That’s how people shop.

Marketers know this as “the sales funnel”. The concept has different names and maybe an additional element or two, but it works pretty much the same way since the concept was first described—in the late 19^th century.

The advent of AI and the avalanche of data has distorted the shape of the funnel. If it is not shaped like a funnel any more, then what is it? There are different opinions out there.

Think about a spider web

Ryan Brock, chief solution officer at marketing strategy platform DemandJump, no longer sees a funnel.

Starting in 2012, Brock realized that the funnel concept was not making sense. He was creating separate marketing campaigns for each persona, developing specific content that would mark the customer’s interest at the top, middle and bottom of the funnel. “I was trying to move people along what I assumed the journey was.”

The customers had been living in the Internet age for a long time, Brock noted. “People learn in a lot of different ways,” he said. That’s when he noticed that how people shopped resembled a spider web more than a journey down a funnel.

“The spider web is built so that the spider can go from anywhere to anywhere,” Brock said. Look at the customer’s search behavior, look at the context of their search, look at the Google recommendations, and you will notice that the same words, terms, and topics will come up all the time. This creates “inflection points” between your solution and their search. The customer can start their search anywhere on that web, but you know where the web strands come together, Brock explained.

AI, coupled with search, will only make the journey faster. For a simple answer to a question, the first answer may be good enough. There is a trust issue here. “You risk [getting] the wrong information, but you save time.” Brock said.

The AI/search combination cannot understand a culture or understand a context that is “too unique”, but it can be used to uncover complex topics and is research-oriented, Brock noted, if you care to research the customer experience.

Dig deeper: What is sales enablement and how do these platforms help bridge the marketing-sales divide?

Everybody still calls it a ‘funnel’

“There are 77,000 different paths to a purchase,” said Don Simpson founder of sales intelligence platform Lift AI. “We try to use the funnel because that is what everyone is used to.”

Simpson deals with B2B sales, often featuring a year-long sales cycle punctuated by lots of customer research. There are many different customer journeys that must be tracked, “from the initial time the customer surfaces all the way to the sales process,” he said. “We try to see the customer journey and predict the likelihood to convert.”

Lift AI does that analysis at the web page level. How people engage with the online material may be indicative of what they will do next. The client’s web site is treated as a “buyer intent tool.” Simpson said. “We track from the first visit to the sale close. We predict conversion in real time.”

The data gained by Lifts AI’s modeling can be fed into a sales tracking tool. “You can play with (the data) and build models based on that with varying degrees of success.” Simpson said. Developing and tracking that situational awareness on the web site has led to increases in sales on an order of magnitude.

Marketers want to put in “what they think is important,” Simpson said, but what is important is that the model is accurate. Model accuracy is a sign that the marketer is on the right track. If that rate is 85-90%, then it is “accurate,” Simpson explained. “You keep tweaking and refining until you get accurate. ” So with AI, the marketers hypothesis is actually no longer as important.

A funnel? Yes, but a dark funnel

For Latane Conant, CMO at ABM platform 6sense, the funnel is still there. “It feels nice, it feels tidy, and it feels organized. But it’s not our reality.” With the shift to digital buying, we get “The dark funnel”.

“[B]uyers do their research anonymously instead of through a conversation with a seller. They’re still showing signals of where they are in the buying journey, but now those signals are happening out of plain sight,” Conant said. 

For example, only three percent of visitors will fill out a form, Conant noted. The rest of them go un-noticed, unless de-anonymized. “Yet only 26% of B2B organizations do actually de-anonymize this traffic,” Conant said.

“The truth is the buying journey has never been linear. Buyers don’t progress neatly from one stage to the next,” Conant continued. “Buyers may spend a month in the awareness phase, a week in the consideration phase, and a day in the decision phase before reverting to the consideration phase. Or they may jump straight from awareness to decision/purchase…It doesn’t follow a pre-set cadence, which is why it’s so important to have the intent data, and the AI to distill it into insights, so you can track buyer readiness at any given point.”

Funnel or not, it’s still about data

Marketers can get a handle on divining buyer intent, provided they ask the right questions and find the right answers. The “shape” of the customer journey is less important than the data being analyzed.

Marketers should start investigating “the questions that matter to you the most,” Brock said. Look at search behavior itself. Do research. Will the AI supplant your voice, or will it find people who are looking for something unique? Carefully develop content to appeal to customers at those information junctions. A sales web has inflection points, and an algorithm can provide content to suit the customer at those points, he noted.

Simpson took a more direct approach. “People come to your store for a reason,” he said, so buyer intent is already there. Identify the behavior that shows likelihood to convert. Engage in a conversation with that cohort. Apply the time and resources to that audience in buy mode, and capitalize on opportunity, he said.

Conant offered these three steps to meet the challenge:

1. Use AI to be more human.  Artificial intelligence can draw customer information from many sources before engaging them on their own personal terms.
2.  Use AI to be more efficient. Predictive AI can take over routine tasks, like crafting personalized messages aimed at specific customers.
3. Use AI to streamline workflows. Use AI to augment CRM, finding accounts and adding meaningful data, then getting that information to the sales and marketing teams.

“As the sales journey and funnel evolve in this anonymous buying age, demands on marketers are higher than ever.” Conant said. “Rather than seeing AI as another complicating factor, I look at it as our lifeboat — the way we’re going to meet these rising demands without killing ourselves in the process.”

The post If it’s not a sales funnel, what is it? appeared first on MarTech.

This is the profile of your typical smart phone user, surfing the net, looking for the next thing. As they flip from page to page and scroll up and down, they may experience one of six basic emotions: fear, anger, joy, sadness, disgust and surprise.

If the page view sparks the right emotion, then that viewer could be turned into a lead. But which emotion can do that? Can this be done in a loud, distracting environment (like in real life)? And can you score the interaction for ad effectiveness use it to optimize a campaign?

First, some background

The hypothesis that all humans feel one of six basic emotions was proposed by psychologist Paul Ekman. His work also inspired others working the intersection of psychology and marketing, looking for ways to measure emotional response so they can sharpen their approach to consumers.

Machine learning and AI modeling have been used by various businesses, all taking different approaches to the reading of emotions through human facial expressions. Some of these approaches were limited by technology, requiring the subject to sit in front of a desktop PC, either in a lab or at home, so that the digital camera could scan their faces and calibrate these images with the software, Max Kalehoff, VP of growth and marketing at Realeyes told us.

With people using smartphones, staying still long enough to be calibrated was not going to work.

Dig deeper: You smiled, so we think you like this product

Cue the face

Realeyes built its facial recognition app for mobile on previous work. It’s AI had been trained on close to one billion frames. Those images were then annotated by psychologists in different countries to take account of cultural nuances. The algorithm in turn was trained by using these annotations, Kalehoff explained, yielding over 90% accuracy.

The potential for Realeyes to work on the mobile platform intersects with the explosion of social media, and in this realm the app is agnostic. It does not matter what the user is looking at — TikTok, YouTube, Facebook, Instagram. The Realeyes app is gauging their reaction.

“To (the best) of our knowledge, this is the first time it’s been done,” Kalehoff said “We are answering a demand to provide detection of attention to creatives in a mobile environment.”

To put Realeyes on the smartphone, users have to opt-in, and are then directed to an environment where they can look at some ads. They are told to scroll through some screens, “doing what they normally do,” Kalehoff said. A small app will reside on the phone helping measure visual attention data and clickstream interaction data. “Our definition (of attention) focuses on a stimulus while ignoring all other stimuli,” he said. “The experience for the participants is under three minutes.”

Looking for data in the right places

What Realeyes looks for depends on the media the consumer is viewing. One outcome sought is what they call a “breakthrough.” “Real people try to avoid ads,” Kalehoff noted, so breakthrough occurs when an ad successfully gets someone’s attention despite a naturally distracting environment.

This matters as people “swipe, skip or scroll” past ads to get to content. They will swipe on TikTok, scroll through Facebook or Instagram, or skip in YouTube, Kalehoff observed. Did the ad get through?

Then there is the type of viewing, like Netflix or Hulu, where the consumer’s involvement is passive. Here Realeyes is looking for “in focus reaction.” Is the viewer paying attention to the ad? What are they seeing, second by second, and is that creating a positive or negative impression?

Then there is online shopping, for example on Amazon. Here validating visual data gets a four-question follow-up, testing for brand recognition, ad recall, trust in the brand and likability of an ad.

The simplicity of Realeyes’ approach is that scanning for facial expression will work anywhere with anything. As two-thirds of the digital media spend goes to three or four major platforms, “you only have to go to a few places to get where the attention resides,” Kalehoff said.

Room for improvement

The foundation of Realeyes is the training database that informs the AI of the meaning of a facial expression. Porting the app to the handheld means being able to spot smiles and frowns, then using that information to correct a bad impression or improve on a good one.

Still Realeyes is aware there is room for improvement. It has had to work on adjusting its face-reading app to work in low-light conditions while remaining accurate, Kalehoff pointed out. The AI has also received additional training recognizing different skin tones and again delivering accurate readouts.

There are also some upsides. Realeyes can tell if the same face appears more than once. This can be an issue with paid surveys, where a subject may want to participate more than once to score a little extra cash, Kalehoff noted.

As for practical application Realeyes worked with Mars Inc. on a project to boost sales using increased attention metrics. The experience yielded an 18% sales increase across 19 markets, optimizing the ad spend by about $30 million, Kalehoff said. Even a five percent increase in “creative attention” can lead to a 40% increase in brand awareness.

The post Scanning the faces that scan the mobile screens appeared first on MarTech.

Data analysis is supposed to replace hunches with facts. Brands don’t want to risk millions of campaign dollars on someone’s gut instinct. The marketer, ideally, has a goal, a clear threshold of success that must be crossed to achieve results. So how do you get there?

Data analysis is the “GPS.” The whole point of data analysis is to understand what is going on and to use that information to make the right decision. It’s “ready, aim, fire” (data, analysis, action). But sometimes the order gets mixed up, resulting in people drawing the wrong conclusions and acting on that basis. The process then becomes “ready, fire, aim”, or even more comically, “fire, aim, ready”.

“The biggest test of data is analytics,” said Mark Stouse, chairman and CEO of Proof Analytics.  “It contextualizes the data, making it extraordinarily difficult to manufacture conclusions, whereas data visualization alone makes it easy.”

Can data identify what’s causing something?

Can one gauge causality from data alone? Stouse believes not. Marketers can try by extrapolating from historical data, then check to see if this extrapolation was correct. “If everything is stable, extrapolation can work. But when the variety, volatility and velocity of change is great, extrapolation has zero value.”

“Data is indeed always about the past, and it has no innate ability to forecast. Past is not Prologue,” he continued. “But multivariable regression is the proven approach to taking data representing the relevant factors (the known knowns) — as well as some potentially important stuff (known unknowns) — and turning that into a calculated historical portrait of causality. That, in turn, creates a forecast against which you can understand the accuracy of the model vis-à-vis a comparison between forecast and actuals.”

Erica Magnotto, director of SEM at Accelerated Digital Media, sees the value of historical data, but only if there is room for retroactive perspective and predictive planning. “Forecasting campaign success should be based on trending data and performance like year-over-year and month-over-month. This should create close to accurate predictions on future success. If the forecasted data indicates a slower month or potential downturn in the market, optimizations can be made in real time to promote efficiency and conservative scale. If forecasting indicates a stronger month, then it’s time to start planning for scale, testing and additional campaign launches.”

Marketers should also be aware of hiccups in the model. Magnotto noted that there is a difference between normal “ebb and flow”’” of performance versus a crash/spike. “Data occurring outside of the normal margin of ebb and flow could indicate that immediate action in the account is necessary. Marketers should also not assume user behavior will always be consistent so it’s important to understand benchmark performance so abnormal user (or campaign) behavior can be detected,” she said.

Dig deeper: Marketing analytics: What it is and why marketers should care

What can marketers do?

Marketers must be analytical, open-minded, and humble at the same time. This alone can be a challenge when there are always some people who can be too self-assured, or fixated on the trivial at the expense of the substantive. Still, there are approaches to check mistakes before they happen.

Magnotto focused on knowing the data, the customer, and acknowledging reality. She offered this checklist for agencies, but the main points on it apply to brands too:

1. Understand basic excel/sheets principals and how to pivot large sets of data downloaded from any platform. 

2. Understand basic comparison formulas and default ways to look at data trends (month-over-month, year-over-year, period-over-period, week over week).

3. Have agreed upon primary KPIs and secondary KPIs with the client. 

4. Always speak the client’s language and incorporate the client’s source of truth data into reporting. This will ensure more productive conversations and help marketers navigate away from making mistakes or misreading performance. 

5) Know when to admit defeat in a campaign strategy. If a “great idea” is not working, then be comfortable allowing the data to speak for itself and changing strategies. 

6) Always QA reporting. Apply QA to formulas, timeframes, numbers, etc. If something looks too good to be true when analyzing data, it probably is. QA for mistakes that may be leading to that anomaly. 

Stouse stressed avoiding a fixed mindset. “Blindness to analytical reality is about choosing not to see, because what is there offers a challenge to what you believe.” he said. “The opposite of analysis is a certainty you have chosen and justified without any real basis except your own self-interest.  More mistake have been made in the name of certainty than anything else I can think of.”

The post Data plus analytics is the route to the truth appeared first on MarTech.

So how is it that smart people can misinterpret their data? How do they get it wrong?

It’s human nature

The problem with humans is that they are…human. They will make mistakes, not all of them consciously.

“Most mistakes are made by people not peeling back the onion,” said T. Maxwell, owner of digital marketing agency eMaximize and a member of the Forbes Agency Council.

“For instance, they look at their Monthly Visitors and use it as a key metric to measure growth. When you dig a little deeper you notice that 40% of those visitors are from India and Russia and are most likely bots.”

“Unlike computers, humans are emotional creatures that have embedded experiences, which means their interpretation of data can be led by things like assumptions and recency bias. Interpreting data is more about relinquishing those thought processes,” said Erica Magnotto, director of SEM at Accelerated Digital Media.

“Bias is anything that changes the outcome of a model when it should not have,” added Mark Stouse, chairman and CEO of Proof Analytics. “The best way to break free of bias is to realize that you have it, and then take the steps to enlarge the circle in terms of what you think is relevant to a decision,” Stouse continued.  “This is really the practical value of diversity and inclusion in an operational sense.  It enlarges your perspective and bears against missing something important that could introduce bias into your thinking.  Bias is usually the result of thinking too narrowly.”

Many ways to measure

Which leads to the data itself. There are many ways to measure it. That does not mean that what you are measuring helps create greater understanding. Some metrics are meaningful, others not so much.

 “Often working with a client that has a ‘little’ knowledge of digital marketing is painful and slows things down,” Maxwell said. “[T]hey fall for every gimmick they see on social media and ask their agencies to investigate hoax marketing strategies instead of deploying sound digital strategies with proven, best practices. It’s the job of the agency and the owner to choose which metrics are important,” Maxwell added.

A single course of truth is needed, Magnotto echoed. “[B]oth the client and marketer need to agree on the platform that is considered the source of truth for tracking primary KPIs and other performance.” That way, all parties are looking at the same information the same way.

“It’s the marketer’s responsibility to create reporting that marries both their platform data and the source of truth data for optimal visibility into performance,” Magnotto continued. “For large accounts the primary KPIs should be discussed frequently, and reporting should be consistent so that the client is bought into the agency’s methodology and can acknowledge/agree with the performance being presented. For a client with multiple goals, it’s important to categorize primary and secondary KPIs so there is clear prioritization when looking at reporting.”

“Data is always the numerator in the equation, not the denominator,” Stouse said. “The question or decision dictates the model, and the needs of the model dictate the data required to arm the model.  In general terms, there are two kinds of potentially relevant data — what measures what you are doing (what you control), and what measures what is a relevant headwind or tailwind (what you don’t control).”

Measuring change or changing the measure

And make some allowance for the unpredictable to happen, like pandemics, bad weather, economic recessions or supply chain issues, Maxwell noted. The more this happens, the more optimization is needed to keep a campaign on track.

“Savvy marketers are adjusting marketing campaigns in real-time,” Maxwell said. “It may take two to three months to get an advertising campaign dialed in, so that it only needs minimal tweaks going forward.”

“There does need to be enough data to indicate next steps in optimization; that amount of data is dependent on client KPIs, spend, and time needed for data collection,” Magnotto noted. “In an account with a large threshold of conversions you may only need to run an A/B experiment for a week to collect enough data to confidently pivot into new strategies, whereas smaller accounts may need 30 to 60 days.”

“Research tells us that the unaided human brain cannot handle more than three to four variables. After that, things go tilt,” Stouse said. “And when time lag is a big part of the equation, it makes things even harder. This is why most people default to very short-term evaluation, and they justify it because they’ve always heard that if you manage the minutes the days will take care of themselves,” Stouse continued.

“That’s true if you understand the causality that’s driving the overall situation, but if you don’t, managing the short game will not mean that you’ll win the long one.”

The post Don’t apply wishful thinking to your data appeared first on MarTech.

Shlomi Ron, founder and CEO of the Visual Storytelling Institute, first explained NFTs and crypto to MarTech almost two years ago. (See “A Guide to Visual Storytelling” Part I and Part II.) We thought it would be a good time to talk with him again to gain a better understanding of how NFTs, crypto and blockchain work together to propel Web3.

Step 1: Crawl

NFTs and crypto are each hard to understand when viewed in isolation. They must be taken together to see how they work. As Ron put it, “[They] are like the building blocks for Web3 technology.”

“Think of NFTs as one-of-a-kind treasures, like rare collectible toys, that can’t be replicated or traded for anything else. They help keep track of who owns unique digital things like digital art and in-game items on the blockchain,” he said.

“Cryptocurrencies, on the other hand, are like digital wallets filled with virtual cash. They help make payments and transactions on the blockchain secure and trustworthy,” Ron continued.

“Both NFTs and cryptocurrencies help Web3 work smoothly and securely, like puzzle pieces fitting together,” he said. “You can pay with crypto currency to buy NFTs in order to buy goods, services, and other digital assets.”

Step 2: Walk

Which leads to the tangible rewards Web3 must deliver. The whole point of this technology package is to engage customers in such a way as to increase brand loyalty. Just remember that the reward cannot be commonplace, like a coupon or a free pizza.

The NFT/blockchain combination can be crafted to deliver a customer reward that cannot be copied or faked, Ron pointed out. “By offering exclusive rewards, brands can encourage customers to engage with them and develop a sense of loyalty as they receive rewards that are otherwise unavailable.”

“Think of them as the evolution of the good ol’ loyalty points that airline companies offer but with a long-term ownership benefit,” Ron said. “[B]y using blockchain technology to track and reward customer engagement, brands can provide a sense of security and trust, which can further increase loyalty.”

Dig deeper: Web 3? It’s the web we hope for, not the one we know

Step 3: Run

Some brands are already trying out Web3 solutions, with varying degrees of success. Ron listed a few such efforts.

• The Gap used the Tezos blockchain platform to launch its own NFT program. NFT owners got a limited edition hoodie.
• Gucci turned to the Roblox platform to launch its “Gucci Garden” for a two-week period. “It attracted 20 million players despite being open for just 2 weeks,” Ron said.
• Gucci Town was the permanent follow-up, launched last year. “It sold one NFT accessory for over $4,000, exceeding the price tag of the physical version of the accessory,” Ron noted.

A more extensive use case popped up last December, when The Art Basel Miami art show saw a number of brands tried out their Web3 offerings on the public. “Phygitals (Rtfkt’s Nike co-branded Space Drip sneakers), distributed NFTs as a reward for doing something, such as attending a fashion show (Altuzarra) or being a member of a special app (Adidas),” Ron said. “Token gated entrances allowed NFT collectors exclusive access to events by DressX or Bored Ape Yacht Club.

“But it doesn’t end there. After people attended these exclusive events, they received NFTs called POAPs (Proof of Attendance Protocols) – paving the way for future rewards.” Ron added.

Step 4: Fly

Web3 is a promising start set against the dismal background of the “Crypto Winter”.

“The key triggers include an overvalued market, with some coins like Bitcoin reaching all time high $65k in Nov 2021.”  Lack of regulation and rampant speculation fueled the chaos. Follow this with “the crash of the Luna and Terra stable coins, [and] the FTX’s crypto currency exchange bankruptcy. Last year’s volume of crypto abuse didn’t help,” Ron said.

A few things must happen to build public confidence in NFT and crypto if Web3 is going to succeed. “The public needs to have better understanding of how these technologies work in lock step with better usability,” Ron said. Right now, these processes are disjointed. “People need to be sure that their transactions are secure and the NFTs they buy are authentic,” he said.

“In short, to cross the chasm from the recent Early Adopters stage to Early and Late Majority, we need better usability, security, regulation and stronger utility,” Ron continued. “Embedding crypto and NFTs in everyday use cases that will drive perceived benefits.”

Ron reminded us that we are still in the early days of Web3. That world is “still wide open for everybody to innovate in.”

The post So how is this Web3 supposed to work? appeared first on MarTech.

Web 2.0 made the web page interactive.

Web 3.0 runs on blockchain, enables the use of crypto-currencies and NFTs, and uses AI.

Say that again?

Bringing many existing, but cutting-edge technologies together, Web3 is supposed to be “the next big thing”, taking the internet into the 21st century. But like any new technology, Web3 is long on concepts and short on implementation. People are still trying to figure out how to use it.

It beta be good

Major brands are giving the technology a test drive. Last November, Nike announced the creation of .SWOOSH, a Web3 platform now in beta testing to create an online digital community. Fans will eventually create interactive digital objects (shoes, jerseys),  adding up to a digital collection to be launched later this year.

Likewise, Starbucks started a blockchain-based loyalty program last December. Starbucks Odyssey, also in beta testing, will offer coffee themed NFTs and an enhanced loyalty program that goes beyond offering free drinks. Interactive activities (“journeys”) will allow fans to earn collectible “journey stamps” (NFTs). Those journeys could be watching videos or visiting different stores to try different drinks, later to be redeemed for benefits or experiences.

No matter how a Web3 platform is constructed, the goal is to get customers more engaged with a product. The challenge is figuring out how to build the program that makes best use of the platform. There is no roadmap for this…yet. Firms and clients are only now trying to chart that course.

Leading edge, bleeding edge

In Nike’s case, selling sneakers ad infinitum made no sense. Consumer demand was not sustainable in the long term, noted Blair Richardson, director at Rehab. The British agency began working with Nike in 2018, “and we proved the case for digital collectibles over the space of two years, with small prototypes within closed groups.” He said. “Time will tell whether mass audiences will lean in, but Nike is confident enough to have since developed the dedicated division: Nike Virtual Studios for this exact reason – satisfying consumer demand for both physical and digital products.”

Australia-based Mooning is working with the Accor Hotel Group to run a NFT Art Gallery, featuring the work of women digital artists from around the world, said Lisa Teh, director. “Titled Digital E/Scapes, this activation was also designed to educate hotel guests about NFTs. The artwork is being auctioned off (anyone can purchase it while visiting the gallery or online) and the proceeds will be split between the artist and a charity of their choice.”

Didn’t this balloon burst already?

Still there are practical, as well as conceptual, obstacles to be overcome by agencies and clients alike. Recent failures inspire caution, not action.

Web3 “is evolving at an exponential rate, but there is still a lot of friction for the consumer.” Teh noted. “[A]fter the crypto crash last year and the rollercoaster ride that is the NFT market, a lot of people lost confidence in the sector.”

Teh used Bored Ape Yacht Club as an example. The group raised the awareness of NFTs, along with public disbelief. They “couldn’t believe people were paying millions of dollars for what they perceived to be a jpeg of an ape.” Teh said.

Yet there is a practical dimension to explore for NFTs. “We are only now starting to see more real-life applications…such as digital tickets, memberships, collectables, etc. The fact that the blockchain enables you to track ownership in a way never before…makes it game changing technology,” Teh explained.

“Brands and creators have gone from a position of FOMO to actually assessing where web3 can add value to their audience.” Richardson said. “There needs to be a clear use case opportunity and a problem to solve, otherwise audiences won’t see enough value and it will live and die in the hype cycle. This requires extensive research, ideation, prototyping and validation.”

“We believe that the technologies must run in the background, if web3 is to really catch on with a mass consumer audience.” Richardson continued. “Brands and creators really need to communicate more in terms of the value web3 brings, as understanding of all the tech components and their connection is still very limited.”

See and seize the future

Digital marketers are not condemned to learning Web3 by trial and error. Experience in Web2—what we have now—and well-known marketing practices, provide a solid foundation to build on. The challenge will be separating novelty from utility.

“I believe clients understand the key pillars of Web3 for their audience; ownership, community,” Richardson said. “However, until there are case studies and huge success stories that validate brands moving budget from Web2 to Web3, it will remain on the edges and more within innovation teams. Or worse, ‘gimmick’ marketing stunts.”

“I would encourage people to look at what some of their favorite big Web2 brands are doing.” Teh added. “Many are moving away from the cliched NFT drops and looking at how the technology can solve real world issues. This will help them understand the potential of the space.” Those big brands will put the tech in context, making it easier to understand, she said.

“We are also a long way off mass adoption. Having big companies like Starbucks, Nike…invest in the space will certainly accelerate that.” Teh added. Clients must look past the hype to understand the underlying technology and its potential for change. Then “they’ll realize it’s not a fad and is here to stay.” She said.

The post Web 3? It’s the web we hope for, not the one we know appeared first on MarTech.

The concept goes by many other names — tiger team, SWAT team, pods, task force, skunk works. But the general feature set is remarkably alike — a small group of people pulled from different specialties working together to solve problems. The customer value squad must be empowered to make decisions and be held accountable for the results.

Elements of such an approach were outlined last year in the two part series, “Return on investment is missing in action,” and “Static ROI metrics, meet dynamic marketing situation.”

Schaub’s customer value squad will run against the grain of the organizational chart, where power is hierarchical. In traditional companies, each department is a fiefdom. It jealously keeps data — and power — within the confines of its silo. A CVS must be able to draw information into its well in order to assess situations and act in real time, thus cutting through silos.

The legacy that refuses to die

Those silos marketers are trying to crack today are a legacy of the industrial age, Schaub told us. Early in the 20^th century, scientific management and organizational structure replaced the “chaotic, bespoke” way of making things. “The idea of grouping specialists together was a novelty,” she said. Efficiency optimized processes and management was professionalized.

“Over time, specialists became silos,” Schaub explained. “Once you put people together who are alike, they form their own cultures.” Information then travels vertically, not laterally. “Need to know” traps data. This way of doing things that can be resistant to change.

Only here, change is represented by “VUCA” — volatility, uncertainty, complexity and ambiguity. The vertical, hierarchical structure of silos become ill-suited to operating in such a fast-moving, ever-changing business environment, Schaub pointed out.

Around what mission should a team be organized? “It depends on what area of value you are delivering,” Schaub said. It could be geographic, as companies tries to improve sales in specific regions. It could be account-based. It could be about revenue operations. Or product service. Or design support. Or the team could address marketing across channels.

Here the size of the team matters. Around six to ten is ideal, Schaub said. “Getting beyond that, you start formalizing and coordinating,” she said. Companies will also have to fill skills gaps, which become obvious when the customer value teams begin operations. You may need product people, communications specialists, or account managers. “There is no checklist,” Schaub said.

Not unique to marketing

The concept of the small team is not new. It has grown out of necessity in other industries and professions.

Schaub found her inspiration for the customer value squad in an unlikely place: the hospital operating room. In that space you find a collection of specialists: a surgeon, an anesthesiologist, several operating room nurses. Each has their own different skill set. But they have one mission: the care of the patient.

Human bodies are similar, but “every situation is a little different” for the team, Schaub said “They are empowered in the moment to make decisions,” she said, and they are held accountable for the outcome.

The specialists are drawn from different departments to form the team. There is still a head of a nursing and a head of surgery, Schaub pointed out. “[But does] the surgeon ask the boss to take action when the patient is bleeding out?”

This illustrates a point: that silos with a vertical hierarchy will inhibit agility, being ill-suited to acting fast when a situation changes on the line or at the edge.

Again, this kind of organization is not unique to the OR. Sports teams operate this way, Schaub noted. So does the military. A VUCA environment moves too fast for a top-down organization to manage. But a small, networked group can handle it, since it is closer to the action.

Yes, but how?

Implementing a customer value squad is a change project. It can happen one of two ways.

The first is the ideal situation, where top managers see the need for customer value squads (or something like them). They will retain consultants, then spend several years retooling the corporate culture to embrace agility. This eventually produces exceptional results, Schaub explained. This approach is rarely seen, she added.

The second approach is more likely. That is when a company takes one piece of its business and undertakes a small, incremental change, Schaub continued. It could be customer success management or account-based marketing, but it must be customer-focused, she said.

It starts by forming just one customer value squad. “Put the best people there and push it through.” Schaub said. Keep breaking things and fixing them until you succeed. These break-fix cycles are short, but iterative. Team members learn the lessons taught by failure, adapt, then try again until they find the right solution. This is more of a methodology rather than a recipe.

Once the team has ironed out its difficulties, it can then be replicated throughout the enterprise. Schaub likened this to opening a chain of candy stores. Start with one store, get it right, then scale up in batches.

Customer value squads will vary in composition but will have three basic components. The “people piece” is network organization, Schaub said. The method piece is agile operations. The brain piece is intelligence/analysis. People will find their way towards “complexity-wise marketing” as they understand how these pieces fit together.

That will take time. A company will have to tolerate a measure of small failures in this learning process. But the results will exceed the costs, provided all are pointed towards the same goal.

The post How to build a customer value squad — and why appeared first on MarTech.

The kid mail poured in. Santa made the call who was naughty or nice. Elves made the toys, loaded the sleigh and hitched up a team of reindeer. Then Santa took off on Dec. 24 to make the deliveries, following midnight westward from Siberia to Alaska.

The entire team was focused on Christmas. You were not just making toys. You were creating delight — and delivering it, too. When the same kid wrote Santa for another gift a year later, you had a repeat customer. This only worked if Santa delivered Christmas on time, every year.

Well, COVID-19 pretty much destroyed that business model. The last two years of Christmas have been hell on Santa.

The COVID outbreak of 2020 forced Santa and his reindeer to fly masked. Covering Rudolph’s red nose robbed the reindeer team of its navigational beacon. Several near misses with airliners followed, along with by some directional errors, leaving some kids with no gifts on Christmas morning. Santa still dropped off goodies on the day after, but it was not the same — for the kids or for him. Warm milk and cold cookies were a meager reward.

Things did not improve in 2021. Supply chain disruptions persisted, shredding the “just-in-time” inventory system at Santa’s workshop. If toy parts can’t get to the North Pole, well, then gifts can’t get to kids by deadline, either. Elves worked overtime past New Years. Fed-up, overworked reindeer refused to fly after January 2. Santa had to outsource deliveries to FedEx and UPS, who were already swamped by returns from pre-Christmas online shopping.

But 2022 was going to be different. Christmas must be fixed at all costs. Santa did not expect such a simple goal would be so hard to accomplish. It sorely tested his patience.

Santa vs. insanity

COVID crashed demand, so supply shriveled. Then COVID passed, to an extent anyway, and demand returned — exceeding supply, which, in turn, messed everything up. Santa had to diversify his supply chain in a hurry to handle Christmas 2022. He was never picky about who he sourced toy parts from, since Christmas was for everyone.

Well, geopolitics does not believe in Santa Claus. The U.S. imposed sanctions on companies doing business with China, then extended those sanctions to Russia after they invaded Ukraine in February. The last thing Santa needed was for the U.S. State Dept. to label him a sanctions evader. He could retaliate by leaving a load of coal for the diplomats, but that would not fix things.

There are obscure, near-criminal firms that specialize in evading sanctions using front companies in other countries. Ebenezer Scrooge knew a few shady operators who could move goods to Russia and China, as well as Iran and North Korea. Santa looked the other way. Deals were made. Toys would be delivered. The media was kept in the dark.

Sourcing parts from other countries that don’t believe in Christmas was also “challenging”. Explaining Christmas to suppliers in some remote parts of the world took too long, so Santa just ordered the damn parts. Money helped.

Santa was feeling the strain. It was only May.

Dig deeper: 5 tips for building customer trust during the supply chain crisis

There is power in a union

Just-in-time delivery was dead. Maintaining inventories and building resilience was the plan. By June, 2022, the newly formed North Pole LLC had branches on every continent, running a network of warehouses and workshops, some in places that had no snow year-round.

But for the North Pole elves, this was taking things too far. The understanding was that only elves could do Christmas work. The guy who organized the Amazon warehouse workers in Staten Island was seen nosing around Santa’s workshop. Yes, the elves formed a union. (Coincidence?) The reindeer, not be to be outdone, formed one too.

Labor talks were tense. But a deal was worked out by September. Elves and reindeer could unionize at the North Pole. Outsourced locations could hire humans, but they must be supervised by unionized elves. UPS and FedEx would do 40 percent of the deliveries, so long as the reindeer handled remaining North Pole traffic. Elves still handled e-mail.

So far, so good. But things took a turn for the worse in September. North Pole business practices conflicted with EU regulatory protocols protecting the data privacy of minors. Santa hired trade lawyers to go to Brussels to “work something out.” The deal went down as follows: minors could message the North Pole with their toy requests, provided their parents checked a permission box on the landing page of the Santa Workshop web page.

Elves, however, lacked the technical know-how to run the IT systems needed to store, sort and parse the data rising from gift requests. Here the union president of the Local Elvish Christmas North Pole Workshop Local 01 conceded, allowing outsourced IT operated by humans to run the marketing tech stack. AWS was allowed to accommodate any sudden spike in demand by adding more servers.

Santa thought the elves and reindeer were on his side. He gritted his teeth, signed the papers that needed signing, and got on with it. Christmas was more important than worrying about problems.

Are we there yet?

Good kids would still get toys for Christmas. Bad kids would still get coal in their stockings, of course. Things were just hanging together by a thread at the workshop when the next problem arose in November.

That’s when the U.N. held its COP 27 Conference in Egypt. There, the nations of the world pledged to take concrete steps to reduce carbon emissions. That meant getting rid of coal, since burning the stuff put out a lot of carbon into the atmosphere, raising global temperature.

Now Santa had a meltdown. How dare those national leaders outlaw coal right before Christmas? Those kids deserved it after being naughty the entire year! Was COP 27 really about climate change?

Santa was NOT going to let those naughty boys and girls get away with this, even if they were grown-ups! He must transition to greener forms of deadbeat gifts if he was going to stay in business. Santa switched from coal to seaweed. (Where do we source seaweed?) Japan obliged. They were even nice enough to throw in some Pokemon cards for free, just for the elves.

With that last problem solved, a very exasperated Santa Claus supervised the loading of the sleigh. He double-checked his manifest of gifts for the naughty and the nice. He taxied the sleigh to Runway 27 (West), shook the reigns, and took off into a brisk headwind, with Rudolph’s red nose blinking.

Just get through this and there will be a mug of rum-spiked hot chocolate waiting at journey’s end., Santa thought.

Hopefully the next Christmas will be better.

The post Claus and effect appeared first on MarTech.

The technique exploits a simple truth: The front end of the web site, which you see, must talk to the back end to extract data, and display it. A web crawler or bot can gather this information. Further work can organize the data for analysis.

Digital marketers are forever seeking data to get a better sense of consumer preference and market trends. Web scraping is yet one more tool towards that end.

First crawl, then scrape

“In general, all web scraping programs accomplish the same two tasks: 1) loading data and 2) parsing data. Depending on the site, the first or second part can be more difficult or complex.” explained Ed Mclaughlin, partner at Marquee Data, a web scraping services firm.

Web scraping bears some resemblance to an earlier technique: web crawling. Back in the 1990s, when the internet occupied less cyber space, web crawling bots compiled lists of web sites. The technique is still used by Google to scrape for key words to power its search engine, noted Himanshu Dhameliya, sales director at process automation and web scraping company Rentech Digital.

For Rentech, web scraping is just obtaining “structured data from a mix of different sources,” Dhameliya said. “We scrape news web sites, financial data, and location reports.”

“Web scraping data is collected on a smaller scale,” said George Tskaroveli, project manager at web scrapers Datamam, “still amounting to millions of data points, but also collecting on a daily or more frequent basis,” he said.

“The defining features of modern web scraping are headless browsers, residential proxies, and the use of scalable cloud platforms,” said Ondra Urban, COO at scraping and data extraction firm Apify. “With a headless browser, you can create scrapers that behave exactly like humans, open any website and extract any data… [M]odern cloud platforms like AWS, GCP, or Apify allow you to instantly start hundreds or thousands of scrapers, based on the current demand for data.”

Which party data?  And how to get it

There is a spectrum of data gathering, ranging from zero-party to third-party data, that marketers are forever picking through for the next insight. So where does web scraping fit into this continuum?

“Web scraped data is most closely related to third-party data.” Said Mclaughlin, as marketers can then join this data with existing data sets. “Web scraping can also provide a unique data source that’s not heavily used by competitors as may be the case with purchased lists.” He said.

“Ninety-five percent of the work we do is third-party [data],” said Dhameliya. Scraping aims for the data trafficked between the front-end and back-end of the web site. That may require an API crafted to tap this data stream, or using JavaScript with a Selenium driver, he explained.

Most of Rentech’s work is for enterprises seeking marketing intelligence and analysis. Bots are tasked with periodic visits of web sites, sometimes seeking product information, Dharmeliya said. Some web sites limit the number of queries coming from a single source. To get around that, Rentech will use AWS Lambda to execute a bot that will launch queries from multiple machines to get around query limitations, Dhameliya explained.

It is not humanly possible to go through all the data to weed out “nulls and dupes,” Tskaroveli said. “Many clients collect data with their own devices or use free-lancers. It’s a huge problem, not receiving clean data,” he said. Datamam relies on its own in-build algorithms to go through the “rows and columns”, automating quality assurance.

“We write custom python scripts to scrape websites. Usually, each one is customized to handle a specific website, and we can provide custom inputs, if needed,” said McLaughlin. “We do not use any AI or machine learning to automate the production of these scripts, but that technology could be used in the future.”

 Any data that can be manually copied and pasted can be automatically scraped.” Mclauglin added. “[I]f you find a website with a directory of a list of potential leads, web scraping can be used to easily convert that website into a spreadsheet of leads that can then be used for downstream marketing processes.”

“Social media are a different beast. Their web and mobile applications are extremely complex, with hundreds of APIs and dynamic structures, and they also change very often thanks to regular updates and A/B tests,” Ondra said. “[U]nless you can train and support a large in-house team, the best way to do it is to buy it as a service from experienced developers.”

“If [the client] is in ecommerce, you might get away with an AI-powered product scraper. You risk a lower quality of data, but you can easily deploy it over hundreds or thousands of websites,” Ondra added.

(Once market data is flowing in, it needs to be managed. That’s discussed in depth here.)

Scrape the web, but use some common sense

There are limits — and opportunities — that come with web scraping. Just be aware that privacy considerations must temper the query. Web scraping is a selective, not a collective, drag net.

Data privacy is one of those limits. “Never collect the opinions or political views or information about families, or personal data,” said Dharmeliya. Evaluate the legal risk before scraping. Do not collect any data that is legally risky.

It’s important to understand that web scraping isn’t — and for legal reasons shouldn’t be — about collecting personal identifiable information. Indeed, web scraping of any data has been controversial, but has largely survived legal scrutiny, not least because it’s hard to draw a legal distinction between web browsers and web scrapers, both of which request data from websites and do things with it. This has been litigated recently.

Facebook, Instagram and LinkedIn do have rules governing which data can be scraped and which data is off-limits, Dharmeliya said. For example, individual Facebook and Instagram accounts that are closed are private accounts. Anything that feeds data to the public world is fair game — New York Times, Twitter, any space where users can post commentary or reviews, he added.

“We don’t provide legal advice, so we encourage our clients to seek counsel on legal considerations in their jurisdiction.” McLaughlin said.

Dig deeper: Why marketers should care about consumer privacy

Web scraping is still a useful adjunct with other forms of data gathering.

For Datamam clients, web scraping is a form of lead generation, Tskaroveli said. It can generate new leads from multiple sources or can be used for data enrichment to allow marketers to gain a beter understanding of their clients, he noted.

Another target for web-scraping bots is influencer marketing campaigns, noted Dhameliya. Here the goal is identifying influencers who fit the marketer’s profile.

“Start slow and add data sources incrementally. Even with our enterprise customers, we’re seeing huge enthusiasm to start with web scraping, as if it were some magic bullet, only to discontinue a portion of the scrapers later because they realize they never needed the data,” Ondra said. “Start monitoring one competitor, and if it works for you, add a second one. Or start with influencers on Instagram and add TikTok later in the process. Treat the web scraped data diligently, like any other data source, and it will give you a competitive edge for sure.”

The post How web scraping can be a valuable data source appeared first on MarTech.

That quaint 20th century practice of getting people to sit around a table and talk about a product is still with us. Marketers still use it to gain insights into why consumers want to buy something.

Only the venue has changed. Now focus groups meet up in a zoom call, not in a conference room. They can still yield useful starting points for further analysis, confirming trends or discarding hunches.

That quaint 20th century practice just got a 21st century upgrade, and by means no one saw coming.

A bad case of the flu

Blame the COVID pandemic for changing the focus group.

Risk of spreading the virus back in 2020 shut down any chance of getting a dozen total strangers into a room to talk about a product. For some firms, this challenge proved to be a blessing in disguise.

“Four months pre-COVID, I saw the need for comprehensive online focus group technology,” said Ron Howard, CEO of consumer research and survey firm Mercury Analytics. The upsides were apparent: no time expended on travel, no travel expenses, no need for geographic proximity, no long lead times arranging for the group to meet. “You could do a focus group tonight with people across the country.”

The transition from physical meetings to online was made easier by Zoom’s rapid adoption during the pandemic. People were familiar and comfortable with the online format, so the crossover was easy.

Using Zoom, one can break up the focus group into smaller sections for shorter sessions. Thus a 6–10-person session that once took two hours can be divided into a pair of four-person sessions running one hour each, noted Jenny Karubian, CEO at market research company Ready to Launch Research. “Zoom fatigue is real.” She added.

Ready to Launch is based in los Angeles, which had to endure mask mandates that ran longer than the rest of the U.S., Karubian explained. So, any face-to-face groups would have to be masked, and that was a problem.

“It’s difficult to pick up non-verbal behavior while wearing a mask,” Karubian said. “Ninety-three percent of communication is non-verbal.”

Dig deeper: You need a market research mind-set

Q before Q

Focus groups still need…focus. But maybe not in the way a marketer expects.

“What is the big impact of this project?” asked Nitin Sharma, CEO of research and journey mapping firm Gold Research. Even the best-done focus group is not actionable if “people lose sight of the goal of the project. You collect a lot of data that leads you to analysis paralysis,” he said.

Which leads to the data itself. Focus groups exist in contrast with big data. The sample size of a focus group is usually measured in single- or low-double digits. This is a far cry from the 1,000-strong sample size that is supposed to produce a finding with a plus-or-minus three percent of standard deviation. The touchy-feely analysts handling focus groups (qualitative data) do not necessarily agree with the number crunchers (quantitative data). One must use both.

Qualitative data “should not be used to make a business decision,” Sharma continued. Rather, one should use it for “hypothesis creation.” Quantitative data can then be used to confirm or refute the hypothesis.

Qualitative must come before quantitative, Karubian added. Any idea generated by a focus group can be validated by the data. That way, one can screen out the “one offs” that can occur in the smaller sample size, she explained. But one can also dive deeper into a trend if several respondents give you the same answer.

Once can also subject focus group data to deeper analysis that can then be fed into the quantitative side. At Mercury Analytics, focus group feedback is recorded and transcribed, and key words in the transcription can be searched for frequency, Howard said. Likewise, the notes taken by the client during the group session can also be indexed and searched.

The whole point is “to give food for thought”, Howard said. You are trying to find ways to reach people, or to find out what triggers them and how to avoid those triggers.

“Quals and quants” must meet up to eliminate their gaps in understanding each other. Here Sharma pointed to a “hypothesis creation workshop,” where the two groups must mesh their findings. Prior to the meetings, the consultant will spend time talking to all the participants, then present an assessment of what was said.

“In the workshop, we keep quiet,” Sharma said, letting the two groups thrash out the data.  Then, “we identify the gaps,” he said. “Do you have the data to fill this?” Findings must be grounded in the business as it looks to make specific improvements in its marketing strategy.

Insights in sight

Marketers are always on an open-minded quest to uncover the preferences of the average consumer. Focus groups are merely a tool to that end, neither a silver bullet nor a waste of time.

“Focus groups are good at the beginning of the process,” Howard said. Their “best value is when something changes,” some event that changes the market, hence requiring the marketer to understand that change, he explained.

You don’t want to talk to the diehard fans of a product any more than you would speak with people who are against you — both groups are unchanging, he continued. It’s the middle, with a preference that leans for or against, that needs to understand a message, he added.

Identify the audience for the focus group, and design the focus group towards that audience, Karubian added. For example, an audience of C-level executives will not have two hours to devote to the focus groups. If you have 10 people using a product, and more than three of the 10 use it the same way, those are going to be the people you talk to some more.

Sharma stressed the need for a realistic goal that grounds the research. If the client is trying to do something specific, like increase sales by 10%, then the focus group can be part of the process that shapes the inquiry. “A lot of clients don’t define the success metric clearly,” he said, resulting in consultants making lists of recommendations to the quals and quants that look little better than a “data dump”.

Clients need to “question and challenge the [consulting] firm” to get them to “show you how to achieve your big goal,” he said.

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