On-Demand Payments Consumer Growth Opens Startup Opportunity

On-demand payments are a significant shift in the way that consumers prefer to shop and buy and the way that merchants push the payment process into the background.

New research identifies how card-on-file solutions have been given a new lease on life and are now the primary consumer method for making payments via mobile devices.

The report – From Card-on-File to On-Demand Payments: New Payment Model and Strategies For Payment Providers – by Mercator Advisory Group describes several examples of third-party solutions that enable consumers to better control and manage their payments.

Examples are also cited in the report indicating how card issuers have responded to on-demand payments in a tactical way but have so far failed to grasp the strategic implications of this model.

“Sometimes changes in technology and consumer adoption occur so slowly that both the nature of the change and the magnitude of the change are misjudged.” said Tim Sloane, VP, Payments Innovation, and author of report.

“On-demand payments may appear to be a simple extension to traditional card-on-file solutions, but nothing could be further from the truth. On-demand payments are a significant shift in the way that consumers prefer to shop and buy and the way that merchants push the payment process into the background. With the issuer’s brand almost invisible at the point of purchase, issuers need to identify new strategies to remain relevant to consumers,” notes Sloane.


Startups should consider the impact of on-demand payments and look for growth opportunities that exploit this trend in consumer shopping preferences.  Technology changes and consumer adoption play big roles in the shift toward this new way of paying for purchases.  The advisory groups report offers insight into other possible reasons for the shift.


Report Highlights

  • Evidence that the on-demand payments model is the primary driver of the explosive growth in digital payments volume, further displacing cash and check transactions
  • Issuers should move from the tactical efforts in place today to a strategic response required to remain relevant with cardholders in the long term
  • Industries once thought to be impervious to disruption are now being radically reshaped due to smartphones and on-demand services


The Achilles Heel of Big Data

You may have heard that Big Data has become the big thing that is propelling companies large and small.  Thanks to the constant innovation in technology, the collection and storage of vast amounts customer information has never been easier than it is right now and it’s only going to get quicker and simpler.

Also, the world of tools analysts can utilize to make sense of this info is growing at a rapid pace.  Vicky Byrom, Senior Analytical Consultant at Aquila Insight, shares her perspective on the issues facing analyst.

There are now mountains and mountains of new data being produced everyday from a variety of interesting, worthy and valuable sources. Alongside this there’s the avalanche of shiny digital toys with which to slice, dice and interpret all this information. However, this leads to the big concern about data quality.

Data Integrity

There have always been problems with data integrity. But now there’s just a whole lot more data to clean up before we can get to the insight. The 2015 Experian Data Quality benchmark suggests that, on average, US companies believe 32% of their data is inaccurate. That’s right, there’s just shy of a third of all data produced which we should be approaching with caution.

digital data

This poor data impacts how effective we can be as analysts. We have to spend more time ‘cleaning’, making approximations or just simply ruling out data sources. If customer data is captured inconsistently or incorrectly, analytical insights can be misleading.

In auto-adapting models, the risk is even more significant as the computer brain, unless instructed to do so, may not see how unreliable the captured information is and could direct customers down the wrong contact strategies. It comes back to the age old saying, “Garbage in = Garbage Out”.

Data Governance

So where is it going wrong? There are some data integrity issues that are too big to fix, inherent from legacy systems. We’ve all encountered the aging, patched up systems that have had work around after work around just added to make sure business as usual can continue.

big data

These systems require large scale overhauls, and major investment in data governance. Fixing them is going to be expensive, time consuming and will need buy in from the guys at the top or it’s just never going to happen.

But there are areas where we can improve and influence change. For starters we can advocate, enthuse and generally shout about the third party solutions that are out there. Ready made packages that can go over the top of those unwieldy existing legacy systems.

They cost a fraction of the price in both time and money compared to fixing what is there or building from scratch and will eliminate that errant one third of the data. We should also be looking to make improvements in the processes where data is captured manually, like in most call centers. As analysts we should have the courage of our convictions, step up and champion the promotion of better, cleaner and more reliable data whenever we can.

The Cost of Poor Data Quality

It often feels like we analysts are the only ones who see just how much potential value is lost from poor and unusable data. It is frustrating when we know how it could benefit businesses in terms of targeting the people we should be talking to, at what price and when we should be doing it.


We need to start talking to many more departments in our organizations to emphasize the real value of data capture. We need everybody that touches the data, directly or indirectly, to realize that it is a monetizable asset and that it’s worth is devalued if it’s not captured correctly.

We need to show stakeholders how they can help us understand and improve the information we capture, get them involved in shaping the analytics to turn data into insight that benefits the whole business and our customers.

The upside of all of this is that with better, more reliable data, analysts can spend more time building new, exciting and most importantly, meaningful models for stakeholders, which becomes a win for everyone.


source: Vicky Byrom, Senior Analytical Consultant, Aquila Insight.

Social Discovery Driven Content Marketing

The age of real-time analytics and social media, makes true customer engagement easier to measure but harder to achieve.  A solid content marketing strategy is even more important to engage customers. The average consumer has become an adept multi-tasker, capable of flitting between multiple devices and services at the same time.

Combine this with the growing use of ad blocking technology and despite today’s hyper-connected digital landscape it’s become much harder for brands to reach consumers through a combination of traditional marketing and digital advertising.

Fortunately, there’s light at the end of the tunnel in the form of data-driven content marketing – a new approach that’s capable of cutting through the oversaturated media environment to reach target audiences with a message that’s both interesting and relevant to them on an individual level.

The role of social discovery in social media

Social media is painted as a challenge to marketing today due to its attention-stealing nature, but it’s the very concept of social sharing that brands should be taking advantage of. Brands are investing heavily (both time and money) into digital channels like Twitter, Facebook, and Instagram, but they’re not taking advantage of the core value proposition these networks offer – social discovery.

social media

From a content marketing perspective, the use of social discovery is a powerful yet non-invasive way to target potential new customers while also maintaining interaction with existing customers that are already engaged.

With more content now available online than ever before it’s become crucial for brands to be present where the customer is, and where conversations are taking place, in order to take advantage of this trend and integrate social discovery into their overall marketing approach. By doing so, they can introduce new products in a far less aggressive and more engaging way.

content marketing

Social discovery and the future of content marketing

It’s clear that social discovery has an important role to play in the future of content marketing, but for brands to truly stand out they need to introduce a data-led approach to their efforts. Where a data-led approach differs to traditional content marketing efforts is in creating a new way of interacting with consumers on a personal level. Creating content based on what’s most likely to be shared over social media will generate the biggest buzz and greatest number of interactions.

By applying data-driven insights to content marketing, it’s not only possible to accurately predict which pieces of marketing collateral will have the greatest success, but also to A/B test variations of the same content to ensure the biggest possible reach is achieved.

big data

Trialing and testing content before releasing it will also help brands gain a reputation for generating content people seek out and share, rather than block. With this in mind, all brands should use data to inform their content marketing approach; to personalize their efforts for different demographics in ways that haven’t previously been possible, and to target consumers based on their individual preferences.

The value of great content

For audiences, great content is still great content. Video, text, or images that drive passionate conversations and fierce loyalty, even in today’s oversaturated digital world, are far more valuable than those that do not. Introducing data-led content to a marketing strategy opens up the opportunity for brands to get closer to their target audience than ever before. By focusing efforts on creating newsworthy, sharable, and actionable content that’s underpinned by data, it’s possible to dramatically increase consumer engagement.



source: Juliette Otterburn-Hall, Chief Content Officer, Beamly

Common A/B Testing Mistakes That Kill Traction

Solutions to Common A/B Testing Mistakes

When was the last time you evaluated your marketing strategy? If you have not done it for awhile, now is the time to revisit your online marketing campaign and assess the impact of your A/B testing.  You might have overlooked the common mistakes of over represented product information and an old approach of dripping market promotion.  Also, check out the video below on A/B testing mistakes.

KISSmetrics explains the common A/B testing mistakes that people make and provides a few useful solutions:

Mistake One: Over representation of your product

A common mistake in marketing strategy is over representation of the product by exploiting fake testimonials.  Do not exaggerate product information or product value. 

product representation

The policy of honesty remains true and applicable in launching and promoting products.  If you continue to bombard your clients with false or misleading information, you will be more likely to increase negative customer reviews and drive bad word of mouth.

Solution One: Enhance the value of the product to deliver value that meets your customer’s needs.  

Stay focused on creating a compelling hook or promise that truly and properly represents your products.  Direct your customers to landing pages that include specific information that addresses their wants, needs, and expectations.

So the question lies with how you would gauge which optimization tool best suits your customers’ needs?  The solution is to simply ask your customers.  Asking the right questions paves way to understanding their main concerns.

product management

Survey tools like Qualarro, can be helpful in uncovering customer needs.  Also, customer development calls, keyword inbound traffic, and heat maps are some tools that if effectively used can help you understand more about your customers.

By gathering survey data, it will become more clear how to create marketing campaigns that will most likely trigger positive responses from customers.  As a result of these combined efforts — knowing the needs of your customers and enhancing the overall value of the product — your clients will continue to love and buy your products.


Mistake Two: Making customers wait too long to experience the value of the product. 

Customers have no extra time to discover your products, so it is important that you help them know how to experience your product.  Once customers love your product, they will use it regularly and pay for it.  Also, you will likely find that customers are willing to give feedback and provide comments to make your product better.


Solution Two: Make them experience the value of the product.

Once you let clients know the true value of your product, and if the product is aligned with what has been promised, then you will be successful at earning their trust.  In return, customers will be happy to share testimonials and recommend your product through social media and word-of-mouth.

Knowing how to avoid common A/B testing mistakes and applying the above solutions will greatly improve your marketing strategy and business profit.  Have you been successful in eliminating some A/B testing mistakes in your business? Let other people read it from your post!

3 Companies Turning Product Challenges Into Product Gold

By Sharon R. Brown

Where’s the pain felt most when you are in the midst of launching a new product or service?  Surprisingly, the challenges typically boil down to one core issue — translating the product vision into verifiable outputs — the deliverables.

We took a look at few startups and SMB’s (small-to-medium-sized businesses) to uncover the biggest challenges that they are experiencing in launching their projects.


Our analysis revealed challenges such as data acquisition, community building, and sensitive data access surfaced as some of the key issues. There are few absolutes in product development.

But, the one constant that is worth emphasizing is that regardless of the industry, if you’re building a product or service from the ground up there will be bumps in the road or potential roadblocks.

Throughout my experience launching numerous products and services across a variety of industries in the corporate sector — and over a decade plus as an entrepreneur — I have always been most surprised that companies often struggle to translate their project vision into actionable plans that can be executed within a measured product development life cycle.

Here is a snapshot of a few companies that are navigating remarkable development challenges in their product releases while launching a unique set of deliverables, despite the roadblocks.


The Data Sensitivity Challenge

LifeSite Vault is a new, secure web-based solution for storing all of life’s vital information and documents. The company’s SaaS platform provides a solution to organize, categorize, manage and safely share information, all in one place.


LifeSite’s Product Challenge:

Gaining the trust of consumers posed the biggest challenge for LifeSite. The confidential nature of the information that the startup’s product was designed to lock up creates a data sensitivity issue. However, LifeSite Vault focused on addressing the safety and security of personal information in the product by engaging security experts during design, development and testing.

The company uses the latest military-grade security protocols, and are continuously monitoring and auditing systems to maintain the highest level of security. The product will be launching a premium paid version by March 2016.


The Infrastructure Challenge

DADA is an online community where visual people draw together. Draw something and someone from anywhere in the world can respond with another drawing. It’s a place to discover that you are an artist, and a place for artists to be discovered.


DADA’s Product Challenge:

The biggest challenge for DADA was figuring how to make it work. Since nothing like the online community has been attempted before, DADA could not translate requirements or adapt the infrastructure from the real or online world. A lot of iteration and experimentation was required to build a system by which people would want to draw.

Getting artist to share their drawings, respond to drawings and create community was a hurdle.  Ultimately, DADA created a community with extraordinary and meaningful engagement.  DADA launched September 2015 and will launch their mobile app March 2016.

big data

The Data Acquisition Challenge

Kehko is a startup that’s building a platform to help socially and environmentally conscious consumers discover companies that are giving back to causes they care about.  The company is in the pre-launch stage.

Kehko’s Product Challenge:

The biggest challenge for Kehko has been identifying social enterprises to include on its platform. With thousands of social enterprises around the world, identifying them has been a real struggle.  The site is expected to launch February 2016.

Over the next few weeks, I will share more of my perspective about some of the best practices for product and project implementation.  Please feel to contact me here and share your experiences and perspectives.


Sharon R. Brown is founder and CEO of eLuminate Network.  Sharon is a tech entrepreneur, product management expert, speaker and author, and regularly speaks on the subjects of product development and project implementation.  You can follow her via @SharonRbrown1 and Facebook.com/eLuminate

eLuminate is the most influential, the most read, and the most popular destination for product and project resources and news for startups and SMBs.


After Your MVP Getting To Scale And Choosing The Team Is The Next Hurdle

By Sharon Brown

Scaling a startup is typically one of the biggest challenges.  When faced with the challenges of scaling your business, consider focusing on what the most important metrics are to converting customers in your pipeline, then optimize, optimize, optimize!

After creating your minimum viable product (MVP) build your team to align with the measurable goals that you need to achieve month-to-month, week-to-week, day-to-day. Hire people with a track record of achievement in a similar capacity.

Everyone you hire should contribute to the core business objectives. It is equally important to choose people that have a passion for the big picture, and with whom you would enjoy working.

Also, it may be time to do a self assessment about your abilities as an entrepreneur, and your ability to communicate a vision, lead a team, and create a culture that will allow your startup to thrive. The culture fit is an important component to scalability that should not be discounted.


Sharon Brown

Sharon Brown

Founder & CEO, eLuminate

For speaking engagements contact Sharon here