Mobile wallet providers made a huge push to acquire users this past holiday shopping season – encouraging consumers to manage card payments through a smartphone app. As an example, Google Wallet offered a compelling incentive by promising users $5 for each person they referred who signed up. Smartphone users could refer up to 20 friends (earning up to $100), while their referred friends also received a $5 gift!
An optimistic study from VentureBeat projected that 30% of U.S. users would adopt the mobile technology for the holidays. But the jury (and the data) is still out as to whether gambles like this worked.
A customer experience quandary: Convenience vs. peace of mind
It’s not surprising that the large players in this new arena have been having a hard time building a customer base. While these providers may be focusing hard on technical capability and device support, consumers are weighing the emotional pros and cons. In the age of sophisticated identity theft, loading all one’s highly confidential payment data onto their phone is no small decision. Sure, it sounds appealing to access mobile wallet convenience from any device, and it’s handy if your favorite merchants support it. But many customers have valid concerns about privacy and security, and many merchants are still not equipped to accept mobile wallets. In the VentureBeat study, 69% of smartphone users said they weren’t sure which stores would accept the type of mobile payments they use.
The buy-in bottom line
- The two early drivers of digital wallet tech, Google Wallet and Square Wallet, are still seeing slow adoption. Google has a market awareness rate of 31%; yet only 5% of those aware actually use it. Square achieved awareness of 15% among U.S. mobile phone users, yet only 2% of those used it. (Forrester report, September 2014)
- Despite Apple Pay’s much touted superior security and ease of use, merchants remain slow to adopt it. Apple Pay accounts for less than 10% of all point-of-sale terminal purchases. (Forrester report, October 2014) Cost is a big obstacle as merchants need to update equipment, and for now they just don’t see clear benefits in accepting mobile payments over cards.
Gaining traction with “native” mobile wallets
For Google and Apple, their mobile wallet success may be largely dependent on merchant adoption—which they can’t control. And that’s created opportunity in the digital wallet space for merchants themselves. The difference? Companies like Starbucks and Macys have honed in on the customer experience, and transferred it to the wallet.
Starbucks’ “native” mobile wallet is an app customers can install on their phone and use in stores. Like Google and Apple, they offer coupons and customer-tailored content. What differentiates Starbucks is that they already know their customer base, and know how to please them.
Macy’s My Wallet is another good example of a seamless customer experience from store through mobile. It allows users to store coupons and offers, calculates savings at checkout, and even applies their selected offer immediately at checkout.
Starbucks and Macy’s have figured out how to amplify customer loyalty by enhancing the in-store experience through mobile engagement. Google, Apple, and Square could learn a meaningful lesson here – it’s about more than providing a service; customers and merchants will need to see clear wins on how the digital wallet experience makes their life easier and better.
Is your company sitting on a gold mine of customer data but not gaining value from it? Like many companies, you may be confused by how to leverage advanced analytics to make your marketing efforts more profitable. We want to tell you the story of Kathy, a marketing director with tons of data at her fingertips and nowhere to take it. You can follow her success story and get started right away.
Kathy leads a team responsible for both customer acquisition and retention. Like many teams at large companies, they have a wealth of data available from channels such as their call centers, website, social networks, and more. But none of the data is integrated, and a lot of it goes unused.
Don’t miss the next free workshop
Increase Business Value with Predictive Analytics
April 23, 2015
10:00 a.m. – 2:00p.m.
San Francisco, CA
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She knew their data –such as Net Promoter Scores, contact center notes, and social commentary– held important insights about purchase behavior, and could make their marketing efforts more effective. But her team was struggling with how to introduce and then leverage analytics as a systematic approach for the business. While predictive analytics has it’s the power to uncover previously hidden patterns across a broad range of data sources, it can seem daunting without previous experience.
Then Kathy discovered an easy way to get started– IBM and Beyond the Arc offer a free quarterly IBM SPSS Modeler workshop. This fun, hands-on class enables beginners to jump right in and start building predictive models they can actually use, guided by data scientists with years of experience.
As a workshop participant, Kathy left with valuable tools: Along with experience using SPSS Modeler software, she had a good sense of what kind of predictive models her company would need to meet key objectives. She also gained confidence in leading an effort to implement analytics solutions at her company, and empowering her teams with the new tools.
Be the next success story! Join our upcoming predictive analytics workshop on April 23, 2015 in San Francisco.
Join us Thursday, April 23 for an exciting hands-on workshop where you can quickly learn to build predictive models with IBM SPSS Modeler to drive actionable insights from your data. Check out our previous blog to discover what kinds of business problems you can be solved with predictive analytics.
April 23, 2015
10:00 am – 2:00 pm
MicroTek Training Center
San Francisco, CA
In this free 4-hour workshop, one of our experienced data scientists will show you how to:
- Gain valuable insight about customer pain points, unmet needs, and more by leveraging multiple data sources.
- Increase marketing impact by predicting customer behavior based on data patterns and sentiment analysis.
- Replace best guesses with real insights you can use to build business value.
Beyond the Arc: Brandon Purcell, Data Science Team Lead
Intro to Predictive Analytics and SPSS Modeler
Applied Learning and Case Studies
Don’t miss this great opportunity – you’ll be able to start building a predictive model within the first 30 minutes!
Space is limited, so please register today >
In our recent post Discover the ROI of predictive analytics with 3 projects, we highlighted some powerful quick wins you can achieve using IBM SPSS Modeler to solve key business problems such as reducing churn and increasing cross-sell and up-sell.
Want a closer look at how Modeler does its magic? Let’s pop the hood and take a quick tour.
We’ll use a project for predicting customer churn, which we’ve found is one of the best ways to learn Modeler. Below you’ll see an example of an analytical stream that predicts churn. We’re using historical customer data from two databases where each row represents one customer.
The building blocks of the stream are called “nodes,” and at each node, Modeler performs operations on the data. The data contains multiple variables, including that variable we want to predict: “Churn” which has two values: “Churn” and “No churn.”
Here’s a breakdown of the nodes in the stream for predicting customer churn (from left to right):
- CRM database and Transactional data nodes: Source nodes reflect where we’re accessing data; Modeler can use a variety of databases such as SQL, SAS, Excel, XML, .csv, etc.
- Merge node: Joins the two data sets together for modeling, with one record for each customer.
- Type node: Labels variables as continuous (numeric) or categorical, and identifies the target variable. In this case the target variable is “Churn”, with two values: “Churn” or “No Churn”.
- Partition node: Partitions the data into two sets. A training set we use to build the model and a testing set we’ll use to evaluate the accuracy of our model.
- CHAID Churn node (pentagon): CHAID (CHi-squared Automatic Interaction Detection) is the decision tree classification algorithm used to build a model to predict our Churn variable. Modeler offers a wide range of modeling algorithms, many of them automated.
- CHAID Churn model node (gold nugget): This is the decision tree model created by the CHAID algorithm to predict Churn. It assigns each record in the data a prediction of “Churn” or “No Churn” and a confidence score for each prediction.
- Evaluation node: This node evaluates the accuracy of our model on the training and testing data sets using a cumulative gains curve or lift curve.
- Database node: We can use our model to score new data and write it into a database.
Once you’ve got predictive data in your database, you can translate it into actionable insights for other teams. In the case of preventing churn, your Marketing team could identify which customers are most likely to leave and engage them with targeted incentives to stay.
Everyone’s talking about Big Data when it comes to customer experience, retention, and profitability. If you want to begin using advanced analytics but don’t know where to start to realize value, here’s a quick guide to get you in the door. Here are three great starter projects using IBM SPSS Modeler that can deliver quick wins for common problems, using your existing data.
Project 1: Reduce customer churn
We’ve seen many companies reduce attrition by as must as 50% by leveraging accurate customer churn modeling.
Start with a project focused on customer churn because the data is relatively easy to understand, and you may uncover insights you can use immediately. Typically it’s less expensive to keep customers than acquire new ones, so you may also see greater returns on your time investment here. ROI is also created by adding up the small values regained across thousands or millions of customers.
- Test case churn: The key is to build numerous models and evaluate them side by side using a test set of data, which helps you fine tune the model that works best for you.
- Implement: Next, you’ll take your customer data and run it through the model. While you’ve used historical data to build the model, the output looks toward the future by predicting which customers are likely to leave. With these insights, your marketers can engage at-risk customers with targeted incentives to stay.
In our next post, we’ll show you a quick walk-through of how Modeler does its magic using a churn model.
Project 2: Increase cross-sell or up-sell
This project is a great next step as it leverages data structures and propensity modeling similar to the churn project. You’ll start with similar inputs, but instead of targeting “churn,” your dependent variable is whether or not a customer was successfully cross-sold by a particular marketing campaign.
- Identify attributes of customers who were successfully cross-sold in the past.
- Develop a model to pin-point current customers with similar attributes.
- Predict who you need to focus on using a confidence score developed from your model.
Cross-sell and up-sell modeling can have an immediate impact on your bottom line. You’ll get insights on how to tailor communications for different channels and customer segments. By targeting the right message to the right customer at the right time, you have a better chance of improving marketing effectiveness, and increasing the value of existing customers.
Project 3: Achieve other business objectives
Your next predictive analytics project might vary depending on the needs of the business, but usually includes one of the following:
- Optimize collections. Create a predictive model to prioritize which customers are more likely to pay, enabling your company to focus collection efforts on more receptive customers to maximize revenue.
- Maintain or increase customer loyalty. Loyalty is increasingly hard to achieve, so it can be valuable to understand which customers are most loyal, and how differing experiences and customer journeys led them there. Harness these insights to identify specifically what’s working well so you can replicate, and even innovate, ways to exceed expectations.
- Predict the behavior of new customers. How will new customers perform in the first 90 days or the first six months? You don’t know their past, but you do have data on how you acquired them. You can build models that predict those most likely to purchase again, respond to additional offers, or be at risk of leaving.
- Fraud detection. Reduce risk and build ROI by detecting fraud faster, quantifying how it happens, and then refining business processes to more effectively prevent fraud.
Modeling fun for everyone
Your company doesn’t need a PhD in math to run Modeler. In fact, the opposite is true; the best modelers are those with a solid understanding of your business objectives, who can also understand what to do with the insights you’ll gain. You can start building predictive models and achieve real ROI in a matter of weeks. If you do have a data science expert on staff, Modeler allows for advanced customization and also interfaces with R, Python, and other languages analysts love.
As Big Data offers many valuable possibilities, predictive analytics is fast becoming an inevitability for all industries. The early adopters will be poised to surpass the competition because the ROI is quick and accumulates at scale. Companies that don’t want to be left behind should start now, and start simple.
Event: BAI Payments Connect, Phoenix, AZ
Presentation: “Taking the Pain out of the Payment”
Speaker: Steven Ramirez, CEO, Beyond the Arc, Inc.
Date: Monday, March 2, 2015, 2:45-3:45 p.m. Mountain Time
While advances in technology have led to a significant spike in new payment methods, the success of a given offering within today’s financial industry depends much more heavily on customer experience than specific product capabilities. To maintain consumer appeal, today’s banks must shift focus from the internal impact of implementing new payments technology to its potential effect on the customer experience.
How do you take the payment out of commerce, so the pain is taken out of the payment? Companies like Uber and Starbucks have achieved this, while Chipotle, other QRSs, and a few retailers allow customers to place an order and the payment just “happens.” What lessons do these examples offer financial institutions and their products?
Join Steven Ramirez, CEO of Beyond the Arc, for “Taking the Pain out of the Payment” during this year’s BAI Payments Connect conference.
Steven will provide detailed guidance on how banking organizations can utilize new payments technologies to simplify the payment process and increase customer satisfaction and loyalty. Key discussion points include:
• The top customer experience issues you need to solve for when creating new payment solutions
• Evaluating how innovators are addressing these concerns
• A deep dive into one innovative payment product to see how it eliminates customer pain points
• Complications that keep FIs from removing payments pain as effectively as nimble competitors
• Embracing third party distributors
Co-author: Corina Jordan
While more and more customers want the convenience of online and mobile banking, concerns over cybersecurity are an increasing challenge for the financial services industry. Consumers fear their financial safety may be more at risk of being hacked as fraudsters reach new levels of sophistication. But will it drive them into the branch instead of banking online? More likely, many customers will seek out a financial services provider that offers the most secure technologies for digital banking. Translation: Security innovations for digital channels will be a powerful driver for retaining customers and attracting new business.
Secure customer satisfaction using the latest technologies
Secure banking starts with the login, and exciting technologies are emerging to make the process more personalized than ever. Finovate Fall 2014 introduced the latest trends in online security, featuring facial recognition and retina scanning designed to reduce the risk of fraudulent access to personal and financial information. A few innovative technologies include:
To enhance customer confidence in using digital channels, Hoyos Labs uses facial recognition and retina scanning instead of usernames and passwords. Implementing this highly individualized mobile app may help businesses increase customer satisfaction — especially in financial services.
With technology that scans over 240 unique points on the human eye, EyeLock has one of the most compelling security technologies on the market. Customers can simply look at a device to confirm their identity for secure access to their accounts. EyeLock can verify customer identity at store locations, as well as via a personal device customers can use to make logging into online accounts fast, easy, and secure.
Leverage technology to keep customers (and keep them engaged)
As online and mobile commerce is rapidly becoming a standard way of life, having advanced security technologies is a necessity. For financial institutions, offering highly personalized secure access to digital banking may make a measurable difference in customer retention and acquisition. Banks that make consumer confidence a key part of their business strategy may gain the competitive edge to differentiate their brand in a crowded marketplace.
Harnessing “big data” with advanced analytics can provide actionable insights that help Chief Marketing Officers (CMOs) drive highly targeted marketing campaigns. Yet surprisingly, more than half of CMOs say big data doesn’t factor into their marketing decisions. Many say the main reason they don’t use big data is because their organization doesn’t have the right technology in place to compile all customer data into a single view. To reap the benefits of big data, CMOs are becoming more tech-savvy.
First, know your customer journey
To help inform smart marketing tech investments, CMOs should gain a comprehensive understanding of the customer journey from initial awareness to purchase, through to an ongoing relationship between the customer and the brand. Consider such questions as:
- Where can technology improve the efficiency of marketing operations?
- Where can you automate the buying journey to deliver a better customer experience?
- Where can you deliver customized offers to increase revenue?
While your organization may capture transactional data from every point in the buying journey, it’s time to think more broadly. For more valuable insights, you’ll want an integrated marketing tech solution that synthesizes structured and unstructured data from many sources into a single view of the entire customer lifecycle. In this way, you’ll be better equipped to understand how customers interact with your company across all channels (e.g., email, phone, print, social media, etc.), and which channels may be more profitable.
With full visibility into the customer lifecycle, CMOs can identify numerous ways to segment customers to deliver the right offers at the right time. They can also more effectively partner with other lines of business on strategies to improve customer experience at every touch point.
Think like a CIO
Once you understand your customer journey and have pinpointed opportunities to segment customers for targeted marketing, you can assess where marketing technology can make a bigger impact, such as driving lead generation or increasing revenue. That’s where the CMO really starts thinking like a CIO. (Some companies that recognize this importance have created a new c-level role: the Chief Marketing Technologist.)
Collaborate with your IT partners to choose technology that will complement your strategy, rather than creating a strategy to suit your technology. For example, suppose you’re trying to increase engagement using video content –what’s the best channel for that strategy? Here are a few key considerations:
- Plan for a comprehensive view by choosing technology that can aggregate and analyze structured and unstructured data from many different sources (e.g., transactions, registration data, call center transcripts, social media, surveys, etc.), and deliver insights in a single, integrated view.
- Leverage existing IT environments and look for opportunities on how they can complement any new marketing analytics technology to optimize data collection from all points in the customer journey. Consider ways to integrate multiple systems to form one, more complete customer record.
- Create an implementation roadmap that prioritizes the technologies that can improve marketing operations or make you more competitive. Work with the CIO and IT team to develop timelines that work for everyone involved.
The big data advantage
Big data is a powerful resource, and data science and predictive analytics provide the keys to unlock actionable insights that can drive measurable ROI in your marketing efforts. It’s a competitive advantage that CMOs can’t afford to be without. Today’s CMO can create a valuable bridge between marketing and IT if they develop the right skills and knowledge to provide effective input into technology decisions. Their deep understanding of the buyer journey can help teams assess the technology needs for critical stages in the customer lifecycle.
In turn, with the right technologies and analytics strategies in place, CMOs can gain a holistic view of the customer experience, and more effectively tailor campaigns to achieve key business goals.
Effective marketing often relies on competitive analysis and consumer research to help identify unmet needs and craft targeted strategies. For Chief Marketing Officers in mortgage, publicly available data compiled through the Home Mortgage Disclosure Act (HMDA) and U.S. Census Bureau can provide a powerful launch point for developing a deeper understanding of their markets and competitors.
Under HMDA, financial institutions are required to provide regulators with data about their mortgage applications and loan originations, as well as purchases of home loans, home improvement loans, and refinancings. Data collected includes variables such as applicant demographics, loan type, property location and type.
Unlocking the value of HMDA data
Forward-thinking mortgage lenders are using HMDA information to identify causes of market-share variance, determine the best product for each market, and gain insight into their competitors’ growth strategies.
How can financial institutions best take advantage of this data? In a recent Scotsman Guide article, Beyond the Arc CEO Steven Ramirez shared how banks can use HMDA data to:
- Determine the best product for each market segment
- Gain insight into the competition’s growth strategies
- Identify causes of market-share variance
Suppose you want to target a specific region with the most relevant lending offers. By analyzing zipcode data and loan type, you can identify the types of financing consumers in a given area have applied for most frequently. You can then develop a strategy to gain a competitive edge, such as promoting a certain type of loan or emphasizing federal home loan programs to attract more first-time homebuyers.
Gain a comprehensive view with Census data
Census data is also publicly available, and includes income, education, and household size, as well as financial details about the residence (e.g., home value, average cost of utilities, real estate taxes) and physical characteristics of housing in a given area (e.g., year built, number of bedrooms, year the owner moved in). Banks can leverage Census data to complement findings from HMDA data to enrich their understanding of the communities they serve.
As an example: Using both HMDA and Census data, you may discover that a competing lender in one of your markets is originating a large number of loans with first-time buyers who are purchasing homes more than 30 years old. Once you’ve identified this trend, you can reassess your strategy in this market. Will you try to compete with the other lender for first-time homebuyers or can you capitalize on a likely need – such as home-equity lines of credit to finance home improvements?
Together, HMDA and Census data paint a more complete picture of your markets to help you identify unmet needs, as well as sources of market-share variance.
Expand your potential with additional data sources
HMDA and Census data only tell part of the customer story, however. To gain a deeper understanding of the patterns revealed in that data, financial institutions should look at other sources of market data such as the Consumer Financial Protection Bureau complaint database, and customer feedback in social media. These publicly available resources are easy to use — it’s just a matter of asking the right questions to glean useful insights from the data, and then taking action based on those insights. Ask yourself: What’s the problem I’m trying to solve? Armed with data, you can reach a fact-based conclusion that will help you put a plan into action.
Co-author: Steven Ramirez
In building a customer-centric business, personas and journey maps are important strategic tools that help provide an in-depth understanding of who your customers are, what they need, and how they interact with your company across all touch points. But more importantly, for sharing customer insights across the organization, these tools can be critical for building buy-in and helping teams take targeted action to improve customer experience.
To get started, you’ll want a clear understanding of what customer personas are, why they’re important, and what makes a good persona. Once you’ve created your personas, you can take a walk in your customers’ shoes with a journey map.
What are personas?
Personas are fictional, yet believable archetypes you can develop to represent your target customers. They go deeper than generalized customer segments by having individual names and stories that reflect personal attributes and behavioral characteristics such as needs, motivations, attitudes, and pain points.
Why are personas important?
Personas have been commonly used to help organizations develop user-centered design. As focus on customer experience has increased in recent years, personas are gaining popularity as a tool to benefit a wide range of departments across an enterprise from sales and customer service to operations and HR.
Personas can help guide customer-centric practices in various ways:
- Develop a deeper understanding of your customers
Like Jeff Bezos’ empty chair that represents “the most important person in the room,” personas help you build empathy with your customers. What are their needs and goals? What motivates them? Why do they behave in certain ways?
- Design processes with the customer in mind
Personas help you understand how your customers interact with your company throughout the entire lifecycle. Do your processes reflect the true customer experience, or do they reflect your internal operations? Personas provide awareness of the many journeys your customers may take, so you can improve them.
To build support for an enterprise-wide customer experience initiative, personas – especially those backed with data and research – can help you describe to executives and stakeholders what a better experience should look like.
Tips for creating highly effective personas
- Align with business objectives to help make your personas powerful tools for teams across the company. Engage key stakeholders to gain diverse perspectives on goals, processes, and issues unique to different lines of business that influence the overall customer experience.
- Use data and research to identify and inform each of your personas. Market segmentation research, surveys, interviews, and social customer insights are all useful methods. This qualitative research can complement your understanding of how customers behave with insight into the “whys,” providing important nuance and detail that humanize your personas.
- Bring your personas to life by crafting engaging, first-person narratives that are realistic representations of your target customers. Give each persona a name and photo to help foster a connection to your actual customers. Include a variety of attributes, such as:
- Demographics: Age, location, education, income, household or family size
- Personal attributes: Their goals, needs, and interests when they interact with your company
- Customer lifecycle: How their needs may vary for different channels and touch points, and how their needs may evolve over time
- Make them eye-catching and memorable with polished, professional quality photos and information layouts for socializing the personas across your organization. The more “real” you can make them, the more your teams can identify with them and map their own actions and attitudes toward delivering the best possible customer experience.
Using personas to map the customer journey
Once you’ve created distinct personas, you can use them to create customer journey maps that describe each persona’s experience at various touch points during their lifecycle with your company. An effective journey map is based on real research and behavior, and should represent the true customer experience– good or bad. That way you build an accurate picture of where you need to make improvements as well as where opportunities exist for cross-sell and up-sell.
Much of the information for creating a journey map will come from your personas (e.g., their goals, motivations, key tasks they want to accomplish, and current pain points), which is why it’s best to create the personas first.
At each step, the journey map should consider factors such as:
- Context – Where is the interaction taking place (e.g., in your store, on the phone, online or mobile, in social media) What is going on around the customer? How might their current context influence how they need to interact and what they want to do?
- Progression – How does each step enable the next?
- Emotion – How does the customer feel at each step? (e.g., are they engaged, bored, or frustrated?)
With a detailed and insightful customer journey map, your business can more effectively assess current and proposed processes, identify targeted actions to resolve pain points, and leverage opportunities for building stronger customer relationships.
The wins of being customer-centric
Companies can use personas and journey maps to rally employees behind the common goal of improving and optimizing the customer experience. That shared commitment is key to building a customer-centric culture. From there, your organization has the best chance to deliver what your customers want, understand how to exceed their expectations, and create experiences that nurture brand loyalty. Bottom line? Investing in small powerful tools can translate into very big wins for you and your customers.