Banking executives around the world are wrestling with the impact of fintech on their businesses. While they understand that consumers want the same simplicity and ease of use in banking channels that they have in digital retail interactions, bank leaders must also weigh the costs and benefits of these innovative technologies. Avoiding disruption and keeping pace with rapid market change is weighed against worries about the stability and security of developing fintech solutions.
For me, the customer experience versus technology issue can be reduced to one idea: my thumbprint. Like many consumers, I do my banking online and on my mobile devices. Channeling Brett King, who famously wrote that banking is no longer somewhere you go but something you do, I use mobile devices for tasks like depositing checks and transferring funds. And because I’m a loyal customer, all of my accounts are with the same bank. To me, digital banking is the only way to go.
Until I upgraded my phone to the iPhone 7. The device has new touch ID fingerprint feature, which lets users unlock their phones, authorize purchases, and access apps using just a thumbprint. Unfortunately, the biometric technology was deactivated in my bank’s mobile app during the move to the new iPhone 7. Until I realized this and activated the touch feature, I had to revert to entering a password, which is an irritating, error-prone exercise that makes me want to avoid opening the bank’s app altogether. If you’re keeping score, it’s fintech 1, Carl’s digital experience, 0.
Modeling interactions on retail experiences
I’m not alone. When it comes to the customer experience, consumers don’t discriminate between retailers and banks. People who can “swipe” on Amazon or continue an interrupted retail transaction across multiple devices understandably expect their bank to offer them relevant products and services in a technically optimized, personalized interaction. Unfortunately, too many banks operate as if they are still in the 20th century, making digital a secondary channel to branches, to the dismay of these highly empowered customers.
Banking leaders know that something needs to change. In a recent survey of 500 executives about their 2017 priorities, 70 percent said that improving the digital customer experience was a top strategic imperative. Redesigning and enhancing the digital experience is key to this shift, says Jim Marous, co-publisher of The Financial Brand and publisher of Digital Banking Report.
Consumers want processes that are seamless, intuitive, and easy, whether they are handling payments, opening new accounts, or engaging in other typical financial activities. And it’s not just millennials who have these expectations. Plenty of GenXers and baby boomers – you know, your most profitable customers – are also digital customers who want a better experience.
Recognizing this reality, one traditional bank in Canada redoubled its efforts to transform itself into a digital financial services company. By combining some cutting-edge technology with its enterprise financial systems, the bank created online channels that allow consumers to choose a product, such as an account or a credit card, and place it in a shopping cart (sound familiar?) and easily “check out.” By making it easy to do business with, the bank helped customers to feel as though their experience is a priority. A growing customer base and an increase in accounts, deposits, and loans prove the bank is on the right track.
Blending old and new
Interestingly, the Canadian bank made this shift without retiring its business systems and handing everything over to a crowd of fintech vendors. This bank and several other financial services firms wanted to enhance rather than replace the solutions that were already working. So they turned to proven, established enterprise software vendors that they already know and partner with.
These vendors offer technology solutions that help banks deliver an optimized customer experience, one that extends across all touch points and is consistent across all channels, yet is also optimized for each channel. These solutions offer the stability and security banks need to protect their data assets, mitigate risk, and meet regulatory requirements – while remaining open to innovative fintech solutions. By offering data analytics that produce actionable insights, these products can help banks can deliver a personalized, contextually relevant experience. Some even offer predictive analytics that highlight likely customer behaviors and prescriptive analytics that support customer recommendations, demonstrating a clear understanding of the consumer’s needs and priorities – not just for today, but for future needs.
An enhanced customer experience is the primary target for today’s banks. Yet financial institutions must find ways to use technology to support this goal without increasing cost or risk. There are many ways to achieve these goals, each as individual as a thumbprint. Do you think your bank is ready to get started?
The fast-paced world of digital marketing is changing too quickly for most companies to adapt. But staying up to date with the latest industry trends is imperative for anyone involved with expanding a business.
Here are five trends that have shaped the industry this year and that will become more important as we move forward:
Email marketing will need to become smarter
Whether you like it or not, email is the most ubiquitous tool online. Everyone has it, and utilizing it properly can push your marketing ahead of your rivals. Because business use of email is still very widespread, you need to get smarter about email marketing in order to fully realize your business’s marketing strategy. Luckily, there are a number of tools that can help you market more effectively, such as Mailchimp.
Content marketing will become integrated and more valuable
Content is king, and it seems to be getting more important every day. Google and other search engines are focusing more on the content you create as the potential of the online world as marketing tool becomes apparent. Now there seems to be a push for current, relevant content that you can use for your services and promote your business.
Staying fresh with the content you provide is almost as important as ensuring high-quality content. Customers will pay more attention if your content is relevant and timely.
Mobile assets and paid social media are more important than ever
It’s no secret that mobile is key to your marketing efforts. More mobile devices are sold and more people are reading content on mobile screens than ever before, so it is crucial to your overall strategy to have mobile marketing expertise on your team. London-based Abacus Marketing agrees that mobile marketing could overtake desktop website marketing in just a few years.
Big Data for personalization plays a key role
Marketers are increasingly using Big Data to get their brand message out to the public in a more personalized format. One obvious example is Google Trend analysis, a highly useful tool that marketing experts use to obtain the latest on what is trending around the world. You can — and should — use it in your business marketing efforts. Big Data will also let you offer specific content to buyers who are more likely to look for certain items, for example, and offer personalized deals to specific groups of within your customer base. Other tools, which until recently were the stuff of science fiction, are also available that let you do things like use predictive analysis to score leads.
Visual media matters
A picture really is worth a thousand words, as the saying goes, and nobody can deny the effectiveness of a well-designed infographic. In fact, some studies suggest that Millennials are particularly attracted to content with great visuals. Animated gifs and colorful bar graphs have even found their way into heavy-duty financial reports, so why not give them a try in your business marketing efforts?
A few more tips:
Always keep your content relevant and current to attract the attention of your target audience.
Always keep all your social media and public accounts fresh. Don’t use old content or outdated pictures in any public forum.
Your reviews are a proxy for your online reputation, so pay careful attention to them.
Much online content is being consumed on mobile now, so focus specifically on the design and usability of your mobile apps.
Online marketing is essentially geared towards getting more traffic onto your site. The more people visit, the better your chances of increasing sales.
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About Sunny Popali
Sunny Popali is SEO Director at www.tempocreative.com. Tempo Creative is a Phoenix inbound marketing company that has served over 700 clients since 2001. Tempos team specializes in digital and internet marketing services including web design, SEO, social media and strategy.
As 2015 winds down, it’s time to look forward to 2016 and explore the social media and content marketing trends that will impact marketing strategies over the next 15 months or so.
Some of the upcoming trends simply indicate an intensification of current trends, however others indicate that there are new things that will have a big impact in 2016.
Take a look at a few trends that should definitely factor in your planning for 2016.
1. SEO will focus more on social media platforms and less on search engines
Clearly Google is going nowhere. In fact, in 2016 Google’s word will still essentially be law when it comes to search engine optimization.
However, in 2016 there will be some changes in SEO. Many of these changes will be due to the fact that users are increasingly searching for products and services directly from websites such as Facebook, Pinterest, and YouTube.
There are two reasons for this shift in customer habits:
Customers are relying more and more on customer comments, feedback, and reviews before making purchasing decisions. This means that they are most likely to search directly on platforms where they can find that information.
Customers who are seeking information about products and services feel that video- and image-based content is more trustworthy.
2. The need to optimize for mobile and touchscreens will intensify
Consumers are using their mobile devices and tablets for the following tasks at a sharply increasing rate:
Sending and receiving emails and messages
Researching products and services
Reading or writing reviews and comments
Obtaining driving directions and using navigation apps
Visiting news and entertainment websites
Using social media
Most marketers would be hard-pressed to look at this list and see any case for continuing to avoid mobile and touchscreen optimization. Yet, for some reason many companies still see mobile optimization as something that is nice to do, but not urgent.
This lack of a sense of urgency seemingly ignores the fact that more than 80% of the highest growing group of consumers indicate that it is highly important that retailers provide mobile apps that work well. According to the same study, nearly 90% of Millennials believe that there are a large number of websites that have not done a very good job of optimizing for mobile.
3. Content marketing will move to edgier social media platforms
Platforms such as Instagram and Snapchat weren’t considered to be valid targets for mainstream content marketing efforts until now.
This is because they were considered to be too unproven and too “on the fringe” to warrant the time and marketing budget investments, when platforms such as Facebook and YouTube were so popular and had proven track records when it came to content marketing opportunity and success.
However, now that Instagram is enjoying such tremendous growth, and is opening up advertising opportunities to businesses beyond its brand partners, it (along with other platforms) will be seen as more and more viable in 2016.
4. Facebook will remain a strong player, but the demographic of the average user will age
In 2016, Facebook will likely remain the flagship social media website when it comes to sharing and promoting content, engaging with customers, and increasing Internet recognition.
However, it will become less and less possible to ignore the fact that younger consumers are moving away from the platform as their primary source of online social interaction and content consumption. Some companies may be able to maintain status quo for 2016 without feeling any negative impacts.
However, others may need to rethink their content marketing strategies for 2016 to take these shifts into account. Depending on their branding and the products or services that they offer, some companies may be able to profit from these changes by customizing the content that they promote on Facebook for an older demographic.
5. Content production must reflect quality and variety
More and more businesses are focusing marketing efforts on content. This means that, as customers have more content to choose from, competition is going to increase significantly.
In 2016, content will remain King, with an increasing focus on variety and and quality. When companies are creating their content marketing strategies for 2016, they may wish to consider the following when they make their final decisions:
Both B2B and B2C buyers value video based content over text based content.
While some curated content is a good thing, consumers believe that custom content is an indication that a company wishes to create a relationship with them.
The great majority of these same consumers report that customized content is useful for them.
B2B customers prefer learning about products and services through content as opposed to paid advertising.
Consumers believe that videos are more trustworthy forms of content than text.
Here is a great infographic depicting the importance of video in content marketing efforts:
A final, very important thing to note when considering content trends for 2016 is the decreasing value of the keyword as a way of optimizing content. In fact, in an effort to crack down on keyword stuffing, Google’s optimization rules have been updated to to kick offending sites out of prime SERP positions.
6. Oculus Rift will create significant changes in customer engagement
Oculus Rift is not likely to offer much to marketers in 2016. After all, it isn’t expected to ship to consumers until the first quarter. However, what Oculus Rift will do is influence the decisions that marketers make when it comes to creating customer interaction.
For example, companies that have not yet embraced storytelling may want to make 2016 the year that they do just that, because later in 2016 Oculus Rift may be the platform that their competitors will be using to tell stories while giving consumers a 360-degree vantage point.
We humans make sense of the world by looking for patterns, filtering them through what we think we already know, and making decisions accordingly. When we talk about handing decisions off to artificial intelligence (AI), we expect it to do the same, only better.
Machine learning does, in fact, have the potential to be a tremendous force for good. Humans are hindered by both their unconscious assumptions and their simple inability to process huge amounts of information. AI, on the other hand, can be taught to filter irrelevancies out of the decision-making process, pluck the most suitable candidates from a haystack of résumés, and guide us based on what it calculates is objectively best rather than simply what we’ve done in the past.
In other words, AI has the potential to help us avoid bias in hiring, operations, customer service, and the broader business and social communities—and doing so makes good business sense. For one thing, even the most unintentional discrimination can cost a company significantly, in both money and brand equity. The mere fact of having to defend against an accusation of bias can linger long after the issue itself is settled.
Beyond managing risk related to legal and regulatory issues, though, there’s a broader argument for tackling bias: in a relentlessly competitive and global economy, no organization can afford to shut itself off from broader input, more varied experiences, a wider range of talent, and larger potential markets.
That said, the algorithms that drive AI don’t reveal pure, objective truth just because they’re mathematical. Humans must tell AI what they consider suitable, teach it which information is relevant, and indicate that the outcomes they consider best—ethically, legally, and, of course, financially—are those that are free from bias, conscious or otherwise. That’s the only way AI can help us create systems that are fair, more productive, and ultimately better for both business and the broader society.
Bias: Bad for Business
When people talk about AI and machine learning, they usually mean algorithms that learn over time as they process large data sets. Organizations that have gathered vast amounts of data can use these algorithms to apply sophisticated mathematical modeling techniques to see if the results can predict future outcomes, such as fluctuations in the price of materials or traffic flows around a port facility. Computers are ideally suited to processing these massive data volumes to reveal patterns and interactions that might help organizations get ahead of their competitors. As we gather more types and sources of data with which to train increasingly complex algorithms, interest in AI will become even more intense.
Using AI for automated decision making is becoming more common, at least for simple tasks, such as recommending additional products at the point of sale based on a customer’s current and past purchases. The hope is that AI will be able to take on the process of making increasingly sophisticated decisions, such as suggesting entirely new markets where a company could be profitable, or finding the most qualified candidates for jobs by helping HR look beyond the expected demographics.
As AI takes on these increasingly complex decisions, it can help reduce bias, conscious or otherwise. By exposing a bias, algorithms allow us to lessen the impact of that bias on our decisions and actions. They enable us to make decisions that reflect objective data instead of untested assumptions; they reveal imbalances; and they alert people to their cognitive blind spots so they can make more accurate, unbiased decisions.
Imagine, for example, a major company that realizes that its past hiring practices were biased against women and that would benefit from having more women in its management pipeline. AI can help the company analyze its past job postings for gender-biased language, which might have discouraged some applicants. Future postings could be more gender neutral, increasing the number of female applicants who get past the initial screenings.
AI can also support people in making less-biased decisions. For example, a company is considering two candidates for an influential management position: one man and one woman. The final hiring decision lies with a hiring manager who, when they learn that the female candidate has a small child at home, assumes that she would prefer a part-time schedule.
That assumption may be well intentioned, but it runs counter to the outcome the company is looking for. An AI could apply corrective pressure by reminding the hiring manager that all qualifications being equal, the female candidate is an objectively good choice who meets the company’s criteria. The hope is that the hiring manager will realize their unfounded assumption and remove it from their decision-making process.
At the same time, by tracking the pattern of hiring decisions this manager makes, the AI could alert them—and other people in HR—that the company still has some remaining hidden biases against female candidates to address.
Look for Where Bias Already Exists
In other words, if we want AI to counter the effects of a biased world, we have to begin by acknowledging that the world is biased. And that starts in a surprisingly low-tech spot: identifying any biases baked into your own organization’s current processes. From there, you can determine how to address those biases and improve outcomes.
There are many scenarios where humans can collaborate with AI to prevent or even reverse bias, says Jason Baldridge, a former associate professor of computational linguistics at the University of Texas at Austin and now co-founder of People Pattern, a startup for predictive demographics using social media analytics. In the highly regulated financial services industry, for example, Baldridge says banks are required to ensure that their algorithmic choices are not based on input variables that correlate with protected demographic variables (like race and gender). The banks also have to prove to regulators that their mathematical models don’t focus on patterns that disfavor specific demographic groups, he says. What’s more, they have to allow outside data scientists to assess their models for code or data that might have a discriminatory effect. As a result, banks are more evenhanded in their lending.
Code Is Only Human
The reason for these checks and balances is clear: the algorithms that drive AI are built by humans, and humans choose the data with which to shape and train the resulting models. Because humans are prone to bias, we have to be careful that we are neither simply confirming existing biases nor introducing new ones when we develop AI models and feed them data.
“From the perspective of a business leader who wants to do the right thing, it’s a design question,” says Cathy O’Neil, whose best-selling book Weapons of Math Destruction was long-listed for the 2016 National Book Award. “You wouldn’t let your company design a car and send it out in the world without knowing whether it’s safe. You have to design it with safety standards in mind,” she says. “By the same token, algorithms have to be designed with fairness and legality in mind, with standards that are understandable to everyone, from the business leader to the people being scored.” (To learn more from O’Neil about transparency in algorithms, read Thinkers in this issue.)
Don’t Do What You’ve Always Done
To eliminate bias, you must first make sure that the data you’re using to train the algorithm is itself free of bias, or, rather, that the algorithm can recognize bias in that data and bring the bias to a human’s attention.
SAP has been working on an initiative that tackles this issue directly by spotting and categorizing gendered terminology in old job postings. Nothing as overt as No women need apply, which everyone knows is discriminatory, but phrases like outspoken and aggressively pursuing opportunities, which are proven to attract male job applicants and repel female applicants, and words like caring and flexible, which do the opposite.
Once humans categorize this language and feed it into an algorithm, the AI can learn to flag words that imply bias and suggest gender-neutral alternatives. Unfortunately, this de-biasing process currently requires too much human intervention to scale easily, but as the amount of available de-biased data grows, this will become far less of a limitation in developing AI for HR.
Similarly, companies should look for specificity in how their algorithms search for new talent. According to O’Neil, there’s no one-size-fits-all definition of the best engineer; there’s only the best engineer for a particular role or project at a particular time. That’s the needle in the haystack that AI is well suited to find.
Look Beyond the Obvious
AI could be invaluable in radically reducing deliberate and unconscious discrimination in the workplace. However, the more data your company analyzes, the more likely it is that you will deal with stereotypes, O’Neil says. If you’re looking for math professors, for example, and you load your hiring algorithm with all the data you can find about math professors, your algorithm may give a lower score to a black female candidate living in Harlem simply because there are fewer black female mathematicians in your data set. But if that candidate has a PhD in math from Cornell, and if you’ve trained your AI to prioritize that criterion, the algorithm will bump her up the list of candidates rather than summarily ruling out a potentially high-value hire on the spurious basis of race and gender.
To further improve the odds that AI will be useful, companies have to go beyond spotting relationships between data and the outcomes they care about. It doesn’t take sophisticated predictive modeling to determine, for example, that women are disproportionately likely to jump off the corporate ladder at the halfway point because they’re struggling with work/life balance.
Many companies find it all too easy to conclude that women simply aren’t qualified for middle management. However, a company committed to smart talent management will instead ask what it is about these positions that makes them incompatible with women’s lives. It will then explore what it can change so that it doesn’t lose talent and institutional knowledge that will cost the company far more to replace than to retain.
That company may even apply a second layer of machine learning that looks at its own suggestions and makes further recommendations: “It looks like you’re trying to do X, so consider doing Y,” where X might be promoting more women, making the workforce more ethnically diverse, or improving retention statistics, and Y is redefining job responsibilities with greater flexibility, hosting recruiting events in communities of color, or redesigning benefits packages based on what similar companies offer.
Context Matters—and Context Changes
Even though AI learns—and maybe because it learns—it can never be considered “set it and forget it” technology. To remain both accurate and relevant, it has to be continually trained to account for changes in the market, your company’s needs, and the data itself.
Sources for language analysis, for example, tend to be biased toward standard American English, so if you’re building models to analyze social media posts or conversational language input, Baldridge says, you have to make a deliberate effort to include and correct for slang and nonstandard dialects. Standard English applies the word sick to someone having health problems, but it’s also a popular slang term for something good or impressive, which could lead to an awkward experience if someone confuses the two meanings, to say the least. Correcting for that, or adding more rules to the algorithm, such as “The word sick appears in proximity to positive emoji,” takes human oversight.
Moving Forward with AI
Today, AI excels at making biased data obvious, but that isn’t the same as eliminating it. It’s up to human beings to pay attention to the existence of bias and enlist AI to help avoid it. That goes beyond simply implementing AI to insisting that it meet benchmarks for positive impact. The business benefits of taking this step are—or soon will be—obvious.
In IDC FutureScapes’ webcast “Worldwide Big Data, Business Analytics, and Cognitive Software 2017 Predictions,” research director David Schubmehl predicted that by 2020 perceived bias and lack of evidentiary transparency in cognitive/AI solutions will create an activist backlash movement, with up to 10% of users backing away from the technology. However, Schubmehl also speculated that consumer and enterprise users of machine learning will be far more likely to trust AI’s recommendations and decisions if they understand how those recommendations and decisions are made. That means knowing what goes into the algorithms, how they arrive at their conclusions, and whether they deliver desired outcomes that are also legally and ethically fair.
Clearly, organizations that can address this concern explicitly will have a competitive advantage, but simply stating their commitment to using AI for good may not be enough. They also may wish to support academic efforts to research AI and bias, such as the annual Fairness, Accountability, and Transparency in Machine Learning (FATML) workshop, which was held for the third time in November 2016.
O’Neil, who blogs about data science and founded the Lede Program for Data Journalism, an intensive certification program at Columbia University, is going one step further. She is attempting to create an entirely new industry dedicated to auditing and monitoring algorithms to ensure that they not only reveal bias but actively eliminate it. She proposes the formation of groups of data scientists that evaluate supply chains for signs of forced labor, connect children at risk of abuse with resources to support their families, or alert people through a smartphone app when their credit scores are used to evaluate eligibility for something other than a loan.
As we begin to entrust AI with more complex and consequential decisions, organizations may also want to be proactive about ensuring that their algorithms do good—so that their companies can use AI to do well. D!
Imagine the following situation: you are analyzing and gathering insights about product sales performance and wonder why a certain area in your country is doing better than others. You deep dive, slice, dice, and use different perspectives to analyze, but can’t find the answer to why sales are better for that region.
You conclude you need data that is not available in your corporate systems. Some geographical data that is available through Hadoop might answer your question. How can you get this information and quickly analyze it all?
Bring analytics to data
If we don’t want to go the traditional route of specifying, remodeling the data warehouse, and uploading and testing data, we’d need a whole new way of modern data warehousing. What we ultimately need is a kind of semantics that allows us to remodel our data warehouse in real time and on the fly – semantics that allows decision makers to leave the data where it is stored without populating it into the data warehouse. What we really need is a way to bring our analytics to data, instead of the other way around.
So our analytics wish list would be:
Access to the data source on the fly
Ability to remodel the data warehouse on the fly
No replication of data; the data stays where it is
Not losing time with data-load jobs
Analytical processing done in the moment with pushback to an in-memory computing platform
Drastic reduction of data objects to be stored and maintained
Elimination of aggregates
Traditional data warehousing is probably the biggest hurdle when it comes to agile business analytics. Though modern analytical tools perfectly add data sources on the fly and blend different data sources, these components are still analytical tools. When additional data must be available for multiple users or is huge in scale and complexity, analytical tools lack the computing power and scalability needed. It simply doesn’t make sense to blend them individually when multiple users require the same complex, additional data.
A data warehouse, in this case, is the answer. However, there is still one hurdle to overcome: A traditional data warehouse requires a substantial effort to adjust to new data needs. So we add to our wish list:
Adjust and adapt the modeling
Develop load and transformation script
Setup scheduling and linage
Test and maintain
In 2016, the future of data warehousing began. In-memory technology with smart, native, and real-time access moved information from analytics to the data warehouse, as well as the data warehouse to core in-memory systems. Combined with pushback technology, where analytical calculations are pushed back onto an in-memory computing platform, analytics is brought back to data. End-to-end in-memory processing has become the reality, enabling true agility. And end-to-end processing is ready for the Internet of Things at the petabyte scale.
Are we happy with this? Sure, we are! Does it come as a surprise? Of course, not! Digital transformation just enabled it!
Native, real-time access for analytics
What do next-generation data warehouses bring to analytics? Well, they allow for native access from top-end analytics components through the data warehouse and all the way to the core in-memory platform with our operational data. Even more, this native access is real-time. Every analytics-driven interaction from an end-user generates calculations. With the described architecture, these calculations are massively pushed back to the core platform where our data resides.
The same integrated architecture is also a game changer when it comes to agility and data optimization. When new, complex data is required, it can be added without data replication. Since there is no data replication, the data warehouse modeling can be done on the fly, leveraging the semantics. We no longer have to model, create, and populate new tables and aggregates when additional data is required in the data warehouse, because there are no new tables needed! We only create additional semantics, and this can be done on the fly.