customer intelligence

8月 112017
 

How can you tell if your marketing is working? How can you determine the cost and return of your campaigns? How can you decide what to do next? An effective way to answer these questions is to monitor a set of key performance indicators, or KPIs.

KPIs are the basic statistics that give you a clear idea of how your website (or app) is performing. KPIs vary by predetermined business objectives, and measure progress towards those specific objectives. In the famous words of Avinash Kaushik, KPIs should be:

  • Uncomplex.
  • Relevant.
  • Timely.
  • Instantly useful.

An example that fits this description, with applicability to profit, nonprofit, and e-commerce business models, would be the almighty conversion rate.  In digital analytics this metric is interpreted as the proportion of visitors to a website or app who take action to go beyond a casual content view or site visit, as a result of subtle or direct requests from marketers, advertisers, and content creators.

{\mathrm {Conversion\ rate}}={\frac {{\mathrm {Number\ of\ Goal\ Achievements}}}{{\mathrm {Visitors}}}}

Although successful conversions can be defined differently based on your use case, it is easy to see why this KPI is uncomplex, relevant, timely, and useful. We can even splinter this metric into two types:

Macro conversion – Someone completes an action that is important to your business (like making you some money).

Micro conversion – An indicator that a visitor is moving towards a macro conversion (like progressing through a multi-step sales funnel to eventually make you some money)

Regardless of the conversion type, I have always found that reporting on this KPI is a popular request for analysts from middle management and executives. However, it isn't difficult to anticipate what is coming next from the most important person in your business world:

"How can we improve our conversion rate going forward?"

You can report, slice, dice, and segment away in your web analytics platform, but needles in haystacks are not easily discovered unless we adapt. I know change can be difficult, but allow me to make the case for machine learning and hyperparameters within the discipline of digital analytics. A trendy subject for some, a scary subject for others, but my intent is to lend a practitioner's viewpoint. Analytical decision trees are an excellent way to begin because of their frequent usage within marketing applications, primarily due to their approachability, and ease of interpretation.

Whether your use case is for supervised segmentation, or propensity scoring, this form of predictive analytics can be labeled as machine learning due to algorithm's approach to analyzing data. Have you ever researched how trees actually learn before arriving to a final result? It's beautiful math. However, it doesn't end there. We are living in a moment where more sophisticated machine learning algorithms have emerged that can comparatively increase predictive accuracy, precision, and most importantly – marketing-centric KPIs, while being just as easy to construct.

Using the same data inputs across different analysis types like Forests, Gradient Boosting, and Neural Networks, analysts can compare model fit statistics to determine which approach will have the most meaningful impact on your organization's objectives. Terms like cumulative lift or misclassification may not mean much to you, but they are the keys to selecting the math that best answers how conversion rate can be improved by transparently disclosing accurate views of variable importance.

So is that it? I can just drag and drop my way through the world of visual analytics to optimize against KPIs. Well, there is a tradeoff to discuss here. For some organizations, simply using a machine learning algorithm enabled by an easy-to-use software interface will help improve conversion rate tactics on a mobile app screen experience as compared to not using an analytic method. But an algorithm cannot be expected to perform well as a one size fits all approach for every type of business problem. It is a reasonable question to ask oneself if opportunity is being left on the table to motivate analysts to refine the math to the use case. Learning to improve how an algorithm arrives at a final result should not be scary because it can get a little technical. It's actually quite the opposite, and I love learning how machine learning can be elegant. This is why I want to talk about hyperparameters!

Anyone who has ever built a predictive model understands the iterative nature of adjusting various property settings of an algorithm in an effort to optimize the analysis results. As we endlessly try to improve the predictive accuracy, the process becomes painfully repetitive and manual. Due to the typical length of time an analyst can spend on this task alone - from hours, days, or longer - the approach defies our ability as humans to practically arrive at an optimized final solution. Sometimes referred to as auto tuning, hyperparameters address this issue by exploring different combinations of algorithm options, training a model for each option in an effort to find the best model. Imagine running 1000s of iterations of a website conversion propensity model across different property threshold ranges in a single execution. As a result, these models can improve significantly across important fit statistics that relate directly to your KPIs.

At the end of running an analysis with hyperparameters, the best recipe will be identified. Just like any other modeling project, the ability to action off of the insight is no different, from traditional model score code to next-best-action recommendations infused into your mobile app's personalization technology. That's genuinely exciting, courtesy of recent innovations in distributed analytical engines with feature-rich building blocks for machine-learning activities.

If the subject of hyperparameters is new to you, I encourage you to watch this short video.

This will be one of the main themes of my presentations at Analytics Experience 2017 in Washington DC. Using digital data collected by SAS Customer Intelligence 360 and analyzing it with SAS Visual Data Mining & Machine Learning on VIYA, I want to share the excitement I am feeling about digital intelligence and predictive personalization. I hope you'll consider joining the SAS family for an awesome agenda between September 18th-20th in our nation's capital.

Hyperparameters, digital analytics, and key performance indicators was published on Customer Intelligence Blog.

7月 062017
 

If there’s one thing today’s organisations can agree on, it’s that the world has changed. In the words of Tony Mooney, former managing director of insight and decision science at Sky, speaking at the recent SAS Data & Customer Experience Forum, “we are now living in a world that is volatile, uncertain, ambiguous and complex.”

As our environment has evolved, so have our techniques for understanding, measuring and motivating people and groups.

Struggling to keep up

The modern consumer is transitioning from digital-first to digital-only and they expect every business in every industry to achieve “digital parity.” In other words, your business needs to be as easy to do business with as the best of what your customer has encountered online and in self-service solutions.

 And it’s not just in the digitally native millennial generation where this transition is occurring. These changes are being realised across the generations because customers who aren’t millennials have been influenced by a millennial outlook. As a result of these changes, our expectation of brand responsibility has evolved as has our interaction with brands in a data-driven world.

Over the last decade we’ve seen a plethora of marketing technologies thrust upon us to harness this new world view and at the same time, influence it. From SMS, web analytics, mobile apps, web personalisation, recommendation engines, conversion optimisation platforms . . .the list goes on. The upshot of the investment in these myriad technologies is that it has created many disconnected silos across the organisation, each with their own set of rules and logic, focused on an individual channel and that frequently don’t speak well together. Unfortunately for consumers, the end result is a fragmented and inconsistent experience and marketers find themselves still failing to deliver a stellar customer experience.

Customer experience has been at the core of conversations about engagement for the last few years. The goal? To interact with customers with the most relevant communications at the right time via the right channel. There needs to be understanding of a customer’s attitudes preferences, interests and needs. These must be balanced with an understanding of customer lifetime value, propensity and risk to make accurate and profitable decisions about the right content, the right offer, the right price or the right product. If this can be achieved at the moment of customer engagement, then those brands won’t be the ones that get left behind.

The real-time opportunity

When we are managing outbound communications to consumers, planning email or direct marketing campaigns, we have time to consider all of the inputs from both a customer insight perspective and an internal business perspective before we decide on the most relevant content. But when a customer proactively engages with us, over the web, via an app, with a call centre agent or in person, we have just milliseconds at worst and seconds at best to make the most accurate and profitable decision. This becomes a major challenge.

According to our recent research and speaking to key decision-makers in consumer organisations, one in five believe the ability to interact with customers and adjust those interactions in real-time (based on the most up to date insight and context), would see revenues jump by as much as 20-40 percent. The majority of decision-makers expect revenues to increase by at least 10 percent.

Daragh Kelly, Data Strategy & Innovation Director at Sky, echoed the thoughts of Mooney at the forum, saying the key to achieving this is “improving all of the small decisions that are made by organisations when they interact with customers." Small decisions are those made in response to an individual customer’s choices and a focus on small decisions offers benefits through a multitude of applications:

  • Improving the management of risk and the matching of price to risk.
  • Reducing or eliminating fraud and waste.
  • Increasing revenue by making the most of every opportunity
  • Improving the utilisation of constrained resources across the organisation, all whilst delivering a superior customer experience.

Organisations need to adapt from making decisions at the speed of the organisation to making decision at the speed of the customer.  For instance, if a customer chooses not to engage with an offer online, based on all the information known about that individual including their lifetime value, propensity and attitudes, as well as new contextual information (e.g., their location, the device they’re using, etc.), they can be served a more suitable alternative within seconds.

Planning and process

To remain relevant in increasingly competitive and disruptive markets and to meet the expectations of the modern consumer, organisations need to put a framework in place to enable them to make better "small decisions" at those moment of customer interaction, which are the true "crunch" moments for individual customers.

Permanent TSB is one organisation that has started to implement such a framework. Underpinned by in depth and advanced customer analytics, the organisation has moved from engaging with customers via outbound only calling campaign structure to developing an omni-channel engagement framework. Through customer analytics, the organisation has been able to prioritise activities and deliver services, offers and updates highly tailored for individual customers.

Businesses like Allied Irish Bank have made analytics a key strategic pillar, with buy-in from the C-level down through the organisation. Customer analytics is being used to drive informed and accurate decision-making right across the business.

Consumers don’t think in channels. They just want to do business with you in a way that is easy, consistent and relevant, regardless of how and when they interact with you. This means they expect you to know them as a customer, to understand their previous engagements and transactions with you and to use the data they make available to you. Only then can you deliver a personalised and relevant experience, every time. Broad brush segmentation approaches to customer interactions based on rules and demographics will no longer cut it. Instead we need to get to a segment of one. Making analytical decisions, based on an in-depth understanding of each individual customer and making those decisions at the speed of the customer rather than at the speed of the organisation, is the key to delivering the superior customer experiences now being demanded by all consumers.

Find out more about how data analytics can deliver personalised decisions to customers in real-time.

The age of now: focusing on the segment of one was published on Customer Intelligence Blog.

7月 062017
 

If there’s one thing today’s organisations can agree on, it’s that the world has changed. In the words of Tony Mooney, former managing director of insight and decision science at Sky, speaking at the recent SAS Data & Customer Experience Forum, “we are now living in a world that is volatile, uncertain, ambiguous and complex.”

As our environment has evolved, so have our techniques for understanding, measuring and motivating people and groups.

Struggling to keep up

The modern consumer is transitioning from digital-first to digital-only and they expect every business in every industry to achieve “digital parity.” In other words, your business needs to be as easy to do business with as the best of what your customer has encountered online and in self-service solutions.

 And it’s not just in the digitally native millennial generation where this transition is occurring. These changes are being realised across the generations because customers who aren’t millennials have been influenced by a millennial outlook. As a result of these changes, our expectation of brand responsibility has evolved as has our interaction with brands in a data-driven world.

Over the last decade we’ve seen a plethora of marketing technologies thrust upon us to harness this new world view and at the same time, influence it. From SMS, web analytics, mobile apps, web personalisation, recommendation engines, conversion optimisation platforms . . .the list goes on. The upshot of the investment in these myriad technologies is that it has created many disconnected silos across the organisation, each with their own set of rules and logic, focused on an individual channel and that frequently don’t speak well together. Unfortunately for consumers, the end result is a fragmented and inconsistent experience and marketers find themselves still failing to deliver a stellar customer experience.

Customer experience has been at the core of conversations about engagement for the last few years. The goal? To interact with customers with the most relevant communications at the right time via the right channel. There needs to be understanding of a customer’s attitudes preferences, interests and needs. These must be balanced with an understanding of customer lifetime value, propensity and risk to make accurate and profitable decisions about the right content, the right offer, the right price or the right product. If this can be achieved at the moment of customer engagement, then those brands won’t be the ones that get left behind.

The real-time opportunity

When we are managing outbound communications to consumers, planning email or direct marketing campaigns, we have time to consider all of the inputs from both a customer insight perspective and an internal business perspective before we decide on the most relevant content. But when a customer proactively engages with us, over the web, via an app, with a call centre agent or in person, we have just milliseconds at worst and seconds at best to make the most accurate and profitable decision. This becomes a major challenge.

According to our recent research and speaking to key decision-makers in consumer organisations, one in five believe the ability to interact with customers and adjust those interactions in real-time (based on the most up to date insight and context), would see revenues jump by as much as 20-40 percent. The majority of decision-makers expect revenues to increase by at least 10 percent.

Daragh Kelly, Data Strategy & Innovation Director at Sky, echoed the thoughts of Mooney at the forum, saying the key to achieving this is “improving all of the small decisions that are made by organisations when they interact with customers." Small decisions are those made in response to an individual customer’s choices and a focus on small decisions offers benefits through a multitude of applications:

  • Improving the management of risk and the matching of price to risk.
  • Reducing or eliminating fraud and waste.
  • Increasing revenue by making the most of every opportunity
  • Improving the utilisation of constrained resources across the organisation, all whilst delivering a superior customer experience.

Organisations need to adapt from making decisions at the speed of the organisation to making decision at the speed of the customer.  For instance, if a customer chooses not to engage with an offer online, based on all the information known about that individual including their lifetime value, propensity and attitudes, as well as new contextual information (e.g., their location, the device they’re using, etc.), they can be served a more suitable alternative within seconds.

Planning and process

To remain relevant in increasingly competitive and disruptive markets and to meet the expectations of the modern consumer, organisations need to put a framework in place to enable them to make better "small decisions" at those moment of customer interaction, which are the true "crunch" moments for individual customers.

Permanent TSB is one organisation that has started to implement such a framework. Underpinned by in depth and advanced customer analytics, the organisation has moved from engaging with customers via outbound only calling campaign structure to developing an omni-channel engagement framework. Through customer analytics, the organisation has been able to prioritise activities and deliver services, offers and updates highly tailored for individual customers.

Businesses like Allied Irish Bank have made analytics a key strategic pillar, with buy-in from the C-level down through the organisation. Customer analytics is being used to drive informed and accurate decision-making right across the business.

Consumers don’t think in channels. They just want to do business with you in a way that is easy, consistent and relevant, regardless of how and when they interact with you. This means they expect you to know them as a customer, to understand their previous engagements and transactions with you and to use the data they make available to you. Only then can you deliver a personalised and relevant experience, every time. Broad brush segmentation approaches to customer interactions based on rules and demographics will no longer cut it. Instead we need to get to a segment of one. Making analytical decisions, based on an in-depth understanding of each individual customer and making those decisions at the speed of the customer rather than at the speed of the organisation, is the key to delivering the superior customer experiences now being demanded by all consumers.

Find out more about how data analytics can deliver personalised decisions to customers in real-time.

The age of now: focusing on the segment of one was published on Customer Intelligence Blog.

6月 162017
 

Tiffany Carpenter, head of customer intelligence at SAS UK & Ireland, looks at the benefits of real-time customer experience and offers a preview into how analytics is powering hyper-personalised customer journeys

In recent years, customer experience has become an important battleground for brands. Yet, in a hyper-connected, hyper-competitive environment where it is becoming increasingly difficult to compete on product or price alone, the concept of customer experience has grown in importance as organisations fight to remain relevant and deliver against customer expectations.

Customers expect the organisations they are interacting with to make it easy to business with them. They expect a seamless experience regardless of how they engage with you whether it be online, via an app, a call centre or in person; and they expect their personal information and data that they have made available, to be used appropriately by organisations to deliver relevant experiences.  To deliver against these expectations,  businesses must first fully understand the wants and needs of current and prospective customers. While this may sound simple enough in principle, most organisations are only using a limited amount of data to try to understand their customers. In fact, most UK organisations admit to using less than half of the valuable data available to them, and they will often analyse it using basic tools or spreadsheets that fail to provide a single view of the customer.

Achieving a segment of one

What’s needed is an approach that allows organisations to concentrate on delivering a superior customer experience by achieving relevancy at every touchpoint based on an understanding of each individual customer – a segment of one.

Today’s customers want the call centre to know when they have just been on the website. They want brands to adjust their marketing strategies if they’ve  made a complaint or negatively reviewed a product or service For businesses, this means having access to a ‘central brain’ that can analyse of all the data available in a timely manner with the ability to inject that insight into any customer interaction across any department and channel -  in real-time if necessary.

This means using data about what’s already happened as well as what’s happening now, to predict what’s going to happen in the future, what the best outcomes will be and make profitable and accurate decisions at each point of a customer interaction.

The central brain

In the race to digitalisation, the mistake many businesses make when trying to achieve a segment of one is placing too much emphasis and narrow focus on digital data. Each lifecycle stage, across each channel is important – from initial consideration, to active evaluation, to the moment of purchase and even the post-purchase experience. Key to successful customer intelligence strategies is tying together offline and online data to get a better understanding of the customer.

Rather than analysing data from a single digital transaction or following customers around in a digital world, It’s more important to understand what happens prior, during and after a digital interaction to create a full picture of behavioural insights. To truly understand customer behaviour and deliver the most value at each customer touch point non-digital data such as demographic, psychographic, transactional, risk and many others types of data - that sit both outside and inside the digital environment - needs to be analysed and mapped to specific stages in the customer lifecycle.

More importantly, once businesses gain these insights, they need to consider how they use this insight to make the right decisions that deliver value to the business. Where appropriate those decisions need to be made in real time and injected into the customer interaction channel at the point of engagement. Each stage of the customer journey needs to be viewed as an opportunity to improve the customer experience. And each stage is an opportunity to gain more insight that can be fed back into marketing processes to draw from the next time. Only then can you deliver the right message at the right time via the right channel.

A personalised experience in real-time

Shop Direct is a great example of a business embracing this approach. Its goal was to make it easier for customers to shop with them, thereby improving the customer experience whilst increasing customer spend. As a 40-year-old business that started as a catalogue company, it was sitting on a huge amount of data that had been captured over the years about its customers and they wanted to find a way to use that data to deliver a highly personalised customer experience.

At the time, a customer shopping for jeans on their Very.co.uk website could be presented with 50 pages of options to scroll through. By analysing the existing data Shop Direct is now able to predict which jeans a customer is most likely to be interested in and personalise the customer’s shopping experience. This is done via an individually personalised sort order in real time to show the products they are most interested in first. Harnessing data and advanced analytics to deliver unparalleled levels of personalistion has seen Shop Direct’s profits surge by 43%.

Group CEO at Shop Direct, Alex Baldock, has said that the company is "all about making it easier for our customers to shop. That's why we're passionate about personalisation. We want to tailor everything for our customer; the shop they visit and how we engage with them - before, during and after they’ve shopped."

The survival factor

In the future, developing a superior customer experience will rely on understanding the balance between delivering the right decision in real-time and giving yourself time to make the right decision. It’s crucial to remember that not every decision about the customer experience needs to be managed in real-time. Organisations have huge amounts of data at their fingertips that they can use to predict and plan to shape products, services and messages.

However, there will be moments when a decision needs to be  made in real-time as to what the right content, message, offer or recommendation for an individual customer might be. This decision should not just be based on what area of a website a customer clicked on, or whether they liked your facebook page. To make accurate and profitable decisions requires insight into offline and online historical data. This must be coupled with real time contextual data as well as a clear understanding of business goals and objectives, and clarity around the predicted outcome of each possible decision. To achieve this, businesses must move away from a channel-specific approach with fragmented systems and rules and embrace a centralised analytical decisioning capability. This would have access to all relevant data, a centralised set of logic and rules, and be able to automate complex analytical decisions at scale and push those out to any channel across any business unit at the right time.

This will need to be what underpins the entire business; the organisations that get this right, will be the ones that survive.

For more insights into how analytics is powering today’s hyper-personalised customer journey, come along to the SAS Data and Customer Experience Forum where we will be announcing headline findings from new research exploring where UK businesses are on the journey to delivering a real-time customer experience.

Transforming the customer experience with analytics was published on Customer Intelligence Blog.

6月 162017
 

Tiffany Carpenter, head of customer intelligence at SAS UK & Ireland, looks at the benefits of real-time customer experience and offers a preview into how analytics is powering hyper-personalised customer journeys

In recent years, customer experience has become an important battleground for brands. Yet, in a hyper-connected, hyper-competitive environment where it is becoming increasingly difficult to compete on product or price alone, the concept of customer experience has grown in importance as organisations fight to remain relevant and deliver against customer expectations.

Customers expect the organisations they are interacting with to make it easy to business with them. They expect a seamless experience regardless of how they engage with you whether it be online, via an app, a call centre or in person; and they expect their personal information and data that they have made available, to be used appropriately by organisations to deliver relevant experiences.  To deliver against these expectations,  businesses must first fully understand the wants and needs of current and prospective customers. While this may sound simple enough in principle, most organisations are only using a limited amount of data to try to understand their customers. In fact, most UK organisations admit to using less than half of the valuable data available to them, and they will often analyse it using basic tools or spreadsheets that fail to provide a single view of the customer.

Achieving a segment of one

What’s needed is an approach that allows organisations to concentrate on delivering a superior customer experience by achieving relevancy at every touchpoint based on an understanding of each individual customer – a segment of one.

Today’s customers want the call centre to know when they have just been on the website. They want brands to adjust their marketing strategies if they’ve  made a complaint or negatively reviewed a product or service For businesses, this means having access to a ‘central brain’ that can analyse of all the data available in a timely manner with the ability to inject that insight into any customer interaction across any department and channel -  in real-time if necessary.

This means using data about what’s already happened as well as what’s happening now, to predict what’s going to happen in the future, what the best outcomes will be and make profitable and accurate decisions at each point of a customer interaction.

The central brain

In the race to digitalisation, the mistake many businesses make when trying to achieve a segment of one is placing too much emphasis and narrow focus on digital data. Each lifecycle stage, across each channel is important – from initial consideration, to active evaluation, to the moment of purchase and even the post-purchase experience. Key to successful customer intelligence strategies is tying together offline and online data to get a better understanding of the customer.

Rather than analysing data from a single digital transaction or following customers around in a digital world, It’s more important to understand what happens prior, during and after a digital interaction to create a full picture of behavioural insights. To truly understand customer behaviour and deliver the most value at each customer touch point non-digital data such as demographic, psychographic, transactional, risk and many others types of data - that sit both outside and inside the digital environment - needs to be analysed and mapped to specific stages in the customer lifecycle.

More importantly, once businesses gain these insights, they need to consider how they use this insight to make the right decisions that deliver value to the business. Where appropriate those decisions need to be made in real time and injected into the customer interaction channel at the point of engagement. Each stage of the customer journey needs to be viewed as an opportunity to improve the customer experience. And each stage is an opportunity to gain more insight that can be fed back into marketing processes to draw from the next time. Only then can you deliver the right message at the right time via the right channel.

A personalised experience in real-time

Shop Direct is a great example of a business embracing this approach. Its goal was to make it easier for customers to shop with them, thereby improving the customer experience whilst increasing customer spend. As a 40-year-old business that started as a catalogue company, it was sitting on a huge amount of data that had been captured over the years about its customers and they wanted to find a way to use that data to deliver a highly personalised customer experience.

At the time, a customer shopping for jeans on their Very.co.uk website could be presented with 50 pages of options to scroll through. By analysing the existing data Shop Direct is now able to predict which jeans a customer is most likely to be interested in and personalise the customer’s shopping experience. This is done via an individually personalised sort order in real time to show the products they are most interested in first. Harnessing data and advanced analytics to deliver unparalleled levels of personalistion has seen Shop Direct’s profits surge by 43%.

Group CEO at Shop Direct, Alex Baldock, has said that the company is "all about making it easier for our customers to shop. That's why we're passionate about personalisation. We want to tailor everything for our customer; the shop they visit and how we engage with them - before, during and after they’ve shopped."

The survival factor

In the future, developing a superior customer experience will rely on understanding the balance between delivering the right decision in real-time and giving yourself time to make the right decision. It’s crucial to remember that not every decision about the customer experience needs to be managed in real-time. Organisations have huge amounts of data at their fingertips that they can use to predict and plan to shape products, services and messages.

However, there will be moments when a decision needs to be  made in real-time as to what the right content, message, offer or recommendation for an individual customer might be. This decision should not just be based on what area of a website a customer clicked on, or whether they liked your facebook page. To make accurate and profitable decisions requires insight into offline and online historical data. This must be coupled with real time contextual data as well as a clear understanding of business goals and objectives, and clarity around the predicted outcome of each possible decision. To achieve this, businesses must move away from a channel-specific approach with fragmented systems and rules and embrace a centralised analytical decisioning capability. This would have access to all relevant data, a centralised set of logic and rules, and be able to automate complex analytical decisions at scale and push those out to any channel across any business unit at the right time.

This will need to be what underpins the entire business; the organisations that get this right, will be the ones that survive.

For more insights into how analytics is powering today’s hyper-personalised customer journey, come along to the SAS Data and Customer Experience Forum where we will be announcing headline findings from new research exploring where UK businesses are on the journey to delivering a real-time customer experience.

Transforming the customer experience with analytics was published on Customer Intelligence Blog.

1月 282017
 

Digital intelligence is a trending term in the space of digital marketing analytics that needs to be demystified. Let's begin by defining what a digital marketing analytics platform is:

Digital marketing analytics platforms are technology applications used by customer intelligence ninjas to understand and improve consumer experiences. Prospecting, acquiring, and holding on to digital-savvy customers depends on understanding their multidevice behavior, and derived insight fuels marketing optimization strategies. These platforms come in different flavors, from stand-alone niche offerings, to comprehensive end-to-end vehicles performing functions from data collection through analysis and visualization.

However, not every platform is built equally from an analytical perspective. According to Brian Hopkins, a Forrester analyst, firms that excel at using data and analytics to optimize their digital businesses will together generate $1.2 trillion per annum in revenue by 2020. And digital intelligence — the practice of continuously optimizing customer experiences with online and offline data, advanced analytics and prescriptive insights — supports every insights-driven business. Digital intelligence is the antidote to the weaknesses of analytically immature platforms, leaving the world of siloed reporting behind and maturing towards actionable, predictive marketing. Here are a couple of items to consider:

  • Today's device-crazed consumers flirt with brands across a variety of interactions during a customer life cycle. However, most organizations seem to focus on website activity in one bucket, mobile in another, and social in . . . you see where I'm going. Strategic plans often fall short in applying digital intelligence across all channels — including offline interactions like customer support or product development.
  • Powerful digital intelligence uses timely delivery of prescriptive insights to positively influence customer experiences. This requires integration of data, analytics and the systems that interact with the consumer. Yet many teams manually apply analytics and deliver analysis via endless reports and dashboards that look retroactively at past behavior — begging business leaders to question the true value and potential impact of digital analysis.

As consumer behavioral needs and preferences shifts over time, the proportion of digital to non-digital interactions is growing. With the recent release of Customer Intelligence 360, SAS has carefully considered feedback from our customers (and industry analysts) to create technology that supports a modern digital intelligence strategy in guiding an organization to:

  • Enrich your first-party customer data with user level data from web and mobile channels. It's time to graduate from aggregating data for reporting purposes to the collection and retention of granular, customer-level data. It is individual-level data that drives advanced segmentation and continuous optimization of customer interactions through personalization, targeting and recommendations.
  • Keep up with customers through machine learning, data science and advanced analytics. The increasing pace of digital customer interactions requires analytical maturity to optimize marketing and experiences. By enriching first-party customer data with infusions of web and mobile behavior, and more importantly, in the analysis-ready format for sophisticated analytics, 360 Discover invites analysts to use their favorite analytic tool and tear down the limitations of traditional web analytics.
  • Automate targeting, channel orchestration and personalization. Brands struggle with too few resources to support the manual design and data-driven design of customer experiences. Connecting first-party data that encompasses both offline and online attributes with actionable propensity scores and algorithmically-defined segments through digital channel interactions is the agenda. If that sounds mythical, check out a video example of how SAS brings this to life.

The question now is - are you ready? Learn more here of why we are so excited about enabling digital intelligence for our customers, and how this benefits testing, targeting, and optimization of customer experiences.

 

tags: Customer Engagement, customer intelligence, Customer Intelligence 360, customer journey, data science, Digital Intelligence, machine learning, marketing analytics, personalization, predictive analytics, Predictive Personalization, Prescriptive Analytics

Digital intelligence for optimizing customer engagement was published on Customer Intelligence.

1月 162017
 

In the word of digital marketing, one of the more controversial moves I’ve seen recently was from U.K. car insurer Admiral. The company recently announced that it would begin offering car insurance discounts to less risky customers based on voluntarily provided social media data. The insurer would analyze Facebook likes […]

Digital footprints in the sand … a source of rich behavioural data was published on SAS Voices.

12月 062016
 

As data-driven marketers, you are now challenged by senior leaders to have a laser focus on the customer journey and optimize the path of consumer interactions with your brand. Within that journey there are three trends (or challenges) to focus on:

  • Deeply understanding your target audience to anticipate their needs and desires.
  • Meeting customers’ expectations (although aiming higher can help differentiate your brand from the pack).
  • Addressing their pain points to increase your brand's relevance.

customer journey

No matter who you chat with, or what marketing conference you recently attended, it's safe to say that the intersection of digital marketing, analytics, optimization and personalization is a popular subject of conversation. Let's review the popular buzzwords at the moment:

  • Predictive personalization
  • Data science
  • Machine learning
  • Self-learning algorithms
  • Segment of one
  • Contextual awareness
  • Real time
  • Automation
  • Artificial intelligence

It's quite possible you have encountered these words at such a high frequency, you could make a drinking game out of it.drinking-game

There’s a lot of confusion created by these terms and what they mean. For instance, there is hubbub around so-called ‘easy button’ solutions that marketing cloud companies are selling for customer analytics and data-drive personalization. In reaction to this, I set off on a personal quest to research questions like:

  1. Does every technology perform analytics and personalization equally?
    • What are the benefits and drawbacks to analytic automation?
    • What are the downstream impacts to the predictive recommendations marketers depend on for personalized interactions across channels?
    • Should I be comfortable trusting a black-box algorithm and how it impacts the facilitated experiences my brand delivers to customers and prospects?
  2. Do you need a data scientist to be successful in modern marketing?
    • Is high quality analytic talent extremely difficult to find?
    • How valid is the complaint of a data science talent shortage?
    • How do I balance the needs of my marketing organization with recent analytic technology trends?

Have I captivated your interest? If yes, check out this on-demand webcast.

It's time to dive in deep and unleash on these questions. During the video, I share the results of my investigation into these questions, and reactive viewpoints. In addition, you will be introduced to new SAS Customer Intelligence 360 technology addressing these challenges. I believe in a future where approachable technology and analytically-curious people come together to deliver intelligent customer interactions. Analytically curious people can be data scientists, citizen data scientists, statisticians, marketing analysts, digital marketers, creative super forces and more. Building teams of these individuals armed with modern customer analytics software tools will help you differentiate and compete in today's marketing ecosystem.

marketing ecosystem

 

tags: artificial intelligence, Context-aware, customer intelligence, customer journey, Data Driven Marketing, data science, digital marketing, Digital Personalization, machine learning, marketing analytics, Predictive Personalization, Real time Automation, segment of one, Self-learning algorithms

Customer analytics: Think outside the black box was published on Customer Intelligence.

12月 062016
 

As data-driven marketers, you are now challenged by senior leaders to have a laser focus on the customer journey and optimize the path of consumer interactions with your brand. Within that journey there are three trends (or challenges) to focus on:

  • Deeply understanding your target audience to anticipate their needs and desires.
  • Meeting customers’ expectations (although aiming higher can help differentiate your brand from the pack).
  • Addressing their pain points to increase your brand's relevance.

customer journey

No matter who you chat with, or what marketing conference you recently attended, it's safe to say that the intersection of digital marketing, analytics, optimization and personalization is a popular subject of conversation. Let's review the popular buzzwords at the moment:

  • Predictive personalization
  • Data science
  • Machine learning
  • Self-learning algorithms
  • Segment of one
  • Contextual awareness
  • Real time
  • Automation
  • Artificial intelligence

It's quite possible you have encountered these words at such a high frequency, you could make a drinking game out of it.drinking-game

There’s a lot of confusion created by these terms and what they mean. For instance, there is hubbub around so-called ‘easy button’ solutions that marketing cloud companies are selling for customer analytics and data-drive personalization. In reaction to this, I set off on a personal quest to research questions like:

  1. Does every technology perform analytics and personalization equally?
    • What are the benefits and drawbacks to analytic automation?
    • What are the downstream impacts to the predictive recommendations marketers depend on for personalized interactions across channels?
    • Should I be comfortable trusting a black-box algorithm and how it impacts the facilitated experiences my brand delivers to customers and prospects?
  2. Do you need a data scientist to be successful in modern marketing?
    • Is high quality analytic talent extremely difficult to find?
    • How valid is the complaint of a data science talent shortage?
    • How do I balance the needs of my marketing organization with recent analytic technology trends?

Have I captivated your interest? If yes, check out this on-demand webcast.

It's time to dive in deep and unleash on these questions. During the video, I share the results of my investigation into these questions, and reactive viewpoints. In addition, you will be introduced to new SAS Customer Intelligence 360 technology addressing these challenges. I believe in a future where approachable technology and analytically-curious people come together to deliver intelligent customer interactions. Analytically curious people can be data scientists, citizen data scientists, statisticians, marketing analysts, digital marketers, creative super forces and more. Building teams of these individuals armed with modern customer analytics software tools will help you differentiate and compete in today's marketing ecosystem.

marketing ecosystem

 

tags: artificial intelligence, Context-aware, customer intelligence, customer journey, Data Driven Marketing, data science, digital marketing, Digital Personalization, machine learning, marketing analytics, Predictive Personalization, Real time Automation, segment of one, Self-learning algorithms

Customer analytics: Think outside the black box was published on Customer Intelligence.

12月 052016
 

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

The challenge

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

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

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

The approach

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

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

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

The results

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

adele-table

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

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

==

Editor’s note: This post is part of a series excerpted from Adele Sweetwood’s book, The Analytical Marketer: How to Transform Your Marketing Organization. Each post is a real-world case study of how to improve your customers’ experience and optimize your marketing campaigns.

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

How analytics empowers campaign agility was published on Customer Intelligence.