Revelwood

Your SUPER-powered WP Engine Site

Data Science

The Customer Insights’ Imperative for CMOs

by Leave a Comment

“The most critical capability of the CMO is to have a profound, deep understanding of customers and their needs and know how to engage and serve them.  This of course involves knowledge of data and analytics. – Jamie Moldafsky, CMO, Wells Fargo, in “Redefining the CMO,” Deloitte Review, issue 22.

According to a recent study by the CMO Council and Deloitte, “CMOs have been increasingly asked to elevate their activities from brand and marketing plan management to acting as an enterprise-wide revenue driver that taps into the hearts and minds of their customers.”  Based on this research, Deloitte makes three recommendations on where CMOs should start.  One of these recommendations is to “relentlessly pursue customer expertise.”

The imperative is clear: today’s CMO is no longer just the brand steward, but also the customer steward.  He or she should be able to steer marketing strategies and activities that, as the report mentions, “engage customers with messaging that better speaks to their needs and values, establishing an ongoing relationship rather than a transactional one.”

The question then, is how the CMO becomes the customer steward.  Part of the equation involves traditional marketing tools and techniques such as personas, NPS, and focus groups in order to fully understand the customer (or customers) and how to meet their needs.  The other part – as mentioned by Moldafsky in her quote – is having a knowledge of data and analytics.  Today’s CMO must be able to analyze meaningful customer data in order to “lead the customer-centric charge.”

Unfortunately for many CMOs that presents a technical challenge, and for some, one that simply does not fit within their marketing budget.  The technical challenge is how to build an enterprise-class customer analytics platform that enables you to ask vital questions of your data.  And yes, it’s almost always build – even if you buy a software package, most require a significant amount of customization and development in order to make it integrate with your existing systems, deliver the answers to your business questions, and is user-friendly.  On top of that, you may also need a data scientist to build out the questions and structure the data so that those questions can be easily answered.  All of this takes time – many months – and money.  Lots of money.

You might be a CMO with a fairly unlimited technology budget. But chances are you do not have unlimited time. The average CMO has a tenure of just 4.1 years, which is half the average tenure for a CEO, and less than that of a CFO, CIO, or CHRO.

How do you, the ambitious CMO who understands the opportunity to be the customer steward and the challenges presented with a traditional approach to a customer analytics platform, resolve this inherent conflict?  The answer is simple: you seek out technology that is built for marketers, but robust enough to deliver the insights you need. One that works with your data, but that does not involve months of development efforts.

This approach puts you on the path spelled out in the Deloitte report: “CMOs wishing to transform their role can take advantage of their unique position to elevate themselves as the customer expert with stakeholders across the enterprise.”

Read more blog posts on customer insights and analyzing customer data:

Pull a Rabbit out of your Hat – or at Least Pull Insight from Your Data

Revelwood Named IBM Customer Analytics Partner of the Year

Home » Data Science

Online Retailers: Don’t Lose Out on Impulse Purchases

by Leave a Comment

Impulse purchasing accounts for a lot of revenue. According to a survey by CreditCards.com:

  • 5 in 6 Americans say they have made impulse buys
  • 84% of poll respondents say they’ve made an impulse purchase at some time
  • 1/3 of consumers who make more than $75K a year have made an impulse purchase of $1,000 or more.

Traditional retailers employ a number of strategies to generate impulse purchases. These include a transaction building strategy, where the retailer strategically places complementary products next to each other; need recognition, where you display products relevant to a target market’s needs and interests; and cross-merchandising, where a retailer suggests products that work well together.

But capturing impulse purchases is largely the domain of brick and mortar stores. The same study shows that nearly 8 in 10 impulse purchases are made in a physical store. The challenge facing today’s online retailers is how they can increase impulse purchasing when customers shop on their sites.

Amazon has mastered the art of deploying some of these strategies – particularly serving up complementary products, displaying products that target a customer’s need, and suggesting products that go together. Netflix does this as well, although not in the context of impulse purchases and/or increasing the customer’s basket. By using customer personalization journeys, Amazon and Netflix have demonstrated how powerful recommendation engines can be.

In the case of Amazon, the retailer (or to more accurately describe the business, the distributor), has developed algorithms based on the items in the customer’s shopping cart, items the customer has rated and liked, and what other customers have viewed and purchased. According to a recent article on MarTechAdvisor, 35% of all Amazon’s sales are “estimated to be generated by the recommendation engine.”

Netflix relies on its recommendation engine to increase viewership and lower customer churn. But custom-built, AI-based recommendation engines do not come cheap. The same article in MarTechAdvisor states “Realizing the importance of having the best recommendation engine, Netflix puts a lot of effort into its algorithm. Updates to the algorithms are researched and tested by a team of over 70 engineers. In 2009, Netflix offered a $1 million prize in an open competition to any research team which could improve on the efficiency of their algorithms.”

Retailers and distributors who do not have the deep pockets to invest in a custom-built, multi-million-dollar development effort might feel they can’t make use of a recommendation engine. Not only is there significant up-front cost, but given the nature of AI, machine learning and predictive analytics, there needs to be an on-going commitment to development efforts. One can’t build a recommendation engine and then let it sit idle. It must be continually improved to leverage the advancements in the field.

But then there’s the potential for increased purchases and capturing those impulse buys. Retailers need to ask themselves not only if that investment in a custom system will pay off (likely it will), but also will it pay off in a timeframe short enough to make a difference for the retailer?

Read more blog posts on customer analytics:

Net Promoter Scores and Customer Outcomes

Using Customer Analytics to Solve Critical Business Challenges

Home » Data Science

Net Promoter Score and Customer Outcomes

by Leave a Comment

We’ve worked with hundreds of companies who primarily measure their customer relationships with one metric: Net Promoter Score (NPS). NPS measures customer experience and predicts business growth by asking one simple question: how likely are you to recommend the company’s product or service? The score is calculated on a 0 – 10 scale based on the response, with those scoring 9 – 10 considered “Promoters,” those scoring 7 – 8 considered “Passives,” and those scoring 0 – 6 considered “Detractors.” The more Promoters you have, the higher your business growth potential is. NPS is seemingly easy to measure and easy for employees throughout the organization to understand.

Marketing and service groups who measure NPS tend to swear by it – and proudly proclaim their 5, 10, or more point increases in NPS that are attributable, loosely, to a new initiative or campaign. Sounds great. So what’s the problem?

Here’s the Problem…

Asking the Right Questions about Customer Outcomes

While Finance is watching the numbers, Marketers are too often focused on moving metrics like NPS, Facebook likes, and Instagram followers that don’t necessarily contribute to the bottom line. You’ve got all this focus on the customer and customer-related activities to move NPS, while NPS doesn’t impact what you think it does.

In fact, research shows that NPS is not correlated with the financial outcomes most organizations care about. According to “Which Customer Metric Best Predicts Financial Performance,” by The American Marketing Association, “Net Promoter Scores are are not backed by any credible evidence of financial predictive ability but have caught on because they seem simple (one item measures it all), prescriptive (you can compute the promoter score for every customer segment) and falsely encouraging (increasing promoter score is believed to increase sales, revenue, share and profits).”

The article goes on to explain that studies show that a higher NPS doesn’t correlate to increased sales, profitability, cash flow, market share, or shareholder value. Instead, customer outcomes such as purchases, attrition, etc., play a huge part in the financial success of an organization. Many marketers are just asking the wrong customer questions.

Asking the Right Questions

What’s the best way to measure and improve customer relationships? There is a better approach to customer measurement. It’s more complicated than a 0-10 scale, but customer relationships can be measured and improved scientifically. We guide our clients towards three major areas of the customer relationship:

  • Retaining the Relationship
    • How do you keep each individual customer’s business?
    • Which customers do you not want to keep?
    • How long have you got with each customer?
  • Growing the Relationship
    • Which customers are likely to buy more (or less)?
    • What is each customer’s next best recommended product or service?
    • How do you sell more, and in a personalized way?
  • Beginning the Relationship
    • Which type of customers do you really want to acquire?
    • How do you attract the right customers?

Attentive readers will note these items are listed in reverse chronological order, and that is intentional. Revelwood recommends starting at the end of the customer relationship and working backward for two reasons:

  • Working backwards through the life cycle is easier due to data availability and complexity
  • This approach yields a faster ROI and is easier to justify internally

Finding the Answers

Financial success won’t come on a ten-point scale. Answering these and many other customer analytics questions is possible and easier than ever before. By leveraging historical data, statistical models, and a bit of know-how, Revelwood helps its clients achieve measurable, justifiable improvements in customer outcomes. Asking and answering the right customer questions will definitively add to the organization’s top and bottom lines. And that’s a score you should promote.

Contacting our Experts

Email info@revelwood.com to set up a discussion to learn more.

References

Home » Data Science

Turn Forecasting into a Competitive Advantage

by Leave a Comment

Have you thought about how to turn financial forecasting into a competitive advantage? In today’s disruptive, high-stakes economy, maximizing any and every competitive advantage can mean the difference between just surviving versus thriving. And if you did turn your standard forecasting activities into a competitive advantage, what would that mean for you and your team in the Office of Finance?

A new report from the Financial Executives Research Foundation (FERF) makes a strong argument for how and why forecasting can become a much more strategic activity for your organization. According to FERF, “More effective forecasting and a deeper understanding of how markets are likely to evolve can provide competitive advantage by improving a company’s agility and its ability to enhance products, target customers more effectively or gain other operational insights.”

Here are some examples of how you can turn forecasting into a competitive advantage:

  • Use demand planning to anticipate the likely effects of variables such as marketing campaigns, market conditions in a given region, price discounts and more.
  • Generate stronger insights from a greater volume and variety of data to replace gut instinct and ensure more confidence in the numbers and advice coming from the Office of Finance
  • Develop insights into how markets are likely to evolve in order to enhance product offerings and target customers more effectively
  • Use scenario modeling to explore potential outcomes and identify the activities that have the greatest effect on your business
  • Analyze accounts receivable to create profiles that rank customers in the likelihood of them paying invoices promptly
  • Apply point-of-sale data to optimize inventory levels to adjust purchases from suppliers

As the report states, “the growing use of analytics to enhance forecasting represents the latest development in the increasing recognition that an organization’s data has evolved from a byproduct of its interactions with customers and stakeholders into a powerful source of organizational value.”

Home » Data Science

Cognitive Computing Adopters are More Profitable & Innovative than Their Peers

by Leave a Comment

According to a report from the IBM Institute for Business Value, 89% of cognitive computing adopters are more profitable and more innovative than their industry peers. That’s an incredibly strong argument for embracing – or if you are not yet ready, exploring – cognitive computing. Think of the impact greater profits and innovation could make on your organization. Could it position you to be a disruptor in your industry? Could you finally beat the competitor with whom you’ve been neck-and-neck with for years? Would it enable you to expand into new markets? Or give back more to your employees?

In this report, Analytics: Dawn of the Cognitive Era, IBM found that 74% of organizations “are pervasively using at least one type of prescriptive analysis. And once a majority of organizations achieve a widespread use of an analytic level, the competitive advantage evolves to the next level of analytic maturity. Competitive differentiation is now greater for those using the fifth level: cognitive.

Interestingly, this research found that cognitive computing is being used in each of the 19 industry groups surveyed, and across all geographic regions. Those using cognitive are outperforming their peers in a number of ways, including:

  • Revenue creation
  • Effectiveness
  • Profitability
  • Innovation

They are also using cognitive systems in a number of different ways, such as for knowledge distribution, research and product development, external constituent and customer management, and sales and service activities. The survey found that most of these organizations, however, started their cognitive journey with a specific use case.

To achieve success with cognitive computing, organizations need more than just technology, data and analytics. According to the report, entry into the cognitive era also “requires a corporate culture and mindset ready to engage with the science of data and strong governance to create agility and speed … a cognitive environment is an analytic evolution, building on the analytics of today to create the marketplace of tomorrow.”

Download this report and you’ll learn how to:

  • Establish an organization cornerstone for cognitive
  • Build a solid cognitive data foundation, and
  • Focus on the skills of the future

Whether you are ready to evolve your use of analytics into the cognitive era or are just starting your research on cognitive, provides practical steps (as well as mini case studies) on how to begin your journey into cognitive computing.

Read more blog posts on cognitive computing:

What the Heck is Cognitive Computing?

New Research: Cognitive Computing will be a Game-Changer for Marketing

Embracing Cognitive Computing

Home » Data Science

New Research: Cognitive Computing will be a Game-Changer for Marketing

by Leave a Comment

Marketing leaders—those outperforming their peers—recognize that cognitive computing and artificial intelligence technologies are game-changers for marketing. The IBM Institute for Business Value recently conducted a study examining how likely Chief Marketing Officers (CMOs) and heads of sales are to embrace cognitive computing. The Institute surveyed 525 CMOs and 389 heads of sales across 18 industries. The research found that:

  • 88% of the outperformers believe they are ready to adopt cognitive computing
  • 91% of the outperformers believe that cognitive computing will be important to their organization’s future
  • 93% of them believe that cognitive and AI is mature enough to be market ready
Marketers embrace cognitive computing

As Maria Winans, Chief Marketing Officer for IBM Watson Customer Engagement recently told Forbes, “The survey further validated that marketers and sales leaders believe that this technology is game changing.”

The challenge may be how quickly marketers will adopt cognitive computing. The research found that the majority of survey respondents are in the early stages of considering and evaluating cognitive technologies. The report, however, points out, “But the longer they linger there, the greater their risk of falling further behind those companies that are already implementing and operating cognitive today.” Put that statement in the context of an additional finding: 61% of marketing and sales executives surveyed expect cognitive computing will be a disruptive force in their industries.

The question you should ask yourself, then, is where do you want to be when the marketing industry fully embraces cognitive? Your peers are saying it’s both a game changer and a disrupter. To use the terminology of Geoffrey Moore’s Crossing the Chasm, do you want to be an early adopter, do you want to be in the early majority, or do you want to be in the late majority or a laggard? Can you afford to be left behind as your discipline and your industry changes? Or do you want to ensure that you—and your organization—become one of the outperformers?

Read more blog posts about cognitive computing:

What the Heck is Cognitive Computing?

Embracing Cognitive Computing

Home » Data Science

Revelwood Named Ingram Micro IBM Analytics Growth Partner of the Year

by Leave a Comment

Business travel can sometimes be more obligation than enjoyment. There’s always the chance of flight delays and other transportation snafus, long days, and often, the expectation of keeping up with your day job while being out of the office for work. But sometimes there’s that special business trip you look forward to!

That’s how we feel about the annual Ingram ICE event. “ICE” stands for Ingram Cognos Elite and it’s an exclusive program for IBM business partners who demonstrate expertise in selling IBM Cognos solutions. Every year, this event is held in a different location. This year, the ICE event took place in sunny Key Largo, Florida which is always a fun place to have a business trip. To make it even more exciting, this was the second year in a row that Revelwood was named the Ingram Micro IBM Analytics Growth Partner of the Year!

“Revelwood has done an incredible job growing their IBM analytics business through portfolio expansion and new client acquisition,” said David Hino, senior vendor business manager for IBM Software, Ingram Micro. “They are part of an elite group of Ingram Micro and IBM partners that many could learn from.”

Revelwood celebrates earning the Ingram Micro IBM Analytics Growth Partner of the Year award for 2016

From left to right: Vincent Zandvliet, global managing director for IBM/Ingram Partnership, IBM; Scott Zahl, vice president & general manager for Advanced Computing Division, Ingram Micro; Ken Wolf, CEO, Revelwood; Dave Hino, senior vendor business manager for IBM Software, Ingram Micro; Laurie Evans, worldwide channels leader for analytics, IBM.

Our CEO, Ken Wolf, was on-hand to accept the award, but it was earned by the entire Revelwood team. As Ken said in our press release, “Our team earned this award because we have continued to grow our established practice in financial and operational analytics, while also building a successful and award-winning practice in customer analytics and artificial intelligence.” As you can see from the photo, Ken had a great time at the event!

Home » Data Science

How will Artificial Intelligence Impact your Industry?

by Leave a Comment

Corporate investment in artificial intelligence (AI) is predicted to triple in 2017, becoming a $100 billion market by 2025. Last year alone saw $5 billion in machine learning venture investment. In a recent survey, 30% of respondents predicted that AI will be the biggest disruptor to their industry in the next five years.

How is AI impacting your industry? What opportunities do you see for the application of AI in your company? Unless you’ve been living in a cave for the last five years, you know that IBM has taken a leadership role in the AI space with Watson. So, what exactly is Watson? Before answering that question, let’s first examine IBM’s definition of “cognitive capability”, which is at the heart of artificial intelligence. According to IBM, something has cognitive capability if it:

  • Understands domains deeply.
  • Reasons toward specific goals.
  • Learns continuously from experience.
  • Interacts naturally with people and systems.

An example might be GM’s OnStar Go, announced in October 2016 as the auto industry’s first cognitive mobility service that will use machine learning from Watson to understand user preferences and recognize patterns found in your decision data. From that, customers will receive personalized marketing services from numerous partners such as Glympse, iHeartRadio, Mastercard and Parkopedia.

Back to Watson…what exactly is it? IBM has branded many of its products with the Watson name, such as Watson Analytics for Social Media, Watson Marketing Insights and Watson for Drug Discovery. These are solutions that solve specific business problems, while incorporating cognitive capabilities to enhance the user experience and accomplish things on a scale that only a machine can. At their core, however, is Watson itself. Watson is a set of APIs that can be called from any application or service that perform specific cognitive functions. Examples include Visual Recognition, Speech to Text, Tone Analyzer and Natural Language Classifier. You build or enhance your applications by adding whatever relevant Watson API functionality you need. Watson APIs are easy to access, test and use from IBM’s Bluemix cloud platform. IBM has made relatively simple to add artificial intelligence capabilities to just about any type of software or application service.

At Revelwood we’ve been working with Watson on applications that combine Watson’s cognitive capabilities with data-driven insights and predictive models. The resulting solutions are capable of understanding call center phone conversations and predicting future customer actions, or in some cases Watson is able to handle customer transactions directly, bypassing the human channels. Our goal is to leverage these IBM Watson capabilities in creative ways to help bring you new opportunities.

Don’t get left behind the AI revolution. Contact us to learn more about how artificial intelligence can help your business keep up with today’s technology disruption and outpace your peers.

Read more about artificial intelligence and cognitive computing:

What the Heck is Cognitive Computing?

Embracing Cognitive Computing

Home » Data Science

Predictive Analytics in Insurance

by Leave a Comment

While we’re primarily known for our expertise in financial performance management and IBM Cognos TM1 and now IBM Planning Analytics, we’ve been growing an elite practice for predictive and advanced analytics for a few years now. In fact, at the IBM World of Watson conference in late October 2016, IBM announced that Revelwood was selected as the Customer Analytics Business Partner of the Year. This was for our work in designing and developing customer analytics solutions.

Two of these solutions just also happen to fall into the category of predictive analytics. One focuses on how an insurance company is using predictive customer analytics in a for sales and marketing and the other is a B2B retailer who is using predictive customer analytics in a digital marketing platform.

This insurance company wanted to get more meaningful insight and business value from its customer and marketing data. It wanted to use this data to determine where to invest in marketing programs, and how to prioritize sales efforts in order to maximize revenue and profits.

We decided to use IBM Predictive Customer Intelligence, Watson Analytics, and Expert Storybooks to create an application that would leverage the right data in order to segment the insurance company’s members and create prioritized lead lists. Now, with these prioritized lists, the company’s sales agents have moved from an average sales success rate of rate of 12% to an average success rate of 45%.

Home » Data Science