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 172016
 

 

 

Some iot-security-challengesestimates suggest that the number of connected objects will be more than 50 billion by 2020. Each of us will own between six and 10 connected objects. But what exactly is the Internet of Things (IoT)? Wikipedia describes it as “the network of physical objects — devices, vehicles, buildings and other items — embedded with electronics, software, sensors, and network connectivity that enables these objects to collect and exchange data. “From product to service: a paradigm shift?

The numbers show that the industrial sector is very much an ‘early adopter’ when it comes to seeing the added value of connected objects.

The IoT, or Internet 4.0, has given industry a new way to organize production. This ‘Internet of smart factory objects’ is characterized by continuous and immediate communication between the various tools and workstations integrated into production lines and supply. The objective is to make a ‘smart’ factory, capable of greater flexibility in production and more efficient allocation of resources.

 

But above all, the manufacturing industry has been among the first to see that the Internet of Things is a fantastic opportunity. In particular, it has enthusiastically adopted the ability to monitor machines remotely to avoid or reduce downtime by anticipating wear. This change has enabled the sector to reinvent itself by offering its customers a ‘service and a solution,’ rather than ‘machine and product.’ In other words, companies no longer sell compressors, they sell compressed air; not drills, but holes.

Connected objects speak. But will they speak in your best interests?

A connected object has three parts:

  • A sensor in the object to collect information
  • A system that collects and manages the information received
  • A network to connect them together

But it’s important to remember that in a machine-to-machine (M2M) model, objects can communicate and make decisions to help you get the best outcomes.

My thermostat, for example, receives information that all the house lights are off and it can therefore reduce the temperature. Or it gets the message that my garage door is open, and knows I’ve returned and it must warm up the house. Even simpler, my washing machine will recognize the QR code on my favourite shirt and set itself to the right temperature.

But in reality, which objects will communicate? And will all brands speak to each other? Have we really thought about this? And are you ready? Because things are already talking, and soon they may start to retain information and even adapt.

The Internet of Things could shake up distribution business models

20 years ago, barcodes enabled retailers to find out what customers were buying through loyalty cards. Retailers quickly realised the power of transactional data and how it could be used. And there is no reason for them to change.

Retailers (whether “pure players” or “bricks & mortar”) do not want to run the risk of being by passed by the IoT. And there’s plenty of potential for that.

Your connected printer, for example, may be set up to go straight to the brand to organize new ink cartridges, avoiding any intermediaries.

Amazon saw this threat before many of its competitors. In April 2015, in Seattle, the company launched its Dash button, a barcode reader connected to the Wi Fi home network. It can scan products or use voice recognition to order from a list of products that will be delivered by Amazon.

Now Amazon has unveiled Amazon Dash Replenishment. This allows a directly-connected device to place an order for consumables. No need for any button this time. Now the machine automatically places the order when it decides it is necessary. Printer cartridges, reams of paper, softener salt ... the list of available consumables is pretty extensive.

By managing direct communication to these connected objects, Amazon has managed to make itself indispensable to consumers and manufacturers, and so maintain its position in the value chain. Other retailers will have to follow suit if they want to remain competitive.

As Harold Grondel, of Productize, the first agency for the IoT, says, “Once upon a time, someone noticed that some devices had a battery, and how much value that added. Before long, the same thing will happen with connectivity: More and more devices will be connected, and our lives will change again.”

It’s becoming increasingly apparent that some of the biggest disruption from the IoT will happen with streaming data. Given customers’ expectations for real-time engagement and the accelerating speed of business, being able to make fact-based automated decisions are what will determine organizations’ readiness.

This paper, Understanding Data Streams in IoT, does a great job exploring the opportunities with streaming data using SAS Event Stream Processing. I hope you find it helpful for getting ready for the IoT disruption!

tags: customer intelligence, disruptive, IoT

Disruption from the Internet of Things: Are you ready? was published on Customer Intelligence.

6月 142016
 

“Good afternoon, Mr. Yakamoto. How did you like that three-pack of tank tops you bought last time you were in?” Washington D.C. Year 2054. Chief of PreCrime John Anderton is running from the law for a crime he has not committed yet. After a risky eye transplant in order to […]

Location analytics and the 'Minority Report' approach was published on SAS Voices.

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.

6月 012016
 

I wanted to share some thoughts about when real-time decisioning is actually necessary. I guess the reason for wanting to do this is to balance the view that everything can be actioned by trigger event campaigns executed in real time.

So have a look at the graph below – with the x axis being time, and the y axis being some kind of KPI measure – for example, response (or perhaps the effect on net promoter score).

NPS chart

 

I’ve drawn four lines on this graph to help make the point:

  • The green line – just going flat over time – suggests that sometimes response doesn’t vary much over time. A good example of this would be high-ticket items or a mortgage, perhaps. Realistically – if you send a message to a customer after they have hovered over the mortgage product on your website – it isn’t really going to seal the deal there and then.
  • The red line shows that response can drop over time. I’m not sure it’s quite as linear as this, but essentially, this is a case of ”the sooner the better”, and so real-time decisioning is important – but I would argue not essential. It depends on the cost to execute and the overall ROI.
  • The black line suggests that real-time execution of a decision is essential – if you don’t act quickly, the moment has gone, as has your customer! This is more akin to perhaps helping a customer who is having difficulty on line – e.g. filling out a form – if you don’t help them immediately, they may go away, never to return. But do show a bit of caution, too. Is the profile more like the blue line?
  • The blue shows that if you act too quickly, you may actually destroy responsiveness (or more likely value). Making an offer to a customer on an abandoned cart can come too quickly – equally there are overtones of Big Brother here.

Three ways to determine the best action

So, how do you find out what kind of profile is the most useful for the particular action that you want to take?

Well, first of all, I would just like to give a shout out to marketing nous (i.e., common sense) because in fairness, some of these things can be obvious. For example, if you reduce a customer’s download speed as they approach their limit you help the customer avoid incurring overages – it is clear that more rapid decisioning will result in better responses.

Secondly, the answers are probably in your data. I hear a lot about attrition and churn triggers – such as browsing competitor websites. What could you do? Well, it would be pretty easy to observe when that browsing behaviour occurred and then plot the churn or attrition event out over time.  There are probably many natural tests that are sitting in your data right now.

Finally there is multivariate testing. Why not try making that abandoned cart or basket offer at Hour 1/2/3 or Day 1/2/3?  That way, you can get a feel for how behaviour varies over time so that you can then make the optimal decision for the customer and your business.

If you want to find out a more about how SAS helps its clients act upon insight, visit the customer intelligence solutions page.

tags: data exploration, net promoter score, real-time decisioning

Real-time decisioning: Is it always your best option? 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.

5月 112016
 

“All for one and one for all” is best known as the motto from “The Three Musketeers”, but this phrase could easily sum up the growing trend in social brokers. With advanced analytical techniques like generalized linear modeling insurance companies have created more granular pricing structures. But despite the assertions […]

The post How social brokers is changing insurance appeared first on The Analytic Insurer.