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Editor’s note: This post is part of a series excerpted from Adele Sweetwood’s book, The Analytical Marketer: How to Transform Your Marketing Organization. Each post is a real-world case study of how to improve your customers’ experience and optimize your marketing campaigns.
When in doubt, one of the easiest things marketers can do is send an email blast. The approach is predicated on a strength-in-numbers mentality. If you send out enough messages, somebody, somewhere, will receive it and take the desired action.
While marketers still use blast messages, their value is waning. Why? You are competing for attention with your emails, website, advertisements, collateral, events and any other initiative. People are using their phones, computers, tablets and TVs to consume information. It’s harder than ever to reach, much less sway, a customer.
By 2010, SAS marketing efforts included a blend of blasts and more personalized emails. The marketing team’s goal was to find the right mix of messages and communications methods that would anticipate customers’ needs and turn emails into a conversation with them on their journeys.
The advent of a new customer-journey approach at SAS gave us an opportunity to rethink our email strategy and see what approaches worked best at different phases of the journey.
The marketing team looked at historical data and asked some questions. For example, where along the path is thought leadership more effective than something product-specific? And where is third-party content more compelling than internal content?
The marketing team members began assembling data on the customer journey and behavior across each phase. They found examples of customers receiving messages that were out of sync with their actual buying stage. For instance, a contact would receive messages designed for the early stages of a journey even after the deal was won (or lost).
Marketing analysts also evaluated and identified content gaps across the customer journey. Looking at the totality of interactions, it was clear that building a conversation with the customer would require an overhaul of the email marketing strategy. Here are some key takeaways from the analysis:
- Scoring allowed the team to assign a value to all actions, not just registrations. Each interaction with SAS was tracked and added to the score. With more pervasive – and more realistic – scoring of these behaviors, the team could further analyze the relative value of different messages and offers.
- Segmentation identified the stage of the customer journey. Once scoring was complete and applied to contacts, the team could choose which message to send based on the stage.
- Automation provided the foundation for faster, analytics-driven communications. With segments in place, the team created targeted and relevant email communications to provide the right message at the right stage of the customer journey.
- Analytics delivered the right business strategy based on the desired outcome. Marketing analysts could evaluate how the entire marketing mix was working to move customers through different stages.
After this analysis, the team created and refined email campaigns to fit the stages of the customer journey. The content for the phases included:
- Need. High-level messaging, including industry-specific content and thought leadership strategies. Blogs and articles at this phase explain the problem and provide a path forward.
- Research. Content that validates the customer’s need to solve the problem. Material here focuses on specific business issues and includes third-party resources like analyst reviews and research reports.
- Decide. Deeper content that provides more product-specific information. This material validates the proposed solution through customer success stories, research reports, product fact sheets and so on.
- Adopt. On-board and self-service content. This stage focuses on introducing customers to support resources and online communities, as well as do-it-yourself material that introduces the customer to the solution.
- Use. Adoption content, such as advanced educational information, user conferences, and product-specific webinars. At this stage, users turn to more technical resources to expand their knowledge.
- Recommend. Content specific to extending the relationship with the customer. This includes speaking opportunities, focus group participation and sales references.
When customers reach the buy phase, interactions occur primarily between sales and the customer. As a result, customers are typically excluded from email communications.
Eventually, our entire online experience will be personalized as a way to best engage our customers and prospects and to help ensure we are communicating with them in a way that they prefer. How do we do this? By using customer experience analytics to track, analyze and then take action when appropriate based on behavior, instead of simply when we want to promote something. In other words, we have adopted an analytical mindset.
How SAS can help
We've created a practical ebook to modernizing a marketing organization with marketing analytics: Your guide to modernizing the marketing organization.
SAS Customer Intelligence 360 enables the delivery of contextually relevant emails, ensuring their content is personalized and timely. Emails sent with SAS Customer Intelligence 360 are backed by segmentation, analytics and scoring behind the scenes to help ensure messaging matches the customer journey.
Whether you're just getting started or want to add new skills, we offer a variety of free tutorials and other training options: Learn SAS Customer Intelligence 360
Machine learning has a high profile currently and is riding a wave of exposure in the media that includes articles about subjects from self-driving cars and self-landing rockets, to computers beating the world’s best players at Go, the most computationally complex board game in the world. Is there an opportunity for your organisation, and the marketers within it, to make use of this “new” technology?
Machine learning techniques were developed as long ago as the 1950s, but with the advent of big data and large analytical engines, the prevalence and the ease of applying the techniques has increased.
Additionally, organisations now understand the value that analytics can bring, so are willing to place it front and center in their plans and invest more time and resources in exploring new and better techniques. Segmentation and predictive models, for instance, have proven themselves time and again in the marketing world, but to a certain extent, they require a higher degree of knowledge to understand. In some cases, a machine learning technique unburdens the user of the statistical work, but provides just as good an answer as a traditional technique. More people, with more data, trying to make more decisions lends itself to a technique that requires less manual intervention.
What it means for marketing
Organizations, large and small, can have huge, complex data that can from the latest advances in machine learning – banks have transaction records, telcos have call details, retailers have purchase records.
Take marketing in our omnichannel world as an example. There are huge amounts of customer interactions and there are business problems, such as attribution and optimizing the customer experience, that are perfect for the latest machine learning techniques. For real-time personalization of experience and real-time calculation of recommendations, great benefit can be gained from self-learning algorithms in reinforcement learning.
But it is important to remember that organizations also have many analytically driven challenges that are smaller, simpler and just as important and valuable to the bottom line of the organization. Again, for marketing, more traditional disciplines like segmentation and propensity modeling are still extremely useful, and organizations need to keep using capabilities like these to ensure the continued benefits from their use.
How SAS can help
SAS has embraced machine learning techniques for many years, and recently took a further step forward with the latest release of our SAS Customer Intelligence 360 suite of products. SAS has built a recommendation engine with the best of both worlds – a predictive model built using traditional techniques (logistic regression) and a machine learning algorithm (using naïve Bayes classifiers). Fortunately, your customers don’t need to understand these techniques – they just want your website to make better recommendations!
For the uninitiated, SAS 360 Engage enables organizations to interact with consumers by allowing them to create, manage and deliver digital content over web and mobile channels. Wait a minute. SAS does more than the analytics? That is correct. SAS 360 Engage is a marketing super force serving as a one-stop shop for data capture all the way through delivering highly-targeted, personalized digital experiences.
Being able to dynamically place content and offers into digital channels – across devices and points in time – is nothing new for savvy marketing brands focused on optimization. As customer journeys spread across fragmented touch points while customers are demanding seamless and relevant experiences, content-oriented marketers have been forced to reevaluate their strategies for engagement. But the complexity, pace and volume of modern marketing easily overwhelms traditional planning and design approaches that rely on historical conventions, myopic single-channel perspectives and sequential act-and-learn iteration.
Presently, marketers primarily use a variety of content optimization approaches that include A/B testing and multivariate testing. A/B testing, at its simplest, is a method of website or mobile optimization in which the conversion rates of two versions of a page are compared using visitor response rates. By tracking the way visitors interact with the content– the videos they watch, the buttons they click, or whether they sign up for a newsletter – you can infer which version of the content is most effective.
Due to the popularity of this technique, SAS 360 Engage supports A/B/n testing. A/B/n testing is an extension of A/B testing, where “N” refers to the number of versions being tested, anywhere from two versions to the “nth” version. For example, when a brand has more than one idea for what the ideal digital experience should be, A/B/n can be used to compare each hypothesis and produce an optimized decision based on data, not subjectivity.
Testing is attractive because it is efficient, measurable and serves as a machete cutting through the noise and assumptions associated with delivering effective experiences. In parallel, the evolving marketing landscape is driving a greater mandate for testing: to operate in more channels, handle more data and support more users. Testing must mature beyond traditional on-site experimentation to fully optimize a multifaceted customer journey.
The majority of today’s technologies for personalization have generally failed to effectively use data science to offer consumers a contextualized digital experience. Many of today’s offerings are based on simple rules-based segmentation to drive recommendations. Building off the benefits of multi-channel A/B/n testing, this is where SAS 360 Engage injects its analytical muscle to differentiate from other personalization technologies. Let's break this down:
- At the conclusion of an A/B/n test, there is usually a winner and one or more losers.
- Is there really one superior experience for your entire marketable audience? Is it possible that experiences should vary by segment?
The time has arrived for predictive marketing to have its moment in the sun, and with Forrester recently naming SAS the leader in customer analytics, it's official - the 800-pound gorilla in advanced analytics is locked in on solving complex issues facing the space of data-driven marketing. Making digital personalization more relevant for target audiences is just like preparing a delicious meal; it all comes down to the ingredients and preparation process to rise to the occasion!
A beautiful and interpretable visualization is generated highlighting what is unique about this segment, as compared to everyone else who was exposed to the test. If the brand wants to target this audience in future campaigns, a single click populates this segment in the platform for future journey orchestration.
If you look closely at the image, you will note in the upper half of the report that the winner of the A/B/n test is variant A. However, the lower half of the report showcases a newly discovered segment. It turns out that when a specific customer segment with recent purchase, stay and amenity activity interacts with this hospitality brand, variant B produces better results. How did SAS 360 Engage do this? By applying automated firepower (i.e. algorithmic clustering) to produce this prescriptive and actionable insight. To learn more about this segment, marketers can profile the audience:
SAS 360 Engage was built with the recognition that some marketing teams don't have data scientists available, and have real needs for analytical automation. To improve upon the concept of A/B/n testing, augmenting this capability with automated, algorithmic segmentation with prescriptive results addresses an important need. Let's assume you've run an A/B/n test with four versions of a page, and variant A was crowned the champion. Wouldn't it be nice to know that if a specific segment arrived at your website, an alternative experience would facilitate a better result?
In today’s digital age, products have become increasingly commoditised, requiring organisations to shift their focus towards ensuring the customer experience becomes their biggest differentiator.
Previously, the customer experience journey was a string of static, one-dimensional encounters. But now, thanks to big data and the resulting innovations it can provide, customer experiences can be a seamless exchange over different channels between people and the organisations with which they choose to do business.
Personalise the process
Successful brands have recognised this trend and are moving away from mass marketing. Instead, they are looking to create a personal, emotional connection with consumers. At the heart of this approach is using data to understand what drives the customer and what doesn’t through holistic customer intelligence. However, delivering a data-driven customer experience doesn’t happen overnight – it requires a mix of activities and competencies, from data integration to technology implementations to training and rethinking processes.
What is becoming clear is demand from the always-on consumer for more personal and relevant communications. Brands need to serve up the right offers at the right time via the right channel. This could be an offer for a product that is highly targeted and appears straightaway on a smartphone screen, without the need for endless scrolling to find a more suitable item.
Driven by data
There is more than meets the customer’s eye when it comes to today’s customer experience journey. It requires a combination of individualised insights and connected interactions, as well as an agile approach, to have meaningful communications with customers on whichever channel they happen to be on.
But it’s not enough to be doing the same things via new channels which continue to pop up as technology develops. Building an effective customer journey requires new ways of exploring customer trends and preferences.
A smarter way of responding to these factors is data-driven customer experience. For example, 357 executives from large organisations were surveyed by SAS and Forbes Insights and found that in order to deliver superior customer experiences, they are beginning to rely on data analytics to better understand customer trends and preferences. For three in 10 enterprises, this approach is already delivering a shift in elevating customer experiences. The benefits are wide-ranging – not only can businesses better target and optimise for specific customers, but it also enables them to deliver consistent context across all its channels of engagement. Across its business, it can enhance revenue generation and enable cost reductions, as well as improve process efficiencies and enable increased business agility.
Smashing the siloes
To achieve this data-driven experience, our research uncovered that there needs to be greater alignment across the organisation of people, processes and technology. While organisations may think this involves only sales and marketing teams, there are also other key players behind customer experience, such as information technology, purchasing and production.
The people behind the processes across the business need to be empowered with training, insights and inspiration for both managers and employees. Getting an entire business to operate in terms of data requires a change in thinking – it isn’t just about machines and systems. The success of a data-driven customer experience relies on the interaction of people who build and manage these systems and their ability to help design key business processes.
The key is that every organisation now has significant stores of data on its customers, but much of it may still be “dark data”—meaning data that is stored and managed but never used. Organisational siloes reinforce this inaccessibility, as data remains within a department or functional area.
Businesses therefore need to break down the siloes and embark on a voyage of discovery, find these hidden assets and bring them together. Deep within data resources and assets may be valuable data lying undiscovered. Recent research by SAS in association with the Economist Intelligence Unit revealed more than half of UK companies are probably only leveraging about half of their available data. Starting the search at CRM, ERP, sales, supply chain or inventory systems is recommended, but other, non-traditional sources could be gold mines as well.
Every industry can have its own unique set of key customer touchpoints and interactions - data formulations that work for one may not be suitable for another. Hospitals could focus on waiting room or emergency care experiences, while e-commerce companies would need specific methods to analyse online check-out processes.
Where are you now?
Regardless of industry, data-driven customer experience is taking a driver’s seat in steering today’s hyper-competitive global economy. It takes a carefully balanced combination of factors to deliver superior relationships with customers, who are now demanding a hyper-personalised service. All the technology is available to make this a reality – it’s now just a matter of adoption.
If you’re unsure of where to start, check out this report on how Data Elevates the Customer Experience. You can find out where you stand by reading the survey and comparing results against other organisations that took part. You can also glean new ideas from your peers about how to transform the customer experience.
How customer intelligence can win hearts and minds was published on Customer Intelligence.
SAS Customer Intelligence 360 is a new digital marketing hub offering that enables users to plan, analyze, manage, and track customer journeys. It includes SAS 360 Discover for digital intelligence and SAS 360 Engage for execution capabilities that enable marketers to dynamically create, manage, and place digital content across a variety of channels. These new enhancements to the our customer decision hub extends the capabilities of an organization to orchestrate omnichannel customer activity. Our intent behind this new offering? To enable our clients to take predictive action through their customer-preferred channels, and deliver a desirable, personalized experience.
Now that I have delivered the trendy marketing description, let's get down to business on the intersection of digital marketing, analytics, optimization, and personalization. No matter who you chat with (insert any marketing cloud vendor who recently delivered you a sales pitch), or what 2016 marketing conference you attended, it's safe to say this is a popular topic. Let's review the popular buzzwords at the moment:
- Predictive personalization
- Data science
- Machine learning
- Self-learning algorithms
- Segment of one
- Real time
It's quite possible you heard these words at such a high frequency, you could have made a drinking game out of it.
What concerns me as I scan the marketing landscape of offerings is the amount of overlap in product descriptions. Does every company do data science equally well? Will the wizardry of automation solve all personalization challenges? While we can all agree that personalization is important, it’s apparent that different organizations can have distinct views of what it actually means, and what it takes to be successful. At the center of the debate is the role of data and sophisticated analytics in personalization best practices. For the past few years, I'm repeatedly asked by clients one specific question:
Do you NEED a data scientist to be successful with data-driven personalization?
At first, I attributed this concern to the perceived lack of analytical talent available for hire. However, there are more than 150 universities offering analytic and data science programs, and talent is flooding into industry. So I placed that stereotype aside and began digging deeper, only to observe that numerous marketing cloud brands were positioning their technology to assume data scientists are NOT needed. This supports the trend of vendors highlighting "easy-button" solutions that address every imaginable obstacle related to scalable personalization. I came across lots of cute descriptions like the "just for them" algorithm, or the "magical machine learning" algorithm. Even imagery of hip marketers wearing augmented reality eyewear while deciphering targeting strategies. Are you kidding me?
When it comes to personalization, I'm uncomfortable with black-box automation, lack of statistical best practices, and the removal of the human analyst. Although
improvements can be achieved in using automated optimization algorithms, they can’t glean the why behind the what. Machine learning doesn't fully replace real learning. Real learning happens when analysts and marketers partner to dig into data, connect the dots and unearth insights that help brands interact with customers. Embedding those insights into hypotheses, then testing and validating completes the process.
In some ways, automating personalization ignores issues that statisticians and data scientists traditionally think about: sampling populations, confounders, model stability, bias and overfitting. In the rush to take advantage of the hype around big data, these ideas seem to be ignored or not given sufficient attention. Marketers need to pay careful consideration to the nuances of consumer behavior, brand management, and the impact of the facilitated experiences they are delivering.
Remember, SAS has passionately loved everything about analytics for 40 years. We do not care for black-box solutions, so we embedded transparent analytical features into SAS 360 Engage. It was developed with this in mind. We are the leader in advanced customer analytics, and deployed our prowess in data science for digital personalization through "blue-box" analytical decision helpers. These decision helpers are specifically built to be integrated and easy to turn on for marketers that prefer automation and efficiency. However, supporting analysts will have the option to dig deeper into the why behind the decisions. Analysts can exploit their own targeting model creations in conjunction with SAS 360 Engage's automated helpers by leveraging our deep library of algorithms and approachable tools. SAS will not limit you to a single algorithm to solve for complex consumer behavior. Compare, contrast, and most importantly, truly optimize!
This concept of "blue-box" addresses an opportunity for SAS to solve for customer journey challenges, while meeting the needs of your team's preferences. We recognize that some departments don't have data scientists available, and have real needs for automation. SAS 360 Engage offers analytical automation without challenging your team to hire new analytic resources immediately. However, as your organization begins to experience success through data-driven methods, your needs will mature. SAS 360 Engage will support that growth trajectory, and offer attractive features for analysts, statisticians, and data scientists to insert their value-add into your personalization mission.
Do I believe a data scientist is a requirement for data-driven personalization? 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 superforces, and more. Building teams of these individuals will help you differentiate and compete in today's marketing ecosystem, and SAS 360 Engage was built for the data-driven.
In the coming weeks, I will be releasing follow-up posts to drill into demonstrations and use cases of SAS 360 Engage. Keep an eye on this space!