SAS Customer Intelligence 360: Digital discovery and engagement brought into focus

Just last weekend, I was considering buying a new camera lens. I already had a few brands in mind, so I looked online at their websites to learn more about their product information. I was able to conduct a comparison on different brands and lenses to narrow down to a specific 50mm lens provided by a major brand. I added the lens to my cart online, but wanted to get a closer look of it, so I chatted online with a representative to see if there were any lenses available at stores near me. This digital channel was my first point of interaction with the brand, but what impact did that have on my buying experience? Would responsive design come479424735 into play? Would the brand proactively contact me about similar products? Or would they simply react to inquiries that I had as a consumer? But today’s consumers expect immediate, individualized messages – would this brand deliver?

The fact of the matter is that a lot of brands don’t have the capabilities to modify messages, offers and interactions across channels, devices and points in time so that they are more relevant to the end consumer.

 Enter SAS

SAS Customer Intelligence 360, launching this month to the marketplace, offers an all-encompassing view of customers no matter how they choose to engage with you across digital properties.

A complete customer view

SAS Customer Intelligence 360 can give you detailed insights from digital channels customers interact with to create the most effective and relevant actions. The solution rapidly transforms digital data into a complete 360-degree view of the customer, meeting each customer’s needs at the right time, place and in proper context. Multiple decision-making methods, such as predictive models and multivariate tests, help ensure that customers gets the most relevant and personalized offers.

Data integration

Data is also easy to integrate with many offline customer channels though SAS Customer Intelligence 360 and its customer decision hub. Customer interactions are based on previous engagements on all other platforms. The data hub is able to convert all of this into customer-focused actions. With this data integration, the Customer_decision_hubbrand is able to gather my interactions and information from all available sources; not just the website, but the call center, mobile apps, social media and point of sale.

Offline customer data can be appended to digital data to further augment the view of me as a customer. These data sources, typically demographic or transactional in nature, gives marketers valuable insight into a customer’s true needs in order to create more relevant offers, better targeted activities and more efficient use of marketing resources. This capability allows the brand to see me more than just page clicks. They’ll see me as a father with young children, interested in photography and seeking to buy a 50mm lens to capture fleeting family moments.

Insights into future actions

You don’t need to be a data scientist to harness the power of predictive marketing; SAS Customer Intelligence 360 includes guided analytics to provide marketers a forward-looking view of customer journeys. This enables them to better understand business drivers and incorporate them into segmentation, optimization and other analytic techniques. Marketers can better forecast how customers will perform in the future. The solution acts as the data scientist – enabling marketers to become more efficient and effective in the analytical techniques they embed into marketing initiatives.

Web data collection

Each web page is embedded with a single line of HTML that automatically collects page information without expensive tagging. With this feature, the webpage configuration might change simultaneously with what I click on, the order and timing of my clicks, each keystroke, etc. Dynamic data collection offers me more relevant content as I navigate through the brand’s site. Any customer activities are recorded privately and securely over time so that once a customer is identified, the information is connected automatically.

Simply put, SAS Customer Intelligence 360 offers marketers the confidence to manage their digital customer journeys in a more personalized and profitable way. Marketers gain a complete view of their customers and transform this data using analytical insight into customer-centric knowledge and future actions. With this solution, brands can interact with customers on a personalized level and customers will be more satisfied with their entire relationship with a brand, not just a single transaction. Customer loyalty goes up and attrition goes down.

And as for me, I got the lens I was looking for, and was satisfied with the customer experience. Of course I have ideas on how to improve it on behalf of this brand, and SAS Customer Intelligence 360 fits into that picture.

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Structured versus unstructured data in retail

Of course everyone has heard all the hype on big data and how it can help business’ become more successful. But have you thought about the different types of big data? How the different types of data can support different initiatives within your business?

Structured versus unstructured data in retail is a key topic to first understand in order to create a successful plan. Structured data is data that sits in a database, a file, or a spreadsheet. It is generally organized and formatted. In retail, this data can be point-of-sale data, inventory, product hierarchies, ect. Unstructured data does not have a specific format. It can be customer reviews, tweets, pictures, and even hashtags.

So now that you know what structured versus unstructured data in retail is, let’s talk about how to use it. Customer reviews are a great way to understand why a certain product is or isn’t working. Word clouds are a tool to visualize large amounts of customer reviews. Finding key words that are continuously being used canRetail-Transaction_50B9900 give insight in to product defects. For example, if ‘fits small’ is frequently used then you can be proactive by adding this to the product description or above the size selection. This will reduce customer returns and money lost on shipping fees.

Unstructured data can also be analyzed for sentiment analysis. This gives insight in to whether the customer’s response is positive, negative, or neutral. A great example of this is being able to analyze your customer’s twitter responses. Let’s say you post a tweet with products you are thinking about buying for your spring line and your brands hashtag. This enables retailers to understand your customers’ response before you even buy the product. This technique can also be used in-season and give insight to merchants on areas of opportunity or risk so that open to buy can be managed. Break down the silos between merchandising and marketing and enhance collaboration.

It doesn’t take a data scientist to use unstructured data analytical techniques either. If you’re looking to use unstructured data in your business process, check out more information on SAS Visual Analytics. Also, take a look at the 2015 Forrester Wave report where SAS was named a leader in Big Data Predictive Analytics Solutions.

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Introduction to SAS for digital personalization and attribution

As promised a couple of weeks ago, I am very happy to share Part 2 of a webcast series highlighting how SAS participates in the space of digital analytics for data-driven marketing with applications for personalization and attribution. Before launching the video, let me set some context for what you are about to see.

Why do we care about the intersection of digital analytics and personalization? Honestly, it is increasingly important to predict how customers will behave so you can personalize experiences with relevance. The deeper your understanding of customer behavior and lifestyle preferences, the more impactful personalization can be. However, digital personalization at the individual level remains elusive for most enterprises who face challenges in data management, analytics, measurement, and execution. 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 marketing easily overwhelms traditional planning and design approaches that rely on historical conventions, myopic single-channel perspectives and sequential act-and-learn iteration.

The majority of technologies in use today for digital personalization have generally failed to effectively use predictive analytics to offer customers a contextualized digital experience. Most are based on simple rules-based recommendations, segmentation and targeting that are usually limited to a single customer touch point. Predictive MarketingDespite some use of predictive techniques, digital experience delivery platforms are behind in incorporating predictive analytics to contextualize experiences using 1st-, 2nd- and 3rd-party customer data. In my opinion, I believe the usage of digital 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 massive opportunity. 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, as it allows the data to prioritize our strategy, and help us focus on what is important.

Moving on to our second subject of interest, we transition to the wonderful world of marketing attribution. At the very core of this topic, modern marketers recognize that customers expect brands to deliver relevant conversations across all channels at any given moment. The challenge is to uncover the interactions that drive conversions through integrated measurement and insights. However, organizations struggle to employ a holistic measurement approach because:

  1. It's confusing to distinguish among the measurement approaches available.
  2. Marketers bombard customers with extraneous content.
  3. Today's misaligned data makes customer level measurement a very difficult task.

It seems like attribution has been a problem for marketers for a very long time. According to a popular quote by Avinash Kaushik of Google:

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

The question is: Why is it challenging? SAS strongly believes three years later that we are living in a game-changing moment within digital analytics. Marketers are being enabled 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. The time has arrived for algorithmic attribution . . . Attribution

 

Did I whet your appetite? I hope so...please enjoy episode two of our two-part webcast series, now available for on demand viewing:

 

SAS for Digital Analytics: Personalization and Attribution [Part 2]

 

SAS Customer Intelligence offers a one-stop modern marketing platform to comprehensively support the objectives of predictive personalization and algorithmic attribution - from digital data collection, management, predictive analytics, omnichannel journey orchestration, delivery across online and offline channels, and measurement. On April 19 at SAS Global Forum 2016, SAS Customer Intelligence 360 will make its debut, and subjects like digital intelligence and predictive personalization will be primary topics. This new offering will drive unprecedented innovation in customer analytics and data-driven marketing, putting predictive analytical intelligence directly in the hands of digital and integrated marketers responsible for the customer experience.

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.

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The role of contextual marketing for in-store sales

All of us we have had the unfortunate experience of going to a store and encountering a salesperson who is unable to give us expert advice about a product or service. It’s not because salesperson is unwilling to help us, but rather because he or she does not know in enough about the products the company sells.

In fact, the Retail in Belgium survey carried out by Vlerick Business School and Insites Consulting revealed that "not less than 44 percent of consumers feel they know more than the seller about [a] product, after searching online information.” I’m sure these numbers are similar in other countries.

Another challenge for in-store sales is managing the peaks and uneven shopping traffic throughout the day, week or year while having enough competent salespeople for floor coverage.

Are brick-and-mortar operators losing ground over what was considered their strength to ecommerce – expert knowledge? Fortunately, it’s not a bleak as it appears. Traditional retailers have options, but they must be innovative and aggressive to challenge current trends.

Data management and analytics to the rescue

We know that shoppers often use the web to research products before visiting a store, but how can retailers add value once shoppers enter the store? That's where contextual marketing play an important role.Albert pic

Recent technologies such as iBeacons and wi-fi tracking enable retailers to recognize and accurately locate customers as they travel through a store. The next step is persuading a customer to download and enable an app that allows a retailer to better understand individual shopping behavior. The enticement for shoppers is that retailers can make in-store offers in real time via push notifications.

For price-conscious shoppers, downloading an app means they won’t miss out on any promotions. For others, the incentive is learning about new products or offering an improved shopping experience. Being able to meet these goals requires detailed knowledge of individual buying motives. Customer segmentation and creating a customer typology will help.

Once your tracking strategies are enabled, centralized data management for phygitals, both physical and digital data sources, will improve the level of service. The app enables a store to recognize when a shopper enters the store, and using the customer’s transactional history and other data (such as recent online research on the company website), the retailer can improve and enhance the customer experience.

For example, using recommendation engines, you can propose related products based on its location in the store. And all this in real time please because in this context, "right time is real time." If you want to know more about how predictive analytics makes offers more relevant, do not hesitate any longer and read the excellent blog post written by my colleague Adrian Carr about the topic.

The application can also be used by customers to request product/service assistance during their store visit. The sales staff can quickly access information about the customer to better respond to their inquiries.

Needless to say, a customer needs to have a positive initial experience with the app or it will become just another unused app on their smartphone.

If you want to find more information about how SAS enhances the customer experience through contextual marketing, have a look at SAS® Real-Time Decision Manager.  You can also look at our Customer Decision Hub approach to managing customer interactions across all channels.

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Introduction to SAS for digital analytics and segmentation

Digital analytics primarily supports functions of customer and prospect marketing. When it comes to the goals of digital analysis, it literally mirrors the mission of modern marketing. But what exactly is today's version of marketing all about?Modern Marketing

Honestly, we've been talking about this for years. And years. We ALL know it's what we should be doing and conceptually it's very simple, but practically, it has been very hard to achieve. Why?

Even with great web analytics, there have always been critical missing insights, which meant we didn't know for certain what the next-best-interaction for each customer was at any point in time. In addition, the development of insights and the use of analytics to define high-propensity audience segments has been distinctly slow and batch-driven in nature, delaying relevant delivery of targeted interactions. So we may get the message right, but we probably don't deliver it in a timely, consistent way, which has a dramatic impact on customer responsiveness and marketing effectiveness.

So in today's connected, always-on, highly opinionated world, we need to be a little sharper in meeting our customer's basic expectations, never mind surprising, delighting, and impressing them. While the concept of customer-centricity continues to increase in importance, improving our analytical approach to support this premise is vital.

SAS recognizes today's modern marketing challenges with digital and customer analytics. It is our mission to enable marketers to benefit from approachable and actionable advanced analytics to make more powerful decisions within today’s complex and interconnected business environments. That sounds great, right? I sense some of you reading this are raising an eyebrow of suspicion at this very moment.

Practically speaking, we want to show you exactly what that means. On March 29th, 2016, we aired episode one of a two-part webcast series, and it is now available for on demand viewing:

SAS for Digital Analytics: Introduction & Advancing Segmentation [Part 1]

We genuinely hope the webcast provided a proper introduction to how SAS participates in the space of digital analytics for data-driven marketing, and please come back in a couple of weeks when we will post Part 2 in this series entitled: SAS for Digital Analytics: Personalization & Attribution [Part 2]

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.

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Web analytics vs. digital intelligence - what's the difference?

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 languaData-Scientistges, 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 “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.

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Who's the king of advanced customer analytics?

The age of the customer is upon us. As data-driven marketers, we are now challenged by senior leaders to take a laser-focus on the customer journey, and optimize the path of consumer interactions with your brand. Within that journey, there are a number of trends (or challenges) to focus on:

  1. Deeply understanding your target audience to anticipate their needs and desires
  2. At a minimum, meeting their expectations (although aiming higher can help differentiate your brand from the pack)
  3. Address their pain points to increase your brand's relevance

According to Forrester Research in their Forrester Wave™: Customer Analytics Solutions, Q1 2016 report, no matter where customer insight professionals sit within the marketing organization, line of business or centralized customer intelligence team, it is their job to employ advanced customer analytics to generate this insight. In the study, Forrester employed a 33-criteria evaluation of customer analytics vendors, and identified the 11 most significant software providers in this category. Forrester researched, analyzed, and scored them. This evaluation details their findings about how well each vendor fulfills their criteria and where each vendor stands in relation to each other. Click here to see the results! ForresterAs I imagine many of you reading this blog posting are users of SAS technology, let's say it together:

That's right baby!!! #ILoveSAS

Okay, after completing my little happy dance, let's get serious again. The report begins with a very interesting headline, that some of you might find debatable, that reads as:

You don’t need a Ph.D. for advanced customer analytics

How does that make you feel? I would imagine for some, it might be inspiring, motivating, and aspirational. For others, you might be raising an eyebrow in suspicion. Personally, based on my interpretation, I believe it speaks to the exciting era of approachable analytic technology and innovation that we are living in. The authors of the report proceed to share observations on the current bottleneck of available data science talent, and the market demand for their talent far exceeding supply. Due to this phenomenon, the job of deriving valuable insights is increasingly becoming the responsibility of data-driven marketers who need friendly tools for data management and analytics.

Marketing analytic ninjas - please stand up!

The next big headline stirs the geeky juices of data science enthusiasts by stating:

Don’t assume you can use traditional analytics to uncover deep customer insights

Calling for the usage of a new breed of algorithms, methods, and advanced analytical techniques, leveraging all your data (vs. statistical sampling), and moving past the reliance of reports (because let's be honest, many organizations are still drowning in endless dashboards). Additional differentiators that are called out include:

  1. Delivering output at the customer level - We are living in the age of the customer, not the age of the aggregated, siloed marketing channel. Customer-centric analysis is a transformation of approach for many organizations, but the benefits can be massive.
  2. Predicting the future - Across the entire customer journey, predictive marketing is all the rage!
  3. Leveraging new data types and sources - Semistructured and unstructured data continue to be under-exploited by marketing organizations, and this needs to come to an end. The technology and tools are in place and available today to enable analysis of these data sources

Jumping ahead past the evaluation's criteria scores (link available below for those yummy details), here is what Forrester had to state about SAS:

"Consider SAS if you are looking to drive innovation in customer analytics. As the only company in this evaluation whose sole focus is on analytics, SAS excels at analytics production — that is, turning data into insights. It invests heavily in its customer intelligence suite to ensure that its solutions anticipate and even drive innovation in this space for marketers. With real-time data ingestion and integration, regular addition of new algorithms, and a robust selection of model management and deployment options, the limitations you will encounter with using SAS for customer analytics are less likely to be with the core functionality than with your team’s ability to use them."Innovator

Wow! We love this viewpoint, and are extremely pleased with the assessment. SAS is strongly committed (maybe obsessed) to innovating customer analytics by adapting solutions to meet our clients’ ever-changing data, business and deployment needs.

  For those of you interested in reading the entire press release, it is available here.

When you're ready to explore more, please start with these technologies:

We look forward to beginning a customer analytic journey with you soon.Forrester2

The Forrester Wave™ is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave™ are trademarks of Forrester Research, Inc. The Forrester Wave™ is a graphical representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.

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.

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Talk to your customers in real time using analytics

Using real-time analytics, your conversations with customers can be more relevant and have a more personal touch. When you’re engaged in conversation, you may not be aware of it, but your brain is taking in and using massive amounts of information.

First, you recall previous conversations and everything you already know about the other person. You are also analyzing and building on what they are saying. realtimeFinally, depending on the nature and intensity of the relationship, your responses will be casual or formal in tone (or somewhere in between).

How much better would your conversations be if you could be more aware of the communication nuances as the conversation is occurring? And perhaps more importantly, what if you could do the same with your conversations with customers?

The answer for customer interactions is integrating real-time analytics into your business. By helping you to develop a comprehensive understanding of your customer, analytics will help you determine the next best offer, or at least the right message to send.

Using real-time analytics, your decision will be based on the context and history of interactions between the consumer and your organization – a complaint a few seconds earlier, a product ordered online or a basket abandoned. This dimension adds the immediacy necessary to any meaningful dialogue. SAS® Real-Time Decision Manager provides the technology and software to enable this. My colleague Andrea Sangalli has discussed this in his post dealing with the power of contextual marketing in real time.

When ‘Right Time’ is ‘Real Time’

The instantaneous nature of this information gathering and analysis is especially important in the connected world in which we operate. The marketer does not have the luxury of being able to take their time in responding to a prospect or customer. Incoming interactions, initiated by the consumer, override outgoing actions and are also much more precise and promising in terms of conversion. Now, it is the consumer who decides what he or she wants, when and by what channel.

For example, a customer logs into his bank account online to look at the interest rate on his saving account, then an associated FAQ page followed by the page that shows him the steps needed to move an account to another bank. This is a strong signal to the bank that it needs to take action. Additionally, he may call the bank’s call center to explore other options.

Predictive analytics will allow call center personnel to offer the bank’s best response to that customer based on his recent queries and previous history. It can also make sure that the incentive is appropriate for the size and importance of the account.

If analytics had been used without this real-time information, a next best offer would have been sent, but perhaps too late because the client would already have contacted the call center and the call handler, unaware of the online search for information, might have missed the opportunity to make the right offer. One customer lost – and how many more in future?

For more on this topic, download our white paper Customer Intelligence in an Era of Data-Driven Marketing. Take a look at chapter 3, which is about campaigns in real time. Happy reading!

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When it comes to one-to-one marketing, even identical twins differ

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.

 

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SAS named a winner on the 2016 CRM Watchlist

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.

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