How far can IoT take customer intelligence?

There is some debate about the extent to which the internet of things (IoT) is affecting customer intelligence. We have the potential for something new: IoT could mark the end of segmented marketing and the start of “momentary marketing”’. But is this really happening yet? Current deployments give us some idea of how far this could go. Early adopter examples suggest IoT for customer intelligence will be an evolution. Read More »

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Winning the customer experience war begins in the data trenches

Back in 2001, when I started working in the enterprise marketing software business, customer relationship management or CRM was seen as the cure all from a sales and marketing perspective.

“If only we could more quickly send direct mailers offering a buy one, get one video rental, we could corner the market” one executive told me. CRM deployments at that time were costly and resource intensive.

My how times have changed.  But one thing hasn’t changed – there remains three critical components to consider when standing up a solid customer intelligence software solution – data, insight and action.

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SAS CI community: Your ticket to speedy SAS answers

My smartphone’s alarm clock app recently did something it’s never done. It failed to go off. I slept through my exercise class. Grrrr!

After texting an apology to my trainer, I looked online for a solution. Google knows everything, right? I found the answer in an online support community, tweaked the app and life went on. Who doesn’t scour the web for quick answers these days? Read More »

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Are marketers ready to navigate IoT privacy concerns?

Privacy is a perennial issue. Whether it is a data breach at the IRS, deliberate leaks such as the Panama Papers or simple non-compliance of data rules, privacy is a regular source of news stories and concern to those responsible for managing personal data of any kind. The Internet of Things (IoT) has potential to make this level of concern look small. IoT data can expose more—and more intimate—information than has ever been shared in the past, and customers are waking up to this. Read More »

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Customer experience has an impact on brand equity

In my first post, I discussed the importance of brand equity and its relationship to good customer experience.

Consider this scenario of an organization where brand equity was negatively impacted by a fractured customer experience. In this case the “brand” is the corporate brand.

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Brand equity is built on customer experience

When it comes to strong brand equity, everyone in the organization has to have a seat at the table. Brand equity is the result of positive interactions and transactions between the consumer and brand – across all touchpoints and all communication channels. It is built over time by brands being loyal to customers by providing them with the products, services, and interactions that they expect and value.

However, building brand equity is increasingly complicated by the number of touchpoints a brand has with consumers. As consumers, we have all experienced the frustration of being over-communicated to. Our mailboxes and inboxes are flooded, sometimes with conflicting messages. Or we continuously see marketing communications for things we already purchased or do not want. And even though we may be good customers of a brand, sometimes one channel does not recognize us or treat us as a loyal customer.

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How artificial intelligence will enhance customer experiences

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

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

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

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


A cognitive computing use case

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

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

cognitive computing

Analytics: the core of machine learning

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

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

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

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

The newest addition to artificial intelligence

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

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

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Real-time decisioning: Is it always your best option?

I wanted to share some thoughts about when real-time decisioning is actually necessary. I guess the reason for wanting to do this is to balance the view that everything can be actioned by trigger event campaigns executed in real time.

So have a look at the graph below – with the x axis being time, and the y axis being some kind of KPI measure – for example, response (or perhaps the effect on net promoter score).

NPS chart


I’ve drawn four lines on this graph to help make the point:

  • The green line – just going flat over time – suggests that sometimes response doesn’t vary much over time. A good example of this would be high-ticket items or a mortgage, perhaps. Realistically – if you send a message to a customer after they have hovered over the mortgage product on your website – it isn’t really going to seal the deal there and then.
  • The red line shows that response can drop over time. I’m not sure it’s quite as linear as this, but essentially, this is a case of ”the sooner the better”, and so real-time decisioning is important – but I would argue not essential. It depends on the cost to execute and the overall ROI.
  • The black line suggests that real-time execution of a decision is essential – if you don’t act quickly, the moment has gone, as has your customer! This is more akin to perhaps helping a customer who is having difficulty on line – e.g. filling out a form – if you don’t help them immediately, they may go away, never to return. But do show a bit of caution, too. Is the profile more like the blue line?
  • The blue shows that if you act too quickly, you may actually destroy responsiveness (or more likely value). Making an offer to a customer on an abandoned cart can come too quickly – equally there are overtones of Big Brother here.

Three ways to determine the best action

So, how do you find out what kind of profile is the most useful for the particular action that you want to take?

Well, first of all, I would just like to give a shout out to marketing nous (i.e., common sense) because in fairness, some of these things can be obvious. For example, if you reduce a customer’s download speed as they approach their limit you help the customer avoid incurring overages – it is clear that more rapid decisioning will result in better responses.

Secondly, the answers are probably in your data. I hear a lot about attrition and churn triggers – such as browsing competitor websites. What could you do? Well, it would be pretty easy to observe when that browsing behaviour occurred and then plot the churn or attrition event out over time.  There are probably many natural tests that are sitting in your data right now.

Finally there is multivariate testing. Why not try making that abandoned cart or basket offer at Hour 1/2/3 or Day 1/2/3?  That way, you can get a feel for how behaviour varies over time so that you can then make the optimal decision for the customer and your business.

If you want to find out a more about how SAS helps its clients act upon insight, visit the customer intelligence solutions page.

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SAS 360 Discover: Predictive marketing's new secret weapon

In April, SAS 360 Discover was introduced at SAS Global Forum 2016. Since my career started at SAS over five years ago, I have been anticipating this important announcement. In my opinion, this is a major breakthrough for the space of digital intelligence.

In my first year working at SAS, I learned of research and development to address industry needs for digital marketers. Although technologies from Google, Adobe and others address web analytics with measurement reporting, there was a shortcoming.

Historically, web analytics has always had a huge data challenge to cope with since its inception. And when the use case for analysts is to run summary reports, clickstream data is normalized:

Data Aggregation for Web Analytics

It nicely organizes raw clickstream into small, relevant data for reporting. However, this approach presents challenges when performing customer-centric analysis. Why? Holistic customer analysis requires the collection and normalization of digital data at an individual level. This is one of the most important value props of SAS 360 Discover.

Multi-source data stitching and predictive analytics require a data collection methodology that summarizes clickstream:

Data Aggregation for Advanced Analytics

The data is prepared to contextualize all click activity across a customer's digital journey in one table row, including a primary key to map to all visits across browsers and devices. The data table view shifts from being tall and thin to short and wide. The beauty of this is it enables sophisticated analysis to prioritize what is important, and what isn't. This concept of data collection and management is considered a best practice for advanced customer analytics.

How many marketers do you know who wake up in the morning and claim they can't wait to hear about how analysts are spending 80 percent of their time preparing raw web behavior data, rather than focusing on analysis and actionable insights? None, you say? Exactly! Wouldn't you rather hear your marketing analysts spend their time doing this?

20-80 Rule

I have always appreciated SAS for what it can do with structured, semi-structured, and unstructured information, but there has always been one dependency – where do I point SAS to obtain the originating data? SAS 360 Discover eliminates this requirement, and provides data collection mechanisms for your brand's website(s) and mobile apps.



In addition, the raw semi-structured data streams SAS natively collects are run through a pre-built relational data model using SAS Data Management for various forms of contextualization that stretch far beyond traditional web analytic use cases.

Data Model

The output of this data model schema summarizes all digital visitor behavior at this level of detail:

  • Customers.
  • Anonymous visitors.
  • Sessions (or visits).
  • Interactions (or clicks/hits).

Complete View

The data model schema will allow for additional configurations and introduction of other digital data sources to accommodate your organization's evolving needs. More importantly, the benefits of the output are profound, and listed below is a summary of SAS 360 Discover benefits:

  • Digital data normalization to support online and offline data stitching of customers.
    • When offline data is residing in your organization's data warehouse, information is available at the customer level (not a click or hit level). That's a problem when you want to link it with web or app data. The amount of time analysts spend reshaping raw HIT extracts from their web analytics solution is astonishing, and quite difficult. Customer analysis requires online/offline data stitching, and overcoming this obstacle was a problem SAS set out to solve.
  • Measurement reporting and visualization of customers and segments.
    • The reporting remains critical as an entry stage for analytics. SAS believes there should be no limit to how many reports and dashboards can be produced to meet business objectives. In other words, unlimited ad hoc reports using SAS Visual Analytics, which is the analysis tool that is packaged with SAS 360 Discover
  • Predictive analyticsmachine learning, and data science  of customers and anonymous traffic.
  • Fueling the SAS customer decision hub
    • Brands gain a competitive edge if they stop perceiving customer engagement as a series of discrete interactions and instead see it as customers do: a set of interrelated interactions that, when combined, make up the customer experience. By folding in all known customer level information into a common hub, SAS can analyze, score and take intelligent, contextual actions across channels.


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

  1. Customer-centric rather than channel-centric
  2. Focused on enterprise goals as opposed to departmental
  3. Enabled for audience activation and optimization
  4. Analytical workhorses

I suspect you would love to see demonstrations of the data that SAS 360 Discover collects from websites and mobile apps in action:

  1. Decision Trees
  2. Clustering
  3. Forecasting
  4. Logistic Regression

In addition, here is the on-demand video of the SAS Global Forum 2016 keynote presentation of SAS Customer Intelligence 360.

As a marketing analyst at heart, it is extremely gratifying to share my excitement for SAS 360 Discover.  The time for predictive customer marketing in the digital ecosystem is here, and the 800-pound gorilla in advanced analytics has just unleashed your new secret weapon.

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SAS 360 Engage: Being in the moment with your customers

If nothing else you read this year puts your marketing efforts in perspective, this quote from Henry Ford should: “It is not the employer who pays the wages. Employers only handle the money. It is the customer who pays the wages.”

Our customers deserve our respect. And I believe that, by and large, marketers treat their customers with the respect they deserve. But like with anything in life, creating happy customers only happens by making their experience relevant and satisfying – a complex task in an ever-changing environment.256722970

That’s why SAS has introduced its new SAS Customer Intelligence 360 platform. It gives you the ability to integrate data from all of your customer touchpoints and share and gather customer intelligence across your entire organization (not just customer-facing departments).

One of the important modules of the new platform is SAS 360 Engage that does just what the name implies – helps you better engage with your customer by using analytical insights to make the right offers, faster.

Marketing mindfulness

In the omnichannel world that marketers now inhabit, you know that you have to be hyper-alert at all times, or you miss an opportunity because you were too slow or make a misstep you’re not in a channel that your customer prefers.

The real power of SAS 360 Engage is in its ability to help you respond effectively to changes in customer behavior – say for example, the customer breaks a pattern of channel preference and moves to a different channel. You’ll be able to recognize behavior shifts and choose the best action for each interaction.

Personalizing, and even individualizing, the content that is placed on digital properties leads to higher engagement, loyalty and retention. It makes perfect sense. If customers see content that is relevant to them on a webpage or in a mobile app – they are much more likely to remain engaged – versus seeing generic content that is targeted to everyone. Targeting this “segment of one” increases uptake rates of offers and messages.

I can’t think of an industry or organization that doesn’t have customers –  no matter whether you call them clients, patrons or patients – that won’t benefit from more relevant engagements.

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