Initiative: Customer Intelligence

2月 292016
 

Consider the last email or digital ad you received from a favorite retailer. It may have included an offer to save 20 percent on your next online purchase, or an invitation to shop in store during an exclusive sale. You don't think too much about this brand’s customer relationship management (CRM) or marketing capabilities, because you don’t have to.

Why? Because the most sophisticated brands employ tools that can tailor an email or a social media post to their buyer’s sweet spot. Powered by data and analytics, these CRM tools do the heavy lifting for marketers to engage their customers in more personalized, authentic ways.

CRM Watchlist 2016

Often recognized as a forerunner in CRM software, SAS Customer Intelligence has added a new accolade to its trophy case as a winner oCRM-Watchlist-Winner-2016-2.jpgn the 2016 CRM Watchlist. The annual list – curated by leading CRM industry analyst, Paul Greenberg – includes the dominant companies to watch in the CRM market. As Greenberg notes in his announcement blog post on ZDNet, the competition was especially stiff this year, with 131 vendors vying for the winners spot. With each submission, Greenberg reads and scores the company (which is weighted), which is then followed up with extensive research analyzing the vendor in the markets it addresses.

One important distinction of the Watchlist is the winner's impact within the CRM space. Greenberg cites that “the impact has to be obvious, both in the prior year and in the anticipated next two or three years.” And “that there is no doubt at all that your company is making a major impression on a market and actually changing or strengthening that market by its presence.”

The impact is not only from the strength of our SAS customer intelligence offerings,  but as a whole company. Greenberg states, “To have an impact, the company has to be pretty much a complete company who has been doing this long enough to have established a rhythm that leads to impact. The company has to be well rounded -- it has financial stability, solid management, excellent products and services, culture, and a strong partner ecosystem to help sustain its efforts.”

The SAS customer intelligence team is honored to earn a spot on the winners list for 2016, demonstrating SAS's commitment to helping brands deliver customer experiences that matter.

tags: CRM, customer intelligence, marketing

SAS named a winner on the 2016 CRM Watchlist was published on Customer Intelligence.

2月 042016
 

Today’s retailers have access to vast stores of data that allow them to create the personalised retail experience that customers have come to expect. Used in the right way, analytics can be the key to bringing customers in through the door, building a better online experience, or simply helping weather slow periods by enabling faster, more efficient and nimble supply chain changes.CustomerDigitalMarketing

Interestingly, customer perceptions towards a brand can significantly change following Black Friday discounting and January sales. Pricing strategy can be confusing at the best of times. Given the option of £50 off on a high-value product compared to a 70 percent discount on a low value item: which is the better offer? Which deal do consumers trust, and why? More importantly, how does it impact the brand perception?

One thing’s for sure: brand loyalty and consumer trust are no longer guaranteed in today’s fast-changing retail environment. Retailers must find alternative ways to engage with consumers.

The dark art of forecasting

For years, retailers have collected customer data through loyalty cards, email marketing promotions, point-of-sales tills, as well as online browsing behaviour and purchase orders. What has changed over the years is the technology that allows retailers to analyse and understand data. The advent of technologies like Hadoop has enabled the development of advanced analytics solutions to produce more insightful and timely answers for retailers, which is of huge value during peak trading seasons.

Another interesting point here is that often the data collected spans the last three years of trading or more. This is traditionally used by store managers to forecast pricing and demand. Yet we know that outdated historical data, coupled with unpredictable external factors like weather conditions, regularly produce inaccurate forecasts.

As a result, retailers are still making predictions largely based on gut feel rather than objective insights. More often than not, they are also spontaneously reacting to competitors’ price cuts without carefully calculating their profits and loss potential.

Brands switch off

Another common retail pitfall is the effect of over marketing with a general blanket message. Unaware of the consequence of blasting out daily and sometimes twice-daily promotional emails in the hope of catching shoppers’ bargain hunting instincts, retailers are actually turning customers away. Consumers will easily unsubscribe from brands they previously loved, especially if they feel they are bombarded with irrelevant and unwanted product recommendations that only clog up their inboxes.

True personalisation demands an intimate understanding of the customer, and willingness from the individual to participate.

One of our retail customers has been using data analytics to deliver personalised product displays. These are highly targeted to improve conversion and avoid endless page scrolling. Instead of feeling like every other shopper each time a customer enters the site, they can now enjoy a genuinely personal engagement with the retailer. Behaviour is predicted and product recommendations are based on browsing, and a wealth of customer and transaction data.

With a more personalised customer experience, shoppers will be able to purchase products at the best price, while receiving the best in-store, online and post-sales service.

As retailers have better insights into customer behaviour and spending patterns, they will be able to personalise the experience and product recommendation. In turn, shoppers will feel they are receiving better deals and being looked after by the retailer. Some customers may also find themselves receiving extra benefits or upgrades through loyalty schemes, enhancing the overall experience with the brand.

SAS has worked for many years with dunnhumby, the power behind Tesco’s Clubcard. Today there is a need to market to customers across multiple channels. SAS has also worked with Callcredit Information Group, specialists in marketing services, analytics and data, to deliver a market leading omnichannel marketing and analytics service. This partnership has delivered a solution to ASDA. It provides an easy-to-use interface, with repeatable processes to enable organisations to deliver more campaigns – from simple to complex – in a shorter space of time.

Sail through the online retail revolution

On Black Friday alone, shoppers spent £1.1 billion with UK online retailers, setting a new internet retail record which underlines the online shopping revolution. The true impact of this year’s trading results are yet to be seen but one thing is certain – retailers can no longer rely on historical data alone to forecast pricing models or stock performance.

The cost for retailers can be drastic if they cannot accurately analyse the net profitability impact ahead of the peak trading period.

Big data enables retailers to more accurately forecast actual consumer demand, based on the very latest fashions and trends, as well as the timing and the likely location for that demand. For example, big data can be used as follows:

  • Analyse the data and visualise the demand forecast on a specific product in a specific store on a specific time and day of the week. This enables the retailer to ensure they get specific stock to that location.
  • Predict what else the customers might like to buy in the same transaction and personalise the product recommendations accordingly.
  • Predict the behaviour of customers by channel. For example, how customers might move between online browsing and in store purchasing, or their preference for home deliveries or Click & Collect.
  • Use demand insight to negotiate more effectively what the retailer buys from suppliers and when. Also, strategically plan for a more coherent and cheaper supply chain and transportation journey, allowing for external factors like extreme weather conditions.
  • Understand which segment of the customer demographics are most valuable to the business and devise a more effective way to nurture their spending and relationship with the brand.
  • Delight your customers by presenting them with product and brand recommendations which you already know they will want and like.

In principle, the knowledge of who wants what and when is an art form in retailing that is rooted in the golden era of advertising and knowing your customer. Retailers that have enhanced their skills in this area will continue to grow and prosper, which is why demand for data analytics technology will continue to grow over the coming years.

Find out more about how to make effective pricing decisions.

tags: big data analytics, brand loyalty, customer loyalty, pricing, retail, retail analytics

Unlock your retail potential with better pricing models and brand loyalty was published on Customer Intelligence.

1月 292016
 

Martech is moving fast - pulling in other segments of technology as it pushes forward into 2016. We are well on the way to 2020 - where WalkerInfo predicts that customer experience will be the primary differentiator for consumers over the traditional four P's of marketing (product, place, price and promotion). In addition, a Salesforce survey of 5000 B2B and B2C marketers shows that keeping up with trends is one of the biggest problems marketers face today. While technology and consumer behavior are involving in unpredictable ways, so are the ways marketers are reacting to these new developments. Here are a few things that I see coming in marketing’s future, and I think they will be here before we know it.marketing future

Future trend 1: Nonhuman marketing

Today, businesses have more data than ever before, including network and device data, collecting from proliferating channels. Our televisions, phones, Fitbits and Apple Watches are becoming connected, rapidly changing consumer behavior. For example, the multiscreen trend in which people watch television while also using other digital devices presents a great opportunity for marketers. Combining this data with traditional demographic and geographic consumer data can greatly improve marketing efforts.

All of this connected technology will become platforms for my life, creating a myriad of consumer data and better experiences. With this trend, marketing will need to direct its attention to all devices, and not just to individuals. SAS is combining streaming analytics off all of these device types with traditional data like demographic, social, and transactional data to help brands inform marketing efforts and send marketing messages and offers to channel touch points.

Future trend 2: Consolidation

Businesses strive to achieve a single customer view because they understand the value to their bottom line, including making marketing more effective and gaining better insight into customers to create brand value and greater loyalty. Brands are starting to understand that customer experience makes the sale, not product or price. By removing intermediaries, supply chains are shorter and technology becomes more user-enabling.

Today, some businesses serve the sole purpose of aggregating and sourcing products and services for consumers. At amazon.com, I can buy anything from a TV to a pair of socks in a single click – without ever interacting with Samsung or Woolrich as brands. So who do these brands market to? Not me the consumer – but Amazon as the aggregator – and the message to that aggregator will obviously be much different than the marketing messages of today. SAS is working in a B2B context with many brands to understand how and who to market to in order to break through the noise and deliver offers to businesses and intermediaries as we see business models change as we move into 2016.

Future trend 3: Applied analytics and inherent intelligence

I talk to people about marketing software technology every day. Never once have I heard someone say – “I really love my marketing technology software – I wish I could spend more time in it, you know,just clicking around and hanging out.”

Instead, marketing analysts want to quickly enter the software, perform an action, complete a task and exit the application so that they can get on to more strategic parts of their job. Marketing technology vendors are answering marketers’ desire for faster and more intuitive software.

The ability to embed and apply analytical techniques – things like automatically derived segmentation, applied marketing activity optimization, embedded forecasting, and other techniques – all in an effort to infuse machine learning style techniques and behaviors into marketing interfaces. SAS is working to infuse these cognitive analytical behavior techniques into marketing technology software. After all, every marketer (including myself) wants to simplify and optimize workloads! If a machine can tell a marketer the best time to execute a campaign, to whom, which content to use, and when to send it out – why wouldn’t a marketer oblige? I know I would.

tags: customer intelligence, digital marketing, marketing analytics, martech

Martech moves forward in 2016 was published on Customer Analytics.

1月 262016
 

I begin this blog post with one goal in mind. I want to raise awareness on the subject of customer and marketing analytics, and why this field is exploding in interest and popularity. Let's begin with a primer for the uninitiated, and lay down some definitions:

Customer Analytics: The processes, technologies, and enablement that give brands the customer insight necessary to provide offers that are anticipated, relevant and timely.

Marketing Analytics: The processes and technologies that enable brands to assess the success of their marketing initiatives by evaluating performance using important business metrics, such as ROI, channel attribution, and overall marketing effectiveness.

If you aren't a fan of textbook definitions, here is a creative alternative:

Still not on board? Here's my perspective on the subject:

Customers are more empowered and connected than ever before, with access to information anywhere, any time – where to shop, what to buy and how much to pay. Brands realize it is increasingly important to predict how customers will behave to respond accordingly. Simply put, the deeper your understanding of customer buying habits and lifestyle preferences, the more accurate your predictions of future buying behaviors will be.

Marketers need to be enabled to benefit from approachable and actionable advanced analytics to make more powerful decisions within today’s complex and interconnected business environments.  In my mind, the big picture boils down to one, two or three core enablers, based on your organization's goals and preferences:

Image 2

 

 

 

 

Marketing analysts tasked with making sense of customer data, big or small, have to migrate through a complex maze of myths and realities about technology platforms, advanced analytics solutions and, most importantly, the magnitude of customer analytics efforts. On the surface, it appears that customer analytics is a well-entrenched discipline in many organizations, but under the hood, old problems persist around data integration and data quality while new ones emerge around the real-time application of insights and the ability to rein in digital data for customer-based analysis.When I speak with clients, there are two key themes that I continually hear:

  1. Data is a big challenge. As customer interactions with brands increase and diversify, brands need to integrate data effectively in order to provide the contextual and real-time insights their customers are growing to expect. Haven't you grown tired of saying we spend 80 percent of our time on data management related tasks, and 20 percent on analysis?
  2. Analytic talent is hard to find. Brands struggle to find individuals with the right analytic skills to meet the challenges they are facing today. Without the talent to unlock actionable insights, modern customer analytics cannot meet its potential. (Given my public affiliation with The George Washington University's M.S. in Business Analytics program, I'd recommend checking it out if you are hunting for quality talent.)

To me, these themes point to a workflow entitled the marketing analytics lifecycle:

Image 3

 

 

 

 

 

 

With the growing importance of customer analytics in organizations, the ability to extract insight and embed it back into organizational processes is at the forefront of business transformation. However, this requires considerations for where relevant data resides, the ability to reshape it for downstream analytic tasks (predictive modeling vs. reporting), and how to take action on the derived insights. Furthermore, there are the roles of different people within the organization that need to be considered:

  • Marketing Analyst/Technologist
  • Data Scientist/Statistician
  • Marketing Manager
  • Supporting IT Team

Customer analysis touches all of these roles, and to enable this audience comprehensively, all aspects of the marketing analytics lifecycle must be supported. To directly address this, I want to to highlight what SAS is doing to help our clients meet these challenges.

Marketing Analytics Lifecycle Stage #1: Integrate and Prepare Data

Customer analytics is highly dependent on the quality of the ingredients we feed into analysis. Now, the digital marketing industry has been taken by storm by the emergence of Digital DMPs, like Oracle BlueKai, Neustar, and Krux, who aim to provide marketers support in programmatic ad buying and selling. Marketers and publishers are learning that harnessing their first-party data; developing single and consistent identities for their consumers across devices and systems, like email and site optimization; and gaining access to second-party data are mission critical. However, the subject of data mining and predictive analytics has largely been ignored by the Digital DMP space. Brands who want to exploit the benefits of advanced analytics have additional considerations to support their data management challenges. The following video highlights how SAS helps manage and prepare data of all sizes, from 1st party customer data to clickstream and IoT, specifically for analytics:

 

Some of you might be questioning the value of this, so let me offer a different perspective. Over the past few years, I have developed a personal frustration of attending various marketing conferences and repeatedly observing high-level presentations about the potential of analytics. Even more challenging has been the recent trend of companies presenting magical (i.e., "easy-button") black-box marketing cloud solutions that address every imaginable analytical problem; in my opinion, high-quality advanced analytics has not reached a point of commoditization, and remains a point of competitive differentiation. Do not be mislead by sleight-of-hand magic!

Marketing Analytics Lifecycle Stage #2 & #4: Explore Data, Develop Models, and Deploy

What types of marketing challenges are you attempting to solve with customer analytics? Srividya Sridharan and Brandon Purcell are two leading researchers in the space of customer insights, and recently released a report entitled How Analytics Drives Customer Life-Cycle Management recommending the deployment of various analytical techniques across the customer life cycle to grow existing customer relationships and provide insight into future behavior. Highly recommended reading! Let's review some of the most common problems (or opportunities) we view at SAS with our clients.

Image 4

Within each of the categories, a myriad of analytic techniques can be executed to assist and improve your brand's abilities to address them. The following video is a demonstration of how I used SAS Visual Statistics and Logistic Regression analysis to understand drivers by marketing channel of business conversions on a website or mobile app. The benefit of understanding these data-driven drivers is to influence downstream marketing personalization and acquisition campaigns. In addition, capabilities related to group-by modeling, deployment scoring and model comparison with other algorithmic approaches are highlighted.

 

 

Big digital data, scalable predictive analytics, visualization, approachability, and actionability. Stay thirsty my friends, because it is our clients who are expressing their needs, and SAS is stepping up to meet their challenges!

If you would like to learn more on how we address other marketing and customer analytic problems, please click on any of the following topics:

  1. Personalization
  2. Attribution
  3. Segmentation
  4. Acquisition
  5. Optimization

With that said, we have one final stage of the lifecycle to review.

Marketing Analytics Lifecycle Stage #3: Explain Results and Share Insights

An individual's ability to communicate clearly, succinctly and in the appropriate vernacular when presenting analytical recommendations to a marketing organization is extremely important when focused on driving change with data-driven methods. I recently wrote a blog post on this topic entitled Translating Predictive Marketing Analytics, and if you're tired of reading, here's another video - this time focused on explaining the results of analytical exercises in easy-to-consume business language.

 

As I close this blog post, I want to leave you with a few thoughts. For your brand's customers, technology is transparent, user-enabling, and disintermediating. The journey they embark with you on is fractured and takes place across channels, devices, and points in time. The question becomes – are you prepared for moments of truth as they occur across these channels over time? Customer analytics represents the opportunity to optimize every consumer experience, and revisiting a point I made earlier, the deeper your understanding of customer buying habits and lifestyle preferences, the more accurate your predictions of future buying behaviors will be.

If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

 

tags: Business Analytics, business intelligence, customer analytics, customer intelligence, Customer Lifecycle Management, Data Mining, Digital Intelligence, marketing analytics, Marketing Attribution, personalization, Predictive Marketing, segmentation

The analytics of customer intelligence and why it matters was published on Customer Analytics.

1月 192016
 

Marketers have used segmentation as a technique to target customers for communications, products, and services since the introduction of  customer relationship management (i.e., CRM) and database marketing. Within the context of segmentation, there are a variety of applications, ranging from consumer demographics, geography, behavior, psychographics, events and cultural backgrounds. Over time, segmentation has proven its value, and brands continue to use this strategy across every stage of the customer journey:

  • Acquisition
  • Upsell/cross-sell
  • Retention
  • Winback

Let's provide a proper definition for this marketing technique. As my SAS peer and friend Randy Collica stated in his influential book on this subject:

"Segmentation is in essence the process by which items or subjects are categorized or classified into groups that share similar characteristics. These techniques can be beneficial in classifying customer groups. Typical marketing activities seek to improve their relationships with prospective and current customers. The better you know about your customer's needs, desires, and their purchasing behaviors, the better you can construct marketing programs designed to fit their needs, desires, and behaviors."

Moving beyond the academic interpretation, in today's integrated marketing ecosystem, SAS Global Customer Intelligence director Wilson Raj provides a modern viewpoint:

"In an era of big data, hyperconnected digital customers and hyper-personalization, segmentation is the cornerstone of customer insight and understanding across the modern digital business. The question is: Is your segmentation approach antiquated or advanced?"

This provides a nice transition to review the types of segmentation methods I observe with clients. It ultimately boils down to two categories:

  1. Business rules for segmentation (i.e., non-quantitative)
  2. Analytical segmentation (i.e., quantitative)

Let's dive deeper into each of these...

Business Rules For Segmentation

This technique centers on a qualitative, or non-quantitative, approach leveraging various customer attributes conceptualized through conversations with business stakeholders and customer focus groups to gather pointed data. This information represents consumer experiential behavior, and analysts will assign subjective segments for targeted campaign treatments. Although directionally useful, in this day and age of data-driven marketing, it is my opinion that this approach will have suboptimal results.

Analytical Segmentation

Within this category, there are two approaches marketing analysts can select from:

  1. Supervised (i.e., classification)
  2. Unsupervised (i.e., clustering)

Supervised segmentation is typically referred to as a family of pattern analysis approaches. Supporters of this method stress that the actionable deliverable from the analysis classifies homogeneous segments that can be profiled, and informs targeting strategies across the customer lifecycle. The use of the term supervised refers to specific data mining (or data science) techniques, such as decision trees, random forests, gradient boosting or neural networks.  One key difference in supervised approaches is that the analysis requires a dependent (or target) variable, whereas no dependent variable is designated in unsupervised models. The dependent variable is usually a 1-0 (or yes/no) flag-type variable that matches the objective of the segmentation. Examples of this include:

  • Product purchase to identify segments with higher probabilities to convert on what you offer.
  • Upsell/cross-sell to identify segments who are likely to deepen their relationship with your brand.
  • Retention to identify segments most likely to unsubscribe, attrite, or defect.
  • Click behavior to identify segments of anonymous web traffic likely to click on your served display media.

After applying these techniques, analysts can deliver a visual representation of the segments to help explain the results to nontechnical stakeholders. Here is a video demonstration example of SAS Visual Analytics within the context of supervised segmentation being applied to a brand's digital traffic through the use of analytical decision trees:

 

Critics of this approach argue that the resulting model is actually a predictive model rather than a segmentation model because of the probability prediction output. The distinction lies in the use of the model. Segmentation is classifying customer bases into distinct groups based on multidimensional data and is used to suggest an actionable roadmap to design relevant marketing, product and customer service strategies to drive desired business outcomes.  As long as we stay focused on this premise, there is nothing to debate.

On the other hand, unsupervised approaches, such as clustering, association/apriori, principal components or factor analysis point to a subset of multivariate segmentation techniques that group consumers based on similar characteristics. The goal is to explore the data to find intrinsic structures. K-means cluster analysis is the most popular technique I view with clients for interdependent segmentation, in which all applicable data attributes are simultaneously considered, and there is no splitting of dependent (or target) and independent (or predictor) variables. Here is a video demonstration example of SAS Visual Statistics for unsupervised segmentation being applied to a brand's digital traffic (including inferred attributes sourced from a digital data management platform) through the use of K-means clustering:

 

Keep in mind that unsupervised applications are not provided training examples (i.e., there isn't a 1-0 or yes/no flag type variable to bias the formation of the segments). Subsequently, it is fair to make the interpretation that the results of a K-means clustering analysis is more data driven, hence more natural and better suited to the underlying structure of the data. This advantage is also its major drawback: it can be difficult to judge the quality of clustering results in a conclusive way without running live campaigns.

Naturally, the question is which technique is better to use in practice – supervised or unsupervised approaches for segmentation? In my opinion, the answer is both (assuming you have access to data that can be used as the dependent or target variable). When you think about it, I can use an unsupervised technique to find natural segments in my marketable universe, and then use a supervised technique (or more than one via champion-challenger applications) to build unique models on how to treat each cluster segment based on goals defined by internal business stakeholders.

Now, let me pose a question I have been receiving more frequently from clients over the past couple of years.

"Our desired segmentation strategies are outpacing our ability to build supporting analytic models. How can we overcome this?"

Does this sound familiar? For many of my clients, this is a painful reality limiting their potential. That's why I'm personally excited about new SAS technology to address this challenge. SAS Factory Miner allows marketers to dream bigger when it comes to analytical segmentation. It fosters an interactive, approachable environment to support working relationships between strategic visionaries and analysts/data scientists. The benefit for the marketer campaign manager is the ability to expand your segmentation strategies from 5 or 10 segments to 100's or 1000's, while remaining actionable within the demands of today's modern marketing ecosystem. The advantage for the supporting analyst team is the ability to be more efficient, and exploit modern analytical methods and processing power, without the need for incremental resources.

Here is a video demonstration example of SAS Factory Miner for supersizing your data-driven segmentation capabilities:

 

I'll end this posting by revisiting a question we shared in the beginning:

Is your segmentation approach antiquated or advanced?

Dream bigger my friends. The possibilities are inspiring!

If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

 

tags: Clustering, CRM, Data Driven Marketing, Data Mining, data science, Decision Trees, marketing analytics, personalization, segmentation

Analytical segmentation for data-driven marketing was published on Customer Analytics.

12月 302015
 

I get the most interesting insights from questions my kids ask me about my work. Why? Because they know very little about big data or analytics, and the questions they ask are sometimes about things that I’ve taken for granted. Every time that happens, it reminds me that the questions you ask are just as important as the answers you get, and that different perspectives are often needed to see the whole picture of a situation.

Elephant1

Photo: Barry Butler, Chicago, IL, taken in Botswana.

That last point is colorfully laid out in the old Indian story of the Blind Men and an Elephant, which is described in Wikipedia as:

A group of blind men (or men in the dark) each touch an elephant to learn what it is like. Each one feels a different part, but only one part, such as the side or the tusk. They then compare notes and learn that they are in complete disagreement.

The issue, of course, is that none of them have all the information to describe the entire beast.

The elephant story and my kids’ questions also remind me of a lesson I learned years ago using SAS in a college econometrics class – don’t ignore the outliers in your model for two good reasons:

  1. Outliers highlight potential flaws in your model – or aspects of your data that your model does not do a good job of explaining.
  2. Outliers may be the leading indicators of a new trend that will completely upend your original model.

So the opportunity before us this coming year relates to elephants – how we’ll recognize them and what we’ll do with them once we see them. And one thing is for sure – we all have elephants. The key is to find them and to recognize the opportunities they represent.

From the old story, we can appreciate how in order to recognize the elephants, we need all the data so analytics can describe the relationships to gain meaning and compel action. For individuals, this process is (and should be) fairly simple because rarely are we so complex that we need advanced analytics to manage our personal matters. For organizations, getting all the data is more complex the more products and customers you have. But that complexity comes at a time when big data technologies make it easier and faster to find the insights and turn them into business decisions due to:

Many organizations have embraced big data and time and time again have found that it provides key answers but it also prompts questions that previously weren’t known to be asked. And because of that cheaper computing and distributed processing, we now can forgo sampling and extrapolating to analyze entire populations of data, and do it more quickly and efficiently than ever before. Restated in terms of the elephant story - not only can we recognize the elephant, we can do it more quickly. And we can find out if the elephant is alone or in a pack, whether or not the elephant is healthy or ailing, and so on.

Big data also should prompt us to question assumptions, especially in marketing. The reason is that our customers' behavior and expectations have changed so radically, we need to ask if our marketing has changed in kind? How has your marketing changed in the last 5-10 years? Have you changed how you’re organized and how you do your planning? Has your approach to engaging your audiences changed? If the answer is no to any of those questions - simply ask yourself why and then think about whether or not the answer you get is good enough.

Elephant2

Photo: Barry Butler, Chicago, IL, taken in Botswana.

One thing is for sure – our customers have changed. They also have access to more data, and easier access to computing power. And mobile devices are a key part of that empowerment, but the more important change is how mobile is changing customers' behavior and expectations, as we found in research with Northwestern University's Kellogg School of Management and summarized in a report titled, Understanding the Mobile Consumer.

Along with mobile devices, social media are creating expectations of immediacy and relevance that are redefining what constitutes a satisfactory customer experience. The relationships between customers and brands are changing, but opportunities await all organizations that modernize their marketing to keep pace with their customers by using big data.

To me, that’s the kind of elephant to watch out for in 2016. And however you choose to ring in the new year, enjoy the moment and mark it with the optimism that great things are on the horizon thanks to big data.

And as always - thank you for following!

tags: analytics, big data, Hadoop, Internet of Things, mobile

The elephants to watch out for in 2016 was published on Customer Analytics.

12月 302015
 

I get the most interesting insights from questions my kids ask me about my work. Why? Because they know very little about big data or analytics, and the questions they ask are sometimes about things that I’ve taken for granted. Every time that happens, it reminds me that the questions you ask are just as important as the answers you get, and that different perspectives are often needed to see the whole picture of a situation.

Elephant1

Photo: Barry Butler, Chicago, IL, taken in Botswana.

That last point is colorfully laid out in the old Indian story of the Blind Men and an Elephant, which is described in Wikipedia as:

A group of blind men (or men in the dark) each touch an elephant to learn what it is like. Each one feels a different part, but only one part, such as the side or the tusk. They then compare notes and learn that they are in complete disagreement.

The issue, of course, is that none of them have all the information to describe the entire beast.

The elephant story and my kids’ questions also remind me of a lesson I learned years ago using SAS in a college econometrics class – don’t ignore the outliers in your model for two good reasons:

  1. Outliers highlight potential flaws in your model – or aspects of your data that your model does not do a good job of explaining.
  2. Outliers may be the leading indicators of a new trend that will completely upend your original model.

So the opportunity before us this coming year relates to elephants – how we’ll recognize them and what we’ll do with them once we see them. And one thing is for sure – we all have elephants. The key is to find them and to recognize the opportunities they represent.

From the old story, we can appreciate how in order to recognize the elephants, we need all the data so analytics can describe the relationships to gain meaning and compel action. For individuals, this process is (and should be) fairly simple because rarely are we so complex that we need advanced analytics to manage our personal matters. For organizations, getting all the data is more complex the more products and customers you have. But that complexity comes at a time when big data technologies make it easier and faster to find the insights and turn them into business decisions due to:

Many organizations have embraced big data and time and time again have found that it provides key answers but it also prompts questions that previously weren’t known to be asked. And because of that cheaper computing and distributed processing, we now can forgo sampling and extrapolating to analyze entire populations of data, and do it more quickly and efficiently than ever before. Restated in terms of the elephant story - not only can we recognize the elephant, we can do it more quickly. And we can find out if the elephant is alone or in a pack, whether or not the elephant is healthy or ailing, and so on.

Big data also should prompt us to question assumptions, especially in marketing. The reason is that our customers' behavior and expectations have changed so radically, we need to ask if our marketing has changed in kind? How has your marketing changed in the last 5-10 years? Have you changed how you’re organized and how you do your planning? Has your approach to engaging your audiences changed? If the answer is no to any of those questions - simply ask yourself why and then think about whether or not the answer you get is good enough.

Elephant2

Photo: Barry Butler, Chicago, IL, taken in Botswana.

One thing is for sure – our customers have changed. They also have access to more data, and easier access to computing power. And mobile devices are a key part of that empowerment, but the more important change is how mobile is changing customers' behavior and expectations, as we found in research with Northwestern University's Kellogg School of Management and summarized in a report titled, Understanding the Mobile Consumer.

Along with mobile devices, social media are creating expectations of immediacy and relevance that are redefining what constitutes a satisfactory customer experience. The relationships between customers and brands are changing, but opportunities await all organizations that modernize their marketing to keep pace with their customers by using big data.

To me, that’s the kind of elephant to watch out for in 2016. And however you choose to ring in the new year, enjoy the moment and mark it with the optimism that great things are on the horizon thanks to big data.

And as always - thank you for following!

tags: analytics, big data, Hadoop, Internet of Things, mobile

The elephants to watch out for in 2016 was published on Customer Analytics.

12月 222015
 

Although the title of this blog posting has all the ingredients to attract the eyes of an analyst, the content is targeted for all personalities of a digital marketing organization. Before we jump into the marketing analytic use case regarding forecasting, scenario analysis, and goal-seeking  for digital analytics, let's spend some time on the magic of stories. As Tom Davenport stated in his fantastic article titled, Telling a Story with Data:

"The essence of analytical communication is describing the problem and the story behind it, the model, the data employed, and the relationships among the variables in the analysis. When the relationships among variables are identified, the meaning of the relationships should be interpreted, stated, and presented relevant to the problem. The clearer the results presentation, the more likely that the quantitative analysis will lead to decisions and actions—which are, after all, usually the point of doing the analysis in the first place."

While creative visionaries and data scientists are both tremendous organizational assets within a team, it is the alliance between these two segments that will push marketing forward. Although aspirational, this is a difficult challenge to overcome. Let me begin by sharing a bit of my story - one that began with a four year career start in graphic design and creative marketing communications, and then taking making a leap to the quantitative side of marketing. I've seen and listened to how DIFFERENT these two segments of the marketing world are, and now as a preacher for the potential of marketing analytics, one's ability to make analysis interpretable and approachable is critical.

Google recently published a nice article titled, Staffing Your Marketing Measurement Team: Why You Need Data Storytellers, and one takeaway that I love from this piece is:

"The true value of data emerges when marketers are able to use it to tell a meaningful story. Enter the data storyteller, or marketing measurement analyst. This is the person who can push the tools, translate insights across the business, and motivate stakeholders to participate."

This quote nails the crux of the issue - if we don't take ACTION on the insights of analytics, it was nothing but a school project. Influencing decision-makers within an organization isn't easy, and if they do not understand the analysis, nothing will ever change. There are people who are good at creative marketing strategy, and there are people who are good at marketing analytics. However, there aren't many people who can toggle between the two, and serve as the translator who inspires both sides.

In my personal opinion, the recent surge in analytic technologies becoming more approachable is key. The special ingredient in that trend is visualization and analytics joining forces in ways we have never seen before. Why is this happening? Seeing and understanding data is richer than creating a collection of queries, dashboards, and workbooks. According to the infamous American mathematician John W. Tukey:

"The greatest value of a picture is when it forces us to notice what we never expected to see.”

The "ah-ha" moment. The best part of my work day!

In addition, when analytics becomes approachable, interpretable, and transparent to the entire marketing organization, the behavioral change of how we work together highlighted in this video becomes a reality:

Visual Analytics represents a new category of interactive and collaborative technology to provide a path to be curious and innovative. Marketers are imaginative, and are constantly pushing to analyze new and exciting data sources (i.e. clickstream, social, IoT wearables, etc.), which require the ability to scale to very large amounts of information. However, what is different here is the ability to perform sophisticated analysis, and produce visualizations to support data-driven storytelling.

Finally, we arrive at the digital analytic use case. The intention is to highlight my personal approach to tip-toeing that fine line of producing meaningful analysis, while narrating the marketing storyline. Here is the description of the business case, and my demonstration video.

Business Challenge:

How do I allocate digital media spend to drive more traffic to my website in a future time period?

Marketing Applications:

  1. Identify the most important acquisition channels (i.e. attribution)
  2. Simulate & optimize ad spend to acquire incremental traffic and meet business objective

Let me know what you think in the comments section below. If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

tags: data visualization, Digital Analytics, Digital Intelligence, digital marketing, Forecasting, Goal-seeking, marketing analytics, predictive analytics, Predictive Marketing, Scenario Analysis, visual analytics, visual statistics, web analytics

Forecasting, goal-seeking, and magical stories for digital analytics was published on Customer Analytics.

12月 092015
 

Broadly speaking, the holy grail of digital media measurement is to analyze the impact and business value of all company-generated marketing interactions across the complex customer journey. In this blog post, my goal is to take a transparent approach in discussing how data-driven marketers can progress past rules-based attribution methods, and make the business case for leveraging algorithmic applications.

Let's begin with a video example that pokes humor at the common problems related to multi-channel marketing attribution. The business challenge is that everybody believes they should have more marketing budget because their tactics are supposedly responsible for driving sales revenue.  The video suggests that challenges arise rapidly when supporting analysis to justify these claims isn't sound. While the video is fictional, the problems are very real. With that said, there are three main drivers to getting digital attribution analysis right:

  1. Allocating credit across marketing channels more accurately
  2. Providing invaluable insights to channel interactions
  3. Empowering marketers to spend more wisely in future media activity

Have you ever given thought to the many ways that a customer can find your brand's digital properties? Organic results on a search engine, display media campaigns, social media links, re-targeting on external sites, and the list goes on in today's fragmented digital ecosystem. One thing is for certain - consumer digital journeys are far from linear. They can occur across multiple platforms, devices and sessions, and organizations are challenged with gaining an accurate understanding of how:

  • External referral clicks (or hits) are mapped to channels and visits
  • Visits are mapped to anonymous visitors
  • Anonymous multi-channel visitor journeys are mapped to identifiable individuals across different browsers and devices

With careful consideration towards the areas of data management, data integration, and data quality, analyzing customer-centric (or visitor-centric) channel activity on their journeys to making a purchase with your brand can have immense benefits. Ultimately, marketers desire to apply a percentage value that can be attached to each channel's contribution to the purchasing event (or revenue). This is critical, as it allows the organization to determine how important each channel was in the customer journey, and subsequently, influence how future media spend should be allocated.

Sounds fairly easy, right? Well, as Avinash Kaushik (digital analytics thought leader at Google) stated in his influential blog post on multi-channel attribution modeling:

"There are few things more complicated in analytics
(all analytics, big data and huge data!)
than multi-channel attribution modeling."

The question is...why is it challenging? Avinash's blog post was written in the summer of 2013, and I strongly believe 2.5 years later we are living in a game-changing moment within digital analytics. Marketers are being enabled by technology companies with approachable and self-service analytic capabilities, and this trend directly impacts our ability to improve our approaches to problems like attribution analysis. However, rules-based methods of attribution channel weighting continue to be far more popular in the industry to date, which contradicts the recent analytic approachability trend. Before we dive into algorithmic attribution, let's review the family of approaches commonly applied in rules-based attribution:

Last Touch & First Touch Attribution

Suneel 1Allocates 100% of the credit to either the last or first touch of the customer journey. This approach has genuine weaknesses, and ignores all other interactions with your brand across a multi-touch journey. It is stunning, in my opinion, that web/digital analytic technologies have traditionally defaulted to this approach in enabling their users to perform attribution analysis. The reason for this was last/first touch attribution was easy, and could claim ownership of the converting visit (although that is only partially true). Thankfully, times are changing for the better, and this rudimentary approach has proven ineffective, guiding marketers (for the sake of job security) to try more intelligent methods.

Suneel 2Linear Attribution

Arbitrarily allocates an equal credit weighting to every interaction along the customer journey. Although slightly better than the last and first touch approaches, linear attribution will under-credit and over-credit specific interactions. In a nutshell, it over-simplifies the complex customer journey with your brand.
.

Time Decay & Position Based Attribution

Suneel 3Time decay attribution arbitrarily biases the channel weighting based on the recency of the channel touches across the customer journey. If you are bought into the concept of recency within RFM analysis, there is some merit to approach, but only when comparing with other rules-based methods. Position based attribution is another example of arbitrary biasing, but this time we place higher weights on the first and last touches, and provide less value to the interactions in-between. As Gary Angel (partner & principal of the digital analytics center of excellence at Ernst & Young) stated in his recent blog posting:

"There’s really no reason to believe that any single weighting system somehow captures accurately the right credit for any given sequence of campaigns and there’s every reason to think that the credit should vary depending on the order, time and nature of the individual campaigns."

Although there are some other minor variants to the rules-based method approaches, highlighted above are the majority of approaches that the digital marketing industry commonly uses. As a principal solutions architect at SAS, I have the opportunity to meet with clients across multiple industries to discuss and assist in solving their marketing challenges. When it comes to attribution, here is a summary of what I have seen clients doing in 2015:

Buying Web/Digital Analytics Software That Includes Rules-Based Attribution Measurement

This is typically when an organization invests in a premium (or more expensive) software package from their web/digital analytics technology partner, which includes out-of-the-box attribution capabilities. Here is a video example discussing how one of the most popular web analytic platforms in the world includes capabilities for various methods of rules-based attribution.

Two takeaways from this video that I love are:

  1. Suneel 4Comparing the attribution problem to soccer (or futbol), and accepting that we cannot give 100% credit to the goal scorer. There is a build up of passes to set up the goal (i.e. purchase), and each of these events (i.e. marketing channel touches) contribute value. Even though names like Messi, Ronaldo, and Neymar are commonly known in soccer, ignoring names like Iniesta, James Rodríguez, or Schweinsteiger would be a travesty.
    .
  2. Focus on the journey, and performing visitor-centric analysis as compared to visit-centric analysis

The difficulty I possess with the video is leveraging the term "data-driven attribution" when rules-based methods are the only approaches highlighted. In my opinion, we are only grazing the surface of what is possible. Algorithmic attribution, on the other hand, assigns data-driven conversion credit across all touch points preceding the conversion, using data science to dictate where credit is due. It begins at the event level and analyzes both converting and non-converting paths across all channels. Most importantly, it allows the data to point out the hidden correlations and insights within marketing efforts.

Have you ever wondered why web/digital analytic software doesn't include data mining and predictive analytic capabilities? It has to do with how digital data is collected, aggregated, and prepared for the downstream analysis use case. Suneel 5

Web/digital analytics has always had a BIG data challenge to cope with since it's inception in the mid 1990's, and when the use case for users is to run historical summary reports and visual dashboards, clickstream data is collected and normalized in a structured format as shown in the schematic to the right.

This format does a very nice job of organizing clickstream data in such a way that we go from big data to small, relevant data for reporting. However, this approach has limited analytical value when it comes to attribution analysis, and digital marketers are only offered rules-based methods and capabilities.

Data mining and predictive analytics for algorithmic attribution require a different digital data collection methodology that summarizes clickstream data to look like this:

Suneel 6

Ultimately, the data is collected and prepared to summarize all click activity across a customer's digital journey in one table row. The data table view shifts from being tall and thin, to short and wide. The more attributes or predictors an analyst adds, the wider the table gets. This concept is referred to as preparing data for the analytic base table (or input modeling table). This is the best practice for advanced algorithms to be used to fit the data. Shhhhhhhh! We'll keep that insightful secret between us.

More importantly, don't let this intimidate you if you're new to these concepts. It boils down to the ability to reshape granular, HIT-level digital data for the best practices associated with data mining. Can it be done? Absolutely, and algorithmic digital attribution is a prime example of big data analytics for modern marketing. The question I challenge my clients with is to consider the arbitrary (or subjective) nature of rules-based methods, and associated limitations. Although they are easy to apply and understand, how do you know you aren't leaving opportunity on the table? This leads me to the next recent trend of what I observe clients doing.

Buying Algorithmic Attribution Consultative Services

The best way to kick this section off is to share how 3rd party marketing attribution vendors introduce themselves. Here are two video examples to consider:

  1. Video #1
  2. Video #2

How do you feel after watching these videos? If you are raising your hands to the sky thanking the higher forces of the marketing universe, I completely understand. Many of my clients describe their marketing organization's culture as unprepared for algorithmic attribution, ranging from lack of subject-matter expertise, big data hurdles, or employee analytical skills. There can be tremendous value in selecting an external partner to handle analysis and actionability, and accelerate your ability to make better digital media investment decisions. 3rd party attribution vendors have the domain knowledge, technology, and a track record, right?

In addition, this segment of my clientbase seem less concerned with transparently understanding how to analytically arrive to their decision strategy, as long as the financial results of their attribution vendor's services look good compared to baseline KPIs. Although these vendors will never reveal their analytical secret sauce (i.e. intellectual property), digital marketing is an overwhelming ecosystem, and who has the time to discuss analytical model diagnostics, misclassification rates, ROC plots, lift curves, and that silly confusion matrix...

That's one trend I see. The other trend is when a marketing organization is analytically mature, and this leads me to the next section.

Building Algorithmic Attribution Models In-House

Do you want to perform algorithmic attribution analysis yourself and maintain a transparent (white-box) understanding of how your analytic approaches are influencing your digital media strategies? If you answered yes, I believe the best way to take you on this journey is through a case study. I'm a strong advocate of this approach, and believe this is a cutting edge application of marketing analytics:

Case Study: Hospitality Industry

The Challenge:
Unable to scale digital analytics for algorithmic attribution to measure drivers of conversion and advertising effectiveness.

Business needs to understand:
- Drivers of resort hotel bookings online,
- Marketing channel attribution to bookings with statistical validation,
- Insights to allocate future digital media ad spend.

Current Limitations:
- Clickstream and display ad-serving data very large in size,
- Rules-based attribution methods largely inaccurate.

Technical Summary:
- 90 day est. file size for extracted Adobe HIT data: 3.0 TB,
- 90 day est. file size for extracted Google DoubleClick (display media) data: 4.0 TB,
- Analytical data prep, modeling, and scoring workflow must be capable of processing on Hadoop platform (i.e. big data lake).

Digital Data Preparation Summary:
In this exercise, the hospitality brand was extracting raw data from their relationships with these digital marketing technologies into an internal Hadoop data landing zone. Their goal is to start stitching various digital marketing data sources together to gain a more complete view of how consumers interact with their brand. Analytically speaking, this is very exciting because we can gain a better understanding of the value of channel touches, onsite click activity, media impressions, viewability, creative content, ad formats, and other factors that we do not have comprehensive visibility into with traditional web/digital analytics.

One valuable insight I would like to share is if you have never worked with raw clickstream data or display media data before, it would be advantageous to obtain a data dictionary and channel processing documentation from your digital marketing solution vendor(s). For example, every website that has installed web analytic tracking has an array of unique goals, interactions, segments, and other attributes that were configured for that specific business model. Analysts will not understand what eVar 47 is without a translation document. Guess what? eVar 47 is going to have a completely different definition from Brand #1 to Brand #2 to Brand #3. Sorry - there is no easy button for this.

Your analysts will thank you sincerely for taking these steps, and it will improve their ability to succeed. Since this is a SAS Blog, I imagine many of you will want to understand how we worked with the raw digital data in this case study.

1. Data access: SAS Data Loader for Hadoop
2. Visual data exploration to assess data quality issues: SAS Visual Analytics
3. Reshaping the data for analytic modeling (i.e. recoding, transformations, joins, summarization, transpositions): SAS Enterprise Guide

Analytic Model Development Summary:

Now we move on to the fun and sexy step of the process...

Our methodology of approach was to address the digital attribution challenge as a predictive modeling problem. This involves three key goals:

  1. Produce a predictive model that computes the probability of conversion given a set of visitor journey predictors.
  2. Determine the incremental lift in probability of conversion for each channel in a visitor journey, and use this to compute attribution.
  3. Provide insight into the relationship between conversion and the predictor variables (marketing channels, onsite click activity, digital demographics, etc.).

To drill into the details a bit further, I'll break this down in three steps through a hypothetical example:

Step 1 - Create Predictors and Target

  • Convert visitor journeys into a table with rows of channel impression counts and conversion information.
  • This data is used to train (and validate) the predictive model.

Suneel 7

 

 

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Step 2 - Compute Incremental Lift

  • Use the predictive model to compute the incremental lift in probability of conversion by adding one channel at a time in each visitor journey.
  • Example for one visitor journey: Display > Email > Search > Display > $

Suneel 8

Step 3 - Compute Attribution

  • Process all conversion journeys & accumulate channel credit to compute channel attribution.
    .

Suneel 9

Analytic Modeling Results:

Now we can get to the fun and sexy stuff...

This analysis included 17 marketing channels, over 1,000 predictors, ~24,000,000 digital visitor journeys, and a rare conversion event occurrence of less than 1%. Oh my!

Suneel 10

Now let me ask you a question - do you believe there is one piece of math that will solve all of our attribution challenges?

Suneel 11

Absolutely not. The game of digital media investment is all about precision, precision, PRECISION! To maximize precision, in the field of data mining, we employ the use of champion-challenger modeling. Simply put, we throw a bunch of math at the data, and the algorithm that does the best job of fitting the data (i.e. minimizing error) is selected.

Suneel 12

Scaling to large digital data with champion-challenger modeling is not trivial, but through the modernization of analytical processing in recent years, the time has arrived to dream bigger. Random forests, neural networks, regressions, decision trees, support vector machines, and more are all fair game, which means we can produce accurate assessments of marketing channel importance using the power of advanced analytics. Here is a snapshot of our modeling results within this project:

Suneel 13

For those of you unfamiliar with misclassification rates, it's nothing more than a metric to summarize how many mistakes our analytical model is making. The lower the value, the better, and in this exercise, the random forest algorithm did the best job in analyzing and fitting our attribution data. There's your champion!

Next, let's share a lift chart visualization to help us get our heads around what we've accomplished here:

Suneel 14

The beautiful takeaway in this example is we have identified an attractive segment (top decile with highest probability scores) that is 8.5 times more likely to convert as compared to randomly targeting the entire marketable population. Secondly, if we alter that segment view to the top two deciles, they are 4.7 times more likely to convert.

BOOM! This is awesome because we can now profile these segments, and proactively hunt for look-a-likes. In addition, be imaginative in how you might use these segments in other forms of digital marketing activities. For example, A/B testing in web personalization efforts.

But what about the marketing channels themselves? Which ones ended up being more (or less) important)? Well, here is a great visualization for channel weighting interpretation:

Suneel 15

The odds ratio plot clearly highlights these insights in a non-technical manner. Channels above the horizontal line have a positive impact in increasing the probability of a visitor conversion, and channels below the line have a negative impact. For those of you who are unfamiliar with odds ratio plots, they serve as an ingredient to feed into a marketing dashboard that can explain market channel attribution performance.Suneel 16

So how accurate were we? Was this model any good?

True positive rate simply means how accurate was our ability to correctly predict conversions. True negative rate summarizes our ability to accurately predict non-conversions. Given that our original event of conversion behavior was below 1% across a three month time window, our ability to predict conversions based on the modeling insights is a MASSIVE improvement (86.67 times more accurate) versus the mass marketing approach (or pure random targeting). Even though there is still room for improvement, these are very promising results.

To deploy or activate on these insights, this will vary based on your organization's approach to taking action. It may be the scoring of an internal database, or it might be passing the model score code to your digital data management platform to improve their ability to deliver media more intelligently. There are a number of use cases for marketing activation, but by doing this analysis in-house, you will have flexibility to conform to a variety of downstream process options.

Again, I suspect many of you will want to understand how we analytically modeled the digital data in this case study.

  1. Algorithmic modeling: SAS Enterprise Miner (High Performance Data Mining)
  2. Analytic scoring: SAS Scoring Accelerator for Hadoop
  3. Marketing channel performance dashboarding: SAS Visual Analytics

Why Aren’t More Organizations Doing This?

From my experiences in 2015, I believe there are three reasons:

  1. Large data volumes require the use of modern big data platforms
  2. The talent required to unlock the marketing value in that data is scarce, but the climate is improving - if you're searching for talent, please consider the future analysts, data miners, and data scientists we are training at the GWU MSBA program in Washington DC
  3. Organizations are rethinking how they collect, analyze, and take action on important digital data sources

If you made it this far in the blog posting, I applaud your commitment, desire, and time sacrifice to go on this written journey with me. We discussed the current landscape of digital marketing attribution, from methods of approach to providing a real case study in support of making the justification for algorithmic attribution (i.e. it's not a mythical creature from another universe). Digital data mining is on the rise, becoming more approachable, and will provide organizations competitive advantage within their industries for years to come.

Marketing analytics matter!

Let me know what you think in the comments section below. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

tags: Digital Analytics, Digital Attribution, Digital Data Mining, Digital Data Science, digital marketing, marketing analytics, Marketing Attribution, Multi-channel Attribution

Making the case for algorithmic digital attribution was published on Customer Analytics.

12月 042015
 
Steps to mastering omni-channel marketing

Steps to mastering omni-channel marketing

As a customer intelligence adviser, my work exposes me to a wide range of organisations with various marketing challenges and available resources. One of the common themes that have emerged is omni-channel marketing as a business imperative. Changes in the ways customers engage with brands across an explosion of channels have prompted the need for organisations to engage in omni-channel marketing.

Best practices are starting to emerge for mastering omni-channel marketing, and I've seen that they seem to fall into a five-step pattern, which I will lay out for you in this short blog series titled, Five steps to omni-channel marketing.

In the previous posts in this blog series, I’ve explained how you can evolve your marketing from customer segmentation to one-to-one marketing to event driven marketing, and now I’d like to elaborate on how to go on to optimised marketing.

The primary benefit of the previous step is that you become really relevant to your customers, engaging in pull marketing instead of push marketing. In that case, your marketing efforts are firmly based on the customer’s needs, as would be the case in a birthday campaign, or a market basket abandonment campaign, The downside of event-driven marketing is that customers can get over-contacted. I have clients that have more than 400 event driven marketing campaigns waiting to be triggered by customers In those situations, it’s easy to get over-contacted customers and fuzzy messaging since it’s not unusual for a customer to trigger multiple campaigns in short time frame. That outcome just takes you away from what should be your main objective: customer centricity. For that reason, it’s important to manage all of your campaigns and contact rules on a single platform to control the mass of event driven communications and avoid over-contacted customers.optimization scenario 1

From a commercial perspective it’s not interesting to send all the triggered communications to the customer. Every trigger has a communication cost and every contact needs to be relevant. To give you an example, the total cost of producing and sending a catalogue to a customer costs around 1 euro. If you have 1 million customers and you are sending out this catalogue 10 times a year this means that you have invested 10 million euros. So often our clients end up in a situation where they have many campaigns that can be sent through different channels at different time frames. One of the biggest challenges in that scenario is knowing which campaign should be stopped and which should be sent. The picture above shows the complexity of decisions an organization can face when planning their marketing campaigns.

The complexity of making the right decision for every customer increases even more when you want to take into account the corporate commercial strategy. The goal of most organizations is to make profits, so marketing departments just don’t get unlimited budgets, nor do they get unlimited email, call center, sales agent, or catalogue capacity. And any form of customer communications needs to demonstrate some impact, or a return on investment. Ideally, the return is maximised, which can be done by optimising the channel capacities at their respective lowest possible costs using the most relevant offers for every customer.

I have visited some banks that were obliged from a strategy point of view to send all their leads to sales agents, because the worst thing that could happen was that an agent go idle because they didn’t have any leads to handle. In many cases, a lot of leads sent by marketing ended up wasted because they didn’t have the capacity to fulfill all of them. The opportunity cost of those wasted leads should be as unacceptable as any possible idle sales agents.

Another possibility would be to focus on which people are in need and have the highest likelihood to respond to a sales call, crossed with the potential revenue profile of the customer, and then send those clients to the agents based on capacity to fulfill. Some customers might find a sales call intrusive, so another channel would be better for those kind of customers.

The complexity of today’s business environment has made traditional marketing approaches relics of a bygone era where “old fashioned” marketing looked more like broadcasting. Indiscriminately pushing out marketing campaigns now reliably deliver numerous adverse outcomes:

  • Subjective, non-customer centric, planning/decision making,
  • Ineffective use of channels,
  • Poor long term customer value growth,
  • Contact fatigue by over-soliciting customers,
  • No ability to understand trade-off between key strategic decision elements,
  • Hard to define best investment and engagement strategy.

Any one of those outcomes should be avoided. Across industries, I’ve seen the most effective approach is to apply marketing optimisation to select the best customers for every offer, taking into account your commercial strategy. Doing that helps you focus on two levels:

  1. Tactical: The best combination of customers, offers and channels, within a framework of policies and real-world business constraints, to maximise value for the organization.
  2. Strategic: Simulate alternative strategies (what-if analysis) to identify the optimal balance of resources and opportunities which will deliver the greatest return

optimization scenario 2Tactical focus

To explain how optimisation finds the best combination of customers, offers and channels I have prepared a simple campaign example to the right.

In the above picture you see 9 customers who are eligible for 3 campaigns. The value of each campaign is determined by the propensity score multiplied by the expected revenue, which can be used to find a balance between your customer centric strategy and commercial strategy. The goal is to pick the best campaign for each customer, thereby maximising the expected revenue based on the following constraints:

  • A customer can only receive 1 campaign
  • Each campaign should have 3 customers.

Some people will find as a solution 675 or 705 depending on optimization scenario 3which prioritisation method you use (by profile or by campaign). These results are found when you use prioritisation rules. But the maximum expected revenue we can generate from this set up is 780.

You might be able to figure this out manually (as in this simple construct), but it quickly becomes too complex for most business environments, often with 100 or more campaigns, millions of customers and upwards of 40 business constraints. Optimisation will help you focus maximizing revenue and being relevant for every customer.

Strategic Focus:

The optimisation solution will also help you in strategic decision-making as you can immediately simulate the impact of your key decisions on the ROI. Even if given the chance, it’s rare to simply pick the optimal scenario, and typically there is a minimum number of offers per product. Another unlikely strategic scenario is to have any given channel be left unused – if they turn out to be ineffective, they are de-selected.

The optimisation approach allows you to compare different scenarios. Each scenario can have unique set of constraints (e.g. higher call center capacity, tighter budgets per campaign, minimum numbers of product offers) , which allows you to compare the results of each scenario as in the picture the below. You can see the objective value: which is what you are optimising. This can maximize profit, lower costs, etc., limit the number of offers, and the return on investment on your scenario.optimization scenario 4

optimization scenario 5The ROI is not only given at an aggregate level but also calculated by campaign to give insights into which campaigns are bringing value and which are not, allowing you to decide which campaign offers should be increased or decreased. You see for example that we have limited profit from the current account cross-sell campaign in the picture to the right. From a strategic point of view you maybe want to increase this campaign as you have a minimum number of offers you need to make on this product.

To be able to understand the impact of the strategic decisions the optimisation software gives an overview of the opportunity cost of each limitation you give to the current marketing set up. In this case we see that the maximum of 12,000 calls is giving us an opportunity of 2.08 and 4.84. The way it arrives at that opportunity cost is because the software calculates the effect of relaxing or tightening that constraint.optimization scenario 6

This is the way the optimisation step allows you to get more ROI out of your marketing investment. We have several customer cases where ROI get tremendously improved by taking this optimisation step and I urge you to check them out to learn more. The most detailed is a Forrester Consulting case study on Commerzbank called The Total Economic Impact of SAS Marketing Optimization. Other options to learn more about marketing optimisation include a customer success story about Akbank and these two whitepapers you can register to download:

I hope you found this helpful. Check back for the next and final step in this process to omni-channel marketing. I'll comclude with how your marketing can be transformed into onmi-channel real-time dialogues. And as always - thank you for following!

tags: analytics, customer intelligence, Five steps omni-channel, omni-channel, one-to-one marketing

Five steps to omni-channel marketing – step 4 was published on Customer Analytics.