As the lines between what is real and what is digital blur, here are some quotable quotes related to data, analytics, cloud and mixed reality, i have come across. Feel free to use them to illustrate your point as you race to future-proof and build an organization that can learn as quickly as possible.
“The world of your children will be as alien to you, as the world you grew up in was to your great grandparents.” – Quantumrum
“Our intuition about the future is linear. But the reality of information technology is exponential, and that makes a profound difference. If I take 30 steps linearly, I get to 30. If I take 30 steps exponentially, I get to a billion.” — Ray Kurzweil
“You feel it in your workplace. You feel it when you talk to your kids. You can’t miss it when you read the newspapers or watch the news. Our lives are being transformed in so many realms all at once – and it is dizzying.” — Thomas Friedman
“Not everything that can be counted counts, and not everything that counts can be counted” – Albert Einstein
“In God we trust. All others must bring data.” – W. Edwards Deming
Torture the data, and it will confess to anything. — Ronald Coase Read more
Conversational AI is clearly the now and the future. According to Satya Nadella, “Bots are the new apps.” In 2019, 33% of US internet users will augment their digital experience using voice assistants.
In 2016, there were 3 million Amazon Echos and limited consumer use cases. Today, there are over 35 million voice-first devices, 100M’s of voice-enabled devices, and consumers are searching for Voice+Visual experiences. The explosion of consumers expecting voice driven commerce to serve a core demand — information search, product search and purchase — requires brand new capabilities.
What are conversational bots? Any computer program able to interact or conduct a conversation with a human via auditory or textual method. People enjoy the conversational interface. According to Nielsen, people spend more than 4 hrs per day in communication apps (and growing).
What are conversational (or screenless) experiences? Any voice or text-based experience (such as chatbots or voice assistant apps – Siri, Alexa, Cortona, Google Home) that uses natural language understanding to dialog with users.
[Synonyms: Conversational interface, screenless UX, dialog system, conversational UX, conversational app, conversational commerce, chatbot, chatter-bot, bot]
Every industry – from precision medicine to precision agriculture, from personalized retail to personalized banking – is being impacted by Machine Learning.
Machine learning (ML) and deep learning have allowed for completely new possibilities in the realm of digital experiences, targeting and predictions. Companies in every industry are collecting more and more data. It’s now possible to train analytical models that even a few years ago would have been impossible. Business has taken notice of their data sets’ power, leading them to develop completely new products and initiatives.
What is Machine Learning(ML)? ML is a part of the broader fields of Computer Science and Statistics. The goal of ML is to enable computers to learn from their experience in certain tasks. ML also enables the machine to improve performance as their experience grows. A self-driving car, for example, learns from being initially driven around by a human driver; further, as it drives itself, it reinforces its own learning and gets better with experience. In finance, one can view ML as an attempt at uncovering relationships between variables, where given historical patterns (input and output), the machine forecasts outcomes out of sample.
ML can also be seen as a model-independent (or statistical or data-driven) way for recognizing patterns in large data sets. ML techniques include:
- Supervised Learning (methods such as regressions and classifications)
- Unsupervised Learning (factor analyses and regime identification)
- Deep and Reinforced Learning. Deep learning is based on neural network algorithms, and is used in processing unstructured data (e.g. images, voice, sentiment, etc.) and pattern recognition in structured data.
What has changed in the past few years? Machine learning is moving from the fringe (doing data science in silos with dedicated PhDs and data scientists with a variety of niche tools (R, scikit-learn, Tensorflow, etc.)) to the core, supported by Machine Learning (ML) as a service. ML as a service model means business teams can move from a focus on custom engineering to model building and deployment by leveraging a variety of algorithms, common APIs, computing platforms and data services.
In many digital business models which function as a two-sided marketplace – AirBnB, Uber, Lyft, Amazon.com etc. — where you have buyers (or riders) on one side and sellers (or drivers) on the other side, an efficient marketplace comes from dynamic matching, predictions and attribution. Three-sided marketplaces – UberEats (consumers, delivery vehicles/drivers, restaurant – meal preparation) take the complexity to another level. Core platforms are becoming increasingly smart and dynamic learning applications that benefit from knowing user behavior via click-thrus, selections and likes and from knowing people’s historical behavior.
It is mind-boggling to consider how much the technology landscape has changed in only a few years. We are facing the most radical and profound changes since we experienced the mobile and digital transformation that started with the IPhone 10 years ago.
Digital, Analytics/ML (and Big Data) and Cloud Computing have been separate disruptors and tech silos so far. Increasingly they are converging (and overlapping) creating a new DAC space. They are increasingly inseparable as each reinforces the other.
Digital is the means through which consumers interact with the environment and data is the by-product of this interaction.
Analytics – data science, machine learning and AI – is how we extract value from the data and gain insights for action. The ability to see meaning inside the data is no longer a nice-to-have but a need-to-have. Adoption of predictive analytics and machine learning has seen an overwhelming surge. Almost every corporation is investing millions of dollars in becoming data driven. Predictions and prescriptive analytics has moved from the fringe of a few firms doing it well to mainstream.
“Big Data + ML + AI” is becoming the tech stack upon which many modern applications (whether targeting consumers, customers or members) are being built. Data engineering and data pipelines have evolved dramatically in the past decade. Some of the applications we are seeing as big data and AI come together are really game-changing.
Cloud is reimagining how enterprises and consumers orchestrate, automate and run applications, and how Digital and Analytics are delivered. Amazon Web Services (AWS) continues to grow by the sheer velocity, continuous cost reduction and breadth of its product releases. While AWS gets all the press, Google Cloud Platform is one to watch in the next 18 months. GCP uses Google’s core infrastructure, data analytics (BigQuery, Dataflow, Dataproc, Datalab, Dataprep, etc.) and machine learning (Tensorflow) which are formidable capabilities and also evolving continuously.
A good example of a DAC firm is NetFlix. It is a digital experience, driven by analytics (personalization on steroids, A/B Testing of new features), and cloud native architecture based in AWS.
The world is “going DAC” and it’s quite exciting — but this always-connected from-everything-to-everywhere world adds new complexity to software systems. New complexity means more opportunity as chaos reigns.
Disruptive 3Ms – Mobile, Messaging/NLP and Machine Learning… The future is evolving faster than organizations can adapt or adjust. What seems crazy a few years ago is now reality:
- In 2004, the idea that social networks will impact presidential elections by swaying voters would have seemed ludicrous.
- In 2009, the idea that smartphone coupled with AI/ML/NLP/Vision/Real-time Data will create a broad sharing economy seemed crazy.
- In 2015, the idea that wearables will one day replace smartphones seems crazy.
- In 2017, the idea of self-driving cars and trucks on road seemed to be a few decades away, not right around the corner.
- the fact that we have digital assistants – Alexa, Siri etc. – that are incorporated into everyday life was unfathomable a few years ago. With messaging emerging as channel of choice for many, and the demand for unique experiences is growing.
The transformation speed and urgency is picking up in almost every industry.
Many organizations flounder in their digitization efforts not because they lack smart talented people or capability but because they lack clear objectives, leadership, experimental mindset or multi-year roadmaps in aligning messy data silos, legacy applications and infrastructure (and mountains of technical debt) into useful digital architectures -> platforms -> business outcomes.
Every CEO today must have an answer to the question, “What is your digital strategy 3 – 5- 7 years from now? What new capabilities are you creating to compete with digital disruptors?” The challenge is not just the big picture clarity but the subsequent logical breakdown: Strategy -> Capabilities -> Architecture -> Programs -> Projects -> Applications -> Infrastructure
So the first critical question is: What do you really want to achieve? What are the target set of customer journeys or use cases? What are the target business outcomes and potential ROI? Increased customer loyalty? Better customer engagement? A greater share of wallet via cross-sell? New customers? Lower attrition? Cheaper and faster targeting?
In other words, what are the relevant use cases? As the adage from “Alice in the Wonderland” goes: if you don’t know where you are going, any road will get you there.
Starting with a clear objective is essential in order to create the right architecture, pick the right tool to solve the right problem. Some clarity is necessary to drive proof of concepts or even select a technology stack to experiment with.
Consumerization is Driving the Next Generation Architecture
Many enterprises are undergoing a data transformation. Consumerization and evolving customer behavior is forcing companies to change how they market, engage, sell and retain.
“The best minds of my generation are thinking about how to make people click ads…that sucks” – Jeff Hammerbacher
When you talk about Digital and Data Science combo, you normally think of the front-office MarTech (marketing technology) – advertising, marketing, sales, commerce, service. This is where most companies are investing as they race to deal with new customer experiences, better engagement, promotion effectiveness and more efficient commerce transactions.
MarTech is about digital and data science platforms that help deepen relationships with customers, simplify and improve customer experience. They aim to increase digital engagement by delivering differentiated experiences.
MarTech is growing partly because the proliferation of “screens” goes well beyond phones, tablets and desktops. There are exciting new developments as “screens” extend to the TV, wrist, in-home automation or car. The pulse of digital experience is speeding up as new technology like 5G, virtual and augmented reality become more feasible and viable.
MarTech is also evolving with data science, analytics, machine learning and AI. By applying intelligence to interactions, promotions and advertising, market leaders are completely changing the “art of the possible”.
Augmented Reality and Virtual Reality, Speech driven interfaces (e.g., Siri, Cortona, Echo/Alexa) all represent catalysts to the next wave of digital marketing innovation.
As a result, the MarTech (marketing technology) landscape grew even bigger . According to Scott Brinker there are as many as 5000 marketing technology solutions — almost twice as many as 2015. We will definitely see an M&A boom as vendor consolidation becomes inevitable.
“Looking to the future, the next big step will be for the very concept of the “device” to fade away. Over time, the computer itself—whatever its form factor—will be an intelligent assistant helping you. We will move from mobile first to an AI first world.” – Sundar Pichai, CEO Google
Software bots or Robotic process automation (RPA) is becoming mainstream at leading corporations as C-Suite execs look at next generation digital transformation strategies to do more with less.
High-volume, repeatable tasks within existing processes are slowly but surely being automated by software bots. Bots can perform at or better than humans at repetitive tasks using lookup, scraping tools, recording actions, natural language processing, role based access controls and rules engines.
As a result, there is a growing strategic wave of activity around next generation workforce transformation. The quest for operational efficiency and competitive pressures is driving this wave. Outsourcing, offshoring strategies have reached the point of diminishing returns so a new frontier enabled by a workforce of software robots is emerging.
The focus is not just on simple tasks like answering the phone in a call-center but on managing complex data-heavy business outcomes, such as NLP driven interactions via chatbots, preventing credit card fraud, or conducting compliance checks.
In the past year, I have seen a massive uptick of interest in RPA. Corporations like Citibank, American Express, Bank of America are implementing this trend with vendors as they race to cut operating costs and ensure compliance. Everyone under pressure from Amazon is looking for ways to automate faster.
The market opportunity of AI across industries and business processes has been expanding rapidly, with analyst firm IDC predicting that the worldwide content analytics, discovery and cognitive systems software market will grow from US$4.5 billion in 2014 to US$9.2 billion in 2019, with others citing these systems as catalyst to have a US$5 trillion – US$7 trillion potential economic impact by 2025.
Digital robots ∼ Apple Siri, Amazon Echo/Alexa, Microsoft Cortana, IBM Watson, Google Home/DeepMind, Facebook ChatBots, drones and autonomous driverless cars ∼ are now mainstream. What most people are not aware of is the rapidly advancing area of enterprise robots to create a “virtual FTE workforce” and transform business processes by enabling automation of manual, rules based, back office administrative processes.
This emerging re-engineering of key back-office and front-office operations is called Robotic Process Automation (RPA). Machine Learning (ML), guided ML, NLP and graph processing are becoming foundations for the next wave of advanced bot use cases. Speech recognition, image processing, translation have gone from demo technology to everyday use in part because of machine learning.
A whole new ecosystem has emerged – Automation Anywhere, Blue Prism, UIPath, Thoughttonomy, Workfusion, Redwood, Kyron Systems, Softomotive, Kofax Kapow and others.
RPA – What?
“Consumers have elevated expectations based on everything they are doing their everyday world. What they see from Uber, they expect everywhere.”
Who are the most customer obsessed companies? What are elements of a customer obsessed company? What role does world-class product management play in staying customer obsessed?
Amazon is definitely one of them. Google is another. Both firms are admired for their relentless innovation, experimentation and execution. So I did some research into what makes them unique.
Bezos focus has been the same for 20 years: “Start with the customer and work backwards.”
Google’s #1: Focus on the user and all else will follow. According to their philosophy…
“Since the beginning, we’ve focused on providing the best user experience possible. Whether we’re designing a new Internet browser or a new tweak to the look of the homepage, we take great care to ensure that they will ultimately serve you, rather than our own internal goal or bottom line. Our homepage interface is clear and simple, and pages load instantly. And when we build new tools and applications, we believe they should work so well you don’t have to consider how they might have been designed differently.”
Simple questions like ‘Where exactly is customer’s problem?’ and ‘What actions should we take to create value, convenience, and selection?’ are challenging for every organization. Most firms rely on McKinsey, Bain or BCG for this analysis. Doing this analysis with A/B testing, click-thrus, customer feedback and transaction data is the hallmark of Amazon product managers. I think that the product management structure of Amazon is their secret to success and differentiation.
The overall strategy of Amazon is shown here. It’s not a secret for their competitors but something that is extremely difficult to execute globally at scale across multiple categories.
The diverse customer segments across our many businesses include:
- Books, Music, Movies, Video Games and Consoles, Software, and Digital Downloads
- Electronics and Computers, Home and Garden, Grocery, Health and Beauty, Toys, Kids and Baby, Clothing, Shoes and Jewelry, Sports and Outdoors, Tools, Auto and Industrial, and Digital Devices
- Amazon Web Services
See my complementary post Retailers and the Paradox of Digital for competitor execution challenges.
Customer Obsession @ Amazon
Amazon’s mission is to be the earth’s most customer centric company. Both when scoping a new initiative and in every day decisions, they start with the customer and work backwards.
Amazon is customer obsessed, not competitor obsessed.
“We are transforming GE into the world’s premier digital industrial company using our scale and diversity to drive outcomes for customers. The real opportunity for change…surpassing the magnitude of the Consumer Internet…is the Industrial Internet.” – Jeff Immelt, ex-CEO GE
Recently, a client asked me a thought provoking question – which large firm is executing the most interesting and complex data driven transformation today. Took me a while to process this question. The usual familiar list of suspects – Amazon.com, Facebook, Google etc. – ran through my mind. But one firm – GE – stood out in terms of the boldness of their digital vision and complex multi-year digitization they are executing across various industrial businesses.
Industrial Data has strategic value. Reducing operational risk of jet engines, wind turbines, locomotives, gas turbines, healthcare equipment and oil & gas equipment has economic value.
GE is taking a data-driven approach to digital transformation (Industrial Internet project). Specifically, their objective is to drive a “Better Customer Outcomes Using Innovative Data-Driven Apps On a Integrated Platform.”
The business outcomes being targeted include:
- Asset optimization – optimize performance with minimal downtime
- Operations optimization – Increased system and people efficiency
- Process optimization – lower waste (material and cycles)
GE calls this strategy “the power of 1% efficiency improvement”. The context for this improvement is a world where the machines are not just intelligent but self-aware, predictive, reactive and even social. The goal is to wring small improvements and the ensuing savings that they can share with customers.
To understand this data-centric optimization strategy better consider the following “What-If” use cases across Aviation, Energy and Transportation.
GE Aviation Use Case
“Shopping is getting personal/easy/fast. Machine learning is enabling interactions to become more targeted, relevant and intelligent.” Mary Meeker Internet Report 2017
The challenges facing retailers in the midst of this transformation are legion. Traffic at many shopping centers has dwindled, price competition is heating up, and 100+ million Americans are Amazon Prime customers — locking them into a system where they can get free delivery on millions of items and access to exclusive movies, shows and video games.
It doesn’t take genius to predict that digital induced pain for brick-and-mortar retailers is going to get worse. The expectation gap between what consumers are expecting from retailers and what they are receiving is getting wider. Consumers are spreading their buying activity across channels, forcing retailers to spread out their digital investments. This puts significant stress on execution, product/platform management, design and leadership.
Evidence of this value migration from physical to digital is mounting every day. Against that backdrop, Wal-Mart is closing over 269 stores as it retools portfolio. [Walmart paid more than $3 billion for Jet to speed its data driven digital transformation. It’s been acquiring smaller players like ModCloth, Moosejaw and Bonobos to appeal to Gen Y and Gen Z segments.] Macy’s said that it will shutter over 36 stores as store traffic declines faster than expected, and Finish Line said that it would close 150 stores by 2020. Gap, J.Crew, American Apparel, Sears and Kmart, Target, Nordstrom are all facing similar headwinds.
Data drives digital transformation.
Starbucks CEO Howard Schultz laid out his thoughts on the future of retail, “three years ago we began to envision that there would be a seismic change in consumer behavior, and that seismic change was due in large part to e-commerce, search and smartphone shopping.”
It’s fascinating to watch retailers experiment and shift tech/platform strategies to deal with digital disintermediation, showrooming, physical-to-digital channel integration, mobile shoppers, same-day delivery/fulfillment, programmatic targeting, online native models and now the new buzz.. AI and augmented reality.
While most retailers seems to know what to do….they are unable to execute consistently or effectively around more efficient search, pricing, targeting or data mining. A talent gap in many cases. A platform gap in others. Others are hindered by legacy IT apps and infrastructure. Others by silos of data or necessary next generation technology capabilities like A/B testing, data science and machine learning.
This data driven digital retail UX and shopper evolution is a continuation of the trend from 1960s.
Analog to data-driven digital transformation
Amazon.com is the leading private label supplier of batteries (beating Duracell) and 3rd largest supplier of baby wipes (almost catching up with Huggies and Pampers). Mary Meeker, Internet Report 2017
E-commerce is growing at 15% YoY. We are right now in the middle of one of the biggest, most profound digital disintermediation in business especially retail. What makes it challenging to execute is that there is no “one size fits all strategy.” if anyone claims they have the answers, they are probably exaggerating.
Every company in the world is going through some form of digital transformation.
Seismic shifts are putting customers in control. As a result, there are many variants of digital:
- Digital add-ons to existing analog business
- Pure-play digital – Digital First
- Seamless consumer experience across web, mobile, physical channels
- Subscription stores with deeply personalized digital relationships
- Mass-customization of content and products
- Augmented Reality Experiences to complement retail experiences (e.g., Lowes)
CONVENIENCE and VALUE matter most to customers. Digital is all about enabling corporations to leverage the power of new technologies to create new sources of value. However, most organizations are still grappling with digital basics or addressing technical debt that has accumulated. But some leading edge firms (or digital masters) have figured out how to adapt to the fast arriving digital future.
So, who are these digital leaders and what are they doing to attract and retain customer attention?
- In content… National Geographic is considered a digital pioneer. Others like New York Times, Financial Times, Time are innovating with metered paywall technology across many of their properties. Their objective is to drive anonymous visitors to become registered users and convert into brand product purchasers.
- In retail… Amazon.com, Walmart, Target, Apple and Starbucks are often cited as digital masters, due to the significant investments they are seen to have already made both financially and in dedicated resources.
- In pharmacy… Walgreens is a digital master across different properties – Walgreens.com, Drugstore.com, Beauty.com, SkinStore.com (Cosmeceuticals), VisionDirect (Optical).
- In CPG manufacturing…. P&G, Pepsico, Coca-Cola, Nestle and Unilever and are often cited as digital center of excellence leaders, allocating only shared resources to social marketing and e-commerce.
- In industrial… firms like GE, Caterpillar, John Deere are also considered digital leaders. They are leveraging Internet of Things (IoT) to create novel experiences.
But, how are some other companies approaching digital customer experience and engagement? This is a very critical strategic issue as the average consumer spends more time on technology than sleep or eat. How firms grab this consumer attention will be key for future growth.
Source: Wal-mart Labs
For the times they are a-changin’… Bob Dylan
Customer channel behavior and interaction model is evolving constantly. Just when retailers, banks and others think they have multi-channel figured out the channel/interaction game is shifting with chatbots, virtual assistants, speech shopping, and other innovation.
New technologies have emerged to
revolutionize the way end-users
interact with technology and to
reshape businesses. According to Gartner: “Conversational AI-first will supersede cloud-first, mobile-first as the most important, high-level imperative for the next 10 years.”
Basically, customers are not interacting with brands in a linear fashion… they are jumping around from channel to channel and expecting the experience to be seamless and relevant.
For instance, in online shopping, women are more likely than men to reach for their smartphones and tablets to research and make purchases. Of U.S consumers who say they’ve completed a purchase on a mobile device in the last month, 66.5% are women and 33.5% are men. Compare that to 2013, when a greater share of men than women completed purchases on mobile. [BusinessInsider, The e-commerce demographic report].
To better understand, customize and respond based on customer behavior/context/clicks, Fortune 500 companies are making large investments around Programmatic Marketing (“Marketing that learns”). Specifically, the objectives are:
- Visualize and map the 1:1 customer journey by personas.. Customer journeys are an illustration or visual representation of all points of interaction across touchpoints.
- Optimizing on the right journey attributes to increase yields by >30% lift… Uncover the right combination of web, mobile and physical channels, content and experiences that best achieves the target goals
- Enable marketers to identify journey bottlenecks for individuals and aggregates
- Leverage actual behavior data to enhance and personalize the experience for each individual customer
One of most often implemented use case in Programmatic Marketing is customer journey mapping and analytics. Why? Because, deciphering the nuts-and-bolts” of individual customer journeys (and deducing intent) is core to improving customer experience and driving brand loyalty.
Salesforce CEO Marc Benioff said at a recent conference: This is a huge shift going forward, which is that everybody wants systems that are smarter, everybody wants systems that are more predictive, everybody wants everything scored, everybody wants to understand what’s the next best offer, next best opportunity, how to make things a little bit more efficient.
The retail store that does not have a meaningful relationships with the consumer is dead or going to be dead.
But meaningful relationship are easy to engineer. Today’s marketer is faced with an almost impossible task: Create relevant, individualized journeys for a customer whose channel preferences, purchase behaviors, and tastes evolve with unmatchable speed.
The cutting edge in data-driven digital and mobile marketing is “marketing in the moment”, which is the ability to identify and optimize precise moments of marketing influence across multiple channels and devices. In digital advertising, firms like Facebook and RocketFuel are using continuous scoring algorithms that score each moment to predict whether an individual will react favorably to an ad shown (display ads, search, social media and video) at a given time.
So how do marketers (and advertisers) understand what their audience wants or will see as valuable? That’s where data science comes in. When you strip away the rhetoric, data science is just about finding meaningful insights through analyzing large datasets.
Data Science is increasingly fueling data-driven digital marketing strategies at cutting edge firms…. Marketing learns, acts, and evolves across the consumer journey. Programmatic real-time bidding platforms is growing to dominate ad spending.
“Marketing and Advertising That Learns” Strategies
Fintech stands for financial technology. It’s just a blanket term for technology that is disrupting the financial services industry. Payments, Blockchain, Robo-Advisors (or automated investment advisory services) are all segments in Fintech.
Why robo-advisors? We are in the early stages of a shift in wealth management, especially “plain vanilla” investing for the mass affluent and millennial segment. Until recently, you had only two options when investing:
- Do-it-yourself (DIY)
- Hire a registered investment advisor (RIA)
Now there is a third option. Robo-advisors are new a class of financial advisors that provides online, algorithm based portfolio management with minimal human intervention. Robo-Advisors going after the low-end of brokerage/RIA business with automated asset allocation.
The Robo-Advisors market leaders who are serving the mass affluent include are:
- Wealthfront (with over USD 2.6bn in assets under management (AuM) and 20,000 investors);
- Betterment (with over USD 1.4bn in AuM and 70,000 investors); and
- FutureAdvisor (With over $600 million in AUM).
The timing for this market shift coincides with three trends: consumerization, digital tools, and disillusionment with status-quo investment advisors. The gyrating stock market driven by program trading is increasingly bringing Robo-Advisors, algorithmic portfolio management to the forefront. Investors are getting disillusioned with traditional investment advisors who simply track the market indices (SPY, QQQ or Russell 2000) by purchasing ETFs at best.
Many banks and brokerage firms over the years have shifted their focus to serve ultra high net worth (UHNW) and high net worth (HNW) investors, leaving an opportunity for firms to target the “mass affluent” investors, or those with less than $1 million in investable assets. Younger investors are increasingly interested in online digital advice, as opposed to hiring an adviser.
Who are we designing for? What are we designing? What outcomes are we targeting? What are the end-to-end user journeys as boundaries blur between consumers, stores and consumer brands?
How do you approach the messaging and the storytelling, especially given the challenges of channel proliferation? How do you break through the clutter? The first step in every digital strategy is to develop personas that segment the audience and serve as the foundation for customer UX and journey mapping analysis.
The best practice firms start with the user. Working from the perspective of the client who consumes a product or service, they focus on personas or “one idealized digital user.”
The goal is to think about the prospect, consumer, user as a human being. What matters in his or her life. Why? Because users do not wake up in the morning and think, “I need a new app today,” for example. People wake up in the morning and worry about getting to work, getting kids to school, where to meet friends for dinner, paying your bills and saving for the future.
Understanding the persona and the daily journey is critical in modern experience design. If marketing is going to interrupt you with something that they think is important to you, they have to find a way to tell the user about it so that it resonates with the user. There has to be a benefit to user. There has to be substance. Hence the need for real-world story-telling and context.
What is a digital persona?
Personas are fictional characters used to represent specific segments that interact with the brand across a variety of touchpoints. Personas characterize attitudes, values and behaviors of customer segments, and draw from various inputs to accurately depict the customer. They are helpful in distilling key information into more succinct stories that can be quickly understood. Personas are developed using qualitative research interviews, ethnographic studies – talking to real people about their real needs, motivations and behaviors.
Why is digital persona development important? The new battlefield is the customer journey and its various touchpoints across the lifecycle: AWARENESS → CONSIDERATION → PURCHASE → LOYALTY → ADVOCACY.
Across every industry, consumerization is changing how People they interact with businesses. Traditionally, most businesses have followed the same marketing and sales playbook to generate leads, close sales and provide support to their customers as they did 10-15 years ago. Businesses need a more effective way to humanize the target audience in order attract, engage and delight customers who have access to an abundance of information and an ability to block traditional marketing and sales tactics. To do this, businesses need to deliver an customized experience, which enables them to be more helpful, more relevant and less interruptive to their customers.
I believe an effective way to illustrate how people have transformed the way they consume information, research products and services, make purchasing decisions and share their views. You get a sense of this by reviewing these general personas – Digital Susan, Social Ashley, Introvert Dave, Modern Meghan and Traditional Ted. Read more
With digital, value is migrating from outmoded business models to new business designs that are better able to satisfy customers’ priorities.
As digital permeates every nook of business, firms will need diverse set of digital leadership. CIOs who step up to a digital leadership role can expect to contribute in a number of valuable ways, perhaps even assuming the über digital role with responsibility for all things digital. Those who can’t will be relegated to play subordinates to the emerging roles of Chief Digital, Data and Analytics Officer, or to the CMO, CFO, or COO, their portfolio limited to the infrastructure and corporate systems.
People have transformed how they consume information, research products and services, make purchasing decisions and share their views and experiences.
So in every boardroom, the buzzwords are flying – omni-channel, mobile-first, digital engagement, AI-first, digital transformation, Conversational AI, multi-screen engagement, millennial marketing, digital operating models, and so on.
Every leader has a semi-clear idea of what the digital strategy needs to be (Increase digital innovation; Improve customer experience by providing access through preferred channels; and drive cost efficiencies) — but there is very little clarity/consensus in terms of how to crisply translate the digital strategy into a next gen engagement architecture and create tangible ROI.
In other words, there is a growing “knowing–doing” gap emerging.
Mobile devices are ubiquitous and people glued to their phones throughout the day account for more than half of all internet traffic. Influencing the mobile consumer requires understanding the “context” in real time to make an impact and add value to their life. Four facts about him/her are necessary to engineer unique experiences:
- Who is the consumer?
- What do they want (to meet both her emotional and functional needs)?
- What have they purchased in the past?
- When and where do they shop?
The Rise of Mobile Marketing Automation (MMA)
The state of the art in digital marketing is the integration of social, local, mobile — or frequently called mobile marketing automation (MMA). MMA is a hot emerging areas that leading Chief Marketing Officers are focused on for mobile apps, precision targeting, and test & learn campaigns.
What makes MMA different is the focus on the propensity to purchase coupled with location intelligence. Segmenting and reaching audiences based on demographics, psychographics, content or cookies has its uses, but these methods don’t make the association that matters most – propensity to purchase and actual purchase behavior.
Just a few years ago, the category didn’t even exist. The tremendous migration of consumers to mobile as their primary interaction channel has fueled the need for new sophisticated B2C marketing tools. CMOs focused on digital consumer engagement are aggressively piloting new initiatives in this area.
Why? Because Digital shifts power to Consumer. Mobile isn’t the future – it’s the present. With over 2 billion Smartphone users and growing, mobile channel usage is growing exponentially. Consider these statistics….the number of Facebook mobile daily active users recently crossed 800M; the number of mobile-only monthly actives is 600+M users; % of users who only login from mobile devices crossed 30%.
Mobile-first, mobile-only are new behavior patterns in consumer engagement across all demographics.
Consumers – Gen Z, Millennials, Gen X, and Boomers – all expect brand interactions to be relevant to their immediate context. Established brands are scrambling to appear relevant in this mobile-first world, and brand-specific mobile apps are popping up with never-before-seen speed. Marketing requires meeting consumers where they are with laser-like targeting of offer & message – and mobile is a key place to do so.