform?

    I'll share results and what we learn from this test in the upcoming weeks. Regardless of the results though, it's not really about what variation wins, but rather it's about what we learn from simply trying to improve the user experience that allows us to continue to design and build good, effective, optimized user experiences. Tools like SAS Customer Intelligence 360 and it's web testing and targeting capabilities allow us to do that faster and more efficiently than ever.

     

     

     

     

     

     

     

    Using SAS at SAS: SAS Customer Intelligence 360, a/b testing and web optimization was published on Customer Intelligence Blog.

9月 012017
 

For those of you using SAS Customer Intelligence 360, or generally just interested in the space around  web content targeting and personalization capabilities, I wanted to share the latest with what we’re doing at SAS.

As the SAS web experience division, we use SAS Customer Intelligence 360 on sas.com for data collection and a variety of a/b testing and content targeting. One of the things we’re starting to use it for is industry segmentation and targeting. Not in a creepy, over-the-top kind of way, but more subtle; in a way that helps to improve the user experience on sas.com.

One of the larger user segments we have on sas.com are academic-related visitors students, educators and researchers. That’s great for us, because these are important users of SAS.

The other good thing is that we already have great content and resources for those visitors on sas.com as part of our SAS Academic Program section. The challenge and opportunity is: “How do we surface that relevant content to the right user, in a way that then allows them to find what they’re looking for quicker, easier and more efficiently?” That's good for our users and good for us.

For us, part of the answer is to use SAS Customer Intelligence 360 targeting features that allow us to identify the right user, and deliver the right message.

It's as easy as 1-2-3

The way it works is pretty simple:

  1. We set up a spot in SAS Customer Intelligence 360. A spot is a place on a web page where we’ll target and deliver content to. In this instance, the spot we created is the feature banner spot on the sas.com homepage.

  2. Then we simply set up the creative. The creative is the content we'll use to deliver to the spot we created. For this targeting, we created a new homepage feature creative that is designed specifically for our academic segment students, educators, researchers and drives them to a specific call to action to our SAS Academic Program content on sas.com. So most visitors will see a standard promo about X, while students and educators will see the page below.
  3. Lastly, we create the web task. A web task allows us to target content to the website visitor and collect insights and data performance. When we set up this task, one of the steps is to create rules for targeting as applicable. For this experiment, we chose to target by industry group, one of the targeting options within SAS Customer Intelligence 360 that allows us to select an industry group SIC code (Standard Industrial Classification codes). We selected education services. We also created a targeted rule (see the image below) for any referral traffic that comes to sas.com from a top-level domain URL ending in ".edu", since we have a good number of backlinks to sas.com from academic institutions who use SAS.

Once activated, SAS Customer Intelligence 360 recognizes a users IP and associated SIC code, or referral URL, that identified them as a visitor from an academic institution. It then places place them into our segmentation bucket and displays our targeted content and experience.

There's a ton more we can do around this, but to get up and running it's incredibly easy and and proving effective (see the performance tracking below).

If you're interested in learning more, here are the latest and greatest in free tutorials and SAS Customer Intelligence 360 documentation.

 

Using SAS at SAS: 3 simple steps to better web content targeting was published on Customer Intelligence Blog.

5月 112017
 

Multivariate testing (MVT) is another “decision helper” in SAS® Customer Intelligence 360 that is geared at empowering digital marketers to be smarter in their daily job. MVT is the way to go when you want to understand how multiple different web page elements interact with each other to influence goal conversion rate. A web page is a complex assortment of content and it is intuitive to expect that the whole is greater than the sum of the parts. So, why is MVT less prominent in the web marketer’s toolkit?

One major reason – cost. In terms of traffic and opportunity cost, there is a combinatoric explosion in unique versions of a page as the number of elements and their associated levels increases. For example, a page with four content spots, each of which have four possible creatives, leads to a total of 256 distinct versions of that page to test.

If you want to be confident in the test results, then you need each combination, or variant, to be shown to a reasonable sample size of visitors. In this case, assume this to be 10,000 visitors per variant, leading to 2.5 million visitors for the entire test. That might take 100 or more days on a reasonably busy site. But by that time, not only will the web marketer have lost interest – the test results will likely be irrelevant.

A/B testing: The current standard

Today, for expedience, web marketers often choose simpler, sequential A/B tests. Because an A/B test can only tell you about the impact of one element and its variations, it is a matter of intuition when deciding which elements to start with when running sequential tests.

Running a good A/B test requires consideration of any confounding factors that could bias the results. For example, someone changing another page element during a set of sequential A/B tests can invalidate the results. Changing the underlying conditions can also reduce reliability of one or more of the tests.

The SAS Customer Intelligence 360 approach

The approach SAS has developed is the opposite of this. First, you run an MVT across a set of spots on a page. Each spot has two or more candidate creatives available. Then you look to identify a small number of variants with good performance. These are then used for a subsequent A/B test to determine the true winner. The advantage is that underlying factors are better accounted for and, most importantly, interaction effects are measured.

But, of course, the combinatoric challenge is still there. This is not a new problem – experimental design has a history going back more than 100 years – and various methods were developed to overcome it. Among these, Taguchi designs are the best known. There are others as well, and most of these have strict requirements on the type of design. safety consideration.

SAS Customer Intelligence 360 provides a business-user interface which allows the marketing user to:

  • Set up a multivariate test.
  • Define exclusion and inclusion rules for specific variants.
  • Optimize the design.
  • Place it into production.
  • Examine the results and take action.

The analytic heavy lifting is done behind the scenes, and the marketer only needs to make choices for business relevant parameters.

MVT made easy

The immediate benefit is that that multivariate tests are now feasible. The chart below illustrates the reduction in sample size for a test on a page with four spots. The red line shows the number of variants required for a conventional test, and how this increase exponentially with the number of content items per spot.


In contrast, the blue line shows the number of variants required for the optimized version of the test. Even with three content items per spot, there is a 50 percent reduction in the number of unique variants, and this percentage grows larger as the number of items increase. We can translate these numbers into test duration by making reasonable assumptions about the required sample size per variant (10,000 visitors) and about the traffic volume for that page (50,000 visitors per day). The result is shown below.

A test that would have taken 50 days will only take18 days using SAS’ optimized multivariate testing feature. More impressively, a test that would take 120 days to complete can be completed in 25 days.

What about those missing variants?

If only a subset of the combinations are being shown, how can the marketer understand what would happen for an untested variant? Simple. SAS Customer Intelligence 360 fits a model using the results for the tested variants and uses this to predict the outcomes for untested combinations. You can simulate the entire multivariate test and draw reliable conclusions in the process.

The Top Variant Performance report in the upper half of the results summary above indicates the lift for the best-performing variants relative to a champion variant (usually the business-as-usual version of the page). The lower half of the results summary (Variant Metrics) represents each variant as a point located according to a measured or predicted conversion rate. Each point also has a confidence interval associated with the measurement. In the above example, it’s easy to see that there is no clear winner for this test. In fact, the top five variants cannot reliably be separated. In this case, the marketer can use the results from this multi-variate test to automatically set up an A/B test. Unlike the A/B-first approach, narrowing down the field using an optimized multivariate test hones in on the best candidates while accounting for interaction effects.

Making MVT your go-to option

Until now, multivariate testing has been limited to small experiments for all but the busiest websites. SAS Customer Intelligence 360 brings the power of multi-variate testing to more users, without requiring them to have intimate knowledge of design of experiment theory. While multivariate testing will always require larger sample sizes than simple A/B testing, the capabilities presented here show how many more practical use cases can be addressed.

Multivariate Testing: Test more in less time was published on Customer Intelligence Blog.

12月 162016
 

We've been saying that the customer is queen or king for quite some time now. And in the coming year, that will be truer than ever. The customer determines where he or she finds information and which channel and which supplier gets the sale. And there is an abundance of these suppliers (certainly online). Customer loyalty, it seems, is as good as dead. Yes, of course, we are faithful to our local baker and tailor, but for items we don't buy everyday and where there is no personal relationship with the supplier (nor does there always need to be one), we don't really care where we order from. Right?customer loyalty

Today, many consumers make their choice based on only two criteria: price and reviews – the latter providing some confidence about product quality and supplier reliability. And it's an obvious choice. Why would you pay top price for an OEM device charger that you can get from a Chinese web shop for a fraction of the price – unless you need it tomorrow, of course? Virtually no supplier has a monopoly today, and you can switch to a new supplier with just one mouse click.

So does that leave all companies having to compete solely on price? No, that would create an unhealthy market situation. Aiming for good reviews is a great idea, of course, but is merely a partial solution. To encourage customer loyalty in the long term, you need to focus heavily on the last touch point in the customer journey. The three elements are essential in these efforts: data, analytics and real-time decisioning.

Determining the right data

Customers leave a data trail behind in various channels. This data enables you to build up a wealth of information about the customer. This is nothing new, but I have noticed that a lot of companies have difficulty in determining what data from this data stream they should add to the customer profile. By analysing the data, you can determine whether data can be assigned as a fixed value to the customer, or is of only temporary relevance, such as a location, for example. In addition, you can really get to know your customer by analysing this data, using this knowledge to predict behaviour and responding to this behaviour in real time.

Predicting behaviour

Using analytics to predict customer behaviour is the key to success in the last step of the customer journey. In this way, you can create the ultimate balance between customer service-driven interactions and marketing and sales-driven interactions. Just think how valuable it would be to know at this last touch point whether you should persuade the customer with your service, or use a combination offer with a product from the same line?

Helping customers make decisions in the moment

By using data strategically, you can predict where the customer has a need. You know what motivates him to actually make a purchase at that critical decision point. Responding smartly to this will increase customer satisfaction and make those customers more loyal. As a result, you will see that price and reviews are indeed important, but that customers still need a supplier who knows and recognises them, and responds to their needs.

To learn more about creating fiercely loyal customers, download our free ebook, Keep them coming back: You guide to building customer loyalty with analytics.

tags: customer data, customer experience, customer journey, customer loyalty, predictive analytics, Predictive Marketing, SAS Customer Intelligence 360

How do you revive customer loyalty in the digital age? 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.

9月 012016
 

Machine learning has a high profile currently and is riding a wave of exposure in the media that includes articles about subjects from self-driving cars and self-landing rockets, to computers beating the world’s best players at Go, the most computationally complex board game in the world. Is there an opportunity for your organisation, and the marketers within it, to make use of this “new” technology?

The buzz

Machine learning techniques were developed as long ago as the 1950s, but with the advent of big data and large analytical engines, the prevalence and the ease of applying the techniques has increased. machine learning

Additionally, organisations now understand the value that analytics can bring, so are willing to place it front and center in their plans and invest more time and resources in exploring new and better techniques. Segmentation and predictive models, for instance, have proven themselves time and again in the marketing world, but to a certain extent, they require a higher degree of knowledge to understand.  In some cases, a machine learning technique unburdens the user of the statistical work, but provides just as good an answer as a traditional technique. More people, with more data, trying to make more decisions lends itself to a technique that requires less manual intervention.

What it means for marketing

Organizations, large and small, can have huge, complex data that can from the latest advances in machine learning – banks have transaction records, telcos have call details, retailers have purchase records.

Take marketing in our omnichannel world as an example. There are huge amounts of customer interactions and there are business problems, such as attribution and optimizing the customer experience, that are perfect for the latest machine learning techniques. For real-time personalization of experience and real-time calculation of recommendations, great benefit can be gained from self-learning algorithms in reinforcement learning.

But it is important to remember that organizations also have many analytically driven challenges that are smaller, simpler and just as important and valuable to the bottom line of the organization. Again, for marketing, more traditional disciplines like segmentation and propensity modeling are still extremely useful, and organizations need to keep using capabilities like these to ensure the continued benefits from their use.

How SAS can help

SAS has embraced machine learning techniques for many years, and recently took a further step forward with the latest release of our SAS Customer Intelligence 360 suite of products. SAS has built a recommendation engine with the best of both worlds – a predictive model built using traditional techniques (logistic regression) and a machine learning algorithm (using naïve Bayes classifiers).  Fortunately, your customers don’t need to understand these techniques – they just want your website to make better recommendations!

tags: big data, customer experience, machine learning, predictive analytics, recommendation engine, SAS Customer Intelligence 360

Machine learning and what it means for marketing was published on Customer Intelligence.