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Category: Analytics strategy (Page 2 of 4)

How Today’s Analytics Change Recruiting

February 26, 2018 /

HR is late to the analytics game by modern standards, and yet, HR metrics is not a new concept. The difference is that modern analytics enable HR professionals and recruiters to measure more things in less time and derive more insight than ever before.

Rosemary Haefner

Rosemary Haefner

“If you’re looking at recruiting, there have always been metrics such as time to hire and cost per hire, but you’re seeing other channels and avenues opening up,” said Rosemary Haefner, chief human resources officer at online employment website, CareerBuilder.com.

The “time to hire” or “time to fill” metric measures how many days it takes from the time a requisition is posted until the time an offer is accepted. The longer a position remains open, the higher the cost of talent acquisition. In addition, if a position remains open, an intervention may be necessary to ensure the work at hand is getting done.

If time to fill were the only measure of success, then, in theory, the faster a position is filled, the better. However, as most working professionals have experienced, the person who can be hired the fastest isn’t necessarily (and probably isn’t), the best candidate.

On the other hand, moving too slowly can cost organizations sought-after talent.

“There’s the time to fill, the cost of the person you hire, whether that person is high-potential and what their expected tenure in the organization is. That’s an example of four interrelated metrics,” said Muir Macpherson, Americas analytics leader, People Advisory Services at EY. “HR needs to stop thinking about individual metrics and consider the problem they’re trying to solve and how to optimize across a set of metrics simultaneously.”

Beyond keywords

Talent marketplaces and talent acquisition software made it easier to navigate a sea of resumes using keywords and filters. In response, some candidates stuffed their resumes full of keywords so their resumes would rank higher in searches. If one’s resume ranked higher in searches, then more people would see it, potentially increasing the candidate’s chance of getting interviews and landing a job.

Masterful keyword use demonstrated an awareness that the recruiting process was changing from a paper-based process to a computer or web-based process. However, other candidates who might have been better fits for positions risked getting lost in the noise.

The whole keyword trend was a noble effort, but keywords, like anything else, are not a silver bullet.

With today’s analytics tools, HR departments and search firms can understand much more about candidates and the effectiveness of their operations.

“You can use a variety of big data and machine learning techniques that go way beyond the keyword analysis people have been doing for a while that integrates all of the data available about a candidate into one, unified prediction score that can then be used as one additional piece of information that recruiters and hiring managers can look at when making their decisions,” said Macpherson.

Data impacts recruiters too

Recruiters now have access to data analytics tools that enable them to better match candidates with potential employers and improve the quality of their services. Meanwhile, HR departments want insight into what recruiters are doing and how well they’re doing it. The Scout Exchange marketplace provides transparency between the two.

“We can look at every candidate [a recruiter] submits to see how far they got in the process and whether they got hired. We use that for ratings so [companies and the recruiters they use] can see the other side’s rating,” said Scout Exchange CEO Ken Lazarus.

The site enables organizations to quickly find appropriate recruiters who can identify the best candidates for a position. HR departments also allows HR departments to see data and trends specific to their company.

Bottom line

Analytics is providing HR departments, recruiters and business leaders with quantitative information they can use to improve their processes and outcomes.

“Knowledge is power and having that data is helpful. For me, the first step is knowing what you’re solving for,” said CareerBuilder’s Haefner.

Right now, HR analytics tend to emphasize recruitment. However, attracting talent is sometimes easier than retaining it so it’s important to have insight throughout the lifecycle of employee relationships. EY’s Macpherson said HR departments should think in terms of “employee lifetime value” similar to the way marketers think about customer lifetime value.

“[HR analytics represents] a huge opportunity because for most companies, people and compensation are their biggest costs and yet there has been very little effort put into analyzing those costs or getting the most out of those investments that companies are making,” said EY’s Macpherson.

How the IoT Will Impact Data Analytics

February 22, 2018 /

IoT devices are just about everywhere, in cities, on oil rig, and on our wrists. They’re impacting virtually every industry, and their growth is outpacing organizations’ ability to make the most of that data.

To give you an idea of scale, IDC expects global IoT spending to reach nearly $1.4 trillion by 2021, up from $800 billion in 2017. The IoT is all around us, in many cases fading into the backgrounds of our homes and lifestyles, all the while generating massive amounts of data. The trick is driving value from that data.

The Balance of Data is Shifting

Over the past decade, we’ve witnessed several shifts in enterprises’ ability to deal with data. While different companies and industries are at different stages of maturity, we’ve seen and continue to see analytics evolving, whether it’s adding unstructured analytics capabilities to structured analytics, third-party data sources to our own, or IoT data to enterprise data. Slowly but surely, we’ve been seeing the balance of data shift from internal data to external data, particularly as more IoT devices emerge.

Edge analytics helps separate meaningful data from all the noise, which usually means identifying, and perhaps reacting to, exceptions and outliers. For example, if the temperature of a piece of industrial equipment rises beyond a threshold, maintenance crews may be alerted, or the equipment might be shut down.

Organizations attempting to manage IoT data using their traditional data centers are fighting a losing battle. In fact, Gartner noted that the IoT is causing businesses to move to the cloud faster than they might move otherwise. In other words, when so many things are happening in the cloud, it makes sense to analyze them in the cloud.

Data and Analytics Strategies: Top-down and Bottom-up

The sheer amount of data organizations must deal with increases greatly with the IoT, and there are still philosophical debates about how much data should be kept and how much data should discarded. Gartner strongly advises its clients to be smart about IoT data, meaning that one should not save all the data hoping to drive value from it in the future, but instead focus on strategic goals and how IoT data fits into that.

We often hear how important it is to align analytics efforts with business goals. At the same time, we also hear how important it is to uncover unknown opportunities and risks simply by allowing the data to speak for itself. Some of the most sophisticated companies I’ve talked to over the last several years are doing both, with machine learning identifying that which was not obvious previously. In Gartner’s view, “data and analytics must drive business operations, not reflect them.”

One major challenge organizations face, practically speaking, is operationalizing analytics — with or without the IoT. The core problem is moving from insights to action, which can’t be solved completely with prescriptive analytics. It’s a larger problem that has to do with company culture, stubborn attitudes and the very real challenges of integrating data sources.

Meanwhile, some organizations are pondering how they can use the IoT to improve customer experience, whether that’s minimizing transportation delays, improving environmental safety or otherwise eliminating friction points that tend to irritate humans. Humans have become fickle customers after all, and each touch point can affect a brand positively or negatively.

For example, Walmart placed kiosks in some of its stores that retrieve online orders, scan receipts and trigger the conveyor belt delivery of the items a customer purchased. The kiosks address a customer pain point which is walking all the way to the back of the store and waiting several minutes for someone to show up only to be told the order can’t be located.

Now think about what Walmart gets from the kiosk: trend data about customer use and experiences that may impact staffing, inventory management, marketing, supply chain. Clearly, the data will also indicate whether the kiosk idea is ultimately a good idea or a bad idea.

In the pharmaceutical industry, GSK has been working with partners to develop smart inhalers that track prescription compliance and dosing. The data helps inform research, and it also has value to doctors and pharmacies.

Similarly, enterprises can use IoT data to develop predictive models that help improve business operations, logistics, supply chain and more, depending on the nature of the sensors and the device.

Why Privacy Is a Corporate Responsibility Issue

February 11, 2018 /

Many organizations have Corporate Responsibility programs that focus on social issues and philanthropy. Especially in today’s Big Data era, why is privacy not part of the program?

Today’s companies are promising to lower their carbon footprints and save endangered species. They’re donating to people in developing countries who have far less than we do, which is also noble. But what about the fact that American citizens are a product whose information is bought, sold, and obtained without consent? In light of recent events, perhaps the privacy policies deserve more consideration than just two linked words at the bottom of a website home page.

“Privacy is a big issue for a host of reasons — legal, ethical, brand protection and moral,” Mark Cohen, Chief Strategy Officer at consultancy and technology service provider Elevate. “[Privacy] is an element of corporate culture [so what goes into a privacy policy depends on] your values and priorities.”

Problems with Privacy Policies

There are three big problems with privacy policies, at least in the US: what’s in them, how they’re written, and how they’re ignored.

One might think that privacy policies are tailored to a particular company and its audience. However, such documents are not necessarily original. Rather than penning a privacy policy from scratch, some are literally cutting and pasting entire privacy policies regardless of their contents. In fact, the people who are simply grabbing another company’s privacy policy might not even bother to read the content before using it.

The boilerplate language is also a problem. In-house counsel often uses freely available forms to put together a privacy policy. They may use one form or a combination of forms available to lawyers, but again, they’re not thinking about what should be in the document.

In addition, the documents are written in legalese, which is difficult for the average person to read. Businesses are counting on that because if you don’t know what’s in a privacy policy, what you’re giving away and what they intend to do with your information, you’ll probably just hope for the best. Even better, you’ll click an “I agree” button without knowing what clicking that button actually means. It’s a common practice, so you’re not alone if that’s the case.

Oh, and what’s stated in the documents may or may not be true, either because the company changed the policy since you last read it or they’re ignoring the document itself.

“After May 2018 when the new GDPR [General Data Protection Regulation] goes into effect, it’s going to force many companies to look at their privacy policies. their privacy statements and consents and make them more transparent,” said Sheila Fitzpatrick, Data Governance & Privacy counsel and chief privacy officer at data services for hybrid cloud company NetApp. “They’re going to have to be easily understandable and readable.”

Businesses Confuse Privacy with Security

Privacy and security go hand-in-hand, but they’re not the same thing. However, the assumption is, if you’re encrypting data then you’re protecting privacy.

“Every company focuses on risk, export control trade compliance, security, but rarely you find companies focused on privacy,” said Fitzpatrick. “That’s changing with GDPR because it’s extraterritorial. It’s forcing companies to start really addressing areas around privacy.”

It’s entirely possible to have all kinds of security and still not address privacy issues. OK, so the data is being locked down, but are you legally allowed to have it in the first place? Perhaps not.

“Before you lock down that data, you need the legal right to have it,” said Fitzpatrick. “That’s the part that organizations still aren’t comprehending because they think they need the data to manage the relationship. In the past organizations thought they need the data to manage employment, customer or prospect relationships, but they were never really transparent about what they’re doing with that data, and they haven’t obtained the consent from the individual.”

In the US the default is opt-in. In countries that have restrictive privacy policies, the default is opt-out.

The Data Lake Mentality Problem

We hear a lot about data lakes and data swamps. In a lot of cases, companies are just throwing every piece of data into a data lake, hoping it will have value in the future. After all, cloud storage is dirt cheap.

“Companies need to think about the data they absolutely need to support a relationship. If they’re an organization that designs technology, what problem are they trying to solve and what data do they need to solve the problem?” said Fitzpatrick.

Instead of collecting massive amounts of information that’s totally irrelevant, they should consider data minimization if they want to lower privacy-related risks and comply with the EU’s GDPR.

“Companies also need to think about how long are they’re maintaining this data because they have a tendency to want to keep data forever even if it has no value,” said Fitzpatrick. “Under data protection laws, not just the GDPR, data should only be maintained for the purpose it was given and only for the time period for which it was relevant.”

The Effect of GDPR

Under the GDPR, consent has to be freely given, not forced or implied. That means companies can’t pre-check an opt-in box or force people to trade personal data for the use or continued use of a service.

“Some data is needed. If you’re buying a new car they need financial information, but they’d only be using it for the purpose of the purchase, not 19 other things they want to use it for including sales and marketing purposes,” said Fitzpatrick.

Privacy may well become the new competitive advantage as people become more aware of privacy policies and what they mean and don’t mean.

“Especially Europeans, Canadians, and those who live in Asia-Pacific countries that have restrictive privacy laws, part of their vetting process will be looking at your privacy program,” said Fitzpatrick. “If you have a strong privacy program and can answer a privacy question with a privacy answer as opposed to answering a privacy question with a security answer, [you’ll have an advantage].”

On the flip side, sanctions from international countries can destroy a company from reputational, brand and financial points of view. The sanction under the new GDPR regulation can be as high as 4% of a company’s annual turnover.

Your Data Is Biased. Here’s Why.

January 11, 2018 /

Bias is everywhere, including in your data. A little skew here and there may be fine if the ramifications are minimal, but bias can negatively affect your company and its customers if left unchecked, so you should make an effort to understand how, where and why it happens.

“Many [business leaders] trust the technical experts but I would argue that they’re ultimately responsible if one of these models has unexpected results or causes harm to people’s lives in some way,” said Steve Mills, a principal and director of machine intelligence at technology and management consulting firm Booz Allen Hamilton.

In the financial industry, for example, biased data may cause results that offend the Equal Credit Opportunity Act (fair lending). That law, enacted in 1974, prohibits credit discrimination based on race, color, religion, national origin, sex, marital status, age or source of income. While lenders will take steps not to include such data in a loan decision, it may be possible to infer race in some cases using a zip code, for example.

“The best example of [bias in data] is the 2008 crash in which the models were trained on a dataset,” said Shervin Khodabandeh, a partner and managing director of Boston Computing Group (BCG) Los Angeles, a management consulting company. “Everything looked good, but the datasets changed and the models were not able to pick that up, [so] the model collapsed and the financial system collapsed.”

What Causes Bias in Data

A considerable amount of data has been generated by humans, whether it’s the diagnosis of a patient’s condition or the facts associated with an automobile accident.  Quite often, individual biases are evident in the data, so when such data is used for machine learning training purposes, the machine intelligence reflects that bias.  A prime example of that was Microsoft’s infamous AI bot, Tay, which in less than 24 hours adopted the biases of certain Twitter members. The results were a string of shocking, offensive and racist posts.

“There’s a famous case in Broward County, Florida, that showed racial bias,” said Mills. “What appears to have happened is there was historically racial bias in sentencing so when you base a model on that data, bias flows into the model. At times, bias can be extremely hard to detect and it may take as much work as building the original model to tease out whether that bias exists or not.”

What Needs to Happen

Business leaders need to be aware of bias and the unintended consequences biased data may cause.  In the longer-term view, data-related bias is a governance issue that needs to be addressed with the appropriate checks and balances which include awareness, mitigation and a game plan should matters go awry.

“You need a formal process in place, especially when you’re impacting people’s lives,” said Booz Allen Hamilton’s Mills. “If there’s no formal process in place, it’s a really bad situation. Too many times we’ve seen these cases where issues are pointed out, and rather than the original people who did the work stepping up and saying, ‘I see what you’re seeing, let’s talk about this,’ they get very defensive and defend their approach so I think we need to have a much more open dialog on this.”

As a matter of policy, business leaders need to consider which decisions they’re comfortable allowing algorithms to make, the safeguards which ensure the algorithms remain accurate over time, and model transparency, meaning that the reasoning behind an automated decision or recommendation can be explained.  That’s not always possible, but still, business leaders should endeavor to understand the reasoning behind decisions and recommendations.

“The tough part is not knowing where the biases are there and not taking the initiative to do adequate testing to find out if something is wrong,” said Kevin Petrasic, a partner at law firm White & Case.  “If you have a situation where certain results are being kicked out by a program, it’s incumbent on the folks monitoring the programs to do periodic testing to make sure there’s appropriate alignment so there’s not fair lending issues or other issues that could be problematic because of key datasets or the training or the structure of the program.”

Data scientists know how to compensate for bias, but they often have trouble explaining what they did and why they did it, or the output of a model in simple terms. To bridge that gap, BCG’s Khodabandeh uses two models: one that’s used to make decisions and a simpler model that explains the basics in a way that clients can understand.

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BCG also uses two models to identify and mitigate bias.  One is the original model, the other is used to test extreme scenarios.

“We have models with an opposite hypothesis in mind which forces the model to go to extremes,” said Khodabandeh. “We also force models to go to extremes. That didn’t happen in the 2008 collapse. They did not test extreme scenarios. If they had tested extreme scenarios, there would have been indicators coming in in 2007 and 2008 that would allow the model to realize it needs to adjust itself.”

A smart assumption is that bias is present in data, regardless.  What the bias is, where it stems from, what can be done about it and what the potential outcomes of it may be are all things to ponder.

Conclusion

All organizations have biased data.  The questions are whether the bias can be identified, what effect that bias may have, and what the organization is going to do about it.

To minimize the negative effects of bias, business leaders should make a point of understanding the various types and how they can impact data, analysis and decisions. They should also ensure there’s a formal process in place for identifying and dealing with bias, which is likely best executed as a formal part of data governance.

Finally, the risks associated with data bias vary greatly, depending on the circumstances. While it’s prudent to ponder all the positive things machine learning and AI can do for an organization, business leaders are wise to understand the weaknesses also, one of which is data bias.

How to Teach Executives About Analytics

January 4, 2018 /
If your data is failing to persuade executives, maybe it’s not the data that is the problem. Here’s how to change your approach to fit the audience.

One of the biggest challenges data analysts and data scientists face is educating executives about analytics. The general tendency is to nerd out on data and fail to tell a story in a meaningful way to the target audience.

Sometimes data analytics professionals get so wrapped up in the details of what they do that they forget not everyone has the same background or understanding. As a result, they may use technical terms, acronyms, or jargon and then wonder why no one “got” their presentations or what they were saying.

They didn’t anything wrong, per se, it’s how they’re saying it and to whom.

If you find yourself in such a situation, following are a few simple things you can do to facilitate better understanding.

Discover What Matters

What matters most to your audience? Is it a competitive issue? ROI? Building your presence in a target market? Pay attention to the clues they give you and don’t be afraid to ask about their priorities. Those will clue you in to how you should teach them about analytics within the context of what they do and what they want to achieve.

Understand Your Audience

Some executives are extremely data-savvy, but the majority aren’t just yet. Dialogs between executives and data analysts or data scientists can be uncomfortable and even frustrating when the parties speak different languages. Consider asking what your target audience would like to learn about and why. That will help you choose the content you need to cover and the best format for presenting that content.

For example, if the C-suite wants to know how the company can use analytics for competitive advantage, then consider a presentation. If one of them wants to understand how to use a certain dashboard, that’s a completely different conversation and one that’s probably best tackled with some 1:1 hands-on training.

Set Realistic Expectations

Each individual has a unique view of the world. Someone who isn’t a data analyst or a data scientist probably doesn’t understand what that role actually does, so they make up their own story which becomes their reality. Their reality probably involves some unrealistic expectations about what data-oriented roles can do or accomplish or what analytics can accomplish generally.

One of the best ways to deal with unrealistic expectations is to acknowledge them and then explain what is realistic and why. For example, a charming and accomplished data scientist I know would be inclined to say, “You’d think we could accomplish that in a week, right? Here’s why it actually takes three weeks.”

Stories can differ greatly, but the one thing good presentations have in common is a beginning, a middle, and an end. One of the mistakes I see brilliant people making is focusing solely on the body of a presentation, immediately going down some technical rabbit hole that’s fascinating for people who understand it and confusing for others.

A good beginning gets everyone on the same page about what the presentation is about, why the topic of discussion is important, and what you’re going to discuss. The middle should explain the meat of the story in a logical way that flows from beginning to end. The end should briefly recap the highlights and help bring your audience to same conclusion you’re stating in your presentation.

Consider Using Options

If the executive(s) you’re presenting to hold the keys to an outcome you desire, consider giving them options from which to choose. Doing that empowers them as the decision-makers they are. Usually, that approach also helps facilitate a discussion about tradeoffs. The more dialog you have, the better you’ll understand each other.

Another related tip is make sure your options are within the realm of the reasonable. In a recent scenario, a data analyst wanted to add two people to her team. Her A, B, and C options were A) if we do nothing, then you can expect the same results, B) if we hire these two roles we’ll be able to do X and Y, which we couldn’t do before, and C) if we hire 5 people we’ll be able to do even more stuff, but it will cost this much. She came prepared to discuss the roles, the interplay with the existing team and where she got her salary figures. If they asked what adding 1, 3, or 4 people looked like, she was prepared to answer that too.

Speak Plainly

Plain English is always a wise guide. Choose simple words and concepts, keeping in mind how the meaning of a single word can differ. For example, if you say, “These two variables have higher affinity,” someone may not understand what you mean by variables or affinity.

Also endeavor to simplify what you say, using concise language. For example, “The analytics of the marketing department has at one time or another tended overlook the metrics of the customer service department” can be consolidated into, “Our marketing analytics sometimes overlooks customer service metrics.”

You’re Doing Analytics Wrong. Here’s Why.

December 1, 2017 /

Businesses across industries want to compete on insights, and yet many of them aren’t getting the ROI they expect. Somewhere between raw data and actionable insights, processes break down or they don’t exist. It’s not just a tool problem. Organizations need to change the way they think and behave.

Data is data

Every company has data, but not all organizations treat data as a valuable corporate asset.  If they did, they probably wouldn’t have multiple copies of customer records, none of which is “the golden record.”

“There’s no documentation so there’s no traceability,” said Ivan Chen, director of Enterprise Business Analytics at GPU manufacturer NVIDIA. “If you really want to get value out of your data, you have to put structure around it. We realized that, so we’re putting plans in place to provide a foundation for analytics.”

Chen is wrapping up the first month of a three-month pilot that will enable self-service analytics.  As a first step, his team is inventorying all of NVIDIA’s data assets, documenting them and putting them in a central repository.

“We’re codifying all the business logic in the repository so that people can see how the transformation that was done,” said Chen. “That way, there’s a baseline for discussion.”

In the next phase, analysts will get access to self-service analytics.

“The fundamental reason analytics is not successful is because different functions have different goals,” said Chen. “Business professionals are trying to answer questions about the market, but the market conditions keep changing so you have to keep enhancing the data in reports. IT is supposed to make those changes, but IT has other priorities, so the whole thing breaks down.”

NVIDIA dedicated three IT professionals to the pilot who are supporting Chen and his team. Before the pilot began, it took IT six months enhance the data in a report. Now it takes two or three days.

Analytics and outcomes aren’t aligned

When analytics and business outcomes aren’t linked, it’s impossible to understand the ROI. All too often, people believe analytics is the outcome, rather than a means to an outcome.

“Analytics are viewed as how many tools or dashboards you have, or how many reports you generate, ” said Isher Kaila, CEO of management consulting firm Sapphire Nine Consulting.  “No one is anchoring that to the amount of insights you are delivering, and by extension, what those insights translate to in terms of business outcomes.”

Visualizing data in different ways can help clarify what the data says. However, it’s also possible to visualize data over and over without producing any insights.

“You have to understand which insights are necessary for you to deliver an outcome, which people are attached to generating those insights, and who you will hold accountable for optimizing the insights linked to your outcomes,” said Kaila. “Most organizations do a pretty good job of managing their reporting tool environment, but someone needs to be accountable for transforming insights into outcomes.”

You’re stuck in the past

More companies are using predictive analytics to accomplish something proactively, although a lot of companies are still doing business through the rear-view mirror.

“If you’re just showing me the same data in a new way, how will that show me what I should be thinking about going forward? Is it challenging me to think about how I can optimize my business model? Are there processes I need to tweak? How much money am I leaving on the table? Are we cannibalizing our own revenue? Those are the kinds of questions you should be able to ask of your data,” said Kaila.

Some organizations have hired a chief analytics officer or a chief data officer to ensure that data can be used as a strategic asset. That person is responsible for bridging the gap between business and IT, orchestrating resources, and driving value from analytics.

“Leaders need to ensure accountability for insights,” said Kaila.  “If you do that, you’ll be able to align the definitions, the processes and ultimately how you operationalize predictive insights.”

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Analytics work best when they’re executed as repeatable, sustainable processes that transcend any particular role. In many organizations, analytics are executed as a set of discrete functions and tasks, negatively impacting the potential value analytics can provide.

“Executives should be aware that they’re likely not even harnessing the value of their analytics spend today. There’s an analytics value chain in which outcomes are generated by insights, insights are generated by accountable business owners, and the accountable business owners have a strong partnership with their technical partners,” said Kaila. “Too often we see a lack of alignment around metrics and insights.”

Insurance Struggles with Lead Gen & Data Analytics

November 22, 2017 /

The International Institute for Analytics (IIA) recently published a report stating that the insurance industry was the least mature of 12 vertical markets it studied. Insurance companies have lots of data, but they’re having trouble making sense of it.

“We have this data, but we can’t make heads or tails of it because we have data integration problems and there’s no data governance,” said Samantha Chow, senior analyst at market research firm Aite Group. “[Insurance carriers] are hiring data analysts and data scientists, but it’s very fragmented. They don’t have the support they need [to improve] their targeting, products, pricing — all of these things they’re trying to do.”

Lead generation is a huge problem. Older agents are retiring and more business is transacted online, which means the approach to lead generation must evolve with the industry. At the present time, lead generation involves a complicated web of data, external partners, and internal systems, all of which need to be orchestrated into compelling offers that are relevant to individual consumers “in the moment.”

Insurance companies also want to improve their ability to act on “triggers” that suggest a prospect’s interest in a particular product. For example, multiple mortgage loan pulls on a credit report indicate that the prospective home buyer will probably need homeowners insurance. To get information they lack, insurance companies use third-party sources such as credit information provider Transunion, data company Lexis/Nexis, and partners who specialize in social media analytics.

Addressing lead generation

Aite Group recently published a report based on interviews with 80 lead generation vendors and more than 30 insurance company and agency lead-generation and marketing executives. Chow said orchestrating information is the biggest problem the insurance industry faces today, which is why some carriers turn to vendors such as data integration platform provider LeadCloud.

Meanwhile, data acquisition costs are rising because insurance companies don’t know how to target prospects younger than the Baby Boomer generation.

“Getting a 30-year-old to understand the value of life insurance is difficult,” said Chow. “Learning how to target them and speak to them adds to the acquisition costs.”

It doesn’t help that the information insurance companies provide to consumers can be more confusing than clarifying. Because consumers have trouble differentiating products, insurance companies such as Geico, Progressive, MetLife, and Allstate spend lots of money on radio, TV, and pay-per-click advertising promoting their brands.

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To reduce lead-generation costs, insurance companies need to improve their ability to use data and analytics.

“We’re seeing acquisition costs go down [among auto insurance carriers] but [for the rest] it’s going to be learning who your target market is, having the supporting data, and being able to hone in on that particular consumer. It’s not going to be easy,” said Chow.

Making sense of data still difficult

The insurance industry also has challenges with data access. Data quality is a problem because different systems say different things about the same person or issue.

Further complicating the matter is the number of systems insurance companies have. They have dedicated systems for claims, underwriting, new business, customer service, and policies. Worse, there are often duplicates of systems because one may cover claims from 1990 to 2000, while another covers claims from 2001 and later, for example.

“On average, some of the top tier carriers have over 26 or 27 legacy policy administration systems they’re running on at one time,” said Chow. “If you have a life insurance policy and an auto policy or a dental policy with [a particular carrier], they can’t get data from one policy to the next policy. They can’t merge that together so they can learn more about you.”

Even if insurance companies could unearth “the golden leads,” loyalty may be an issue for some types of insurance. Chow said most people won’t move their life insurance policy from one carrier to another. Yet, most consumers shop for auto insurance based on price. When it comes to health insurance, customer loyalty depends on the carrier’s willingness to make good on its promises and streamlining the claims process.

Perhaps the insurance industry lags behind in its intelligent use of data because its technology stack and business processes are complex and fragmented. Still, if insurance companies want to remain competitive, they must be able to use data more adeptly to quickly identify quality leads and compete more effectively.

How Customer Intelligence Impacts Customer Loyalty, Wallet Share

November 22, 2017 /

Many of today’s companies talk about getting a 360-degree view of their customers and how that will enable them to increase share of wallet and improve customer loyalty. As a consumer and an industry observer, I would argue that these “360-degree views” are aspirational at best. Striving for a holistic view of customers is a noble goal and a necessary one; however, achieving customer intelligence nirvana is easier said than done.

“The vast majority of the people we talk to aspire to get a 360-degree view of the customer, but the reality is they may not have closed the circle,” said Julio Hernandez, partner, global customer Centre of Excellence Lead and US Customer Advisory Practice Lead at KPMG . “A 360-degree view of the customer is really knowing who the customer is, what they’re doing and why they’re doing it. It’s also bringing in the right information sources, and the information sources are continuously evolving.”

Companies should also understand how valuable their customers are and where they are on their journeys. However, truly understanding customers and marketing to them appropriately is still difficult despite all the technology and data that’s available now.

Part of the problem is what Hernandez calls “The New Year’s Day” problem, which is saying one thing and doing another.

“It goes back to the 360-degree view and having multiple sources of data, combining them, and combining [different] types of analysis to get a better picture of what the customer wants and is doing,” said Hernandez. “You have to start with what you’re trying to achieve with customer insights. That drives how you harness the analytics and how you look at the data. If you expect to just look at all your data and [get] all the insights in the world, you’re going to come up short.”

Increasing Share of Wallet

Businesses purchase a lot of third-party data to better understand their customers’ economic means, what they’re buying and where they’re buying it to understand their share of wallet. If they have a loyalty program, they have insight into what their share of wallet looks like.

“You have to make some inferences about what’s your share in the marketplace is in different categories,” said Hernandez. “You can also triangulate and come up to a number about what I’m actually selling to that person versus the inferred wallet [because] you won’t know for sure exactly what their wallet is.”

Businesses should also consider the attributes of their best customers and then identify customers who share the same attributes but spend less. That way, the company can intervene with some sort of marketing campaign that encourages the latter group to spend more.

Improving Customer Loyalty

Businesses with loyalty programs get varied results depending on the benefits their programs provide and the degree to which companies leverage that information.

“Loyalty cards are interesting because they’re trying to [get] you to clearly state who you are when you’re using the card and then they can track your basket and your purchases,” said Hernandez. “But you have to step back and ask how do you as an organization define loyalty? Is it someone who stays with you on an ongoing basis? If so, that’s great, but if you’re a utility and I continue to business with [you], that doesn’t necessarily means that I’m loyal. It means I’m lazy or I don’t have substitutes.”

Money isn’t everything. If two customers spend the same amount of money, but one is a brand advocate on social media, the latter is considered more valuable now.

“When you think about loyalty, it’s also about what are they’re doing with their loyalty,” said Hernandez. “Are they engaging with your services? Are they proponents of it? Those are things that help you determine what kind of loyalty you have.”

Hotels Check In with More Analytics

October 28, 2017 /

Hotels continue to invest in analytics so they’re in a better position to optimize revenue, deliver better customer experiences and improve operations. Like other organizations, hotels realize they can improve their competitive position using data and analytics more effectively than others. To do that, they need to integrate data coming from different functional areas and connect internal data with external data to make material improvements across the board.

Revenue Optimization

Hotels are moving past historical data and current bookings to maximize room occupancy and profitability. To improve their effectiveness, they’re using competitive data, weather data, event data, predictive capabilities, and more. Starwood Resorts is experimenting with machine learning and neural networks to change pricing dynamically, rather than twice or three times per day or seasonally.

The goal is to optimize the profitability of each room, not just hotels at large

Customer Loyalty

Customer loyalty programs have evolved with data analytics capabilities to provide guests with better, albeit different, experiences. For example, some guests care more about the quality of concierge services than Wi-Fi. Hotels must understand such differences to understand a guest’s preferences and cater to those preferences. After all, every customer has an individual expectation of what a “good” hotel experience is, not just the Premier or Platinum members. While the most profitable and loyal customers deserve exclusive benefits, catering to them should not be done at the expense of other guests.

To provide better experiences at all levels, hotels are attempting to understand their customers more holistically than they have in the past to provide a relevant experience. Having a “special requests” textbox in a booking application yields some information, so are customer requests and feedback recorded at the front desk. Another way to understand customer preferences is to slice and dice room offerings based on non-traditional amenities, such as allergen-filtering, in addition to the usual size, price, category and smoking/no smoking designations. Tracking a customer’s preferences over time, helps too in an effort to anticipate guests needs and desires.

These days, loyalty isn’t something that kicks in at check-in. Hotels are using search, online bookings, social media, call center data, front desk data and surveys to better understand customer journeys and what people want.

Moving up a level or two, some are targeting Millennials, which included Pokemons in the pool and on beds at Marriott hotels, clearly a non-traditional “benefit,” though an attractive benefit for those caught up in the Pokemon Go craze. The campaign happened to be a very smart marketing move from a social media point of view — free advertising.

The relationship among marketing, customer loyalty and revenue optimization enables a continuous feedback loop where insights from one bucket inform the others. And that’s not all.

Operations

Third party data can be very valuable from a predictive point of view when it impacts hotel occupancy and profitability. Weather and event data are two examples. Here in Sedona, Ariz., wildfires and heavy monsoon rains can cause massive hotel room cancellations. In other cities, popular concerts, sports games and flight cancellations cause a spike in demand. While those things may seem intuitive, actual data feeds can help hotels plan for the dips and spikes more accurately, so they can right size things like staff on hand and supply orders.

From an internal perspective, hotels need to monitor and constantly improve the efficiency of individual functions such as housekeeping, not only to reduce costs, but to keep up with competitors’ improvements. Some operational information is used to craft marketing messages such as Starwood Hotel’s “Smart check-in.”

Analytics is also providing insight into age-old issues such as sluggish room service. Is the problem too many simultaneous orders, too few members of the kitchen staff, poor kitchen management, something else or a combination of things? Operational analytics provides some insight as will guests’ social media posts, survey data, call center data, front desk data, etc.

Mobile

Hotel chains have mobile apps that give them even more insight into customer behavior, especially as they expand out from reservations made using a mobile device to keyless entry (using a smartphone), mobile food orders and more.

Some hotels are adopting “mobile first” strategies given the popularity of the devices and the fact that more hotel customers are using mobile devices instead of laptops to book rooms.

Hotels face many of the same problems enterprises face generally, not the least of which is connecting dots in a way that is valuable to their organizations and customers.

How Analytics Are Changing Frequent Flyer Programs

October 24, 2017 /

Airlines gather and analyze more data than ever before to improve operations and deliver better travel experiences. One of the most effective data-gathering tactics has been the establishment of frequent flyer programs.

In the beginning, those programs enabled the airlines to identify their “best” customers — those who flew more than others or spent more for seats than others. Today, frequent flyer status is determined by a much more sophisticated calculus that involves many data points , some of which are very creative. The data provides insight into customer behavior and preferences, as well as operational issues that need to be addressed for compliance or competitive reasons.

Like other businesses, airlines use increasingly sophisticated website analytics to better understand customer behavior and preferences. They also use mobile app data and social media data and they’ve structured partnerships with other airlines and businesses.

In short, airlines have more insight into travelers’ preferences and behaviors than ever before.

More Perks Mean More Data

Airlines offer branded credit cards because they provide insight into customers’ purchasing habits. Their partnerships with other airlines provide additional information.

Meanwhile, airlines continue to expand their frequent flyer programs beyond hotels and rental cars to include all sorts of things including flowers, a marriage proposal kit, wine and even a dog sled ride. Apparently one gentleman managed to rack up over 1.2 million miles purchasing massive amounts of pudding. If you’re interested in earning up to 250,000 Qantas Frequent Flyer program miles in a single purchase, buy a Jaguar.

Interestingly, Qantas Frequent Flyer members can use their miles to pay for the healthcare insurance Qantas offers. (Imagine that on the US Congressional agenda!) As part of its Wellness program, parents and their kids can earn points just by downloading the app and staying active.

Every swipe, every click, every soccer ball kick now matters.

Analytics Means Business for Some Airlines

Qantas’ Loyalty department is so effective, the company made the group available for hire so other businesses can maximize the ROI of their marketing and loyalty programs. Its strategy caused Virgin Airlines Australia to acquire an analytics company rather than building an internal capability.

Air Canada’s Aeroplan story is also interesting. In 2002, the airline spun off its loyalty program as a separate company. However, in 2020, Air Canada will launch its own loyalty program — again.

“The new program, launching in 2020, will offer additional earning and redemption opportunities, more personalized service and a better digital experience for Air Canada customers,” said Benjamin Smith, president, Passenger Airlines at Air Canada in a press release. “[B]y managing our own loyalty program, we will be able to take better care of our customers by making decisions in real time that address specific needs.”

Mr. Smith makes a good point. Fast data and combined data sources enable airlines to provide contextual experiences. Text alerts of flight delays or gate changes are just two examples. Airports have their own analytics infrastructures which feed certain information to airlines. Not surprisingly, airlines are also monitoring what’s happening in their member-only lounges so they can provide additional competitive benefits and improve operations.

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