personalization

9月 062017
 

When it comes to the SAS web experience on sas.com, support.sas.com (and more), we have a vision and mission to create experiences that connect people to the things that matter – quickly, easily and enjoyably. You could say that we’re a user-task-focused bunch of web people here, and so whether you’re a web visitor to SAS researching a solution area or evaluating a product, looking for how-to content or applying for a job, our goal is to make sure you complete that task easily.

Using tools like SAS Customer Intelligence 360 helps us do this by allowing us to take the guesswork out creating the most optimized web experiences possible through it's IA, machine learning, omnichannel marketing and analytics and more is all the rage – and for good reason – don’t lose sight of the power and impact of good old fashioned a/b and multivariant testing.

The power of small in a big customer journey

If you think of your website as a product, and think of that product as being comprised of dozens, maybe hundreds of small interactions that users engage with – imagery, video, buttons, content, forms, etc. – then the ability to refine and improve those small interactions for users can have big impact and investment return for the product as a whole. Herein lies the beauty of web testing – the ability (really art and science) of taking these small interactions and testing them to refine and improve the user experience.

So what does this look like in real life, and how to do it with SAS Customer Intelligence 360?

On the top right corner of the sas.com homepage we have a small “sticky” orange call-to-action button for requesting a SAS demo. We like to test this button.

A button test? Yes, I know – it doesn’t get much smaller than this, which is why I affectionately refer to this particular button as “the little button that could.” It’s small but mighty, and by the end of this year, this little button will have helped to generate several hundred demo and pricing requests for sales. That’s good for SAS, but better for our site visitors because we’re helping to easily connect them with a high-value task they’re looking to accomplish during their customer journey.

How do we know this button is mighty? We’ve tested a ton of variations with this little guy measuring CTR and CVR. It started off as a “Contact Us” button, and went through a/b test variations as “Connect with SAS” “How to Buy” “Request Pricing” as we came to realize what users were actually contacting us for. So here we are today with our control as “Request a SAS Demo>"

Setting up a simple a/b test like this literally takes no longer than five minutes in SAS Customer Intelligence 360. Here's how:

  • First, you set up a message or creative.
  • Finally, you create your

    Easy breezy. Activate it, let it run, get statistical significance, declare a winner, optimize, rinse and repeat.

    Now, add segmentation targeting to the mix

    So now let’s take our testing a step further. We’ve tested our button to where we have a strong control, but what if we now refine our testing and run a test targeted to a particular segment, such as a “return user” segment to our website - and test button variations of Request a SAS Demo vs. Explore SAS Services.

    Why do this? The hypothesis is that for new users to our site, requesting a SAS demo is a top task, and our control button is helping users accomplish that. For repeat visitors, who know more about SAS, our solutions and products – maybe they are deeper in the customer journey and doing more repeat research and validation on www.sas.com. If so, what might be more appropriate content for that audience? Maybe it’s our services content. SAS has great services available to SAS users - such as Training, Certification, Consulting, Technical Support, and more. Would this content be more relevant for a return user on the website that's possibly deeper in the research and evaluate phase, or maybe already a customer? Let's find out.

    Setting up this segmentation a/b test is just like I noted above – you create a spot, build the creative, and set up your task. After you have set up the task, you have the option to select a “Target” as part of this task, and for this test, we select "New or Return User" as the criteria from the drop down, and then "Returning" as the value. Then just activate and see what optimization goodness takes place.

    So, how did our test perform?

    I'll share results and what we learn from this test in the upcoming weeks. Regardless of the results though, it's not really about what variation wins, but rather it's about what we learn from simply trying to improve the user experience that allows us to continue to design and build good, effective, optimized user experiences. Tools like SAS Customer Intelligence 360 and it's web testing and targeting capabilities allow us to do that faster and more efficiently than ever.

     

     

     

     

     

     

     

    Using SAS at SAS: SAS Customer Intelligence 360, a/b testing and web optimization was published on Customer Intelligence Blog.

8月 122017
 

Optimization is a core competency for digital marketers. As customer interactions spread across fragmented touch points and consumers demand seamless and relevant experiences, content-oriented marketers have been forced to re-evaluate their strategies for engagement. But the complexity, pace and volume of modern digital marketing easily overwhelms traditional planning and design approaches that rely on historical conventions, myopic single-channel perspectives and sequential act-and-learn iteration.

SAS Customer Intelligence 360 Engage was released last year to address our client needs for a variety of modern marketing challenges. Part of the software's capabilities revolve around:

Regardless of the method, testing is attractive because it is efficient, measurable and serves as a machete cutting through the noise and assumptions associated with delivering effective experiences. The question is: How does a marketer know what to test?

There are so many possibilities. Let's be honest - if it's one thing marketers are good at, it's being creative. Ideas flow out of brainstorming meetings, bright minds flourish with motivation and campaign concepts are born. As a data and analytics geek, I've worked with ad agencies and client-side marketing teams on the importance of connecting the dots between the world of predictive analytics (and more recently machine learning) with the creative process. Take a moment to reflect on the concept of ideation.

Is it feasible to have too many ideas to practically try them all? How do you prioritize? Wouldn't it be awesome if a statistical model could help?

Let's break this down:

  • Predictive analytic or machine learning projects always begin with data. Specifically training data which is fed to algorithms to address an important business question.
  • Ultimately, at the end of this exercise, a recommendation can be made prescriptively to a marketer to take action. This is what we refer to as a hypothesis. It is ready to be tested in-market.
  • This is the connection point between analytics and testing. Just because a statistical model informs us to do something slightly different, it still needs to be tested before we can celebrate.

Here is the really sweet part. The space of visual analytics has matured dramatically. Creative minds dreaming of the next digital experience cannot be held back by hard-to-understand statistical greek. Nor can I condone the idea that if a magical analytic easy-button is accessible in your marketing cloud, one doesn't need to understand what's going on behind the scene.That last sentence is my personal opinion, and feel free to dive into my mind here.

Want a simple example? Of course you do. I'm sitting in a meeting with a bunch of creatives. They are debating on which pages should they run optimization tests on their website. Should it be on one of the top 10 most visited pages? That's an easy web analytic report to run. However, are those the 10 most important pages with respect to a conversion goal? That's where the analyst can step up and help. Here's a snapshot of a gradient boosting machine learning model I built in a few clicks with SAS Visual Data Mining and Machine Learning leveraging sas.com website data collected by SAS Customer Intelligence 360 Discover on what drives conversions.

I know what you're thinking. Cool data viz picture. So what? Take a closer look at this...

The model prioritizes what is important. This is critical, as I have transparently highlighted (with statistical vigor I might add) that site visitor interest in our SAS Customer Intelligence product page is popping as an important predictor in what drives conversions. Now what?

The creative masterminds and I agree we should test various ideas on how to optimize the performance of this important web page. A/B test? Multivariate test? As my SAS colleague Malcolm Lightbody stated:

"Multivariate testing is the way to go when you want to understand how multiple web page elements interact with each other to influence goal conversion rate. A web page is a complex assortment of content and it is intuitive to expect that the whole is greater than the sum of the parts. So, why is MVT less prominent in the web marketer’s toolkit?

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

If you want to be confident in the test results, then you need each combination, or variant, to be shown to a reasonable sample size of visitors. In this case, assume this to be 10,000 visitors per variant, leading to 2.56 million visitors for the entire test. That might take 100 or more days on a reasonably busy site. But by that time, not only will the marketer have lost interest – the test results will likely be irrelevant."

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

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

Continuing with my story, we decide to set up a test on the sas.com customer intelligence product page with four content spots, and three creatives per spot. This results in 81 total variants and an estimated sample size of 1,073,000 visits to get a significant read at a 90 percent confidence level.

Notice that Optimize button in the image? Let's talk about the amazing special sauce beneath it. Methodical experimentation has many applications for efficient and effective information gathering. To reveal or model relationships between an input, or factor, and an output, or response, the best approach is to deliberately change the former and see whether the latter changes, too. Actively manipulating factors according to a pre-specified design is the best way to gain useful, new understanding.

However, whenever there is more than one factor – that is, in almost all real-world situations – a design that changes just one factor at a time is inefficient. To properly uncover how factors jointly affect the response, marketers have numerous flavors of multivariate test designs to consider. Factorial experimental designs are more common, such as full factorial, fractional factorial, and mixed-level factorial. The challenge here is each method has strict requirements.

This leads to designs that, for example, are not orthogonal or that have irregular design spaces. Over a number of years SAS has developed a solution to this problem. This is contained within the OPTEX procedure, and allows testing of designs for which:

  • Not all combinations of the factor levels are feasible.
  • The region of experimentation is irregularly shaped.
  • Resource limitations restrict the number of experiments that can be performed.
  • There is a nonstandard linear or a nonlinear model.

The OPTEX procedure can generate an efficient experimental design for any of these situations and website (or mobile app) multivariate testing is an ideal candidate because it applies:

  • Constraints on the number of variants that are practical to test.
  • Constraints on required or forbidden combinations of content.

The OPTEX procedure is highly flexible and has many input parameters and options. This means that it can cover different digital marketing scenarios, and it’s use can be tuned as circumstances demand. Customer Intelligence 360 provides the analytic heavy lifting behind the scenes, and the marketer only needs to make choices for business relevant parameters. Watch what happens when I press that Optimize button:

Suddenly that scary sample size of 1,070,000 has reduced to 142,502 visits to perform my test. The immediate benefit is the impractical multivariate test has become feasible. However, if only a subset of the combinations are being shown, how can the marketer understand what would happen for an untested variant? Simple! SAS Customer Intelligence 360 fits a model using the results of the tested variants and uses them to predict the outcomes for untested combinations. In this way, the marketer can simulate the entire multivariate test and draw reliable conclusions in the process.

So you're telling me we can dream big in the creative process and unleash our superpowers? That's right my friends, you can even preview as many variants of the test's recipe as you desire.

The majority of today’s technologies for digital personalization have generally failed to effectively use predictive analytics to offer customers a contextualized digital experience. Many of today’s offerings are based on simple rules-based recommendations, segmentation and targeting that are usually limited to a single customer touch point. Despite some use of predictive techniques, digital experience delivery platforms are behind in incorporating machine learning to contextualize digital customer experiences.

At the end of the day, connecting the dots between data science and testing, no matter which flavor you select, is a method I advocate. The challenge I pose to every marketing analyst reading this:

Can you tell a good enough data story to inspire the creative minded?

How does a marketer know what to test? 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.

11月 042016
 

The digital age has fundamentally changed how brands and organisations interact with consumers. This shift has been a crucial part of the Third Industrial Revolution and helped spark the era of consumers sharing their data with different organisations. But now organisations are heralding the Fourth Industrial Revolution, and data is […]

Analytics: The lifeblood of the Fourth Industrial Revolution was published on SAS Voices.

10月 052016
 

Energy suppliers are fighting for prime position in the domestic energy supply market. Disillusioned customers, increased competition due to a flood of new entrants and tighter regulations are forcing suppliers to reassess their business models. According to UK regulator Ofgem, there were 3.8 million account switches in the first six […]

Big data customer analytics key to high-performing energy companies was published on SAS Voices.

3月 232016
 

The business opportunity to intelligently manage customer journeys across their lifecycle with your brand has never been greater, but so is the danger of not meeting their expectations and losing out to savvier competitors. In my opinion, the current state of most digital analytic practices continue to be siloed, tactical, and narrowly fixated on channel-obsessed dashboard reporting. That might come across as presumptuous, but keep this in mind - customer-centricity is a hot topic at the C-Suite level, and your CMO has  stated (or will very soon) that your organization is transforming into a personalization super force that will be marketing to the segment of one. If that is the case, the category of digital analytics has got to step up its game!

The antidote is digital intelligence which represents a strategic shift in approach to marketing analysis that uses insights from traditional and modern channels (we're talking online AND offline) to enable actionable, customer-obsessed analytical brilliance.

The era of the empowered customer is unraveling itself — trends in which consumers, not brands, own influence, backed by the rapid rise of digital. I strongly believe that no matter how important a company's products or services are with my life, the majority of brands I do business with continue to perform channel-centric analysis, and remain unaware of the different interactions I have with them across ALL channels. I don't care about your email or search marketing KPIs. What I care about is how you treat Suneel, no matter what device, channel, or platform I select to interact with you on.

Meanwhile, digital marketing spend continues to grow at a tenacious pace, cementing the importance of digital channels in managing the customer journey. Digital marketing is effective in all phases of the customer life cycle, ranging from acquisition, upsell/cross-sell, retention, and winback, proven by the ongoing shift of wallet share to online channels. While these are exciting times for omnichannel marketers, these more holistic approaches bring challenges. In today's fragmented digital landscape, long-established methods focused on web analytics and aggregated customer views are ill-equipped to keep pace with:

Digital interaction bread crumb trails

Customers (and prospects) interact with brands across an array of online channels and devices, creating new paths to generate incremental value associated with marketing-centric KPIs. However, customers expect personalized relevance in moments of truth, raising the bar for analytics and marketing execution. A brand's digital presence is much more than a website, such as social media, mobile applications, and wearable technologies. Conventional web analytics only track onsite behavior and lack the ability to comprehend tech-savvy customers in 2016.

The collapse of the digital silo

Brands typically construct offline and online interaction channels confined from one another, so let's reflect on that for a moment. Isn't it time we recognize that customer data is customer data, regardless of where the ingredients are collected? To deliver comprehensive customer insights, brands seek to merge digital and offline data sources together. Digital & customer analytics teams are attempting to work together, but their projects struggle due to a clash of approaches & culture. Some of the main drivers are:

  1. Data — Customers leave trails of information for marketers to chew on, and are available in structured, semistructured, and unstructured formats. There's no excuse anymore for brands to not be able to work with all three. Approachable technology exists to integrate multiple sources of online and offline customer data in meaningful ways to analyze and take action on.
  2. Skills — Have you ever sat in a meeting with data scientists and web analytic ninjas? It's like they speak two different languages, and communication between these two segments is critical for an organization to innovate in its commitment to customer analytics.
  3. Analysis — There is a reason why there is so much discussion around the application of advanced analytics. In many ways, digital marketing is ripe for analytical maturity, ranging across segmentation, attribution, and personalization. The discipline has proven its value to help differentiate a brand from its competition. When are the days of Data-Scientist“good enough” analytics going to end? Let's keep the science in data science, and stop succumbing to the false hype that sophisticated predictive marketing can be accomplished through black box, easy-button solutions.

Dynamic interaction management

Brands seek to react intelligently to shifts in consumer behavior in milliseconds, which makes the intersection of predictive analytics and data-driven marketing vital for orchestrating the customer journey. To reach your target audience in opportunistic micro-moments, the requirement of real-time actionable analytics with direct connections to personalization and marketing automation systems is the queen bee. The sole dependence on isolated, retrospective reports and dashboards of aging web analytic solutions has serious limitations in modern marketing.

Given the investment and revenue at stake for most brands, it is increasingly important to champion support of the development and continuous optimization of digital channels. Simply put, analytical sophistication lives at the center of that process. Yet most organizations continue to approach digital analytics focused on discerning traffic sources and aggregated website user behaviors. Given the intricate complications and aspirational promise of digital marketing, brands should consider modernizing and maturing their approaches to customer analytics because:

  • CX matters: Customers don't care about the challenges related to identity management across multiple visits (or sessions), browsers, channels, and devices. Does your web analytic platform support your team's abilities to recognize and track customers, not clicks or hits, across the fragmentation of touch points? With careful consideration towards the areas of data management, data integration, and data quality, analyzing customer-centric (or visitor-centric) digital activity on their journeys to making (or not making) a purchase with your brand is absolutely feasible.
  • "Good enough" analytics must end: Digital analytic teams must graduate from machine gunning their organizations with traffic-based reports that summarize the past to producing predictive insights that marketers can interpret, and take action with. I'm always impressed by web analytic teams that produce an array of historical reports with beautiful visualizations, segmenting and slicing away at their tsunami of clickstream data. However, how much impact and relevance to the business can this approach have? Customer-centricity demands that we re-engineer our thinking, and make the shift from reactive to predictive marketing analytics.
  • There's nothing exciting about siloed channel analysis: To deliver the elusive and mythical 360 degree view of customer insights, it turns out you don't need magical wizards like Gandalf or Albus Dumbledore by your side. Have you ever wondered why web analytic software doesn't allow you to perform data stitching with offline data sources? How about data mining and predictive analytic capabilities? Well, it boils down to how digital data is collected, aggregated, and prepared for downstream use cases.

Web 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 analysts is to run historical summary reports and visual dashboards, clickstream data is collected and normalized in a structured format as shown in this schematic:

Data Aggregation for Web Analytics

This format does a very nice job of organizing clickstream data in such a way that we go from big data to small, more relevant data for reporting. However, this approach presents challenges when performing customer-centric analysis which requires data stitching across online and offline data sources. Why you ask? Because you cannot de-aggregate data that was designed for channel and campaign performance summarizations. Holistic customer analysis, from a digital viewpoint, requires the collection and normalization of granular, detailed data at an individual level. Can it be done? Of course it can.

Multi-source data stitching, data mining and predictive analytics require a specific digital data collection methodology that summarizes clickstream data to look like this:

Data Aggregation for Advanced Analytics

Ultimately, the data is collected and prepared to contextually summarize all click activity across a customer's digital journey in one table row, including a primary customer key to map to all visits across channels and devices. 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. The beauty of this approach is it allows marketers and analysts to be curious, add more data sources, and allow algorithmic analysis to prioritize what is important, and what isn't. This concept is considered a best practice for advanced customer analytics.

  • Beware of blind spots: As time passes, customers in every industry are progressively sharing more data about themselves through existing and emerging digital outlets, such as mobile applications, wearables, and other connected technology. The opportunity to ingest and analyze these new sources should excite any marketer who claims to be data-driven. However, does your web analytics platform allow you to analyze these new digital touchpoints? A brand's ability to absorb, integrate, analyze, and derive marketable insights from emerging data sources is key in this new paradigm to avoid being blindsided by customers and the competition.

The path to digital intelligence from traditional web analytics needs to cover the diversity of data, advanced analytic techniques, and injection of prescriptive insights to support decision-making and marketing orchestration. Digital intelligence is a transformation for web analytic teams — making it a competitive differentiator if executed well. It aims to transform brands to become:

  1. Customer-centric rather than channel-centric: As customers and prospects weave across an ocean of marketing channels and connected devices, digital intelligence supports the integrated analysis of interactions in concert, rather than with disconnected channel views. In addition to visibility across all channels, analysis is highly granular to identify, track, and prioritize next-best-actions for individuals. In other words, hyper-personalization to the segment of one!
  2. Focused on enterprise goals as opposed to departmental: To enable omnichannel analytics, digital intelligence is highly dependent on customer data management capabilities across all data types – structured, semistructured, and unstructured. This includes fusing interaction and behavioral data across all digital channels with first-party offline customer data, as well as second- and third-party data (if available). This enriched potpourri of data must be prepared to feed the analytical ninjas that sit within the marketing organization, line of business or centralized customer intelligence team, because it is their job to exploit this stream of information and generate insights for the organization as a whole.
  3. Enabled for audience activation and optimization. The mission of digital intelligence is the direct application of analytics to generate data-driven evidence that helps business stakeholders make clearer decisions. The potential of data mining exponentially increases with richer customer data to support segmentation, personalization, optimization, and targeting - in other words, connecting data and analytics to the delivery of relevant content, offers, and awesome experiences.
  4. Analytical workhorses: The incredibly fast-moving world of digital interactions and campaigns mean that marketers desperately need quicker analysis. Waiting days or weeks for reports and research equates to failure. Digital intelligence delivers efficiency at a pace that more nearly matches users' decision-making schedules.

SAS Customer Intelligence offers a one-stop modern marketing platform to comprehensively support the mission of digital intelligence - from digital data collection, management, predictive analytics, and marketing delivery across online and offline channels. On April 19 at SAS Global Forum 2016, SAS Customer Intelligence 360 will make its debut, and digital intelligence will be a primary topic. This new offering will drive unprecedented innovation in customer analytics, putting predictive analytical intelligence directly in the hands of digital marketers, business analysts, and data scientists. In the last few months, industry analysts have previewed and validated our abilities in advanced and customer analytics.

We are very excited for the future and potential of digital intelligence. The question is...

Are you excited?

 

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: customer intelligence, Data Mining, data science, Digital Analytics, Digital Intelligence, marketing analytics, personalization, predictive analytics, Predictive Marketing, segment of one, web analytics

Web analytics vs. digital intelligence - what's the difference? was published on Customer Intelligence.

3月 072016
 

Rand Merchant Bank (RMB) ran an advert a few years ago, showing identical twins born 10 minutes apart. The advert shows how this small difference had a great bearing on their lives, with two very different personalities developing, and, of course, both eventually finding contrasting but equally fulfilling careers at the bank.twins

I was reminded of this recently when discussing the differences in behaviour within very similar customer segments. No matter how granular and analytically-advanced your segments, there are minor differences between the individuals in each one that cause them to behave differently from their peer group. In the words of the advert, they are “identical in every way, except the way they think."

Data-driven marketers are trying to overcome this problem with a segment-of-one approach. Listening to, understanding and then acting on the unique nuances in behaviour of each individual means truly personalised experiences.

Will we be seeing truly personalised experiences in the near future?

Imagine an online fashion retailer. Two identical male shoppers, in the young professionals segment, interested in smart suits and accessories, both shopping online, both presented with the same offers for the latest Italian designs . . . but one day, one of these shoppers clicks on the kids’ clothing section, not the men’s.

Is this a mis-click? Or does this young man have a new niece or nephew, or maybe a newborn son we didn’t know about?

And more importantly, what does this mean for the way that we handle his visits and make offers to him? Do we move him into the new parents segment? How do we know if this is a permanent change in behaviour? Can we triangulate this behaviour with any other that we’ve seen, to provide clues?

The technology is available to do this. We have big data processing power and the analytical capabilities to sift through this data to uncover relevant patterns of behaviour. We can even do it in real time, or close to it.

But the complexities in data collection and integration slow these efforts down. Companies are overwhelmed by the volume of the data, and struggle to identify common threads across multiple sources. Organisational silos, channel isolation and segment-based thinking all hamper company-wide efforts to develop the elusive 360-degree view of individual customers that would allow real-time analysis of their behaviour.

How can organisations realign around a segment of one?

I suggest that customer-centricity is developing a new meaning. It is now understood that realigning the organisation’s data, people, processes and technology around its customers is the only way to achieve truly personalised experiences. It is also understood that these experiences will be the cornerstone to winning and retaining customers.

But there is another problem. Even if an organisation could collect all its data and analytics in one place and build an intelligent view of each customer’s unique past  behaviour, it would struggle to react quickly enough to nuances in real-world behaviour. And this is what is required for true personalisation.

The most vital component for marketers and data analysts is a centrally-managed data, analytics and real-time decision-making engine at the heart of all marketing efforts. This centralised engine should act as the channel-agnostic and context-sensitive brain. It would be working in the background during all interactions across all channels and make real-time decisions for these channels about what messages to provide to each customer.

Many organisations make the mistake of building personalisation logic, but limiting it to a particular channel, usually the website or mobile channels. If these channels operate in isolation and don’t listen to (or feed) the centralised brain, insights and decisions made on this channel do not inform, nor are informed by, any other channel, whether it’s the call centre, in-branch staff or batch email marketer.

The work of the centralized brain

Let’s go back to our example. Our young male customer’s recent change in behaviour cannot be handled in isolation. As soon as this behaviour occurs, the central engine should move into action.

It is constantly listening for new contextual information, such as website or mobile app clickstream data. When it obtains new data, it runs it through a real-time process to decide if this new information should change our predetermined action for this person. This process considers all available data, such as:

  • Engagements with the brand in the past minutes or hours (since the last batch analytical processes ran).
  • Insights on social media using text analytics.
  • Previous browsing history to check whether this is an isolated incident.
  • Purchase history to see if he does this at the same time every year.

At the end, the brain will make a decision about whether to override or append the predetermined scores or segments. It will determine the best action to take for that individual in that moment (our segment of one), and this action will immediately be available to all other channels, brands and data sources.

Is this really worth all the effort, time and expense? Well, SAS’ customers think it is. One mobile operator is able to detect real-time context in airtime balance thresholds. Their problem was that they could not send personalised offers until a few hours after the threshold was reached. And by then, the offers were often no longer relevant. The company was hovering at a 5 percent response rate to its offers no matter what it tried.

But when the company introduced real-time centralised decision-making with personalisation, response rates rose to 24 percent, generating tens of millions in incremental revenue per year. This was far beyond expectations and will only improve as the company’s capabilities mature.

Time to change

Is your organisation treating your customers like they are all twins?  Improving your segmentation abilities is an evolutionary journey, and I urge you to start immediately with what you have.  The white paper by Suneel Grover, Analytics in Real-Time Online Marketing, discusses how your organisation can take the first steps to detect, analyse and respond to the rich data that your customers are already giving you on digital channels.

This SAS eBook is another great primer on the concept of contextual marketing.

 

tags: analytics, marketing, one-to-one marketing, personalization, real-time decisioning, segment of one

When it comes to one-to-one marketing, even identical twins differ was published on Customer Intelligence.

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.

11月 032015
 

In anticipation of SAS Forum Portugal 2015, I wanted to kick off my first contribution to the SAS Customer Analytics Blogosphere sharing an interview I completed with Sofia Real on the topics of modern digital marketing, predictive analytics, optimization, and personalization. Does that sound like a nasty traffic jam you might want to avoid? Absolutely not, as the time has arrived for predictive marketing to have it's moment in the bright sun, and with Gartner recently naming SAS a Leader in digital marketing analytics, it's official - the 800 pound guerrilla in advanced analytics is locked in on solving complex issues facing the space of data driven marketing. Making digital personalization more relevant for target audiences is just like preparing a delicious meal; it all comes down to the ingredients and preparation process to rise to the occasion!

1. How can analytics help the everyday life of a marketer focused on website or mobile app content strategy and optimization? 

Optimization is a core competency for digital marketers. As customer interactions spread across fragmented touch points and consumers demand seamless and relevant experiences, content-oriented marketers have been forced to re-evaluate their strategies for engagement. But the complexity, pace and volume of modern digital marketing easily overwhelms traditional planning and design approaches that rely on historical conventions, myopic single-channel perspectives and sequential act-and-learn iteration.

Presently, marketers primarily use a variety of online testing approaches that include A/B testing and various methodologies within multivariate testing (MVT) for optimizing content. A/B testing is a method of website or mobile app optimization in which the conversion rates of two versions of a page (version A and version B) are compared using visitor traffic. Site or app visitors are presented either version A or B. By tracking the way visitors interact with the content they are shown – the videos they watch, the buttons they click, or whether they sign up for a newsletter – you can infer which version of the content is most effective. Multivariate testing uses the same core ingredients as A/B testing, but it can compare more than two variables. In addition, it reveals more information about how these variables interact with one another.

Lastly, for digital marketing practices with an advanced analytic strategy, the usage of data mining and predictive analytics to prioritize and inform the marketing teams on what to test, and to analytically define segment audiences prior to assigning test cells, is a best practice, in my opinion. Marketers are very creative, and can imagine hundreds of different testing ideas – which tests do we prioritize if we cannot run them all? This is where advanced analytics can help inform our strategies in support of content optimization. To bring this to life, check out a video example I created of predictive marketing analysis using SAS Visual Analytics and Decision Trees to provide digital-centric insights!

Suneel.Figure11

2. What are the advantages of using these various optimization approaches? Are they restricted only to the marketing department?

Online testing is appealing not only because it is efficient and measurable, but also because it cuts through noise and assumptions to help marketers present the most effective content, promotions, and experiences to customers and prospects. The evolving digital marketing landscape drives a greater mandate for online testing: to operate in more channels, handle more data and support more users. Online testing must move beyond traditional on-site experimentation to fully optimize a multifaceted digital customer experience.

The majority of today’s technologies for digital personalization have generally failed to effectively use predictive analytics to offer customers a contextualized digital experience. Many of today’s offerings are based on simple rules-based recommendations, segmentation and targeting that are usually limited to a single customer touch point. Despite some use of predictive techniques, digital experience delivery platforms are behind in incorporating predictive analytics to contextualize digital customer experiences.

There are three areas where current trends in digital personalization are falling short:

  • Single-channel digital interactions: Most online experience delivery platforms offer predictive analytic capabilities for a single section of a website in order to support marketing acquisition (rather than the entire digital journey), but do not provide (or limit) functionality for integrating predictive insights across multiple data sources (online and offline), primarily because cloud-based solutions were not designed to incorporate on-premises first-party offline data. In other cases, uploading that data would violate internal IT policies regarding the sensitivity of sharing customer data and associated risks.
  • Black-box vs. white-box scoring: Many digital experience delivery technologies offer predictive capabilities, but do not offer transparency. That is, they aim to provide insights for a specific scenario (such as next best offer recommendations) with algorithms that are more or less opaque. Marketers or their supporting analysts can’t see into the process of the prediction, limiting their ability to improve the predictive model while minimizing false-positives and false-negatives.
  • Extreme dependency on business rules: Other platforms rely heavily on predefined (or subjective) customer profiles and interaction campaign design. As firms who have adopted this approach begin to mature, these rules expand exponentially, forcing marketers and campaign planners to manage hundreds of rules. Business rules have a place in predictive analytics, but they are the bread, and predictive models must be the filling in between the bread.

There is a broad selection of standalone predictive analytics solutions that can support the delivery of exquisite digital experiences. These solutions enable any department (not just marketing), data scientists and developers to design, develop and deploy predictive models to websites and mobile applications. Standalone predictive solutions surpass embedded predictive capabilities that are found in many digital experience platforms because they have the ability to:

  • Incorporate large and varied data sets from numerous sources, producing unanticipated insights. Unlike the digital experience platforms, which aim to own the data, predictive analytic capabilities can support either cloud-based or on-premises platforms, enabling marketers to find customer patterns across a variety of internal and external data silos. Often, the goal-oriented nature of predictive analytics leads to unexpected customer insights that firms might not have found by using traditional segmentation methodologies. The key is to ensure that the data sources are available for real-time personalization applications, meaning that clickstream data (historical and in-session), demographics and other valuable inputs can be processed, analyzed, scored and treated within milliseconds.
  • Allow for monitoring of predictive models and adaptation to new developments. Over the long term, data-driven marketers must evaluate predictions for effectiveness. If a model’s predictive confidence level drops below a certain threshold, its business value decreases, and it might become no more useful than rules-based personas. When a model becomes unacceptably inaccurate, users should be able to modify the algorithms and variables that are used to make the predictions in order to return to higher accuracy levels.
  • Provide both the predictive insights and the logical rules. Despite their power, predictive models must also be constrained with information about the real world in order to deliver the most value.

I am a strong believer in supporting my thoughts and opinions with real evidence. Check out another video example I created using SAS Visual Statistics to perform approachable, analytical segmentation (rather than subjective rules-based approaches) using both clickstream behavioral data and third-party append data (sourced from a partnered MSP or digital DMP) to provide insight into informing personalization strategies and increasing relevance.

Suneel.Figure12

3. How does this all fit in a modern marketing omni-channel strategy?

Most organizations have several customer-facing web and mobile applications with varying levels of visitor traffic. Before undertaking a digital-personalization initiative, the organization has to first identify the most suitable digital application for personalization and its related content management systems. Some of the factors that go into this decision include:

• Average number of daily visitors
• Geographical and time-of-day distribution of visitors
• Purpose of the web application
• Existing hosting platform (cloud or on-premise)
• Ease of website modifications for personalization

After the most suitable web application and its related content management system have been identified, the following components (implemented by what I will refer to as engines) are recommended for a robust digital-personalization solution:

• Collection Engine: Collects digital behavioral data, for every session and every user accessing any of the digital properties of the organization
• Normalization Engine: Transforms raw digital behavioral data into a normalized data model, suitable for data-stitching with offline data, as well as for feeding business intelligence reporting, and predictive analytics
• Analytical Engine: Consists of all tools and processes used by organization to analyze the normalized data and build predictive marketing models
• Decision Engine: Uses the output of the predictive marketing analytical models and processes to perform decision orchestration in staged or real-time consumer interactions (both outbound and inbound processes)
• Personalization Engine: Presents optimized and contextually aware content across marketing channels (online or offline) using treatments received from Decision Engine

4. What are main steps a company must take to adopt this kind of procedures? Does it Imply changes in the traditional processes?

I would like to highlight three phased approaches, based on varying levels of digital marketing and analytic maturity of an organization:

Startup Phase

In this phase, the enterprise installs and configures the required tools and software to work in conjunction with its digital application to personalize content (by using a rules-based randomization model) and collect required data that will be used in upcoming phases.

Suneel.Figure10

 

Analytics Phase

During this phase, the organization assembles the data captured by the collection engine and merges it with internal customer data into a common analytical data mart for building models to support staged personalization.

Suneel.Figure25

Operational Execution Phase

During this phase, the enterprise monitors analytical performance and continues to improve its predictive models by periodically downloading data that was captured by the collection engine and deploying model scoring to the real-time decision engine.

Suneel.Figure27

For readers who made it this far, I thank you for your attention and commitment to this blog posting. If you enjoyed the content, and would like to dive deeper into my thoughts about making digital personalization delicious by leveraging predictive analytics, please consider downloading a technical white paper I authored earlier this year here, or viewing an on-demand webinar available here.

Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

 

tags: customer intelligence, Data Driven Marketing, Digital Analytics, Digital Data Mining, Digital Intelligence, digital marketing, personalization, predictive analytics, Predictive Marketing

Digital marketing, predictive analytics, and making personalization delicious was published on Customer Analytics.