marketing analytics

8月 112017
 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

1月 282017
 

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

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

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

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

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

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

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

 

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

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

1月 162017
 

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

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

12月 062016
 

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

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

customer journey

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

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

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

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

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

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

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

marketing ecosystem

 

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

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

12月 062016
 

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

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

customer journey

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

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

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

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

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

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

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

marketing ecosystem

 

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

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

11月 172016
 

At SAS, we've worked hard to transform ourselves into an analytical marketing organization. And it's an ongoing journey. As new tools and data sources appear, we'll continue to grow, change and improve. As the leader of this effort, I wish there had been a how-to guide available when we started […]

Three steps to modernizing your marketing organization was published on SAS Voices.

11月 022016
 

Leads are the lifeblood of any sales effort. But not all leads are created equal. Some have a high value for an organization and represent a realistic opportunity to win business. Others are early-stage engagements that take months or years of development.

Because of this disparity, the question “What is a lead?” puzzles many organizations. Sales and marketing groups have worked for years to formalize the definition of a lead and what it means within an existing business model. Regardless of your definition, one thing is consistent – marketing has to adapt its strategies to bring in more, better, or just different mixes of leads. The key question is: “How do you get there?”

The challenge

Over the years, the SAS marketing organization built a complex method of passing leads from marketing to sales. The process was similar to what other companies have in place, that is, leads that met a set of rules were qualified and then sent to a salesperson to follow up. The system was effective but difficult to manage, leadsespecially when business needs changed.

To build a new model to score and qualify leads, the marketing team looked at existing data and then conferred with their counterparts in sales to reorient the lead management process to accomplish two main goals:

  • Increase the number and percentage of leads that convert to opportunities. This meant identifying the best leads and finding a faster way to pass more high-qualified leads to sales.
  • Improve the outcomes from the lead conversion process. Obviously, high-quality leads are essential to creating a larger pipeline of deals. The team needed a better way to score, and then prioritize, leads.

An added wrinkle was that the project had to be global. For example, a lead in Australia would have the same meaning as a lead in Germany. That way, the company could compare lead performance across geographies and fuel global decisions about what strategies would be more effective.

The approach

While the previous rules-based model was geared more toward quantity, the team opted for a model-based approach to lead scoring that emphasized quality based on likely outcomes. The team developed an analytics-driven model that could evaluate the range of customer behaviors (registrations, website page views, e-mail clicks, and so on) to identify the best leads.

Beyond the quality-versus-quantity discussion, the sales and marketing teams agreed that the timing of the lead handoff to sales was also important. To accomplish this effectively, the model evaluated many behaviors, and once certain criteria were met, the information was added to the customer relationship management (CRM) system. To improve the lead conversion process, the team also focused on converting more sales-ready leads. Not only did the new scoring model evaluate more behavioral data, but that information was passed on as a “digital footprint” for each lead. The salesperson can see interactions for the lead from within the CRM system, giving her important information to guide her initial outreach.

Additionally, the team decided not to send all leads to the CRM system. Because the model does a better job of classifying better leads, those that aren’t routed to sales go to a lead-nurturing pro- gram, where the contact receives a cadence of relevant e-mails. The contact’s behavior when receiving those e-mails (click-thrus, registrations, website visits, etc.) are all fed into the model.

The results

When the lead-scoring model was still in the early stages, the initial feedback was positive. Salespeople appreciated that the leads were more qualified and reliable. Rather than sifting through dozens of contacts, they know that leads indicate an interest in SAS and its solutions. That was once a luxury for a salesperson. Now, it’s an everyday reality.

To fine tune the model, analysts track the total number of leads passed to sales and the number of leads that convert to opportunities. The marketing team wants to make sure rates continue to rise for both numbers. If there is a plateau or a decline, the analysts receive rapid feedback and can adjust programs as necessary.

SAS marketing analysts can also fine tune the model as sales requirements change or the market evolves. The model is more flexible than the rules-based approach, allowing the team to rapidly adjust strategies. The team can adjust the lead conversion rate if there is a shift in internal focus or if a sales group an increase or decrease in capacity.

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

==

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: customer journey, email marketing, lead scoring, marketing analytics, marketing campaigns, sales leads, SAS Customer Intelligence 360, segmentation, The Analytical Marketer

Scoring leads to drive more effective sales was published on Customer Intelligence.

9月 272016
 

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.

The challenge

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 conversationproduct-specific? And where is third-party content more compelling than internal content?

The approach

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.

The results

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

 

tags: customer journey, email marketing, marketing analytics, marketing campaigns, SAS Customer Intelligence 360, segmentation, The Analytical Marketer

Moving from blasts to conversations was published on Customer Intelligence.