customer experience

10月 122017
 

I’ve been making note of recent interactions and observations with different companies that I’ve done business with lately. My goal is to share with you real ways in which excellent customer experiences are shaped. Observation #1: Witty conversations rule As I write this, I’m sitting at a Panera Bread restaurant [...]

Two memorable multi-channel customer experiences was published on SAS Voices by Lonnie Miller

10月 032017
 

Real-time customer experiences are an elusive yet coveted goal for all organizations. As competition intensifies in every single industry, organizations want to ensure they are providing customers with unforgettable results. Satisfactory is no longer enough. Not if you want your customers to come back. Forrester Principal Analyst Rusty Warner’s research [...]

SAS is revolutionizing the real-time customer experience was published on Customer Intelligence Blog.

8月 242017
 

Your business is in a battle to retain and increase your share of wallet from a happy customer base. Winning takes analytics-driven real-time customer experiences. If you’re unconvinced of the value that using analytics brings to the customer experience, a recent Forbes Insight survey of nearly 360 executives makes a compelling case, with benefits including:

    • Faster decision-making: 62 percent.
    • Better insight into a common view of customers: 51 percent.
    • Greater confidence in decision-making: 49 percent.
    • Greater engagement with customers: 49 percent.
    • Increased sales revenue: 47 percent.
    • More repeat business from customers: 44 percent.

With these advantage-building benefits you can imagine the risk to your bottom line from saying “no” to taking an analytical approach to delivering customer experience: Forrester puts it at a 14 percent missed growth opportunity.

What consumers want

All modern consumers are transitioning from digital-first into digital-only users – and they expect you and every other business to achieve “digital parity”. In other words, your organisation needs to match or exceed the best experiences your customers have had with other organisations online or in self-serve environments. The result is that expectations of brand experiences are higher than ever in this data-driven world.

Why an analytical approach delivers against consumer expectations

Over the last decade, a plethora of technologies designed to help us better address our customers has come and gone. For consumers, the result has been fragmented and inconsistent experiences and marketers found themselves failing to deliver a stellar customer experience.

An analytical approach to customer experience enables organisations to move from simply reacting to customers in the moment, to predicting those moments and the appropriate outcomes in advance, to deliver a more considered and strategic experience. Integrated analytics and decisioning is the only way to balance an understanding of a customer’s attitudes, preferences, interests and needs with customer lifetime value, propensity and risk. This approach allows you to make accurate and profitable decisions about the right content, the right offer, the right price or the right product in the moment a customer engages with you. In real-time environments, all this decision-making must be done in milliseconds – something a predetermined, rules-based approach cannot deliver.

 How can you capitalise on the real-time opportunity?

At the SAS Data and Customer Experience Forum, the data strategy and innovation director at a leading European broadcaster said that the key to improving customer experience was by “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. Focusing on small decisions offers benefits in many ways:

  • Improving risk management and matching 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 organization.

The result is a superior customer experience.

By deploying a real-time decisioning framework, this leading broadcaster has experienced a significant sales uplift through online cross-sell and upsell activities. Their customer retention team has also used this approach to significantly reduce retention costs across the same number of customers.

Organisations need to adapt from making decisions at organizational speed to making decision at customer speed. For instance, if a customer ignores an offer online, they should be served a more suitable alternative within seconds based on all of their individual data including their lifetime value, propensity and attitudes, as well as new contextual information (e.g., their location, the device they’re using, etc.),

For Shop Direct, a UK-based digital retailer, the use of analytics to deliver a superior customer experience has paid off with a 43 percent  increase in pre-tax profits. CEO Alex Baldock, said, "We're making the most of how well we know our customer and being increasingly tailored to each of them."

Baldock admits personalisation needs to be executed effectively and that crude personalisation can be damaging to the customer relationship. "If a customer buys a pair of slippers and we just target them with slippers for the next year, that could be worse than not personalising at all." (source: essentialretail.com)

Are you ready to take a more analytical approach to customer experience? SAS helps many organisations start their journeys. Read more about our approach to real-time customer experience and how we can help you become a customer-first organisation, too.

Become a Customer First enterprise read:

Customer Intelligence for the always-on digital economy

 

Why success in real-time customer experience is about small decisions was published on Customer Intelligence Blog.

7月 282017
 

Think big, start small, take the analytics-driven approach

You want to be a customer-first organisation, but are the benefits worth it? Forrester reports that customer experience leaders enjoy 17 percent CAGR (compound annual growth rate) as opposed to laggards at 3 percent.[1]

Organisations of all shapes and sizes are embarking on digital transformation – a term that’s become synonymous with putting a slick digital front-end on traditional processes. In reality, true digital transformation is about adapting business culture and processes to work with new technology. This isn’t simple and presents many challenges that must be overcome in order to put the customer first, including:

  1. Functional silos: Beneath the glossy front-end of the customer experience machine sit functional and data silos created because many companies organise themselves around products or channels, not the customer.
  2. Legacy systems: Systems of record and channel-specific technologies, often with their own rules and logic, and little ability to talk to each other, fragment customer journeys.
  3. Cultural change: The various departments that contribute to creating a customer-first organisation have different objectives and key performance indicators. This undermines the collaboration and cultural change necessary to put the customer at the core.

Unfortunately, customers don’t care that your organisation is built on complex legacy structures in the back-end. When they interact with you they expect accurate and timely responses and decisions, regardless of the channel through which they engage.

What time is real time?

These days, organisations need to be able to respond to changing customer expectations and provide a seamless joined-up customer experience at every point of interaction, often in real time. The issue is that "real time" means different things to different organisations.

Many believe that a good real-time customer experience constitutes the ability to react immediately to what the customer is doing right now in a specific channel. Displaying a banner ad based on where a customer clicks on your website, or triggering an encouraging email when someone abandons their cart are nice tactics, but fall short of delivering a customer-first experience.

Excellent real-time customer experiences can only be delivered when you truly understand your customers: and their wants and needs; their price sensitivity and preferences; their propensity to buy; their lifetime value; and their service expectations.

Being a true customer-first organisation requires the capability to collect and analyse the data that customers make available to you, then use it (responsibly) to deliver value back to them. Today, these sources are expanding to include structured and unstructured data from social and multimedia feeds, streaming data from beacons and devices, voice calls, transactions and browsing histories.

Better faster, real-time decisioning

Once you’ve analysed the data to uncover valuable insights about your customers, you need a decisioning framework that allows analytical insights to be applied to both historical and real-time contextual data. It must encompass your organisational goals, all the potential offers and actions that a customer could be presented with, eligibility, budgetary and other constraints in order to infuse deep customer understanding into the decision-making process for each individual customer. Only then will you be empowered to make highly accurate decisions across your business about the right next action, next offer, next content or next recommendation and deliver that real time. Not having these capabilities could signal the loss of competitive ground.

Leading retailers, financial services, telco and media organisations have seen significant improvements in customer experience, profitability and reduced costs by using a customer decision hub.

Where do you start?

Choose a use case; a business challenge you would like to overcome. Once you have achieved your intended goals, replicate the model across other use cases or business problems. This is best illustrated with some of the work we have implemented with a leading European broadcaster and for a well-known insurer.

The broadcaster wanted to use analytical-driven decisions to increase conversion rates. Within weeks its customer decision hub was up and running and over a 6-week period the organisation saw a significant increase in the uptake of online upsell recommendations.

A global insurer used a customer decision hub approach to automate complex claims decisions that were being handled in the call centre. They were able to cut average settlement decisions from 28 days to making decision in real time, and saw a 26 percent improvement in decision-making accuracy while also providing a superior real-time experience for customers.

Get started

We can help you brainstorm your first project and get started with less risk.

Find out how we can help you to become a customer-first enterprise - read Customer intelligence for the always-on economy.

[1] Customer Experience Drives Revenue Growth, Forrester Research, Inc., June 2016

So you want to be a customer-first organisation? was published on Customer Intelligence Blog.

7月 182017
 

In part one of this series, Clark Twiddy, Chief Administrative Officer of Twiddy & Company, shared some best practices from the first of three phases of Twiddy’s journey to becoming a data-driven SMB. This post focuses on phases two and three of their journey. Phase two is about action. Now [...]

How to be a data-driven SMB: Part 2 of Twiddy’s Tale was published on SAS Voices by Analise Polsky

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.

5月 112017
 

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

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

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

A/B testing: The current standard

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

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

The SAS Customer Intelligence 360 approach

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

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

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

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

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

MVT made easy

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


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

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

What about those missing variants?

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

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

Making MVT your go-to option

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

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

2月 282017
 

Do you remember the 90s? It seemed like every company and organization had some sort of strategic plan that had “2000” in its title. And they were all going to achieve and exceed these Year 2000 goals … if their systems didn’t crash at 12:00:01 on January 1, 2000! So [...]

2020 vision for utilities: What’s the prescription? was published on SAS Voices by Mike F. Smith

2月 202017
 

Marketers today use varying adaptations of the customer journey to describe a circular, looped decision pathway with four distinct phases.

Mapping the right data to specific stages of the customer journey is all about getting to know your customers and developing initiatives to put that knowledge into action. Applying analytical models across the key customer journey phases uncovers opportunities to cultivate value generating behaviors and extend the customer’s lifetime value.

  • Initial Consideration Set (Research/Discover). Data and analytics in this phase help you gain deeper customer understanding of customers and prospects. Segmentation surfaces stated and unmet customer needs and buying motivations. Reach the right prospects with look-alike acquisition models and evaluate prospects with lead scoring techniques.
  • Active Evaluation (Explore/Consider). Data and analytics in this phase help you dynamically adapt marketing efforts to customer response – in real-time. Offer optimization techniques can match the appropriate offer based on historical customer response. Amazon’s recommendation engine is a familiar example. Also, A/B and multivariate testing can assess various marketing variables, such as messaging and content types before you roll out initiatives on a wider scale.
  • Moment of Purchase (Buy/Convert). Data and analytics help you understand how and when customers will purchase. Predictive techniques such as propensity models help marketers predict the likelihood that a customer will respond to a specific offer or message and convert. Expand share of wallet with cross-sell and affinity models; or, understand future buying behavior through propensity models.

Post-purchase experience (Use/Maintain/Advocate). Data and analytics in this phase help you uncover patterns of usage behavior and further drive customer engagement. For example, a retail site may tell you the status of your recent order the moment you land on the home page. Churn models such as uplift modeling and survival analysis can provide early warning signs of defection. Preempt customer churn with corrective actions, such as special offers or free upgrades.

Open, unified capabilities needed

Brands that build the most effective customer journeys master three interrelated capabilities: unified customer data platforms, proactive analytics and contextual interactions.

  • Unified customer data platforms: This capability unifies a company's customer data from online and offline channels to extract customer insights and steer customer experience. This includes the ability to cleanse, normalize and aggregate data from disparate systems – within the enterprise and externally – at an individual level.
  • Proactive analytics: Purpose-built data collection and analytics capabilities that incorporates both customer analytics (give brands the customer insight necessary to provide offers that are anticipated, relevant and timely) and marketing analytics (evaluate marketing performance using metrics, such as ROI, channel attribution, and overall marketing effectiveness).
  • Contextual interactions: This capability involves using real-time insights about where a customer is in a journey digitally (browsing product reviews) or physically (entering a retail outlet) or to draw her forward into subsequent actions the company wants her to pursue.

The results are dramatic when marketers can combine data management, analytics and insights execution into unified marketing platform.

Consider gourmet gift retailing icon, Harry & David. By combining data-driven marketing with enriched customer insight, the company transformed its catalog heritage into a contemporary, digital retailing powerhouse. In the past three years, customer retention has increased by 14 percent and sales per customer have gone up 7 percent.

The largest retail group in Switzerland, Migros, used data and analytics to further optimize the customer journey.

The upshot: Change perception to reality

“If change is happening on the outside faster than on the inside the end is in sight.” – Jack Welch

Digitally-empowered prospects and customers are calling the shots, going after what they want when they want it. With a unified view of data and analytics, brands can position themselves in front of their customers’ paths as they navigate the customer journey.

For the brands that can see the world as their customers do – and shape the customer journey accordingly--the reward is higher brand preference, revenue and cost improvements, and a lasting competitive advantage.

Assess your marketing confidence

Take stock of your digital marketing approach with the Marketing Confidence Quotient. This assessment tool quickly identifies and scores your company's strengths and weaknesses across four marketing dimensions: data management, analytics use, process integration and business alignment. It's like having your own personal marketing adviser.

A better approach: Align data and analytics across the customer journey was published on Customer Intelligence Blog.