Research Brief
Elevating Customer Analytics Initiatives
and Building the Coveted
Holistic Customer View
November 2014
Written By: Christy Maver, Actian
Key Takeaways
1. The evolution of Big Data and analytics coupled with the explosion of digital
communication channels has created new challenges and significant opportunities for marketing executives; the new marketing manifesto requires brand experiences that surprise customers through unique, personalized engagement.1
2. Marketing executives who have embraced analytics are executing impactful customer acquisition and engagement strategies that look very different from traditional
strategies. The traditional 360-degree view of the customer is no longer sufficient, being replaced by a more comprehensive 720-degree view.
3. Making the best use of Big Data and analytics, successful marketers execute targeted advanced analytics projects using purpose-built blueprints in critical areas like customer profiling, micro-segmentation, and next best action.
4. This modular approach allows for quick, measurable results with low investment.
Introduction
The technology revolution that has occurred across web, mobile and social communication channels over the past two decades has moved marketing from the traditional 4 P’s (product, price, place, promotion) to a new world with content marketing, sentiment analysis, and dual-screen promotion where the customer is increasingly in charge. For the past two years, CMOs have forecasted an 8-9 percent yearly increase in budgets devoted to customer relationship management.2 This investment underscores the shift from one-way customer communication to two-way customer engagement.
The evolution of Big Data and analytics coupled with the explosion of digital media has created major opportunities and a new world marketing manifesto that requires brand experiences that surprise customers with unique, personalized engagement. Customer centricity has become the guiding principal as chief marketers attempt to help their companies adapt to customer needs and exceed their expectations. For this reason, customer segmentation and targeted, automated marketing to the individual has become a top, yet daunting, goal.
As if it wasn’t daunting enough, what marketing executives don’t realize is that they are stuck in a Monet painting. They are trying to focus in on each individual customer dot to build a
personalized experience based on an individual customer profile – the coveted 360 degree view. On the surface, there’s nothing wrong with this tactic. It can lead to more granular
1
For additional reading on how marketing executives are creating personalized engagement through customer loyalty programs, see “Keeping Customers: Successful Loyalty through Analytics,” on iianalytics.com.
2
customer segments, optimized pricing structures and customized offers. Yet despite the fact that many are still struggling to achieve it, the 360 degree view is already becoming insufficient. Many analysts and industry experts argue that in today’s world, you need a 720 degree3 view of the customer to make better decisions, execute an Omni-channel strategy and sustain a
competitive advantage.
What is a 720 degree customer view? Returning to the Monet painting helps to explain. Customers in the digital world are much more than their own experiences, preferences and history. They are interconnected. They’re part of a network. They influence, and are
influenced by, those in their network. When you focus on a single customer, you’re looking at the dot, but the dot in isolation is incomplete. It’s only when you see how the dots are
connected that you can form a complete picture – a truly holistic customer view.
When you factor in the explosion of clickstream data, mobile application usage data, social interactions on Facebook, Twitter and in customer support applications – and you start to think about blending new data with the old, interpreting these enriched data sets and reacting in seconds, the realities and expectations of marketing in the digital world crystallize. Multi-channel coverage, which involves integrating media silos, is difficult. As a result, some rich customer data ends up being wasted or unused. Across all industries a new marketing landscape is emerging that is real-time, multi-channel, multi-device, and data-driven.
IT budgets are shifting to the CMO while marketing budgets are growing and moving to digital, bringing increased pressure and accountability. Many CMOs have a strong desire to use analytics and would prefer a do-it-yourself approach, one that doesn’t depend on IT. Yet most haven’t had any training on analytics or customer data management, so maximizing the
resources they do have is critical. An analytics blueprint can address this problem by laying out purpose-built, validated approaches that don’t require extra skills.
Defining an Analytics Blueprint
With so many things keeping marketing executives up at night, an analytics blueprint can help set a clear path to navigate the complexities of marketing in a digital world. An analytics blueprint is a validated approach to a real-world Big Data and analytics problem. It
incorporates stepwise methodologies across analytic workflows, with advanced analytics that span digital and traditional data. It is a useful construct for marketing executives because it becomes a starting point for deeper analysis and better accuracy. A blueprint in the hands of a
3
See “An Accentric Approach to Customer-centricity,” by Sriram Anandan July 9, 2013. Tdwi.org. 720-degree customer views could provide an internal, 360-degree view of the customer (what does the customer need?), and an external, 360-degree view of how the customer perceives the institution. This adds an essential layer to the segmentation process.
CMO is more powerful than alternative approaches because it can jump start analytics projects without requiring additional resources. The heavy lifting has already been done; the proven methodologies and pre-defined advanced analytics workflows are baked in, allowing the CMO to use the blueprint as a complete roadmap to address his or her particular business challenge. This can be a great boost for those looking to get started with analytics, and an important accelerator for those who have started, but haven’t yet moved to predictive analytics.
In our experience, there are three blueprints in particular that are most important to the CMO: Develop a holistic customer view, Create micro-segments, and Predict the next best action.
Develop a Holistic Customer View
Customer analytics is one of the hottest areas within analytics today, and for good reason. Companies of all sizes want to delight their customers, whether it’s a retail consumer, a banking client or a healthcare patient. But before you can delight a customer, you have to know who they are and what they want.
This task is now trickier as customer expectations today are radically different. Customers are more informed, with data from multiple sources available to them 24/7. They’re also more influenced by social networks than ever before. This makes retaining and satisfying customers much more challenging. Without analytics, companies are making decisions based on guesses
at best: which campaigns to run, what pricing structures to use, what to offer customers and when to offer it.
To overcome this challenge, organizations need to create a holistic customer view.
Traditionally, customer views are built using a couple different data sets, perhaps CRM account info with demographics and transaction history sitting in an enterprise data warehouse. However, building a view from these limited data sets can lead to inaccurate conclusions because that only tells part of the story; it only gives you a handful of dots of the painting, but not enough to really let you know what you’re seeing.
It’s only when you leverage additional, digital data sets that you can start to build a more accurate view and truly understand your customer, down to an individual level. This could involve purchased data like lifestyle trends, demographics and household information, or social media data, including keywords and sentiment analysis. Bringing all of this data together brings the individual dots of the painting into focus.
But it doesn’t stop there. Regardless of the fact that a large majority of companies are still struggling to get a 360 degree view, such a view is already outdated. Industry experts are now discussing the need for a 720 degree view, because the segment of one is no longer a segment of one. It’s one plus a network, a complex network of relationships that are core to the
business. This can include social networks, the Internet of Things (IoT) and complex computer systems and networks. Each customer is connected to other individuals and groups. And each customer plays a specific role within that group. Maybe he’s always the first to try out the latest and greatest gadgets and technologies, and he influences those in his network that wait for his trusted advice before purchasing. Targeting this customer with a relevant offer yields results beyond just that person. Or maybe the customer isn’t a big spender, but is very vocal in her social network. This is someone that might be typically overlooked since she doesn’t rank as a high value customer on her own but targeting the right campaign at her can have a ripple effect as she broadcasts the details to her network.
The challenge here is how to get a view of the network. It’s hard enough building a 360 view. How do you build a view that incorporates an individual’s network? Graph analytics is one way to address the problem. With graph analytics, you can take the typical 360 degree view of the customer and turn it into a more comprehensive, 720 degree view of the customer, by adding an individual’s social network to the profile. Graph analysis involves looking at an individual’s social network to identify its key network influencers, the level of influence of the individual, and correlations within the network that trigger increased responses for targeted offers. By identifying the most influential individuals within networks of people, marketers can optimize campaigns. They can track changes in influence over time and make real-time adjustments to leverage the dynamics of influencers and followers among increasingly targeted groups of individuals. They can discover the kind of significant impact that is driven by seemingly
unrelated influencers in adjoining social networks. This is the type of insight that can only be found through analytics and can mean certain advantage over the competition.
Create Micro-Segments
Many customer analytics initiatives start with some kind of segmentation, and rightly so. For those trying to increase conversions and drive more revenue, traditional segmentation models only take them so far. Today’s competitive environment requires deeper discovery and correlation of micro-segments. Leveraging new data types, like the ones mentioned in the Customer Profile section, and blending them together, help to uncover relationships between customers and key purchase drivers, and create new segments for which you can build a better customer experience, with targeted offers, effective offers and meaningful dialogue.
To get even more granular segments, a social network influencer scoring model can uncover relationships between customers and key purchase drivers. Variable-term profitability forecasts can predict the value of each customer along thousands of customer attributes. This allows you to continuously define new segments that your competition isn’t thinking about yet.
Predict the Next Best Action
Once you’ve built the Holistic Customer View and created New Micro-Segments, the next step is predicting the next best action for your customer. This is about making the right offer at the right time. Using a next best action blueprint empowers marketing executives to provide one-to-one marketing and offers that have relevance and value. With a comprehensive view of behaviors and spend, CMOs can meet customer demand and shape what happens next. Using a Next Best Action Blueprint enables CMOs to bring in the necessary data sources to complete the picture: web activity, mobile device data that may be sitting in Hadoop or real-time digital media feeds. Bringing these sources together enables you to round out your customer profile and truly pinpoint the right time to make an offer. Additionally, any next best action campaign will have a strong influencer on the marketing side. The marketing
department will want to put some constraints on it to drive the campaign. This type of data may be sitting on a shared drive or Excel spreadsheet, but it gets blended in with the rest of the data, as well.
With this newly blended data used in your predictive models, you can predict customer life-time value and near-life-time value scores. You can build a predictive product model for every product that’s part of the campaign to determine what the likelihood is that customer A will respond positively to any of those products. Combining business rule triggers from marketing, along with all the predictive models, leads to a solid next best action recommendation engine.
Feeding the output from this recommendation engine into your outbound marketing and inbound responses in real time leads to increased campaign revenue, customer loyalty, call center profits and supply chain optimization.
Best Practices
Utilizing an analytics blueprint can help answer: Who is loyal? Who is likely to churn? Who are my repeat customers? Who is willing to buy more? Who is profitable now and who long term? What should I offer next? How can I influence customer behavior?
But, how you approach each project can have a big impact on results. Three best practices to keep in mind when implementing a customer analytics blueprint are outlined below.
Take Action in Real Time
Probably the most important step to take when implementing blueprints is to take action in real time. It sounds like a given, but many companies struggle to get to that point. For many, the ability to extract signals from the noise of data through advanced analytics is difficult enough. When you know who is likely to churn, take action. Proactively offer a retention bonus to keep that customer from leaving. When you learn who your individual customers are and how loyal they are, you can provide tailored campaigns based on individual preferences. And when you combine what you know of the customer with things like GPS or location data, you can send a mobile coupon to a customer as they walk by your store.
Discover Without Limitations
Traditionally, limitations on the amount of accessible data have defined the level of
sophistication for analytics projects. Yet new technologies like Hadoop and high performance database engines have begun to remove constraints by allowing organizations to incorporate previously untapped data sources into our analysis, leverage larger data sets for more accurate results and make decisions more quickly and in real time, so as not to miss narrow windows of opportunity that used to remain unaddressed. This leaves the door open for strategic
marketers who understand that discovery can lead to a distinct competitive advantage.
New Data Sources Increase Prediction Accuracy
When looking at customers or customer sets, many marketing executives have been dealing with the same type of data for years, namely transaction history and demographics. Yet it’s the new, digital data types that are rich with insights and lead to more accurate decision making. Marketers should not shy away from thinking outside traditional data sets. The more data you
bring in, the more granularity you can create when building segments and profiles. More data in your predictive analytics models yields more accurate results.
Additional Blueprints and Associated Outcomes
The Actian Big Data 2.0 Clear Path Program is one example of how CMOs can empower
themselves to create value with analytics. More importantly, they can do so without having to rely on IT. With a blueprint in hand, CMOs can extend capabilities across the enterprise
without adding new resources.
The Clear Path Program comes from the Actian Analytics Center of Excellence, a team of experienced data scientists and analytics platform experts that help organizations realize extreme value from big data analytics. The team has a long history of implementing solutions for optimal performance and scalability, developing next generation solution architectures that include Hadoop. Through work on real-world customer engagements, the team has developed big data analytics blueprints that help address analytics challenges and accelerate analytics ROI. While the three blueprints outlined in this paper, (Develop a holistic customer view, Create micro-segments, and Predict the next best action), are recommended as top priorities, there are other customer analytics blueprints that can help the CMO and marketing department as well:
Customer Lifetime Value Analysis: Discover and cultivate high-value customers
Campaign Optimization: Rapidly build and refine campaigns
Churn Analysis: Prevent high-value customers from leaving
Conclusion
In thinking of your own organization, where are you on your customer analytics journey? Do you have initiatives underway to deepen your customer relationships and create more meaningful engagements? Have you begun to build holistic customer views? If you think of your customer analytics objectives and opportunities as the painting, is it possible that you’re too close to the canvas and too focused on one dot to see the bigger picture?
In order to achieve true personalization that drives customer delight, step back, allow for a new, interconnected perspective and leverage analytics blueprints to help guide you. You may find the full picture, representing the full possibilities, is more beautiful than you expected.
About the Author
Christy Maver is the Global Product Marketing Director at Actian and leads marketing for all of Actian’s analytics programs. Christy has 15 years of experience with technology marketing and has held a variety of roles from insurance industry consultant to Information Management Communications Strategist to. Prior to joining Actian, Christy was the Big Data and Analytics Product Marketing Manager for IBM. She holds a BA in Economics from Princeton University. You can follow Christy on Twitter @cdmaver.
