marketing automation

1月 112017
 

One of the most powerful sales tools is often something that you can’t foresee or control. Even though customers read papers, visit websites and talk with a salesperson, another factor can make all the difference – a referral from a friend or coworker.

Think about the way that sites like Google, Yelp and others have changed the way consumers make everyday decisions, such asadvocacy choosing restaurants. You can go to the restaurant nearest you or one you’ve visited before. Or, you can try something new by looking at your smartphone to see which dining spot has the highest ratings or the best reviews. Why? People show a preference for the personal experience of those in their networks.

For business-to-business software companies like SAS, the impact of customer advocacy is critical. These influencers can set the tone and provide a consistent positive influence throughout the customer journey. Unfortunately, this type of advocacy is tough to measure and hard to predict.

The challenge: Acquisition and retention

Although a customer may be a single record in your database, she doesn’t exist in a vacuum. Each contact has a connection to others within her business or the industry. Understanding and fostering good relationships can have a huge effect on your retention and loyalty efforts.

During our effort to map a modern customer journey, the SAS marketing team focused on different phases of this cycle. The customer journey contained these phases:

  • Acquisition – which includes need, research, decide and buy.
  • Retention – which includes adopt, use and recommend.

On the retention side, the team knew from anecdotal evidence that some SAS customers were advocates of the technology and for the company overall. In fact, several SAS regional offices and divisions had data confirming the idea that finding and rewarding high-value customers led to big returns. What was lacking was an overarching program for getting customers to advocate for SAS technology.

For a larger effort, the team assessed the customer behavior data, examining those who attended events, provided feedback on surveys, sent ideas to R&D, and generally stayed engaged with the company. From a revenue standpoint, those people were often the ones advocating for the use of new SAS technologies or the expansion of existing deployments.

What was less understood was the reach of these influencers and how their activities affected others. With that information, SAS could identify more advocates and nurture that behavior.

The approach: Identify advocates by scoring BFF behaviors

The SAS marketing team members started by digging into the data that they had on customers. They first identified a segment of the top accounts that contained more than 20,000 individual contacts and the team began to examine the behaviors exhibited by that group including:

  • Live event attendance.
  • Website traffic.
  • Technical support queries.
  • Customer satisfaction survey data.
  • Customer reference activity.
  • Webinar attendance.
  • White paper downloads.

This information provided a better understanding of the range of activities that customers undertake. However, simply cataloging the behaviors wasn’t enough. The team applied a scoring model for different types of interactions. This allowed the team to weight certain activities, helping to further identify which customers were the best advocates—“BFFs” (best friends forever) as the marketing team began to call them.

The results: Advocacy campaigns that matter

SAS marketing used the information to create a model that is the foundation for customer-focused data exploration. The initial effort helped shed light on how influential advocates can shape retention and additional sales. As a result, sales and marketing worked together to highlight BFFs within key accounts in an ongoing effort to foster better relationships with those key individuals.

Initiatives to locate and encourage advocates used the model to identify the likely candidates within customer organizations. The team then designed campaigns and outreach efforts to give these advocates the tools to foster and expand their influence.

The marketing team now focuses on advocacy campaigns that target potential BFFs. The goal is to build more SAS advocacy during the recommend phase of the customer journey.

Acquisition and retention campaigns begin by doing advanced segmentation in SAS Marketing Automation. Campaign workflows are created that are backed by analytics, ensuring that communications to customers are appropriate and relevant. Through the collection of both contact and response history data, attribution can be performed in SAS Visual Analytics that allows marketers to see correlations and cross-promotion opportunities.

Interested in learning how to leverage SAS Marketing Automation techniques for advanced segmentation? Explore our SAS Marketing Automation: Designing and Executing Outbound Marketing Campaigns and Customer Segmentation Using SAS Enterprise Miner course offerings.

==

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: Adele Sweetwood, customer advocacy, customer analytics, customer experience, customer journey, marketing automation, sas enterprise miner, sas marketing automation, segmentation, The Analytical Marketer

Customer advocates: Finding your customers’ BFFs was published on Customer Intelligence.

6月 062016
 

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

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

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

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

algorithms

A cognitive computing use case

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

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

cognitive computing

Analytics: the core of machine learning

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

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

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

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

The newest addition to artificial intelligence

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

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

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

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

6月 062016
 

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

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

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

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

algorithms

A cognitive computing use case

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

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

cognitive computing

Analytics: the core of machine learning

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

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

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

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

The newest addition to artificial intelligence

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

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

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

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

6月 062016
 

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

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

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

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

algorithms

A cognitive computing use case

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

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

cognitive computing

Analytics: the core of machine learning

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

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

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

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

The newest addition to artificial intelligence

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

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

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

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

7月 272015
 

The future of marketing involves touch-screens.In the movie Minority Report, while the leading actor walks through the mall and experiences personalized greetings all around him, there is a clear flash of how the future of marketing may look: a customer journey marked by relevant and personalized experiences.

Getting back to the reality of today, the typical customer journey represents various customer interactions with your brand over time across all the digital and offline channels. When they are done right, each of these touch points builds on the others to play an important role in bringing your customer closer to choosing your brand over others.

The customer journey: what it takes
It’s the “when they are done right” part of that scenario that requires a great deal of effort, particularly with regard to:

  • Personalization: Increasing response rates with uniquely tailored content, delivered instantly.
  • Automation: leveraging content across channels and doing it consistently.
  • Optimization: using real-time analytics to drive the best possible outcomes for each customer and for your organization every time.
  • Innovation: Finding ways to rise above the noise by promising – and delivering - compelling user experiences.

Customers’ Digital Footprint
Luckily, consumers today are always connected, leaving their digital footprint everywhere, and generating a phenomenal amount of data as a result. Marketers can use this data to map customer journey and deliver value, in particular to those more willing to interact with the brand. Customers are constantly sharing and exposing important data, such as:

  • Content viewed
  • Behavioral
  • Location
  • Time
  • Demographics
  • Segment/Individual
  • Device
  • Opinions
  • Health

Companies that are able to use this information to cater to customers’ needs and desires will earn their loyalty by fulfilling a promise. Doing that over and over again cultivates the loyalty and turns those customers into new brand ambassadors.

The Importance of Context
Contextual understanding of consumers, combined with the ability to engage constantly with them on their own terms, enables companies to deliver the promise of customer-centricity. With no understanding of what “context” truly means, it is impossible to understand how it will affect your marketing, your customer relationships, and the larger opportunity for your brand. Knowing the balance of customer and business circumstances, and beginning to gather and leverage the right data to expose each, gives marketers the opportunity to rethink customer interactions, to be more personal and relevant, and to engage customers with highly targeted, relevant and timely messages that convert.

Real-Time Contextual Marketing
The real power of context comes to bear when it can be leveraged in real-time, and there are a few key elements to make it happen:

  • Harnessing Big Data: Capture any data (Identity data, Quantitative data, Descriptive data and Qualitative data) in real-time from any touchpoint. Collecting data in real-time is important, but it’s not enough. You also need to combine it with data from historical context to ensure a consistent customer experience.
  • Real-Time Big Data Analytics: Gain immediate insights into contextual and historical data, providing fast and flexible analytics to make better, faster business decisions to anticipate customer needs
  • The Cross-Channel Experience: Customers expect services to be available when and where they want, contacting you in the channel of their choice, separately or in parallel. With every single customer interaction directly linked to your customer record, it’s possible to grant your customers an excellent cross-channel experience.

Want to learn more about how unique and superior customer experiences can make a difference? A great next step is to read a research report created by Harvard Business Review that examines these very issues: Lessons from the Leading Edge of Customer Experience Management. I promise it’s worth downloading.

tags: big data, customer experience, marketing automation, marketing optimization, real-time decisioning

The power of real-time contextual marketing was published on Customer Analytics.

3月 262015
 

In my ongoing quest to connect people's business problems with sources of technology solutions, my work on the TechnologyAdvice Expert Interview Series puts me in contact with some interesting people behind those solutions. Recently, I caught up with John Balla from SAS and got his insights on how marketing automation relates to mobile customers.

His recent role as a panel moderator and sponsor at the DMA's Marketing Analytics Conference in Chicago put him on my radar screen for the fascinating intersection of mobile with analytics, marketing automation and big data platforms. Here are a few of the highlights from our conversation:

TechnologyAdvice: The session you presented at the Marketing Analytics Conference covered mobile engagement and the differentiating role of analytics. Can you give us some highlights from that presentation?

John Balla at the Marketing Analytics Conference.

John Balla at the Marketing Analytics Conference.

John: When you think about mobile in particular, it's such a powerful platform. It's social, it’s search, and it's basic communications like email, text and phone. So mobile is really something that’s affecting the work of marketers in multiple ways. In the past year, I sponsored two studies on what’s happening in marketing with mobile customers that gave me the chance to zero in on the opportunities for marketers with mobile.

  • The first is a study with the CMO Council that we called “Getting in Sync with the Mobile Customer.” It took a pulse on the enterprise view of what's happening with mobile and their customers, and how marketing departments in these organizations are changing to meet the challenge of mobile.
  • The second study was conducted with Northwestern University’s Kellogg School of Management, where Professor Terri Albert and a group of marketing research graduate students looked at how consumers engage with brands and organizations.

Together, the two studies gave me a chance to see the proverbial “two sides to the coin” and draw some important conclusions about mobile engagement, look at where the linkages are and what some of the trends are that were raised in one project and validated in the other. What we found is that mobile is both a challenge and opportunity in important ways. And while best practices aren’t fully-baked quite yet, mobile is still very much an open field for marketers to establish their market and engage in ways that would give them a competitive edge.

TA: What trends in mobile engagement and analytics can give companies a competitive edge when using marketing automation software?

John: As I mentioned, the biggest problems and the biggest challenges are where the opportunities are. And what I'm seeing is that it's the immediacy aspect of mobile -- that need and desire for all to engage in real time -- is where the opportunity lies.

So let's look at a very well talked-about subtopic here: showrooming. 

Showrooming is rooted in the idea that your customers are engaging with your brand online, offline and especially simultaneously. It happens while they're in your store, they’re using your WiFi and looking at your merchandise, but then they’re also looking at what other stores -- your competitors -- are offering. For retailers in particular, that's a big problem, however, it also creates opportunities.

If you're able to engage your customers with a real-time decision engine tied to your automation platform [while they’re in your store], you have an idea of what they want. You can engage them actively knowing what they’re looking at and possibly why, and you increase the chances of keeping that sale in-store or in-house via however you reached their mobile device.

And this is a classic big data issue - it’s mostly driven by customer data. The choice is yours to either respond to it as an opportunity, or see it as a challenge that needs to be managed.

TA: Right, and your competition, they're taking advantage of the opportunities. So it's not only that you're missing the opportunity, but you're falling behind. 

John: Exactly. Then it's a business problem, not so much an IT problem. With mobile changing the way people behave, they really want to hear about what you have to say and what you might offer them when they happen to be looking for it at that moment. But it has to be on their terms - you can’t go around bothering them. It's like the whole idea of the “Do Not Call List,” right? When you have people getting annoyed by getting these phone calls during dinner, then they don't want to talk you about whatever you're selling because they’re busy with something more immediate and (to them) more important. 

It's the same way with mobile. That's why having a robust, real-time decision engine tied to your automation platform is going to help you achieve that relevancy where you're not annoying. I think everybody is still trying to figure how to do that and not be creepy at the same time, though. But that [the creep factor] is going to be something everyone -- marketers, customers, society -- are going to have to get used to and come to terms with.

 TA: With access to all this data, how can businesses find that balance in their marketing automation— of giving the customer relevant and meaningful information that they want — without being creepy about it?

John: It’s the whole idea of how to use big data in a way that's responsible. Using opt-ins, safeguarding data and respecting privacy are key. The relevancy  factor is particularly important because it affects customers and it affects how they perceive you and engage with you. Big data is largely customer data. It's the transaction, it's the engagement and the bread crumbs left across your website. That's where the value is. 

If you’re able to combine it, analyze it, and get the insights and the understanding, then it enables you to be more informed and more educated and appropriate in how you engage with your customer.

It all comes back to marketing automation because when it works well, that is to going to be the engine that controls your engagement with your customers. That's the beauty of analytics. It gives you the ability to combine all this data and use it meaningfully. It's going to give you the ability to drive the insights that make you more effective as a marketer.


To learn more about Marketing Automation Software, big data platforms and CRM, visit http://www.sas.com/customerjourney.

Listen to the entire show above in order to hear our full conversation, or click here to listen later. You can subscribe to the TA Expert Interview Series via Soundcloud, in order to get alerts about new episodes. You can also subscribe to just the Marketing Automation category. 

The podcast was created and published by TechnologyAdvice. Interview conducted by Clark Buckner.

tags: big data, DMA, marketing automation, mobile, real-time decisioning, TechnologyAdvice

The post Interview: How marketing in the moment matters for mobile appeared first on Customer Analytics.

1月 092015
 

Like many people, I had a bit of a break over the holiday, and like many people, I did a little early Spring-cleaning during my time off. But unfortunately, my house is still messy, because the cleaning I did was electronic: After recently merging my four email accounts into Outlook, I noticed the incredible amount of irrelevant marketing emails I was receiving. I made it my mission to unsubscribe from as many of them as possible.

Email is dead

When will email marketing finally be obsolete but still quaint?

I’m sure that you’ve noticed that many websites ask for your email address before you view any of their content. This is where the trouble starts. If I’m just browsing, I might be interested in a single article or post, but I don’t really want a long-term relationship with the content provider. But once you’ve provided that email address, you suddenly find yourself attached to what seems like zillions of emails from organizations you’ve never heard of (oh yeah, that’s because they sold your email address to other providers). The default option for many websites is to sign you up for every email subscription that’s available and then it’s your responsibility (that’s right – YOU!) to determine whether the content is relevant or not. Even if it's a company that you like doing business with, you're likely to be bombarded with content. I’ve seen some organizations where the marketers are actually incentivized on the number of emails (or "leads") that go out, not the quality of the lead or the relevance of the content to a target audience.

The industry term for this effect is “contact fatigue,” which is what led me to my electronic housecleaning. Too many irrelevant messages clogging up my very important inbox. But email is still an important marketing and communication tool if the message is meaningful to your customer!

  • In a 2014 global executive study by Quartz Insights, 60% of executives read email newsletters as one of their first three sources of daily news information; news apps came in at just 28%.
  • According to a study by McKinsey, email is still a more effective way to engage with customers and prospects than social media: 91% of US consumers use email on a daily basis. Email conversion rates are three times higher than social media and average order rates are 17% higher.
  • Forrester Research estimates that the number of marketing emails sent out in the 2013 will be close to 850 billion. In a recent user experience review of 98 email marketing programs, 94 received failing scores. Primary improvement opportunities included the subscription process, the ability to share content, mobile support and preference management.
  • Marketing service provider Experian found that email personalization lifts transaction rates and revenue six-times higher than non-personalized email, but 70% of the brands in their study fail to personalize email messages. Sixty percent don’t give customers the option to select the types of emails they want to receive.

There’s obviously a lot of room for improvement. Online retailer Gilt sends out more than 3,000 personalized variations on their daily sales email. Each message is tailored based on user preferences, browsing and transaction history. By getting the right message to the right customer, they were able to increase customer engagement by getting them to view other product categories; lift conversion rates for women predicted likely to shop in the men’s section; and increase new member purchase conversion rates.

My inbox is a little emptier today, but I would like to hear from you. Make it your New Year’s resolution to send me more relevant emails!


Editor's note:

Recurring themes for marketers over the last few years include both the demise of email marketing, and the enduring importance of email. So when Rachel first submitted this post, a red flag went up for me about the title - I could have sworn I'd seen it before.

Unbeknownst to Rachel, on November 18, 2010, we published this post by Kelly LeVoyer: Email is dead. Long live email! And even after several hundred billion emails over the last 5 years, both Rachel's and Kelly's viewpoints on email are relevant.

Let's zero in on Rachel's point about finding a better way à la Gilt's example - using analytics like marketing automation is how to get there. How about it?

tags: customer analytics, direct marketing, email marketing, marketing automation
1月 062015
 

Companies like Travelocity, Uber and Yelp have forever changed the way consumers plan their travel and entertainment. Can you remember when you had to book your trips through a travel agency? These innovators have raised the bar for other industries, so can the banking experience be changed in the same way? At least one banking executive thinks so.

Omni-channel customers use many channels - sometimes simultaneously.Gareth Gaston is the Executive Vice President of Omnichannel at US Bank, but he hasn’t always been a banking exec. Before his recent move to US Bank, Gaston spent 20 years in the travel industry. It’s probably those years in travel that helped him see what type of experience consumers expect. He says, “The consumer doesn’t give us a pass because we are a bank.”

During his 2014 BAI Retail Delivery keynote presentation, Gaston defined omni-channel marketing as a shift from individual, in-person interactions to transactions across multiple channels. He chuckled at the obvious understatement when he said, “Omni-channel marketing is marketing on all channels where your customers expect to find you.”

Did you hear that? All channels, not just the bright shiny new channels. It’s not about killing the branch and jumping into social and mobile. It’s a call to provide information and functionality to all of the channels where your customers expect it, including social and mobile, the call center, branch and Internet. “Omni-channel is about moving beyond the digital obsession toward customer centricity,” says Gaston. Getting there is a matter of knowing your customers' expectations.

What does your customer expect from your omni-channel strategy?

  1. Speed things up.
    Give them the channels that put them in control of their time – they don’t have time for long waits on the phone, so you might offer online chat, FAQs and social.
  2. Stop surprising them.
    Automatically give them updates and notifications on unseen events that may impact their experience.
  3. Put them in the driver’s seat.
    Customers want to be able to complete most functions across each channel and to pick up where they left off in a prior session.
  4. Put your data to work.
    Consumers assume that you have in-depth information about who they are, the products they have and the interactions you’ve had in the past. They expect you to use that to offer relevant products and tailored services.
  5. Don’t make them ask.
    Wherever possible, make your service automatic. For instance, if I’m a loyal customer with good credit and currently carry one of your credit cards that charges interest, wouldn’t it be in your best interest to offer me your new 0% or rewards card? Automatically.
  6. Appeal to the shopper inside.
    Let them compare your products and pricing side by side with your competitors’ products.

Omni-channel marketing isn’t just about branding and functionality. Gaston says it’s about thinking through fundamental processes and policies including pricing, risk policies, fraud policies and product design. “If done right, omni-channel provides increased acquisition, deeper relationships and increased retention,” he explains.

Here are some companies that are getting it right:

Uber offers transparency that puts the customer in control of the experience. “With the Uber app, you can request a cab, know the wait time for a ride and track the car as it progresses. And you get an estimate of the fare, rather than having to guess while watching the meter fly. And finally, you can review the driver and experience once the ride is over,” Gaston explains.

Amazon collects a vast amount of customer data and uses it to provide a personalized experience. When the customer leaves an Amazon shopping session and returns, Amazon tells him what is in his basket, what others like him have chosen, what he’s chosen before and suggestions for what he should choose today.

Progressive has on-the-spot insurance claims resolution. The customer can use the app to take photos of the damage, request roadside service, submit the claim and monitor the claim via the app. The customer gets peace of mind because the information is entered right away. Good for the customer and good for Progressive.

“Banks have a long way to go to be Amazon-esque,” Gaston says. “The most difficult hurdle to overcome will be integrating your siloes. To create that great customer experience, you’ll need to use all of the customer’s data. It will require an organizational change – a change in culture.”

 


Editor's note:

While customers' expectations may vary slightly among regions or even demographics, this vision for omni-channel and the six customer expectations are spot-on for all industries. And you'll never go wrong by starting with the customer (and the customer's data). That idea is not limited to omni-channel since generally better data means better marketing.

Once you've gotten your data managed, you'll want to use SAS Marketing Automation with its many robust features that enhance the core campaign management function. In a true Omni-channel environment, your customers will use multiple channels whenever it suits them - sometimes simultaneously. In that case, you'll want to use SAS Marketing Optimization so you can maximize your profitability - or however success is defined for your business.

tags: banking, data management, marketing automation, marketing optimization, omni-channel
11月 262014
 

Marketing has evolved - thankfully - from the days when everyone just thought of us as the brochures and tradeshows people. Nowadays, we're as likely to be focused on creating digital content that's worth sharing as we are putting a tradeshow sponsorship together, but all of it is data-driven.

Marketing may seem magical, but it's not pulling rabbits out of hats.

Marketing, while sometimes magical, is not this kind of magic.

So what does it really mean for marketing to be data-driven? And how does marketing turn data "magically" into profits? In the spirit of full disclosure, it's not magical in an abracadabra or Harry Potter sort of way, and it's not entirely mystical in a crystal ball sort of way. But the results can indeed be magical and indeed profitable. And the best magician marketer I can think of to illustrate that point is Jim Foreman, most recently Director of Analytics and Customer Insight at Staples Corp.

Jim spoke recently at a DMA Annual Conference and provided some good, specific examples of how to turn customer insight into profit with analytically-driven marketing automation. So once you collect the customer data and apply the analytics and start getting the insights, what can you actually do with it? That's what Jim delved into and I'm happy to share with you here.

Very simply - the magic starts when you begin to launch successful campaigns. Per Jim, SAS Marketing Automation software "takes all of the good work that our analysts are doing for us – descriptive work, segmentation, modeling – and it lets us bring that right into a campaign, on the fly if we want to." To illustrate his point, he detailed three types of campaigns he led at Staples to great success:

  1. Attrition campaigns:
    Find predictors of existing customers' likely attrition and design campaigns to proactively address those factors.
  2. Browsed-and-abandoned campaigns:
    Identify the online behaviors of new potential customers that lead to abandoned shopping carts and have responses at the ready.
  3. Trigger campaigns:
    Provide suggestions to consumers who may not realize they need something additional to give the end result they want (i.e. a specific type of paper for the printer ink they want to buy).

Once you have your successful campaigns in place and operational, then there are five final steps to take to get to profitability:

  • Identify critical metrics.
  • Use decile analysis.
  • Profile the top 10% of customers.
  • Test the offers, segments and campaigns.
  • Do incremental analysis - refine and retest.

Each of those five final steps are detailed in the paper, How to Turn Customer Insight into Profit with Marketing Analytics. It's an interesting read that includes many practical guidelines from a master data-driven marketer, and some of Jim's more colorful anecdotes, such as whether marketers may have anything in common with blind squirrels. Let me know what you think.

 

 

tags: DMA, marketing analytics, marketing automation
11月 212014
 

Customer experience matters most for loyalty.It seems like every retailer nowadays has a loyalty program. From the local coffee house to “big box” national retailers to almost any online merchant, everyone has a loyalty program. But do people really want them? It turns out the answer is yes – a resounding yes.  But are those programs actually what drive loyalty, or is something else driving behavior?

That question loomed on my mind this year as I had the opportunity to work with two organizations to research customer loyalty - one project focused on the enterprise view, and other to get the consumer view. For the consumer view, I had the privilege to work with graduate students from Northwestern University’s Kellogg School of Management, and that study confirmed a few suspicions I had about customer loyalty programs. And it’s a combination of good news / bad news.

The Bad News
Let’s start with the bad news and get it out of the way. It seems that consumers have come to expect loyalty programs, so in many situations not having one may put you at a disadvantage if your direct competitors have such programs. The other bad news is that so many loyalty programs are tied to discounts and have been aggressively promoted as such that it’s the benefit that consumers associate most strongly with loyalty programs. So, the upshot is that with a loyalty program in place you’ll need to figure out how to operate on slimmer margins, or make other accommodations.

The other bad news is that loyalty programs designed to keep the customer coming back do little to trump a bad customer experience. As a result, retailers must first ensure that they are delivering quality shopping experiences before offering perks for return trips. So as they are designed today, do loyalty programs actually engender loyalty? Apparently not, according to the Kellogg study – it’s a combination of factors.

The Good News
It’s not all doom-and-gloom, however. The good news is that the combination of factors to drive loyalty have many elements that are within your control. And which factors matter depends on the type of retailer you are and what your customer base looks like. Do you emphasize convenience? Low prices? Personalized service? Are your customers “necessity shoppers,” “practical shoppers,” or “pleasure shoppers?” All those factors emerged  in the research and are explored in the report.

But one clear conclusion from the study was that experience matters most – so delivering a good customer experience should be your primary focus through all interactions with your customers (including your loyalty program).  Then from there, it’s a question of reviewing your customer-facing processes and aligning internally so what your customers experience are both consistent and in line with what’s expected. And unless you are a mom-and-pop shop with a limited selection, you’ll need more than a spreadsheet to understand what your customers expect and how you’re delivering on your brand promise.

More good news
The proven way to get a full view of your customers across all channels no matter where they are in their purchase journey is to use marketing analytics. Those same analytically-driven marketing solutions can also be used to orchestrate your customer interactions across your organization and to inform them in real-time as is often the requirement. Want proof? Read these use cases on Staples, Macy’sOberweis Dairy and the luxury goods flash-sale retailer Gilt.

Or you can start by downloading the Kellogg School’s report, Shopper Insights to Improve Retail Loyalty Programs. Another option is to tune in to a December 2, 2015 webinar produced by Loyalty 360, which will highlight the findings of the Kellogg School study and the parallel enterprise-focused study we sponsored this year with the International Institute of Analytics.

And as always – thank you for following!

tags: customer loyalty, customer retention, Kellogg School of Management, marketing analytics, marketing automation, real-time decisioning