SAS Customer Intelligence 360

6月 292016
 

In my first post, I discussed the importance of brand equity and its relationship to good customer experience.

Consider this scenario of an organization where brand equity was negatively impacted by a fractured customer experience. In this case the “brand” is the corporate brand.

Internally, employees knew that the company was in trouble because:

  • It did not have a clear, complete picture of customers.
  • Each business area had their own definition of the customer based on their own partial data.
  • The marketing group targeted middle-aged, price-sensitive customers.
  • Advertising bought media that was targeted to younger audiences.
  • Merchandising targeted affluent households.
  • Customer service had no customer information.
  • Stores, catalog and Internet channels had different marketing programs.
  • Digital channels interacted with customers based on their narrow view of the customer.
  • Data was not shared, so no one had a complete picture of customers.
  • Marketing programs were not coordinated.

Needless to say, this negatively impacted customer experience. Customers were showing up in the stores holding two or three 183061092different promotions valid for that week. Confusion reigned as neither customers or employees were sure which promotions were good in which channel, or which could be used in combination.

The customer experience was a negative one, and marketing response rates declined as did sales and perception of the brand. It did not take long for Wall Street to figure out there were deep problems and the stock price sank.

The Turnaround

What saved this brand?

  • A unified view of the customer.
  • Shared customer insights.
  • Transparency of marketing processes.

The impetus was a turnaround CEO with a maniacal focus on customer and transparent coordination of processes around customer.

Actions

  • His first order of business was to accelerate an already in-progress effort to consolidate customer data across the organization.
  • He made data accessible to all business groups and channels for the tactical customer interaction decisions.
  • For strategic decisions, he demanded an analysis to clearly identify and profile the best and next-best customers. He then required every decision be aligned with these customers.
  • Every business area and every channel needed to show how their resources were being allocated to align with the various customer segments.
  • Interactive channels needed to show how they were supporting consistent messaging to various customer segments and using data to personalize the experience.

Results

  • For the first time, there was transparency of advertising and marketing promotions across all channels.
  • For the first time, business groups were aligned and had a coordinated message to communicate brand value to customers.
  • Customers saw the same messaging across all channels.
  • Customers understood what the brand stood for.
  • Over the next few years, market share increased, stock price soared 800 percent.
  • Employees were confident in their decisions and proud to work for the brand.

This scenario is a great learning experience of what can go right with a brand by consolidating enterprise-wide customer data, and providing transparency across business groups and marketing programs.

Management needs visibility into company-wide plans to make sure that budgets, creatives and programs all support the overall business strategy and the customer experience.

SAS has strong marketing resource management capabilities that are completely integrated with marketing execution capabilities as well as performance metrics. For example, SAS Marketing Operations Management provides the ability to plan, manage and share programs across your SAS Customer Intelligence 360 platform gives you the ability to put those plans into action and engage with customers.

Epilog: Turnaround of the turnaround

Unfortunately, for the organization mentioned above, all the good was undone when a new CEO came in and decided that the current customers were not important for the direction he wanted to take the company. He changed pricing and promotions, corporate logo, store layouts and ditched strong product brands that current customers were loyal to. He severely eroded brand equity among current customers. He insisted that his changes would bring in younger, hipper customers. But it did not because the brand was not one those younger customers valued – no brand equity. That CEO did not last long but the damage was done. The company is now trying to recover from a massive debt burden and damage to its brand equity.

Hope for the future

In our scenario, the current CEO grounds every decision in data and information – not intuition and we will be able to tell a good story of recovery in the future.

tags: brand, brand equity, customer data, SAS Customer Intelligence 360, SAS Marketing Operations Management

Data can help revive brand equity was published on Customer Intelligence.

6月 232016
 

When it comes to strong brand equity, everyone in the organization has to have a seat at the table. Brand equity is the result of positive interactions and transactions between the consumer and brand – across all touchpoints and all communication channels. It is built over time by brands being loyal to customers by providing them with the products, services, and interactions that they expect and value.

However, building brand equity is increasingly complicated by the number of touchpoints a brand has with consumers. As consumers, we have all experienced the frustration of being over-communicated to. Our mailboxes and inboxes are flooded, sometimes with conflicting messages. Or we continuously see marketing communications for things we already purchased or do not want. And even though we may be good customers of a brand, sometimes one channel does not recognize us or treat us as a loyal customer.

All of this is due to a breakdown in sharing of data, customer insights, and marketing plans across the enterprise.   The customer experience is not optimized and this negatively affects brand equity.

Brand equity is important to an organization because it means consumers trust and believe in the value of your brand over other Brand Equity bannerbrands.  Brands with strong brand equity have more pricing power. They are top of mind, and are the brands that customers go to first. And they are brands that consumers are willing and happy to receive communications from, resulting in higher response rates to marketing communications. Strong brand equity increases customer retention, marketing ROI, market share, profits, and shareholder value.

And that is why leading companies invest in improvements in customer experience. An organization that desires to increase brand equity, focuses on customer experience and will leverage all available data and analytics, across the enterprise, to create positive customer perception of their brand.

Forbes survey reveals approaches for building a better brand experience

The Forbes Insights, just published a report based on findings of a survey of business leaders.  In that report they state:

“Business leaders grasp the importance of enterprise-level data analytics for supporting brand and customer-focused initiatives.” It goes on to state:  “Data-driven CX (Customer Experience) is key for surpassing the competition in today’s hyper-competitive global economy. It takes a combination of factors…to deliver highly interactive, consistent, and contextual customer experiences which are critical for supporting the brand.  To achieve this, however, there needs to be greater alignment of people, processes and technology across enterprises—involving not only sales and marketing teams, but also other key players behind customer experience, including information technology, purchasing and production.”   SAS Marketing Operations Management

Today’s rapidly shifting consumers, competition and employee turnover make it even more important to have systems and processes in place to manage across the enterprise. Support for the customer experience need to be embedded across the organization, needs to go beyond one-time initiatives, and needs to be integrated with customer interaction channels.

In support of that, , see SAS Marketing Operations Management that provides the ability to plan, manage and visualize programs across your organization, as well as the new SAS Customer Intelligence 360 platform, which gives you the ability to put those plans into action and engage with customers.

Look for part 2 of this post where I discuss my own experience when brand equity suffered because of a fractured customer experience and how SAS can help you avoid this mistake.

tags: brand, brand equity, customer experience, customer insights, SAS Customer Intelligence 360, SAS Marketing Operations Management

Brand equity is built on customer experience was published on Customer Intelligence.

6月 232016
 

When it comes to strong brand equity, everyone in the organization has to have a seat at the table. Brand equity is the result of positive interactions and transactions between the consumer and brand – across all touchpoints and all communication channels. It is built over time by brands being loyal to customers by providing them with the products, services, and interactions that they expect and value.

However, building brand equity is increasingly complicated by the number of touchpoints a brand has with consumers. As consumers, we have all experienced the frustration of being over-communicated to. Our mailboxes and inboxes are flooded, sometimes with conflicting messages. Or we continuously see marketing communications for things we already purchased or do not want. And even though we may be good customers of a brand, sometimes one channel does not recognize us or treat us as a loyal customer.

All of this is due to a breakdown in sharing of data, customer insights, and marketing plans across the enterprise.   The customer experience is not optimized and this negatively affects brand equity.

Brand equity is important to an organization because it means consumers trust and believe in the value of your brand over other Brand Equity bannerbrands.  Brands with strong brand equity have more pricing power. They are top of mind, and are the brands that customers go to first. And they are brands that consumers are willing and happy to receive communications from, resulting in higher response rates to marketing communications. Strong brand equity increases customer retention, marketing ROI, market share, profits, and shareholder value.

And that is why leading companies invest in improvements in customer experience. An organization that desires to increase brand equity, focuses on customer experience and will leverage all available data and analytics, across the enterprise, to create positive customer perception of their brand.

Forbes survey reveals approaches for building a better brand experience

The Forbes Insights, just published a report based on findings of a survey of business leaders.  In that report they state:

“Business leaders grasp the importance of enterprise-level data analytics for supporting brand and customer-focused initiatives.” It goes on to state:  “Data-driven CX (Customer Experience) is key for surpassing the competition in today’s hyper-competitive global economy. It takes a combination of factors…to deliver highly interactive, consistent, and contextual customer experiences which are critical for supporting the brand.  To achieve this, however, there needs to be greater alignment of people, processes and technology across enterprises—involving not only sales and marketing teams, but also other key players behind customer experience, including information technology, purchasing and production.”   SAS Marketing Operations Management

Today’s rapidly shifting consumers, competition and employee turnover make it even more important to have systems and processes in place to manage across the enterprise. Support for the customer experience need to be embedded across the organization, needs to go beyond one-time initiatives, and needs to be integrated with customer interaction channels.

In support of that, , see SAS Marketing Operations Management that provides the ability to plan, manage and visualize programs across your organization, as well as the new SAS Customer Intelligence 360 platform, which gives you the ability to put those plans into action and engage with customers.

Look for part 2 of this post where I discuss my own experience when brand equity suffered because of a fractured customer experience and how SAS can help you avoid this mistake.

tags: brand, brand equity, customer experience, customer insights, SAS Customer Intelligence 360, SAS Marketing Operations Management

Brand equity is built on customer experience was published on Customer Intelligence.

6月 062016
 

There's no doubt that artificial intelligence (AI) is here and is rapidly gaining the attention of brands large and small. As I talk to customers and prospects, they are interested in understanding how AI and its subcomponents (cognitive computing, machine learning, or even deep learning) are being woven into various departments (marketing, sales, service and support) at organizations across industries.

Here are some examples of cognitive computing and machine learning today at organizations, and how these capabilities will enhance customer experience in the future.

I think it's important to start with a few foundational facts:

  • AI as a practice is not new – John McCarthy and others started their research into this area back in the 1950s.
  • AI and its subcomponents are rooted in predictive analytics (neural networks, data mining, natural language processing, etc., all have their beginnings here).
  • Automation and the use of supervised and unsupervised algorithms are crucial to machine learning and cognitive computing use cases.
  • Deep learning uses the concept of teaching and training to accomplish more advanced automation tasks. It’s important to note that deep learning is not as prevalent from a customer experience perspective as machine learning and cognitive computing. Let's take a look at what AI means for brands as the customer experience becomes the primary differentiator for marketing organizations.

algorithms

A cognitive computing use case

Cognitive computing enables software to engaging in human-like interactions. Cognitive computing uses analytical processes (voice to text, natural language processing and text and sentiment analysis) to determine answers to questions.

For example, a SAS customer uses automation to provide a quicker response to service requests that come in to the brand's contact center. It can send an automated reply to service inquires, direct the customer to appropriate departments, and send customer responses back to the channel – all using SAS solutions. These capabilities reduces the number of replies that require human intervention and improves service response times. This same use case can be applied across industries such as retail, telecom, financial services and utilities. The end result? A happier customer and an improved customer experience.

cognitive computing

Analytics: the core of machine learning

Machine learning uses software that can scan data to identify patterns and predict future results with minimal human intervention.

Analytics play an important role. Model retraining, the use of historical data and environmental conditions all serve as inputs into the supervised and unsupervised algorithms that machine learning uses. For example, some of our large telecom and financial services providers use data, customer journey maps and past patterns to be able to serve timely and relevant offers during customer interactions.

Many of our customers can do in less than one second, and are providing response and replies that are relevant and individualized. Another great example of machine learning is the development work that SAS is doing currently with regard to its marketing software.

Our customer intelligence solutions use embedded machine learning processes to make setting up activities and completing tasks in the software easier for analysts and marketers alike. For instance, the software will automatically choose the optimal customer segment and creative combinations for a campaign. It will also recommend the best time to follow up with a customer or segment and on the customer’s preferred devices. Machine learning also gives marketers the ability to understand how to use and modify digital assets for the most reach and optimal conversions.

The newest addition to artificial intelligence

Deep learning, a newer concept that relies on deep neural networks – is certainly something that is coming to the marketing and service realms. Many companies have started looking at how we teach and train software to accomplish complex activities – drive cars, play chess, make art (the list goes on). As for marketing, I believe we will see deep learning being used to run marketing programs, initiate customer service interactions or map customer journeys in detail.

These are just a few examples of how we are seeing AI improve the customer experience. You and I, as digitally empowered consumers, will certainly benefit from man and machine working together to automate the interactions that we have with brands on a daily basis. I urge you to keep an eye out for how brands big and small are automating the interactions they have with you – I think you will be pleasantly surprised with the outcome.

tags: artificial intelligence, cognitive computing, customer analytics, deep learning, marketing automation, marketing software, predictive analytics, Predictive Marketing, SAS Customer Intelligence 360

How artificial intelligence will enhance customer experiences was published on Customer Intelligence.

6月 062016
 

There's no doubt that artificial intelligence (AI) is here and is rapidly gaining the attention of brands large and small. As I talk to customers and prospects, they are interested in understanding how AI and its subcomponents (cognitive computing, machine learning, or even deep learning) are being woven into various departments (marketing, sales, service and support) at organizations across industries.

Here are some examples of cognitive computing and machine learning today at organizations, and how these capabilities will enhance customer experience in the future.

I think it's important to start with a few foundational facts:

  • AI as a practice is not new – John McCarthy and others started their research into this area back in the 1950s.
  • AI and its subcomponents are rooted in predictive analytics (neural networks, data mining, natural language processing, etc., all have their beginnings here).
  • Automation and the use of supervised and unsupervised algorithms are crucial to machine learning and cognitive computing use cases.
  • Deep learning uses the concept of teaching and training to accomplish more advanced automation tasks. It’s important to note that deep learning is not as prevalent from a customer experience perspective as machine learning and cognitive computing. Let's take a look at what AI means for brands as the customer experience becomes the primary differentiator for marketing organizations.

algorithms

A cognitive computing use case

Cognitive computing enables software to engaging in human-like interactions. Cognitive computing uses analytical processes (voice to text, natural language processing and text and sentiment analysis) to determine answers to questions.

For example, a SAS customer uses automation to provide a quicker response to service requests that come in to the brand's contact center. It can send an automated reply to service inquires, direct the customer to appropriate departments, and send customer responses back to the channel – all using SAS solutions. These capabilities reduces the number of replies that require human intervention and improves service response times. This same use case can be applied across industries such as retail, telecom, financial services and utilities. The end result? A happier customer and an improved customer experience.

cognitive computing

Analytics: the core of machine learning

Machine learning uses software that can scan data to identify patterns and predict future results with minimal human intervention.

Analytics play an important role. Model retraining, the use of historical data and environmental conditions all serve as inputs into the supervised and unsupervised algorithms that machine learning uses. For example, some of our large telecom and financial services providers use data, customer journey maps and past patterns to be able to serve timely and relevant offers during customer interactions.

Many of our customers can do in less than one second, and are providing response and replies that are relevant and individualized. Another great example of machine learning is the development work that SAS is doing currently with regard to its marketing software.

Our customer intelligence solutions use embedded machine learning processes to make setting up activities and completing tasks in the software easier for analysts and marketers alike. For instance, the software will automatically choose the optimal customer segment and creative combinations for a campaign. It will also recommend the best time to follow up with a customer or segment and on the customer’s preferred devices. Machine learning also gives marketers the ability to understand how to use and modify digital assets for the most reach and optimal conversions.

The newest addition to artificial intelligence

Deep learning, a newer concept that relies on deep neural networks – is certainly something that is coming to the marketing and service realms. Many companies have started looking at how we teach and train software to accomplish complex activities – drive cars, play chess, make art (the list goes on). As for marketing, I believe we will see deep learning being used to run marketing programs, initiate customer service interactions or map customer journeys in detail.

These are just a few examples of how we are seeing AI improve the customer experience. You and I, as digitally empowered consumers, will certainly benefit from man and machine working together to automate the interactions that we have with brands on a daily basis. I urge you to keep an eye out for how brands big and small are automating the interactions they have with you – I think you will be pleasantly surprised with the outcome.

tags: artificial intelligence, cognitive computing, customer analytics, deep learning, marketing automation, marketing software, predictive analytics, Predictive Marketing, SAS Customer Intelligence 360

How artificial intelligence will enhance customer experiences was published on Customer Intelligence.

6月 062016
 

There's no doubt that artificial intelligence (AI) is here and is rapidly gaining the attention of brands large and small. As I talk to customers and prospects, they are interested in understanding how AI and its subcomponents (cognitive computing, machine learning, or even deep learning) are being woven into various departments (marketing, sales, service and support) at organizations across industries.

Here are some examples of cognitive computing and machine learning today at organizations, and how these capabilities will enhance customer experience in the future.

I think it's important to start with a few foundational facts:

  • AI as a practice is not new – John McCarthy and others started their research into this area back in the 1950s.
  • AI and its subcomponents are rooted in predictive analytics (neural networks, data mining, natural language processing, etc., all have their beginnings here).
  • Automation and the use of supervised and unsupervised algorithms are crucial to machine learning and cognitive computing use cases.
  • Deep learning uses the concept of teaching and training to accomplish more advanced automation tasks. It’s important to note that deep learning is not as prevalent from a customer experience perspective as machine learning and cognitive computing. Let's take a look at what AI means for brands as the customer experience becomes the primary differentiator for marketing organizations.

algorithms

A cognitive computing use case

Cognitive computing enables software to engaging in human-like interactions. Cognitive computing uses analytical processes (voice to text, natural language processing and text and sentiment analysis) to determine answers to questions.

For example, a SAS customer uses automation to provide a quicker response to service requests that come in to the brand's contact center. It can send an automated reply to service inquires, direct the customer to appropriate departments, and send customer responses back to the channel – all using SAS solutions. These capabilities reduces the number of replies that require human intervention and improves service response times. This same use case can be applied across industries such as retail, telecom, financial services and utilities. The end result? A happier customer and an improved customer experience.

cognitive computing

Analytics: the core of machine learning

Machine learning uses software that can scan data to identify patterns and predict future results with minimal human intervention.

Analytics play an important role. Model retraining, the use of historical data and environmental conditions all serve as inputs into the supervised and unsupervised algorithms that machine learning uses. For example, some of our large telecom and financial services providers use data, customer journey maps and past patterns to be able to serve timely and relevant offers during customer interactions.

Many of our customers can do in less than one second, and are providing response and replies that are relevant and individualized. Another great example of machine learning is the development work that SAS is doing currently with regard to its marketing software.

Our customer intelligence solutions use embedded machine learning processes to make setting up activities and completing tasks in the software easier for analysts and marketers alike. For instance, the software will automatically choose the optimal customer segment and creative combinations for a campaign. It will also recommend the best time to follow up with a customer or segment and on the customer’s preferred devices. Machine learning also gives marketers the ability to understand how to use and modify digital assets for the most reach and optimal conversions.

The newest addition to artificial intelligence

Deep learning, a newer concept that relies on deep neural networks – is certainly something that is coming to the marketing and service realms. Many companies have started looking at how we teach and train software to accomplish complex activities – drive cars, play chess, make art (the list goes on). As for marketing, I believe we will see deep learning being used to run marketing programs, initiate customer service interactions or map customer journeys in detail.

These are just a few examples of how we are seeing AI improve the customer experience. You and I, as digitally empowered consumers, will certainly benefit from man and machine working together to automate the interactions that we have with brands on a daily basis. I urge you to keep an eye out for how brands big and small are automating the interactions they have with you – I think you will be pleasantly surprised with the outcome.

tags: artificial intelligence, cognitive computing, customer analytics, deep learning, marketing automation, marketing software, predictive analytics, Predictive Marketing, SAS Customer Intelligence 360

How artificial intelligence will enhance customer experiences was published on Customer Intelligence.

5月 252016
 

In April, SAS 360 Discover was introduced at SAS Global Forum 2016. Since my career started at SAS over five years ago, I have been anticipating this important announcement. In my opinion, this is a major breakthrough for the space of digital intelligence.

In my first year working at SAS, I learned of research and development to address industry needs for digital marketers. Although technologies from Google, Adobe and others address web analytics with measurement reporting, there was a shortcoming.

Historically, web analytics has always had a huge data challenge to cope with since its inception. And when the use case for analysts is to run summary reports, clickstream data is normalized:

Data Aggregation for Web Analytics

It nicely organizes raw clickstream into small, relevant data for reporting. However, this approach presents challenges when performing customer-centric analysis. Why? Holistic customer analysis requires the collection and normalization of digital data at an individual level. This is one of the most important value props of SAS 360 Discover.

Multi-source data stitching and predictive analytics require a data collection methodology that summarizes clickstream:

Data Aggregation for Advanced Analytics

The data is prepared to contextualize all click activity across a customer's digital journey in one table row, including a primary key to map to all visits across browsers and devices. The data table view shifts from being tall and thin to short and wide. The beauty of this is it enables sophisticated analysis to prioritize what is important, and what isn't. This concept of data collection and management is considered a best practice for advanced customer analytics.

How many marketers do you know who wake up in the morning and claim they can't wait to hear about how analysts are spending 80 percent of their time preparing raw web behavior data, rather than focusing on analysis and actionable insights? None, you say? Exactly! Wouldn't you rather hear your marketing analysts spend their time doing this?

20-80 Rule

I have always appreciated SAS for what it can do with structured, semi-structured, and unstructured information, but there has always been one dependency – where do I point SAS to obtain the originating data? SAS 360 Discover eliminates this requirement, and provides data collection mechanisms for your brand's website(s) and mobile apps.

SAS-Tag

 

In addition, the raw semi-structured data streams SAS natively collects are run through a pre-built relational data model using SAS Data Management for various forms of contextualization that stretch far beyond traditional web analytic use cases.

Data Model

The output of this data model schema summarizes all digital visitor behavior at this level of detail:

  • Customers.
  • Anonymous visitors.
  • Sessions (or visits).
  • Interactions (or clicks/hits).

Complete View

The data model schema will allow for additional configurations and introduction of other digital data sources to accommodate your organization's evolving needs. More importantly, the benefits of the output are profound, and listed below is a summary of SAS 360 Discover benefits:

  • Digital data normalization to support online and offline data stitching of customers.
    • When offline data is residing in your organization's data warehouse, information is available at the customer level (not a click or hit level). That's a problem when you want to link it with web or app data. The amount of time analysts spend reshaping raw HIT extracts from their web analytics solution is astonishing, and quite difficult. Customer analysis requires online/offline data stitching, and overcoming this obstacle was a problem SAS set out to solve.
  • Measurement reporting and visualization of customers and segments.
    • The reporting remains critical as an entry stage for analytics. SAS believes there should be no limit to how many reports and dashboards can be produced to meet business objectives. In other words, unlimited ad hoc reports using SAS Visual Analytics, which is the analysis tool that is packaged with SAS 360 Discover
  • Predictive analyticsmachine learning, and data science  of customers and anonymous traffic.
  • Fueling the SAS customer decision hub
    • Brands gain a competitive edge if they stop perceiving customer engagement as a series of discrete interactions and instead see it as customers do: a set of interrelated interactions that, when combined, make up the customer experience. By folding in all known customer level information into a common hub, SAS can analyze, score and take intelligent, contextual actions across channels.

SAS CDH

The path to digital intelligence from traditional web analytics covers the diversity of data, advanced analytic techniques, and injection of prescriptive insights to support decision-making and marketing orchestration. Digital intelligence is a transformation — making it a competitive differentiator. It aims to convert brands to become:

  1. Customer-centric rather than channel-centric
  2. Focused on enterprise goals as opposed to departmental
  3. Enabled for audience activation and optimization
  4. Analytical workhorses

I suspect you would love to see demonstrations of the data that SAS 360 Discover collects from websites and mobile apps in action:

  1. Decision Trees
  2. Clustering
  3. Forecasting
  4. Logistic Regression

In addition, here is the on-demand video of the SAS Global Forum 2016 keynote presentation of SAS Customer Intelligence 360.

As a marketing analyst at heart, it is extremely gratifying to share my excitement for SAS 360 Discover.  The time for predictive customer marketing in the digital ecosystem is here, and the 800-pound gorilla in advanced analytics has just unleashed your new secret weapon.

tags: 360 Discover, Data Driven Marketing, data science, Digital Analytics, Digital Intelligence, digital marketing, Integrated Marketing, marketing analytics, predictive analytics, Predictive Marketing, SAS Customer Intelligence 360

SAS 360 Discover: Predictive marketing's new secret weapon was published on Customer Intelligence.

4月 222016
 

If nothing else you read puts your marketing efforts in perspective, this should:

It is not the employer who pays the wages. Employers only handle the money. It is the customer who pays the wages.

Henry Ford

Our customers deserve our respect. And I believe that, by and large, marketers treat their customers with the respect they deserve. But like with anything in life, creating happy customers only happens by making their experience relevant and satisfying – a complex task in an ever-changing environment.

That’s why SAS has introduced SAS Customer Intelligence 360 . It gives you the ability to integrate data from all of your customer touchpoints and share and gather customer intelligence across your entire organization (not just customer-facing departments).

One of the important modules of the new platform is SAS 360 Engage that does just what the name implies – helps you better engage with your customer by using analytical insights to make the right offers, faster.

Contextual marketing: Being in the moment with your customers

In the omnichannel world that marketers now inhabit, you know that you have to be hyper-alert at all times, or you miss an opportunity because you were too slow or make a misstep you’re not in a channel that your customer prefers.

The real power of SAS 360 Engage is in its ability to help you respond effectively to changes in customer behavior – say for example, the customer breaks a pattern of channel preference and moves to a different channel. You’ll be able to recognize behavior shifts and choose the best action for each interaction.

Personalizing, and even individualizing, the content that is placed on digital properties leads to higher engagement, loyalty and retention. It makes perfect sense. If customers see content that is relevant to them on a webpage or in a mobile app – they are much more likely to remain engaged – versus seeing generic content that is targeted to everyone. Targeting this “segment of one” increases uptake rates of offers and messages.

It's really the best of all possible worlds --  acquiring new customers while delighting existing customers --  using contextual customer engagement across digital channels and devices. Delivering a contextual offer allows you to boost new-customer acquisition rates and ultimately leads to a stronger, more profitable customer base and higher return on marketing investment.

Editor's note: This is an update of a post that originally appeared in April, 2016

How to provide contextual customer experiences was published on Customer Intelligence Blog.

4月 222016
 

SAS Global ForumImpressive innovations and exciting announcements took center stage (literally) at Opening Session of SAS Global Forum 2016. Near the end of the session, SAS CEO Jim Goodnight shared news about SAS’ new architecture that had everyone abuzz.

SAS® Viya™ - There’s a new headliner in Vegas

“We are unveiling a quantum leap forward in making analytics easier to use and accessible to everyone,” Goodnight said. “It’s a major breakthrough and it’s called SAS Viya.”

Goodnight was also quick to point out that SAS Viya will work with customers’ existing SAS 9 software.

Goodnight invited Vice President of Analytic Server Research and Development Oliver Schabenberger, who led the development work for SAS Viya, to join him on stage to discuss the new cloud-based analytic and data management architecture.

Jim Goodnight makes some exciting announcements at SAS Global Forum 2016 Opening Session

Jim Goodnight shares exciting announcements at SAS Global Forum 2016 Opening Session

“We see great diversity in the ways our customers approach and consume analytics,” Schabenberger explained. “From small data to big data. From simple analytics to the toughest machine learning problems. Data in motion and data at rest. Structured and unstructured data. Single users and hundreds of concurrent users. In the cloud and on premises. Data scientists and business users.”

SAS has developed a truly unified and integrated modern environment that everyone can use, whether you are a data scientist or a business analyst. “The beauty of SAS Viya is that it’s unified, open, simple and powerful, and built for the cloud,” said Schabenberger. “Today we are moving to a multi-cloud architecture.”

Goodnight encouraged customers to be “sure to try it out. I think you will enjoy the new SAS Viya.”

The SAS Viya procedural interface will be available to early adopters in 30 days, with visual interfaces scheduled for a September release. Customers can apply to be part of the SAS Viya early preview program.

SAS Customer Intelligence 360 and SAS Analytics for IoT announced

SAS Viya wasn’t the only “star” of the evening.

Goodnight lauded the company’s continuing efforts to globalize and expand ways to make our software faster and easier to use. On the development side, he highlighted SAS Customer Intelligence 360, SAS® Forecast Studio, SAS® Event Stream Processing, SAS® Cybersecurity and the next generation of high performance analytics.

Executive Vice President and SAS Chief Revenue Officer Carl Farrell took the stage to share examples of the many diverse uses of SAS. “Today, our customers are so much more educated on big data and analytics,” Farrell said. “CEOs are realizing that analytics can help them draw more value for their business around that data.”

Farrell singled out several customers including Idea Cellular Ltd. in India, which is processing a billion transactions a day -- something that was impossible before high performance analytics – and Macy’s customer intelligence project that is focused on making real-time offers to customers as they walk through a store, creating a personal and immediate experience.

Farrell also said he was so proud of the SAS work being done outside of business, in the data for good realm, specifically mentioning work in Chile combatting the Zika virus and the work of the Black Dog Institute, which conducts research to improve the lives of people with mental illness.

“Our customers are doing amazing things with SAS that we couldn’t have imagined 40 years ago, and this is just the tip of the iceberg and there’s so much more to come,” Farrell said.

Jeromey Farmer accepts the 2016 User Feedback Award from Annette Harris.

Jeromey Farmer accepts the 2016 User Feedback Award from Annette Harris,

Speaking of stars, Senior Vice President of Technical Support Annette Harris applauded the SAS Super Users for their work in support communities. “SAS users have a rich tradition of helping each other in peer-to-peer forums,” said Harris.

Harris also recognized the 2016 SAS User Feedback Award winner, Jeromey Farmer, a Treasury Officer from the Federal Reserve Bank of St. Louis, noting that SAS gained strong insights from Farmer into how SAS can more seamlessly integrate in a complex and secure environment.

SAS Executive Vice President and Chief Marketing Officer Randy Guard took the stage to announce SAS® Analytics for IoT and to talk about some macro trends he is seeing, including the digital transformation taking place in business and technology. He cited an IDC report that stated by the end of 2017, two-thirds of all CEOs will have digital transformation – across their company – at the top of their agenda.

Customers want help in managing their data, including streaming data, and want analytics embedded in their applications, he added. He calls the latter “analytics any way you want it.”

Customers also want software as a service, including self-service, and want to know how to monetize the connectivity and continuous load of data. “That hits our sweet spot in analytics at SAS,” he said. “The transformation is under way and we are investing money to make this transition smoother for our customers.”

40 and Forward

Woven throughout Opening Session were references to SAS’ 40 years in business.

Asked about what has changed over the years, Goodnight recalled that when SAS started, there was one product on a single machine. Now we have more than 200 products on dozens of machines. Back then, a computer could process about 500 instructions a second. Now it’s up to 2 to 3 billion instructions a second. The very first disk drives were two feet across, with tapes containing about five million bytes. Now we can get 1.2 terabytes in the size of a K-cup.

As for key milestones over the 40 years, Goodnight said two things came to mind. One was the introduction of multivendor architecture in the mid-1980s so our software could run on all platforms, and the other was the advent of massively parallel computing.

Not surprisingly, given the milestone anniversary year for SAS, the Opening Session ended with a video retrospective looking back on world news from the 1970s through today, with a cameo appearance by Goodnight from the early days of SAS.

If you want to view a recording of Opening Session, visit the SAS Global Forum Video Portal.

tags: SAS Analytics for IoT, SAS Customer Intelligence 360, SAS Global Forum, SAS Viya

Highlights from SAS Global Forum: Opening Session was published on SAS Users.

4月 212016
 

Generating rich customer insights – the centerpiece of successful marketing efforts – is more arduous and crucial in today’s digitally saturated world. Brands must not only understand their customers across all touch points, but analyze and glean patterns from their behavior, and quickly respond to the faintest signs of changing preferences and needs.

 Our multi-screen world creates even more complexity for the marketer. A recent Nielsen study revealed that the typical US consumer now owns four digital devices, and spends 60 hours a week consuming content across devices.

Plus, a majority of US households now own web-connected televisions, computers and smartphones. Amidst all these digital devices, consumers also have numerous choices for how and when they access and engage with that content as part of their customer journey.

Smoother customer journeys, not fragmented hops

Given the growing number of digital touch points where customers now interact with companies, marketing often can’t do what’s needed all on its own. Many brands typically think of customers (and the insights gleaned from them) as being “owned” by particular function – marketing owns brand management; service 483134143owns support; sales owns customer relationships; retail operations own the in-store experience, etc.

As a result, the customer data and corresponding insights are fragmented across these functions. When businesses can’t effectively combine customer insights across multiple digital channels, let alone across multiple customer-facing functions, marketers are less confident about their efforts.

This is why brands must have effective technologies and processes in place so they do not lose track when charting, designing and measuring the customer journey. Whether customers are browsing your brand website, completing a purchase on your mobile app or talking with a service representative via online chat, customers demand to be recognized and treated consistently no matter the channel.

Broader, deeper customer knowledge

To this end, SAS 360 Discover goes beyond channel-level data to collect detailed customer-level data for deeper customer understanding and better marketing decisions. SAS 360 Discover is part of the new SAS Customer Intelligence 360 suite that can help you create a new level of customer experience.

Now, you can go beyond page views and clicks to knowing why customers behave as they do on your digital properties, what are the characteristics of your most profitable customers and which digital interactions successfully resulted in loyal, profitable relationships?

In today’s marketing environment, brands gain a competitive edge if they stop perceiving customer engagement as a series of discrete interactions and instead see it as customers do: a set of interrelated interactions that, when combined, make up the customer experience.

 

tags: customer data, customer experience, digital marketing, omnichannel, SAS 360 Discover, SAS Customer Intelligence 360

SAS 360 Discover: Elevating the role of customer insights for confident digital marketing was published on Customer Intelligence.