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Month: May 2017

Why Marketing Is So Smart, Yet So Dumb

Marketing is considered the most analytically advanced function in most companies. Yet, consumers and businesses are still bombarded with irrelevant promotional messages.

It’s true that marketers have had access to “modern” analytics tools longer than most others in an organization. It started with web analytics and then grew to encompass other digital channels and even offline channels.

In the last decade, there has been a push toward “multi-channel” and “omnichannel” analytics. Multi-channel analytics is designed to optimize marketing effectiveness within and across channels. Omnichannel analytics focuses on improving a continuous user experience across channels.

Marketing analytics is difficult, in other words, despite the availability of more and better tools.

“What are the exact ads, the exact conversations, and the exact place that drove someone to make a purchase on my site or in my store?” said Chris Madden, co-founder of digital marketing agency Matchnode. “I think in 0% of the cases does the super smart, data-driven marketer, CMO, or CEO know what drove the sale.”

It’s Complicated

The number of online channels has exploded over the past couple of decades with the rise of Search Engine Marketing (SEM), social media, and mobile, to name a few. Anyone familiar with even one of those channels knows that change is constant, and if you don’t keep up, you’ll slip up eventually.

“We’ve seen Facebook come out twice in the past year claiming that their method for measuring engagement metrics [was] wrong. There will be more growing pains as these platforms stabilize,” said Mitul Jain, vice president at data science platform provider r4 Technologies.

Meanwhile, brick and mortar entities are tracking what’s happening in stores using kiosks, digital point of sale (POS) systems, customers’ smartphones, and security cameras. They also have e-commerce sites. Their big challenge is to understand the relationship of online and offline channels.

We’re Tracking Activities, Not People

Activities are being monitored in every channel whether posts, clicks, downloads, foot traffic, or credit card swipes, but not all of that information is being stitched together into a coherent, accurate picture.

“Analytics does not do a very good job of knowing that the person on my phone is the same person on my tablet and desktop,” said Matchnode’s Madden. “The marketers who are doing well are those that start with the person.

Attribution is Difficult

Most of the time, there isn’t a 1:1 relationship between a message and an outcome (e.g., a sale, download, or donation). Usually, the final outcome is influenced by several factors that may include search-based research, search or social media advertising, product reviews, direct mail and email offers, apps, and websites.

The natural and incorrect thing to do is to attribute the last interaction to the outcome. The shopper visited the site and bought something, so the ecommerce site gets full credit. However, since several other factors likely influenced the decision, what percentage the sale should be attributed to each? That’s the burning question.

Data Quality Could Improve

Marketing tends to use several different systems and platforms, each of which may differ enough to affect data quality. Perhaps fields or tags are implemented differently, or there are five instances of a customer record, all of which are inconsistent.

“Large sites may not have the code in the right places or double instances of code. For example, an .edu site with multiple departments may have different tracking codes on the same site, which can create a lot of confusion,” said Max Thomas, CMO at fintech startup YayPay.

Thomas audits a client’s data points to make sure they’re correct early in the relationship. Quite often he discovers that the client hasn’t set up website analytics correctly or they haven’t set up conversion tracking correctly. If either or both of those things are true, the client is referencing faulty data.

We’re Biased

Humans are biased creatures. What we perceive is based on beliefs and experience, most of which is subjective. Our subjective view or bias causes us to do many things that skew analytical results such as selecting non-random samples or cherry-picking data.

“The mistake people are making is they don’t let the data talk to them. They’re looking for something in the data that’s not there,” said D. Anthony Miles, CEO and founder of consulting firm Miles Development Industries Corp. “You have to ask what the data is telling you and what it isn’t telling you.”

Marketers tend not to look at analytical results critically, however. They tend to accept analytical at face value unless it’s out of sync with their beliefs. If they were looking at analytical results critically, they’d ask why a particular analytical result occurred or didn’t occur.

“The data can and should tell the story, but we make up our own story and look for data to support it, so we may miss the most important thing because we were looking for something else,” said Matchnode’s Madden.

Pesky PII

Finally, marketing can only be so accurate without a critical mass of Personally Identifiable Information or PII, some of which consumers do not want to give and some of which is illegal. Without the missing data points, it’s difficult to reach consumers with the right message at the right time for the right reason.

The lack of that “last mile” data is the reason why some people think marketing will never be 100% accurate. What do you think? Before you answer, think of a compromising message that might be sent to you, just at the wrong moment.

How Cybersecurity Analytics Are Evolving

As the war between the black hats and white hats continues to escalate, cybersecurity necessarily evolves. In the past, black hats were rogue individuals. Now they’re hactivists, crime groups, and hackers backed by nation states.

“Hackers have gotten a lot more sophisticated,” said Sanjay Goel, a professor in the School of Business at University of Albany. “It used to be they’d break into networks, do some damage, and get out. Now they have persistent attacks and targeted execution.”

Hackers are automating attacks to constantly search for vulnerabilities in networks. Meanwhile, fraudulent communications are getting so sophisticated, they’re fooling even security-aware individuals. Analytics can help, but nothing is a silver bullet.

Moats Are Outdated

Organizations used to set up perimeter security to keep hackers from breaching their networks. Since that didn’t work, firewalls were supplemented with other mechanisms such as intrusion detection systems that alert security professionals to a breach and honey pots that lure hackers into a place where they can be monitored and prevented from causing damage.

Those tools are still useful, but they have necessarily been supplemented with other methods and tools to counter new and more frequent attacks. Collectively, these systems monitor networks, traffic, user behavior, access rights, and data assets, albeit at a grander scale than before, which has necessitated considerable automation. When a matter needs to be escalated to a human, analytical results are sent in the form of alerts, dashboards, and visualization capabilities.

“We really need to get away from depending on a security analyst that’s supposed to be watching a dashboard and get more into having fully-automated systems that take you right to remediation. You want to put your human resources at the end of the trail,” said Dave Trader, chief security officer at IT services company GalaxE.Solutions.

Predictive analytics analyzes behavior that indicates threats, vulnerabilities, and fraud. Slowly, but surely, cybersecurity budgets, analytics, and mindsets are shifting from prevention to detection and remediation because enterprises need to assume that their networks have been breached.

“All the hackers I know are counting on you not taking that remedial step, so when there’s a vulnerability and it’s a zero-day attack, the aggregator or correlators will catch it and then it will go into a ticket system so its three to four days before the issue is addressed,” said Trader. “In the three to four days, the hackers have everything they need.”

Why Break In When You Can Walk In?

Fraudsters are bypassing traditional hacking by convincing someone to turn over their user ID and password or other sensitive information. Phishing has become commonplace because it’s effective. The emails are better crafted now so they’re more believable and therefore more dangerous. Even more insidious is spear phishing which targets a particular person and appears to be sent from a person or organization the person knows.

Social engineering also targets a specific person, often on a social network or in a real-world environment. Its purpose is to gain the target’s trust, and walk away with the virtual keys to a company’s network or specific data assets. Some wrongdoers are littering parking lots with thumb drives that contain malware.

Behavioral analytics can help identify and mitigate the damage caused by phishing and social engineering by comparing the authorized user’s behavior in the network and an unauthorized user’s behavior in the network.

Bottom Line

Breaches are bound to happen. The question is whether companies are prepared for them, which means keeping security systems up to date and training employees.

Far too many companies think that hacking is something that happens to other organizations so they don’t allocate the budget and resources they need to effectively manage risks. Hackers love that.

What A Chief Analytics Officer Really Does

As analytics continues to spread out across an organization, someone needs to orchestrate it all. The “best” person for the job is likely a chief analytics officer (CAO) who understands the business, understands analytics, and can help align the two.

The CAO role is a relatively new C-suite position, as is the chief data officer or CDO. Most organizations don’t have both and when they don’t, the titles tend to be used interchangeably. The general distinction is that the CAO focuses more on analytics and its business impact while the CDO is in charge of data management and data governance.

“The new roles are really designed to expand the use of data and expand the questions that data is used to answer,” said Jennifer Belissent, principal analyst at Forrester. “It’s changing the nature of data and analytics use in the organization, leveraging the new tools and techniques available, and creating a culture around the use of data in an organization.”

Someone in your organization may already have some or all of a CAO’s responsibilities and may be succeeding in the position without the title, which is fine. However, in some organizations a C-suite title and capability can help underscore the importance of the role and the organization’s shift toward more strategic data usage.

“The CAO needs to be able to evangelize the use of data, demonstrate the value of data, and deliver outcomes,” said Belissent. “It’s a role around cultural change, change management, and evangelism.”

If you’re planning to appoint a CAO, make sure that your organization is really ready for one because the role can fail if it is prevented from making the kinds of change the organization needs. A successful CAO needs the support of senior management, as well as the authority, responsibility, budget, and people skills necessary to affect change.

One mistake organizations make when hiring a CAO is placing too much emphasis on technology and not enough emphasis on business acumen and people skills.

The making of a CAO

When professional services company EY revisited its global strategy a few years ago, it was clear to its leadership that data and analytics were of growing importance to both its core business and the new services it would provide to clients.

Rather than hiring someone from the outside, EY chose its chief strategy officer, Chris Mazzei, for the role. His charter as CAO was to develop an analytics capability across EY’s four business units and the four global regions in which it operates.

[Want to learn more about CAOs and CDOs, read 12 Ways to Connect Data Analytics to Business Outcomes.]

Part of his responsibility was shaping the strategy and making sure each of the businesses had a plan they were executing against. He also helped expand the breadth and depth of EY’s analytical capabilities, which included acquiring 30 companies in four years.

The acquisitions coupled with EY’s matrixed organizational structure meant lots of analytics tools, lots of redundancies, and a patchwork of other technology capabilities that were eventually rationalized and made available as a service. Meanwhile, the Global Analytics Center of Excellence Mazzei leads was also building reusable software assets that could be used for analytics across the business and for client engagements.

Mazzei and his team also have been responsible for defining an analytics competency profile for practitioners and providing structured training that maps to it. Not surprisingly, his team also works in a consultative capacity with account teams to help enable clients’ analytical capabilities.

“The question is, ‘What is the strategy and how does analytics fit into it?’ It sounds obvious, but few organizations have a clear strategy where analytics is really connected into it across the enterprise and at a business level,” said Mazzei. “You really need a deep understanding of how the business creates value, how the market is evolving, what the sources of competitive differentiation are and how those could evolve. Where you point analytics is fundamentally predicated on having those views.”

Mazzei had the advantage of working for EY for more than a decade and leading the strategy function before becoming the CAO. Unlike a newly-hired CAO, he already had relationships with the people at EY with whom he’d be interfacing.

“Succeeding in this role takes building really trusted relationships in a lot of different parts of the organization, and often at very senior levels,” said Mazzei. “One reason we’ve seen CAOs fail is either because they didn’t have the skills to build those relationships or didn’t invest enough time on it during their tenure.”

Self-Service Analytics Are A Necessity

Lines of business are buying their own analytics solutions because IT is unable to deliver what they need fast enough. If the company has a data team, lines of business can ask for help, but like IT, the data team is faced with address more problems than there are people to solve them.

Smart IT organizations are building a foundation with governance built in. In that way, business users can get access to the data and analytics they need while the company’s assets are protected.

“IT has become more of a facilitator,” said Bob Laurent, VP of product marketing for self-service analytics platform provider Alteryx.  “If they’re able to give people access to data with the proper guardrails, then they’re out of the business of having to do mundane reports week in and week out.”

The shift to self-service analytics is happening across industries because organizations are under pressure to do more with their data and do it faster.

Meanwhile, average consumers have come to expect basic self-service analytics from their banks, insurance companies, brokerage firms, credit card companies, apps, and IoT devices. For an increasing number of businesses, self-service analytics is a necessity.

Higher Education Improves Performance

Colleges and universities are using self-service analytics to improve admission rates, enrollment rates, and more.

As an example, the Association of Schools and Programs of Public Health(ASPPH) built a system that allows its members to upload admissions data, graduate data, salary data, and financial data as well as information about their grants and research contracts. ASPPH verifies and validates the information and then makes the data available via dashboards that can be used for analysis.

“We needed to give them a place to enter their data so they weren’t burdened with reporting which they have to do every year,” said Emily Burke, manager, data analytics at ASPPH.

More than 100 schools and programs for public health are using the system to analyze their data, monitor trends and compare themselves to peers.  They’re also using the system for strategic planning purposes.

“A university will log in and see [their] university’s information and create a peer group that’s just above them in rankings. That way, they can see what marks they need to hit,” said Burke.  “A lot of them are doing that geographically, such as what the application numbers look like in Georgia.”

Drive Value from Self-Service Analytics

The value of self-service analytics is measured by two things: the number of active users, and the business value it provides an organization.  Knowing that, a number of vendors are now offering SaaS products that are easy to use, and don’t require a lot of training.

ASPPH built its own system in 2012. At the time, Burke and her team were primarily focused on the system’s functionality, but it soon became obvious that usability mattered greatly.

“We built this wonderful tool, we purchased the software we needed, we purchased a Tableau server, and then realized that our members really didn’t know how to use it,” said Burke.

Deriving the most value from the system has been a journey for ASPPH, which Burke will explain in detail at Interop during her Data-Driven Decision Making: Empowering Users and Building a Culture of Data session in Las Vegas on Thursday May 18.

If you’re implementing self-service analytics or thinking about it, you’ll be able to see a demonstration of the ASPPH system, hear Burke’s first-hand experiences, and walk away with practical ideas for empowering your users.

 

Data-Driven Effectiveness Is A Team Sport

Most companies are trying to understand how they can make the best use of their data. They’ve invested in tools and they’ve invested in people, but the results continue to fall short of expectations. Competitors are stealing customers, disruptors are upsetting the natural order or things, and business as usual is showing diminishing returns.

Why companies fall so behind or advance so fast isn’t always obvious, but there’s one thing that separates the leaders from the laggards:  The leaders have integrated data driven decision-making into their culture and business processes. In fact, their ability to use data effectively is part of their core competency.

“Legacy processes and procedures have led to really siloed organizations,” said Rich Wagner, CEO of business performance forecasting solutions provider Prevedere. “The analysts within each function all operate differently. They use different tools, different techniques, and different technologies to build their business plans to run the business so they’re not an integrated group.”

How to Make Teamwork Work

One sign of analytical maturity is the effectiveness of cross-functional problem solving. IT likely has the data, the data team needs to surface insights for the business, and the business has to be confident that the decisions they make advance their objectives. In today’s rapidly changing business environment, cross-functional teams are necessary because their collective knowledge and skills enables more effective problem solving, faster.

“You need to have a team that’s focused on a shared understanding of what the business problem is and what the objective is. Do not pass go until you do that because it’s a recipe for disaster,” said Chris Mazzei, chief analytics officer at professional services organization EY (Ernst & Young).

Unifying efforts doesn’t just happen. Business professionals need to understand what the data team and IT do and vice versa, which is best accomplished by working together to solve a business problem.

“[T]ask a team to solve a pretty big problem with a tight deadline and let each of them see the value that the other brings,” said Prevedere’s Wagner.

Success may also require some self-motivation. There’s significant value in spending time with members of the team that have different areas of expertise. By working together and being inquisitive, individuals can learn more about how other functions operate and why they operate that way, which is essential. Without that, important details may be overlooked.

For example, one of EY’s telecom clients wanted to improve its customer retention model. So the analytics team built a new model that could accurately identify customers who would leave within two weeks. That’s impressive, but marketing and sales needed four to six weeks to intervene.

“Nobody asked the marketing and sales team how far in advance they needed to know [a customer was leaving], said EY’s Mazzei. “We see that all the time.”

One of the cheapest ways to understand what works and what doesn’t is to hear what other companies have done right and wrong.