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.
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.
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?
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.
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:
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.
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: Digitalanalytic 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:
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:
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:
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!
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.
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.
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.
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:
Deeply understanding your target audience to anticipate their needs and desires
At a minimum, meeting their expectations (although aiming higher can help differentiate your brand from the pack)
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!As 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:
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.
Predicting the future - Across the entire customer journey, predictive marketing is all the rage!
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."
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.
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.
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. Finally, 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?
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.
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.
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 on 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.
Today’s retailers have access to vast stores of data that allow them to create the personalised retail experience that customers have come to expect. Used in the right way, analytics can be the key to bringing customers in through the door, building a better online experience, or simply helping weather slow periods by enabling faster, more efficient and nimble supply chain changes.
Interestingly, customer perceptions towards a brand can significantly change following Black Friday discounting and January sales. Pricing strategy can be confusing at the best of times. Given the option of £50 off on a high-value product compared to a 70 percent discount on a low value item: which is the better offer? Which deal do consumers trust, and why? More importantly, how does it impact the brand perception?
One thing’s for sure: brand loyalty and consumer trust are no longer guaranteed in today’s fast-changing retail environment. Retailers must find alternative ways to engage with consumers.
The dark art of forecasting
For years, retailers have collected customer data through loyalty cards, email marketing promotions, point-of-sales tills, as well as online browsing behaviour and purchase orders. What has changed over the years is the technology that allows retailers to analyse and understand data. The advent of technologies like Hadoop has enabled the development of advanced analytics solutions to produce more insightful and timely answers for retailers, which is of huge value during peak trading seasons.
Another interesting point here is that often the data collected spans the last three years of trading or more. This is traditionally used by store managers to forecast pricing and demand. Yet we know that outdated historical data, coupled with unpredictable external factors like weather conditions, regularly produce inaccurate forecasts.
As a result, retailers are still making predictions largely based on gut feel rather than objective insights. More often than not, they are also spontaneously reacting to competitors’ price cuts without carefully calculating their profits and loss potential.
Brands switch off
Another common retail pitfall is the effect of over marketing with a general blanket message. Unaware of the consequence of blasting out daily and sometimes twice-daily promotional emails in the hope of catching shoppers’ bargain hunting instincts, retailers are actually turning customers away. Consumers will easily unsubscribe from brands they previously loved, especially if they feel they are bombarded with irrelevant and unwanted product recommendations that only clog up their inboxes.
True personalisation demands an intimate understanding of the customer, and willingness from the individual to participate.
One of our retail customers has been using data analytics to deliver personalised product displays. These are highly targeted to improve conversion and avoid endless page scrolling. Instead of feeling like every other shopper each time a customer enters the site, they can now enjoy a genuinely personal engagement with the retailer. Behaviour is predicted and product recommendations are based on browsing, and a wealth of customer and transaction data.
With a more personalised customer experience, shoppers will be able to purchase products at the best price, while receiving the best in-store, online and post-sales service.
As retailers have better insights into customer behaviour and spending patterns, they will be able to personalise the experience and product recommendation. In turn, shoppers will feel they are receiving better deals and being looked after by the retailer. Some customers may also find themselves receiving extra benefits or upgrades through loyalty schemes, enhancing the overall experience with the brand.
SAS has worked for many years with dunnhumby, the power behind Tesco’s Clubcard. Today there is a need to market to customers across multiple channels. SAS has also worked with Callcredit Information Group, specialists in marketing services, analytics and data, to deliver a market leading omnichannel marketing and analytics service. This partnership has delivered a solution to ASDA. It provides an easy-to-use interface, with repeatable processes to enable organisations to deliver more campaigns – from simple to complex – in a shorter space of time.
Sail through the online retail revolution
On Black Friday alone, shoppers spent £1.1 billion with UK online retailers, setting a new internet retail record which underlines the online shopping revolution. The true impact of this year’s trading results are yet to be seen but one thing is certain – retailers can no longer rely on historical data alone to forecast pricing models or stock performance.
The cost for retailers can be drastic if they cannot accurately analyse the net profitability impact ahead of the peak trading period.
Big data enables retailers to more accurately forecast actual consumer demand, based on the very latest fashions and trends, as well as the timing and the likely location for that demand. For example, big data can be used as follows:
Analyse the data and visualise the demand forecast on a specific product in a specific store on a specific time and day of the week. This enables the retailer to ensure they get specific stock to that location.
Predict what else the customers might like to buy in the same transaction and personalise the product recommendations accordingly.
Predict the behaviour of customers by channel. For example, how customers might move between online browsing and in store purchasing, or their preference for home deliveries or Click & Collect.
Use demand insight to negotiate more effectively what the retailer buys from suppliers and when. Also, strategically plan for a more coherent and cheaper supply chain and transportation journey, allowing for external factors like extreme weather conditions.
Understand which segment of the customer demographics are most valuable to the business and devise a more effective way to nurture their spending and relationship with the brand.
Delight your customers by presenting them with product and brand recommendations which you already know they will want and like.
In principle, the knowledge of who wants what and when is an art form in retailing that is rooted in the golden era of advertising and knowing your customer. Retailers that have enhanced their skills in this area will continue to grow and prosper, which is why demand for data analytics technology will continue to grow over the coming years.
Martech is moving fast - pulling in other segments of technology as it pushes forward into 2016. We are well on the way to 2020 - where WalkerInfo predicts that customer experience will be the primary differentiator for consumers over the traditional four P's of marketing (product, place, price and promotion). In addition, a Salesforce survey of 5000 B2B and B2C marketers shows that keeping up with trends is one of the biggest problems marketers face today. While technology and consumer behavior are involving in unpredictable ways, so are the ways marketers are reacting to these new developments. Here are a few things that I see coming in marketing’s future, and I think they will be here before we know it.
Future trend 1: Nonhuman marketing
Today, businesses have more data than ever before, including network and device data, collecting from proliferating channels. Our televisions, phones, Fitbits and Apple Watches are becoming connected, rapidly changing consumer behavior. For example, the multiscreen trend in which people watch television while also using other digital devices presents a great opportunity for marketers. Combining this data with traditional demographic and geographic consumer data can greatly improve marketing efforts.
All of this connected technology will become platforms for my life, creating a myriad of consumer data and better experiences. With this trend, marketing will need to direct its attention to all devices, and not just to individuals. SAS is combining streaming analytics off all of these device types with traditional data like demographic, social, and transactional data to help brands inform marketing efforts and send marketing messages and offers to channel touch points.
Future trend 2: Consolidation
Businesses strive to achieve a single customer view because they understand the value to their bottom line, including making marketing more effective and gaining better insight into customers to create brand value and greater loyalty. Brands are starting to understand that customer experience makes the sale, not product or price. By removing intermediaries, supply chains are shorter and technology becomes more user-enabling.
Today, some businesses serve the sole purpose of aggregating and sourcing products and services for consumers. At amazon.com, I can buy anything from a TV to a pair of socks in a single click – without ever interacting with Samsung or Woolrich as brands. So who do these brands market to? Not me the consumer – but Amazon as the aggregator – and the message to that aggregator will obviously be much different than the marketing messages of today. SAS is working in a B2B context with many brands to understand how and who to market to in order to break through the noise and deliver offers to businesses and intermediaries as we see business models change as we move into 2016.
Future trend 3: Applied analytics and inherent intelligence
I talk to people about marketing software technology every day. Never once have I heard someone say – “I really love my marketing technology software – I wish I could spend more time in it, you know,just clicking around and hanging out.”
Instead, marketing analysts want to quickly enter the software, perform an action, complete a task and exit the application so that they can get on to more strategic parts of their job. Marketing technology vendors are answering marketers’ desire for faster and more intuitive software.
The ability to embed and apply analytical techniques – things like automatically derived segmentation, applied marketing activity optimization, embedded forecasting, and other techniques – all in an effort to infuse machine learning style techniques and behaviors into marketing interfaces. SAS is working to infuse these cognitive analytical behavior techniques into marketing technology software. After all, every marketer (including myself) wants to simplify and optimize workloads! If a machine can tell a marketer the best time to execute a campaign, to whom, which content to use, and when to send it out – why wouldn’t a marketer oblige? I know I would.
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 assessthe 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:
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:
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?
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:
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:
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!
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.
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:
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 behaviorswill 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.
Welcome to Customer Intelligence, a blog for anyone who is looking for ways to improve the business of marketing and communicating with customers.
We strive to prompt new thinking in the way you tackle customer-related business issues. And we hope to inspire the use of analytics for everything from multi-level marketing to social media campaigns. Follow us here and on Twitter at @SAS_CI, or check out the Twitter hashtag #sasci.