Submitted article by Romi Mahajan

Chief Commercial Officer, Quantarium

The world of technology is known as much for its hype as it is for its legitimate innovations.  Atone level, this is understandable. Dreamers can only accomplish big things when they dream big and followup on those dreams with supporting rhetoric. At a different level, however, it serves to dupe consumers, customers,and investors into alchemist fantasies that often defy the laws of physics.  What we need, naturally, is a balance.

With AI, and claims about it, we as a community have come to the point where we have to decide if we are okay with “fact-checking.”  Are all claims about AI fair and accurate?  Do all companies that claim to be “doing AI” pass muster in that regard? After all, haven’t many organizations referred to any and all of their “data”initiatives as AI? Have we diluted the term so as to make it meaningless?

Discerning investors have started to kick the AI tires.  Highly skilled Scientists and Engineers increasingly refuse to be beguiled by marketing claims, choosing instead to dig deeply into the code and methodologies surrounding its production before allowing themselves to be recruited by the countless organizations that seek the limited supply of talent. 

Perhaps more relevant, still, is that people in all roles ask one fundamental question:  “Is there any relevant and practical application to the AI work you are doing?”

On all these matters, Quantarium can hold its head high.  From “Real AI” to “Real Applications of AI,”Quantarium is altering and enhancing our notions of what is possible in residential real estate, the world’s single largest asset class.

Take for instance an important but mundane question, likely asked by millions of people every day.  “What is my house worth?”  Several pundits will offer several answers to this question, no doubt.  But, will the answers be accurate and in this case, what does accuracy even mean?  What data goes into the answer?  Do we have all the data we need?  Is it all available? Do the data we collect account for every single aspect of the house that could or should go into the valuation? 

Now take an extrapolation of this question with magnified scale.  Imagine you are the CEO of a large bank that “owns” half-a-million residential mortgages. What is your portfolio worth?  How much risk are you holding in the portfolio?  Are you too exposed in a particular geography or demographic?  Do you have sufficient data and the ability to process and make sense out of it?  Can you do this all with the speed that is called for by regulation and market conditions?

These questions are easy to pose but hard to answer.  Further, while these questions may not seem “sexy,” they underscore the reality of the single biggest source of economic value and for most families the single largest source of equity.  Using AI to drive accuracy, speed, and scale in this market is complex, genuine and incredibly important.Indeed, real AI applied to real industries with real outcomes is the name of the game. Therein lies the balance we seek, the convergence of both the hype and of the reality.

Marketing Darwinism caught up with Samir Saluja, Co-Founder of DeriveOne and former Microsoft Learning Executive.

Marketing Darwinism: Samir, you left Microsoft to start DeriveOne with your partner Jason Talwar. Tell us about that transition.

Samir: Microsoft played a huge role in my development and in my understanding of the needs of customers. It also helped me understand the importance of data and using data for scale. What Jason and I realized is that despite the importance of data in the organizational and business world, most were not conceiving of it correctly or in the most efficient manner. We saw that “data for data’s sake” started becoming the essential mantra for companies and we wanted to help them avoid that trap. Thus we started DeriveOne. We consider Microsoft a key partner so still are in the ecosystem.

Marketing Darwinism: “Data for data’s sake?” What do you mean?

Samir: Data has come to be seen as an important commodity and as such companies are scrambling to ingest it and even hoard it. Ingestion is great but what about digestion? How do you use this data? What about data-overload? What about useless data? What about “fake” data? The point here is that data is not an unalloyed good if not trained on decisions. In fact, we believe you need to look at the decisions first and figure out the data needed based on the decisions not just because “data is good.”

Marketing Darwinism: You have a very varied background. Tell us more about your journey.

Samir: Thanks Paul. I have spent time in the Peace Corp, as an entrepreneur, as a business-owner, as a Microsoft employee and as a volunteer. All of these experiences helped me converge on being who I am today, personally and professionally. The Peace Corp helped me learn to listen, empathize and act. Being an entrepreneur taught me about risk and reward. Microsoft helped me understand the importance scaling through partnerships. DeriveOne has helped me realize the value of focus.

Marketing Darwinism: Why should an organization hire DeriveOne?

Samir: We believe that partnering with our customers in the context of what they need and what their customers need is crucial. We help organizations, medium to large and even some startups, use data to hone and focus their customer segmentation strategy, the cognitive diversity of their teams, and to inculcate the culture of decision-driven data. We believe that methodology matters, eliciting what the true decisions that need to be made is key, and delivering accountability is necessary. We are humbled daily by interest in our company.

Marketing Darwinism: Clement, tell us about Quantarium.

Clement: The company is inspired by insights from Quantum Physics and the potential inherent in applying them to Machine Learning approaches within an A.I. framework. We have organically gathered the analytical methods of fields as far reaching as Quantitative Genetics to build a leading Artificial Intelligence company that enables competitive advantage in vertical industries via advanced predictive and propensity models along with smart decision-engines. To be sure, there remains a lot for Quantarium to accomplish and indeed we have the ambition to match, though we are quite proud of our synthesis to date and the benefits our customers are enjoying each day.

Marketing Darwinism: Can you tell us which verticals you focus on mostly?

Clement: The beauty and peril of A.I. is that it can seemingly apply to everything and that can be intoxicating, thus both market and organizational focus to execute become paramount. Our first salvo is residential Real Estate, a $20 Trillion asset class that represents in a meaningful personal perspective, the most important of all sectors because it constitutes the largest purchase a family ever makes. Quantarium looks at Real Estate from a perspective not only of Data (and there is a lot of Data!) but also of modeling scenarios of what “could” be. For both financial institutions that “own” and service mortgages and for the individuals who own homes, understanding the “deep” economics is very important. From valuations to other analytical models, Quantarium intends to revolutionize the approach and economics.

Marketing Darwinism: Clement, you have a background in large companies like Microsoft, how is it being a CEO of a start-up.

Clement: Thanks for the question. There is nothing headier than building something with world-class people who humble me every day with their vigor and intelligence. At Microsoft, I learned how to manage A+ teams and to think about products and customers at scale. Applying that to the need for speed in the startup world is my biggest challenge and joy.

Marketing Darwinism: Clement, I must ask you this. A.I. has become a “buzz phrase” …how do you distinguish yourself.

Clement: You are certainly correct about that. The technology business is very much about fashion and phraseology. Unfortunately, it is also often about false claims as well. Quantarium’s founders team, with Ph.D’s and accomplished experts in the field, undertook the approach that A.I. is best when it enhances the ability for people to both arrive at a valuable truth in a quicker and more judicious fashion, and then start to predict future truths, or certainties, given the current business exigencies. Quantarium established itself as an A.I. company from the get-go, it’s in our DNA; as a matter of fact, the first platform Quantarium built, QVM/Quantarium Valuation Model relied on M.L./A.I. technologies such as evolutionary programming when “Artificial Intelligence” was not such a buzz phrase yet. Our team consists not only of award winning Mathematicians and Engineers but also of some of the best “technology translators” in the industry. Algorithms and A.I. are indeed assets, however when you add them to the human agency and agility, you get real applications of real A.I.

Marketing Darwinism: What’s in store for Quantarium in the next phase of your growth?

Clement: Good question. While structured as a tridimensional growth approach, with a clear focus on increasing market share, innovation / differentiated I.P. and product expansion, in many ways we’ve been silent for too long. We enjoy genuine, solid relationships with our customers and partners but haven’t “splashed” in the market yet. That has been by design but the time has now come to shout from the rooftops. We’re showing up at conferences like the O’Reilly AI Summit and Strata. While we remain humble and true to ourselves, we are bold at the same time so watch out for us.

About Clement
Clement is Co-Founder and CEO of Quantarium, an Artificial Intelligence company enabling vertical industries via advanced Predictive and Analytic models, and smart decision-ing engines. As the name of the company suggests, inspired by Quantum Physics and fueled by the power and potential of Machine Learning such as synthesizing and leveraging approaches from Quantitative Genetics, towards resolving significant predictive challenges, Clement is an accomplished international professional for leveraging Data Science and A.I. as well as a proven business leader.

With degrees in Computer Science, Clement spent 14 years building large-scale and Enterprise-level software products and services in areas traversing Content Management and Enterprise Search. Responsible for strategy and product development, Clement directed enterprise teams for Microsoft such as SharePoint, and MS Enterprise Search, while building a proven track-record for recruiting and developing teams with exceptional culture. Prior to Microsoft, Clement started and ran several software companies.

Originally hailing from Romania, Clement lives in the Seattle-area with his wife and children. He is actively involved in a variety of philanthropies and applies philosophy to technology as he builds lasting companies.

Here’s a great video of me and Aseem Badshah the CEO of Socedo, a social media lead generation tool, talking about 7 ways Blockchain can transform marketing! We hope you enjoy it …

Fashions change. 

This cliché doesn’t apply just to hemlines and jeans, but to business as well.  Anyone who claims that business is all about logic and data needs to get a reality-check; Marketers are perhaps the worst offenders here, much to their detriment.  Of late, Marketers have suffered from a deep alienation from the real essences of their profession and we hope that 2018 will usher in a return to sanity.

This alienation – or departure from sanity in Marketing- stems from the over-indexing on Data and Measurement.  While this sounds strange, even counterintuitive and heretical, it stands the test of logic and does not require a deep knowledge of Marketing to understand.  Data and Measurement are no doubt valuable but they can also be the refuge of scoundrels.

The key in the above paragraph is the term “over-indexing.”  In other areas of life, the tendency to over-index is called zealotry.  In Marketing, the zealotry of measurement has created an untenable situation in which Marketing is asked to be as resilient as Physics or Mathematics; So too are Marketers, who feel forced to conform to the fashions of the day.  For the past decade or so, the fashion has been “Performance Marketing” or, in a wild conflation of strategy and channel, “Digital Marketing.” 

The genesis story here is a good one.  Marketing for a long time appeared to be a cocktail of guesses mixed with a dose of manipulation.  Organizations started to get frustrated with the lack of predictability and rising costs associated with Marketing and the ecosystem of agencies and media companies that had to be invoked when even considering bringing a product, service, or brand to market.  Theories of consumer reception abounded, but the overall logic of Marketing appeared to be something akin to “do it and it will work.”  Since no company could afford to shut off all Marketing, they continued in an inertial frame for decades.

Then came the Internet.  Almost overnight- or so it seemed- behavior patterns changed.  In addition, the almost infinite real estate and low cost of replication on the Internet, allowed for a completely different cost structure for Marketing. Completing the hat-trick was the fact that digitized Marketing can be “revved” quickly and tests of efficacy can be run in record time.  A heady mix indeed!

And for a while it seemed great.  Marketers could “go to market” quickly and bypass the usual middle-men.

Soon, however, the false “quants” took over and started writing how Marketing was both a “Science” and “Predictive.”  Tomes could be written about the false attribution that plagued the marketing scene with the eminent measurability of Digital Marketing.  We neglected Pater Semper Incertus Est. 

Marketers new to the profession became one-channel ponies. They only knew Digital Marketing. They also grew up under the totalitarianism of measurement.  They believed in the falsity of attribution and hewed only to the channels that provided an easy story for attribution.

Lo and behold, pundits declared the demise of “traditional” marketing.  Some said TV was dead. Others eulogized radio.  Still others print and outdoor.  Digital Marketing was ROI Marketing and ROI Marketing was King (forgive the pun!)

The zealotry created real problems for real Marketers.  First, they were subjected to Wall Street-type time-frames. What would in a sane world take a year, had to be measured in weeks or months.  Second, the need to show ROI created a channel bias in which they were forced to market in only those channels which were eminently measurable.  Third, they lost the Art which defined Marketing and chose, instead, to genuflect at the altar of a false science.  CMOs lost their jobs in 18 months because they could not prove the ROI they agreed to.  Marketing lost its way.

Fast forward to now. 

Are Marketers ready to reclaim their profession?  Are they ready to bring back that Evergreen-yet-needs-to-be-green-again concept that defined their art?  Yes, you know what we mean- The Marketing Mix. 

We predict that 2018 will be the year in which Marketers re-embrace the notion of managing a portfolio of bets, of which some are measurable and others are not.  The rush to measurement restricts the channels Marketers pick to engage with, not unlike a Chef with an infinitude of ingredients but only one ladle and one pan with which to create a gourmet meal.  

The portfolio will no doubt contain elements of Digital Marketing but will also likely concentrate on what the current and future audience really needs and could, thus, index on physical marketing, TV, Radio, Outdoor, even Print.  Who knows.  Why discount ideas and channels a priori? 

Ironically, the zealotry around measurability and ROI lands Marketers in an ironic soup- they restrict themselves from generating real ROI by thinking of it as an input and not as an outcome.

All fashions have their arc.  It’s high time we reclaim Marketing from the ROI zealots and re-engage with the world as it is and as it could be.

Guest post by:
Romi Mahajan, Blueprint Consulting
Steven Salta, Agilysys

No matter how much technology has changed our day to day lives, both at home and at work, what remains essential to running a successful business is customers—how you treat them, how they feel about your product or service, and whether they share those good (or bad) feelings.

In decades past, interacting with customers and helping to manage their problems and expectations was something that was left mostly to humans, which meant any good or bad things could also be subject to staffing or competing deadlines. But technology has helped with that in a unique way: by automating much of the customer journey through artificial intelligence, or AI.

Customers may not realize it, but a part of the process with many companies is already managed by AI. It’s helping with predictive needs, to name just one area. And its use will only continue to grow. This graphic explains what it’s doing and how business will continue to use AI.

Click To Enlarge

Via Salesforce

Data is the watchword in organizations large and small. In fact, how an organization frames data is the single most important determination of future success or failure. As some put it, Data is the new “oil,” the commodity of most value in the modern age.

Many business leaders understand this intuitively. As business-users in the organization are forced to make larger number of critical decisions with larger “payloads” on a more frequent basis, the idea that these decisions must be data-driven is at the fore. Gut instinct is fine but gut instinct inflected with timely, contextual, and comprehensive knowledge of relevant data is a winning strategy.

While the idea of being “data-driven” is fundamental and powerful, most organizations fall short. Intentions are necessary but not sufficient. For most organizations, the technology and operational infrastructure that defines their “data” is predicated on notions that made sense in an earlier era in which there were simply less sources of data and less change to existing sources. The “size” of the data question makes for a complexity that is not pre-defined and therefore the solution to the data problem has to be flexible and adaptive. Data infrastructure maturity is necessary in today’s business environment and has 4 basic qualities: Governance, Security, Agility, and Automation.

Without these 4 qualifiers, 2 core facets of the solution are absent- democratizing access to data and liberating IT from the backlog and fatigue associated with constantly-changing business needs. Business-users work in the “NOW” timeframe while IT has its own rhythms. In order to truly be data-driven in a way that scales, organizations must empower business-users while simultaneously freeing IT to innovate. While there are cultural hurdles to this state, the biggest blockers are infrastructural.

Until very recently, good enough was, alas, good enough. The internecine conflict between Business and IT was considered just a fact of life, a “cost of doing business.” With automation technology, business users’ data needs can be managed on the fly and without the need for reactive hand-coding, conferring agility to the business teams and handing time back to the IT teams to innovate and more resources from lower value tasks to higher value tasks. This structural win-win is available today and harmonizes the needs of Business and IT.

If data is the new oil then an infrastructure to capitalize on it is necessary- an infrastructure that is mature and “Hub”-like. While all organizations are different, they are similar in their data needs and the data platforms that win will accommodate diversity and change inherently.

Guest post by:
Romi Mahajan
Chief Commercial Officer, TimeXtender

Most marketers I talk with today say they are drowning in data. But in reality data they really want sits in disparate systems throughout the organization. Or if their company has invested big money in a traditional data warehouse, the results have fallen short of expectations. This is because traditional data warehouses were never designed to handle the volume, variety and velocity of today’s data-centric applications. So, while most marketers and most companies “talk” about big data they just go on with “business as usual” taking little or no action.

This is not just my opinion. Recently, my UK colleague, Richard Petley, director of PwC Risk and Assurance, conducted a survey of 1,800 senior business leaders in North America and Europe. And only a small percentage reported effective data management practices. 43 percent of companies surveyed “obtain little tangible benefit from their information,” while 23 percent “derive no benefit whatsoever,” according to the study. That means three quarters of organizations surveyed lack the skills and technology to use their data to gain an edge on competitors.

The problem is not access to data. It’s the management of it. What companies really need is the ability to manage large amounts of data in a safe, agile and adaptable fashion. And that means they need a more modern data warehouse.

The overall purpose of a data warehouse is to integrate corporate data from various internal and external sources. Implementing a data warehouse is traditionally a long, costly and risky process. When the solution is ready, it’s often slow, outdated and hard to update as business changes. A modern data warehouse is different – employing new technologies, products, and approaches. Approaches that allow for both speed and agility.

With a modern data warehouse, you only have to query one source to get the data you need. When you add automation to the mix, you can load, clean, integrate, and format the data in record time.

POSSIBLE is a creative agency that brings results-driven digital solutions to some of the world’s most dynamic brands. Every two weeks, analysts faced the herculean task of reporting campaign results based on 10 different data sources, applying 20 different measures on 70+ products delivered by 100+ media partners. They would spend on average 35 hours just processing data before they could begin analysis.

When I spoke to the POSSIBLE team they reported this free demo introduced them to a data warehouse automation tool from TimeXtender. After a surprisingly fast implementation period, the “data munging” performed by POSSIBLE’s analysts has now been reduced 68%. “This has really turned out to be a big win for us. The fact that we can now get actionable data to analysts so much faster allows us to spend more time providing valuable insights to clients,” says Harmony Crawford, Associate Director of Marketing Sciences.

As my colleague Richard Petley likes to say, “Data is the lifeblood of the digital economy.” It can provide insight, inform decisions and deepen relationships, and drive competitive advantage, but only if it’s managed in an agile and adaptable way.

So the next time you find yourself complaining about the problem with big data, stop talking and start researching the modern data warehouse and data warehouse automation.

This week I moderated another Social Media Today webinar as part of their Best Thinker webinar series, this time on the topic of Social Listening: Harness Marketing Insights from Consumer Conversations. This webinar featured Kevin Hack (@kevinhack) head of Social Intelligence in Global Digital Marketing Advancement at The Hershey Company, Kendra Simpson (@Kfoley) Director of Communications at Kohler Company, Drew Neisser (@DrewNeisser) founder and CEO of Renegade and Will McInnes (@willmcinnes) Integrated Marketing Analyst at Union+Webster. This webinar was sponsored by Brandwatch. We discussed tips and tricks for finding and utilizing Social Listening in your organization!

Here are three key takeaways from the webinar:

1. Customer Centric? – How can you declare that you are customer centric if you don’t do Social Listening!
2. ROI of Social Listening – what’s the ROI of not listening to your customers – most likely it more than the cost of Social Listening
3. Social to predict what’s next – more and more social is being used to find opportunities in product development or innovation

To get a copy of the slides or to listen to the replay, please click here. You can also scan the highlights of this webinar on Twitter by reading the Storify below.

Our next webinar is titled Storytelling Gone Wild: The Key to Creating Viral Content be sure to sign up for it or view the schedule of other upcoming webinars here.

Here is a recent interview with my new friend, Gregg Thaler, is a self-professed data quality junkie and the Chief Revenue Officer of RingLead. We discussed some best practices in data. I hope you enjoy!

PD:     How often is poor data the downfall of a marketing campaign?

GT:     Well, if I said every time, that would be a bit of hyperbole, but only a bit. Typically what I find with lists – especially trade show lists – is that they are the Typhoid Mary of duplicate creation in your database. Very often if you think about trade shows, who attends your booth? Many times it’s your customers or existing prospects. And booth staff will scan them indiscriminately. If you import that list without taking preventative measures, then it creates a duplicates horror show.

So, there’s another category of bad lists and those are the lists that are purchased from vendors. And there’s a very fundamental reason why lists purchased from data vendors have data quality challenges. The challenge with contact data is that it ages like fish and not like fine wine. It gets worse as it gets older, not better. Data is foundational. CRM and marketing automation are merely vessels, it is the data they contain that is the true treasure. It’s the single most critical element when it comes to determining revenue success of failure. Everything else you do further down the order of operations, its’ outcome depends completely on the input. What do you get from an automated process when you put garbage in? Garbage out.

PD:     What would you say is one of the biggest mistakes you see B2B marketers making when it comes to data?

GT:     Well, the mistake that I’m going to describe isn’t really limited to marketers. If you want to really truly recognize the strategic benefits of having optimized data, you have to have a mindset to prevent errors at the source. There’s a simple reason for that. Generally speaking, whatever the cost is in the enterprise to get the data right at the point of creation it’s going to cost you ten times that later to fix it. But then, of course, if you do nothing, the damage caused by inferior data could be much worse, 100x worse is possible.

So often marketers come to us, and their hair is on fire they’ve got to dedupe their database right now. Yes, they’re right. You do have to remediate the situation since almost every single contact database is riddled with duplicate and non-standard data. Often, however, I hear brilliant marketers say something incredibly dumb, they’ll say “…we’ll worry about the prevention later.” Are you kidding me?

PD:     Who ultimately owns the data? It is really sales who owns it, or is it really marketing?

GT:     That’s a terrific question. Where should data governance reside? Who is the data steward? Traditionally it has been IT. Increasingly we are seeing the data steward role reside in marketing and sales which is where I rightly believe it should belong. Now, what we see in the market, most often the actual people who perform these data janitor-like tasks, are usually in marketing operations, followed very closely by their colleagues in sales ops. Really the best practice is, I believe that organizations should have a data quality center of excellence typically reporting into sales and marketing operations. Even if the COE is one person.

PD:     Let’s talk about the best way to boost productivity in marketing.

GT:     This has, of late, become a favorite topic of mine. I think it’s something that’s very under-focused on as it relates to peak performance not just for marketers, but throughout the organization. What I’m talking about is first the batch normalization of an org’s data and then the automated enforcement of data standards with technology.

I spoke to two very well-known technology companies. I asked them “how many technologies do you have in your marketing stack?” One responded 35 and another said 22. When you think about the performance efficiencies of all of those applications operating on, what for most people, is a completely non-standard set of data. That contributes to very poor application performance which would then translate into poor performance across the entire marketing stack. It’s a silent killer of revenue and application performance. People probably aren’t aware of how well those applications could perform on a standardized data set. They’ve never seen one. In the benchmarking we’ve done, we’ve seen eye-popping performance gains of up to 600% in duplicate detection in a standardized vs. non-standardized data set. Compound that kind of application performance improvement across the 35 apps that modify that data set and the gains in application performance can be off the charts.

I am completely convinced that data standardization is the single most impactful action, a unique foundation-level enabler that marketers and sales professionals can take to optimize their data for revenue performance and their applications for speed, accuracy and efficient operation.