Why There is Major Disruption Ahead for The Food Service Industry

Guest Post by Matt Gutermuth, Founder G7 Leadership

The moment Julia Child burst on the culinary scene some 50 years ago, bringing refined French cuisine to the masses, Americans tastes and preference changed forever. Today, with celebrity chefs getting constant exposure on Food Network and through traditional media, names like Bobby Flay and Rachel Ray are now as recognizable as major sports figures.

And we now have an entire consumer segment of foodies that eat out more often and are much more discriminating about what they buy at the grocery store. They want organic, grass-fed, and locally sourced whether they are out at a restaurant or shopping their neighborhood grocery store.  In 2016, for the first time in history, retail sales at US eating establishments surpassed those of grocery stores. 

Data: US Census Bureau; US Retail Spending

Data: US Census Bureau; US Retail Spending

And there has been a steady supply of new restaurants to meet this demand. In 2001 there were 469,018 restaurants in the country.  By 2016, the number had jumped to just over 600,000, an increase of 30%. 

But if you start to dig a little deeper into the restaurant growth story, there are some troubling signs and legitimate concerns about overcapacity — or dare we say a “bubble.” According to NPD Group, in 2016 the number of independent restaurants in the US dropped 3%, and the overall number of restaurants (independent and chain) fell by 1%.  While certain segments of food service are maintaining moderate sales growth, NPD data show that the casual dining and midscale/family dining segment continues to show weakness, with visits to casual dining restaurants falling by 4 percent and midscale/family dining by 3 percent in the first quarter of 2017.  In fact, dining out has started to take on a different meaning with restaurants facing further pressure from food retailers like Whole Foods and Wegmans offering prepared foods that can be consumed on site.  Would be foodies also have other ways to get their gourmet fix without actually going a restaurant. A growing number of online providers like Blue Apron deliver restaurant-quality meals with all the prep work already done.

Larger Trends

As we look to the next decade and beyond, there are larger trends that threaten traditional grocery chains as well as restaurants.  As they start to play out, we will see the greatest disruption in food service and retail in the last 50 years. The first major disruption will be dramatic changes in how and where consumers purchase and consume meals, with convenience and choice going to entirely new levels. The other related trend will be the emergence of technological innovators and masters of logistics as the new leaders in food retail and food service.  

Emerging Leaders

We are already seeing the first signs of blurring of lines between food retailers and food service, as prepared foods become a growing and more profitable segment for grocery stores.  Leading the way are Whole Foods and Wegmans, with that latter now taking over Trader Joe’s as America’s favorite grocery chain.  The research firm CRC projects that five years from now prepared foods will represent 6.7% of grocery store sales, up from just 1.7% five years ago. They estimate that prepared food sales could increase from $15 billion five years ago to exceed $65 billion five years from now.  Now, “going out” to eat no longer means a trip to your local restaurant, but is more likely to be Wegmans or Whole Foods. For every dollar spent dining out at either of these grocers is one less spent in a local restaurant.  The reason food retailers have been successful at growing food service is that they meet all the essential elements that drive consumer behavior, which according to NPD’s Warren Solochek, include “convenience, quality food, value, and the experience.”

Lessons from Walmart

When Walmart entered the grocery business, there was a good bit of skepticism, with established grocery chains scoffing at the notion that a traditional retailer could possibly be successful selling food.  “They don’t know our business,” was the mantra at the time. Starting in the late 1980’s, Walmart proved to be a fast learner, launching their first Superstore in 1987, they are now the largest “grocery store” in the country, with over 21 percent market share of the U.S. traditional grocery industry.  They were able to achieve this dominance in large part due to their mastery of logistics honed over the previous decades.  They became, in essence, a logistics company that happened to be in the food retail business, with inventory management and cost control that is the envy of industry.  Now having achieved scale, they are influencing food pricing in the grocery sector, putting downward pressure on competitor’s profitability.

The Future with Amazon

The latest and perhaps most disruptive force now in the industry is Amazon. With the acquisition of Whole Foods, Bezos has done what the naysayers claimed he couldn’t do — quickly scale a physical presence across the U.S.  With over 450 stores located in prime locations, he’s done just that.  So, what does this mean for the industry, and what players will be impacted the most?  Food retail?  Food Service?  The short answer is all of the above.  Amazon’s move into this space is an absolute game-changer, and the impact will be felt across restaurants and grocery stores as Amazon brings core capabilities that are essential to winning in the future:  delivering a superior customer experience using technology and mastering supply chain and logistics.

Amazon is betting that they can innovate and execute better than the incumbents by using technology and analytics to change how consumers as well as culinarians buy, prepare and consume food. They have redefined choice and convenience for consumers in the online retail world, and who’s to say they can’t do the same for food service or retail. Amazon has already begun investing in building out local distribution centers that enable same day delivery of merchandise. How soon will they scale up food delivery to the home?  Or, perhaps even more significantly, when do they start supplying directly to restaurants, offering chefs and owners the same choice, convenience and price transparency that online consumers enjoy now. And, by the way, those chefs and owners are most likely already Amazon customers through Amazon Business. Will Amazon, now that Whole Foods is on board, be able to truly scale farm-to-table with better technology and logistics? Can they deliver the impossible: size, scale, and highly differentiated offerings? 


Food retailers, food service and the entire ecosystems that support them need to reevaluate everything they do, starting with the customer experience using the Amazon standard as a baseline. They need to ask themselves whether the product or service they provide is fast, convenient, transparent, easy? They need to look across all customer touch points to determine what can be improved or automated. They need to examine systems that support order management, order fulfillment, pricing integrity, and delivery to identify gaps in capabilities or intelligence that put constraints on their ability to improve the customer experience.

Finally, they need to challenge all existing performance standards as it pertains to the customer, and ask whether they are good enough to compete in the future. Perhaps the most important first step is to realize that the competitive landscape just shifted dramatically and only those able to adapt and change will survive.

About Matt:  Matt was formerly President & CEO, Safeway.com, and held senior executive positions at Sysco and Winn-Dixie. He is now founder of G7 Leadership, inspiring others to be great leaders by sharing over 25+ years of leadership experience to help others navigate change.


Photo credit: Premshree Pillai via Visual Hunt




Machine Learning and Price Optimization

Determining what price to charge for your product or service can at times be deceptively easy:  figure out what your competition is doing and either match or beat that price. This approach works fine for commodity markets, where price transparency and comparison is easy. But what happens when there aren’t readily available comparisons? Or when you have “similar” product characteristics, but no other information that you can access that may have influenced pricing decisions such as other services included, seasonality, location, etc.  In this case, you often go with a gut feeling or accumulated experience to price products and hope there is adequate demand at your chosen price level.

For most organizations, even if they did have data on all the product characteristics and external factors they would still struggle to process this information in a way informs day-to-day pricing decisions.  The task of combing through and analyzing large data sets, determining correlations and assigning weighting factors to various product characteristics and other variables, is still beyond the capabilities of most organization’s technology stack designed for managing supply chains and customers, not high-powered analytical analysis.

Machine learning technology is starting to fill this gap, and traditional companies and startups are changing how pricing is done using smart analytics, processing power, and human intuition to optimize pricing.  Let’s take a look a couple of real world applications in the insurance and hospitality industies.   

Insurance Industry Application

As one of the largest insurers in the world, AXA has massive amounts of data on customer claim histories, and they are putting this to good use to help prevent large loss claims. Every year, 7%-10% of the company’s customers cause an accident, with most involving small claims of hundreds or thousands of dollars. 

However, approximately 1% of customers involved “large-loss” cases of over $10,000 and Axa needed a better way to predict and hence prevent the number and size of the large-loss cases. They had been using a more traditional machine learning technique called Random Forest but were only getting prediction accuracy rates of less than 40%.  In the hopes of getting better results, they started using Google’s TensorFlow deep learning solution and saw their prediction accuracy climb to 78%.  They were able to do this by tapping into the advanced neural network model that Google had been refining over the years, and combining this with the scale of their cloud offerings to deliver the computing power necessary to handle the processing load. Axa is now in a position to accurately price risk based on better understanding of the attributes of policyholders and other factors that lead to large-loss cases.

AirBnB’s Pricing Algorithm

Airbnb’s pricing challenge is a bit more complicated than most, as the users, not the company are responsible for setting prices. To enable hosts with pricing decisions, the company needed to provide the tools and data to help them optimize the price received while maintaining occupancy levels.  While conducting user research, Airbnb observed that during the initial sign up process, when hosts came to the pricing page they immediately began to search for other similar properties. The problem was that not only was this a laborious and time-consuming process, but they often had trouble locating similar properties. They discovered what most people learn when trying to sell their home, that it’s tough to find exact comparable properties. They also had to contend with pricing comps across an entire city, spanning multiple neighborhoods. They needed a way to automate this analysis and provide meaning price guidance to hosts.  

So the technical team at Airbnb set their sights on solving two problems: 1.  Automate the property comparison process, and 2. Understand supply and demand dynamics to make timely price adjustments. 

Unlike eBay, where there aren’t any location or time dependencies — you can buy and sell anything from anywhere at any time — lodging is very location and date-specific. And in the Airbnb model, can be as varied and idiosyncratic as the people that own the properties. To solve for this, Airbnb developed a list of the prime characteristics of properties, applied weightings to each one based on their importance to potential renters, and then ran these assumptions against years of transaction data to model against actual outcomes (i.e. what was the final price).

Image: Airbnb pricing tool

Image: Airbnb pricing tool

They were looking for how each variable correlated with price to understand the key drivers of value and to inform their pricing engine to make better pricing recommendations. They discovered things like the number of ratings correlated with higher demand, and that the use of certain types of photos translated to higher prices. Surprisingly, the professional photos of living rooms didn’t fare as well as the nice cozy bedroom shots taken by the owner. 

With these new insights, Airbnb was able to provide a more useful pricing tool for hosts that not only allowed them to price their properties based on more comprehensive comparative analysis but also provide dynamic pricing recommendations in response to changing demand. Similar to how airlines handle pricing, hosts get ongoing guidance based on market conditions so they can make adjustments the will drive higher occupancy.


Machine learning augments human decisions by narrowing a set of choices.  But just running a “black box” in the background that produces the miraculous answer is not sufficient. In the above example, insurance agents need to be able to explain the rationale behind auto premium price differences and rental hosts need to understand why and how price recommendations were determined to maintain trust and confidence in the information. It’s important to keep in mind that while the machine learns and provides answers, humans still need to explain what it means and why the results should be trusted.  The product lead for Airbnb put it best, “We wanted to build an easy-to-use tool to feed hosts information that is helpful as they decide what to charge for their spaces while making the reasons for its pricing tips clear.”

A Closer Look at Einstein, Salesforce's New AI Features

Is there any promise for the use of AI in sales and marketing? In a B2B context? Leading CRM solution provider Salesforce seems to think there is. In the past year, they rolled out AI-enabled enhancements to their cloud-based sales, marketing, and support solutions that are designed to deliver more predictive analysis, helping sales reps identify the most qualified leads, and giving marketers the intel to know who to target with what offer. 

To determine whether there is hope for such solutions or just hype, we’ll take a quick look at the major features of Salesforce’s Einstein AI, review some of the early critiques by the experts, and ponder some of the real-world use cases that might yield breakthrough results. Salesforce has deployed Einstein across their entire suite of solutions, but for the brevity’s sake, we’ll focus just on the sales cloud.

Feature overview of Einstein Sales

  • Einstein Lead Scoring: Einstein Lead Scoring models are built specifically for each customer and organization, which ensures that the models are tailored to the business. Einstein Lead Scoring analyzes all standard and custom fields attached to the Lead object, then tries different predictive models like Logistic Regression, Random Forests, and Naïve Bayes. It automatically selects the best one based on a sample dataset.
  • Einstein Opportunity & Account Insights: Sales Cloud Einstein analyzes all the standard fields attached to the Opportunity data in addition to email and calendar data, and then uses machine learning, natural language processing, and statistical analysis to provide sales reps and managers with "Predictions", "Key Moments", and "Smart Follow-Ups."
  • Einstein Activity Capture: This logs historical emails and calendar events from up to six months back for Gmail and up to two years back for Office 365.  It then works in the background to passively capture every email or calendar event sent or received. The captured emails and events are all displayed in an activity timeline, providing a history of the team’s relationship with a customer.
  • Einstein Follow-Ups:  This provides proactive email notifications, letting reps know when a customer needs an immediate response, or set a follow-up reminder.

Early critiques

Having lived through many “hype-cycles” over the years, technology buyers tend to react in the same way whenever there is some breakthrough new technology:  “so, what problem does it actually solve.”  In a recent article on new AI solutions, NextWeb talked about how “AI-powered tools are now helping scale the efforts of sales teams by gleaning useful patterns from data, finding successful courses of action, and taking care of the bulk of the work in addressing customer needs and grievances.”  Techcrunch takes a bit more pragmatic view on Salesforce's AI, “certainly automatic model generation, if it works as described and truly delivers the best models in an automated fashion, is highly sophisticated technology, but in the end, users don’t care about any of that. They want tools that help them do their jobs better, and if AI contributes to that, all the better.” On how to think about AI in the technology solution stack, they noted “the fact is AI is not a product in the true sense, so much as a set of technologies. We should keep that in mind as we judge these announcements, looking at how they improve the overall products and not at the shiny bells and whistles.” 

Possible Use Cases

Complex B2B sales remains a mostly human activity, and any technology deployed to support the process should help augment, not replace human judgment. If applied correctly, AI could help spot consistent patterns that narrow down a list of highly qualified leads for reps to contact given certain triggers. This is no doubt useful and could drive efficiency, but if the objective is to close larger more complicated enterprise sales, the most likely use case could be AI that tells reps who to talk to, but not what to say or do next. As we have discussed before, buyers and the buying process is not perfectly rational, and algorithms need good data

CRM systems can be full of human-keyed data that may be inconsistent, inaccurate, or lack sufficient depth to be meaningful.  Additionally, much of what’s entered can be subjective (close dates, probability of close, deal size) and often overly optimistic. What ultimately matters are customer behaviors: what products did they buy, when did they buy, what did they pay. Using actual prior transaction data for the AI analysis would likely improve relevancy and accuracy of predictions to make marketing and sales more efficient, and more importantly, more productive. 

Lessons from Google Data Centers: “Gaming” Their Way to Better Efficiency

Google data centers consume lots of power.  By recent estimates, they have over 2.5 million servers that consumed 4,402,836 MWh of electricity in 2014, equivalent to the average yearly consumption of about 366,903 U.S. family homes. Over the years they’ve had scores of PhD’s focused on coming up with solutions to optimize data center efficiency. Then they unleashed machine learning on the machines.

Using the same AI technology that taught itself to play Atari and beat the world champion in Go, Google’s DeepMind machine learning algorithms now control 120 different variables in their data centers, constantly learning what combination of adjustments maximize efficiency.  The result?  Deepmind was able to achieve 15% reduction in overall power savings and a 40% reduction of energy used for cooling, translating into hundreds of millions in cost savings.

Commenting on these results, author and MIT professor Erick Brynjolfsson addressed the broader implications: “You can imagine if you take that level of improvement and apply it to all of our systems — our factories, our warehouses, our transportation systems, we could get a lot of improvement in our living standards.”

Apparently, we’ve barely scratched the surface:  According to McKinsey: “while 90 percent of all digital data has been created within the last two years, only one percent of it has been analyzed, across both public and private sectors.” And behemoths like GE are fully on board with advanced analytics, spending $1 billion this year alone to analyze data from sensors on gas turbines, jet engines, and oil pipelines. If they can achieve Google-like results, the implications could be staggering.  

A Thought Experiment

Most organizations don’t have the resources of Google or GE, but they do experience similar problems that could be solved with a better understanding of all the variables that impact performance and a mindset of constant improvement. It’s important to keep in mind; Google already had some of the most efficient data centers in the industry before they unleashed DeepMind on the problem.

Obviously, you can’t snap your fingers and suddenly become Google.  So, perhaps a thought experiment is in order. One where you, for a moment, suspend disbelief, set aside current constraints, and think about what’s possible. With the Google example in mind, in what areas of your organization could you reap the greatest benefit with respect to, for example, production or servicing costs, or close ratios and customer retention that drive revenue?  What are the key variables that impact each of these areas and if you had perfect information what would it tell you? If you come up with, for instance, five variables that impact customer support costs, try to come up with 10 or even 20.  Challenge your team to do the same.  The point is not to engage in some pie-in-the-sky exercise, but to appreciate the level of complexity inherent in any activity within your business, and to start to look for correlations between events, activities, behaviors, and outcomes.

Further, you need to challenge the conventional wisdom in your organization that reinforces that notion that finding the “single cause” for performance issues will result in optimal outcomes, when in fact understanding the broader collection of variables will likely produce better results.  Google identified 120 variables just for data center energy consumption.  How about you? 

Digital Transformation: Where are You Now and Where Do You Need to Be?

We hear a lot about digital transformation and disruption, with boards pushing CEOs to “become digital” and completely rethink their business models. Geoffrey Moore provides an interesting framework for thinking about digital disruption as a continuum or serious of steps with firms having different starting points based on where they are in their life cycle: 1. new entrants incubating and scaling a truly digital business model, or 2. established companies that are modernizing and already scaled “industrial” model. 

Regardless of your starting point, creating and building a strong analytics competency is essential to remain competitive.  His point is that digital is data.  And when we talk about disruption, it’s about how companies use data and analytics to create new business models or services.  

Moore sees competitive firms in the future as those able to read the “signals” from customer data:  

“In the digital economy, such signals live at the intersection of two types of datasets—systems of record, which capture transactional data, and systems of engagement, whose log files capture all the peripheral interactions that occur in and around a transaction.”

Getting to this point requires climbing a series of “stairs” to reach the point of digital disruption. But first you need to figure out where you are now. 

Climbing the Stairs to Digital Disruption

According to Moore’s model, there are five steps that firms must ascend, with each corresponding to their digital IT maturity:  1. systems of record, 2. systems of engagement, 3. engagement analytics, 4, systems of intelligence and, 5. systems of disruption.  

Here’s a quick synopsis of each phase:

  1. Systems of Record:  ERP and CRM systems provide a single view of the customer and streamline the quote to cash process.  Key challenge – systems are still organized around the products, and they make it difficult to get a single view of the customer.
  2. Systems of Engagement:  Mobile applications and omni-channel communications improve customer experience, reduce time to transact, and eliminate disintermediation. Key challenge -- if systems of record are  behind in their "accommodation of customer-centricity," according to Moore, “you now have a ‘two-stair’ challenge ahead of you.”
  3. Engagement Analytics:  Dashboards and reports extract insights from Systems of Engagement about customer preferences, market trends, systems inefficiencies, and user adoption.  Key Challenge – at this phase you still have “human-in-the-loop computing,” that relies on people being able to "detect patterns and infer relationships.”  Innovation still moves at “human-centric pace.”
  4. Systems of Intelligence:  Machine learning detects near-invisible correlations, infers causation, enables prediction, and proposes prescriptions, in order to optimize all types of interaction. Key Challenge -- You need the right talent to “secure the data science expertise to work the algorithms, and then you need to get access to enormous amounts of data to feed the beast.”
  5. Systems of Disruption:  Systems of Intelligence leverage proprietary insights to disrupt inefficient markets with novel digital services.  Key Challenge -- getting through steps 1-4, which ultimately may require a new infrastructure model, a new operating model, and a new business model.

Moore posits that today most established companies operating in more traditional industries (i.e. not the digital natives) are somewhere between systems of record and systems of engagement, with a smaller number of innovators reaching Stage 3 - Engagement Analytics. He warns that established companies need to be firmly at stage 3 by the end of this decade or face a real existential crisis.

The "Two-Stair" Challenge

So, are you facing a two-stair challenge today?  Based on Moore’s framework, the degree of “customer-centricity” you have now in your systems and processes is a good indicator.  Firms that have attained just the systems of record level tend to be more inwardly focused on efficiency and less externally focused on effectiveness of customer interactions. Readjusting your focus externally and understanding your customer using historic transaction data and the “interactions that occur in and around a transaction,” is the key to accelerating your ascent to digital disruption and maintaining competitiveness in the new digital economy.

In our latest eBook: The New Customer Experience: Using Data and Analytics to Drive Digital Transformation, we discuss the key elements of the new B2B customer experience; the four common barriers to digital transformation; your essential analytics toolset; and how to get started down this path using feasibility studies to gauge where you are now and where to invest next in your digital journey. 


Photo via VisualHunt

Using a Journey Map to Improve Customer Experience

The old adage “you never get a second chance to make a first impression” still holds true today. However, the reality is that customers have multiple “first impressions” along their journey, from evaluation to purchase, to post-sales support.  And a bad experience at any point can wipe out any goodwill generated to that point. Gartner calls each of these points a “moment of truth” or critical decisions customers make at various points along their journey that can make or break a relationship— driving the customer to abandon their purchase, or perhaps the relationship entirely.

Companies use a variety of customer surveys and tools to try and gauge customer satisfaction and determine problem areas. While an essential part of a company’s toolkit, surveys are just one source of input to include in a comprehensive customer journey mapping that shows where, when, and how the company dropped the ball. To determine how a journey map might work for you, you need to understand the core elements in your typical map, why they are important, and how you might use them to pinpoint problems and identify opportunities for improvement. 

Primary Components of a Customer Journey Map

There is not one single type of customer journey (that would be too easy), but can be many permutations based what you provide (product or service) and the breadth of your focus (single customer persona or complete process). Regardless, there are some common core elements found in all good journey or experience maps.

The folks at Adaptive Path use “Experience Maps” to capture the complete customer experience and identify areas of customer pain and opportunities for improvement.  It starts with establishing guiding principles and includes the journey model, qualitative insights, quantitative metrics, and key takeaways. It’s an “artifact that serves to illuminate the complete experience a person may have with a product or service.”  


Guiding Principles – These principles define the context for the experience or journey map, and the scope of the analysis, be it specific personas or value propositions.  The objective is to gauge at multiple points across the customer journey, how well the customer experience agrees with these guiding principles.  

Journey Model – This is where you document the path the customer takes, the transitions they have to make from different phases (sales, delivery) and channels (web to phone support).  Here you want to capture not just the steps but illustrate something about the process: what is not working, the scope of the problem (how many customers), and the nature of the activity (linear steps or variable), what systems and tools are involved.

Qualitative Insights – These insights include the “doing” (journey) but also the thinking and feeling—the frame of mind of the customer at any given point in the journey.  They may feel anxious, confused, angry, or disappointed. You also want to understand what they are thinking: “What is the easiest way to get from A to B,” “I want to get the best price but I’m willing to pay more for convenience,” “The answer I’m looking for is not on the website, what now?”

Quantitative Info – Here is where you can use the survey data, web traffic, or abandon rates to understand the source and magnitude of the problem. By including clear metrics on the journey map (survey data in the Rail Europe case), you can quickly pinpoint problem areas. 



Takeaways -- The takeaways should guide decisions related to solving the problems identified in the journey mapping exercise:  reducing pain points and taking advantage of opportunities to improve your customer experience.  These bullet points provide a clear summary for your team as to priorities going forward and areas for investment that will deliver measurable value.


Customer Journey Maps can be a valuable tool to help you isolate customer experience challenges. It can also be an unwieldy, tangled mess if you don’t apply some basic structure to the upfront research, construction of the map, and evaluation of key takeaways.  To help keep you grounded and focused, start with the key principles and use them as “guardrails” to keep you on track to better customer insights.


Photo via Visual hunt


Will Algorithms Replace Human Judgement in the B2B Sales Cycle?

With all the talk about advanced algorithms, artificial intelligence, and chatbots one begins to wonder when virtually every B2C or B2B transaction will be automated.  In this utopian (dystopian?) future, the machines will know exactly what you want, buy it for you, and deliver it to you the same day.  But what role will humans play?  Are we to be disintermediated by the machines?  Replaced by algorithms? Future thinker and researcher Andrew McAfee makes the case that algorithms can and do outperform “experts” that rely on accumulated experience and good old human judgement—but only under certain conditions.

Understanding where you can use advanced algorithms will help you think through where to apply investments in analytics and what complementary skills you need on your marketing and sales teams.

Humans vs. Machines

According to McAfee, there is an abundance of evidence indicating that algorithms outperform human experts in their prediction making prowess.  One research study he cited, which involved the meta-analysis of 136 different studies comparing the prediction accuracy of machine vs. man, showed that in only 8 of the 136 studies the “expert judgments were clearly better than their purely data-driven equivalents.”  He further noted that “Most of these studies took place in messy, complex, real-world environments, not stripped-down laboratory settings.” So, why is this the case? In what situations or conditions do algorithms have the advantage?  And what about human intuition? To answer this, he calls on a bit of theory regarding the ideal conditions for decisions made based on judgment and intuition.  The ideal conditions for human judgment include:

  • an environment that is sufficiently regular to be predictable
  • an opportunity to learn these regularities through prolonged practice

In the medical field, you can find examples of expert judgment that fit the above criteria.  McAfee notes that since human biology changes very slowly, medicine meets the first criteria, but gyrating stock markets certainly don’t.  The second condition benefits from fast and consistent feedback loops that promote learning that can be applied to future decisions.  Anesthesiologists working with dozens of patients experience rapid feedback loops --seeing the effects of their actions -- that help improvement decisions. Where human intuition and judgment tend to break down is in “noisy,” highly variable environments where there are large data sets that aren’t easily interpreted.

Implications for B2B Marketing and Sales

For marketing and sales professionals, there are two aspects of the above analysis that impact that effectiveness of algorithms for lead qualification and selling and perhaps tilt the scales to human decisions:  the availability of complete and accurate data to feed the algorithm, and unknown preferences and biases of real buyers. The quality of the output from an algorithm can be highly dependent upon the veracity of the inputs. If there is missing or incomplete data, or a small sample size is used that skews results, the algorithm could suffer. Additionally, the complexity of the typical B2B sale makes automation with analytics trickier.  In the B2B sale, there is often multiple decision-makers and influencers, they can be less than forthcoming in sharing their intentions, and they face reputational risk when making buying decisions.  All of which requires more handholding, coaching, educating, understanding, and communicating—none of which easily automated.  


If we apply the ideal conditions for intuitive decision-making listed above to the B2B marketing and sales functions, we can see where the line could be drawn between automated algorithms and human decisions.  The first condition describes an “environment that is sufficiently regular to be predictable,” which would apply to a sales process whereby qualified prospects have a consistent set of pain points and requirements.  The second condition is where sales team “learns these regularities” and becomes more adept at educating, positioning, and pricing based on this understanding, resulting in faster sales cycles and higher close rates.

The antithesis of this is random leads that have no consistency and high levels of variability, which limits the intuitive decision-making ability of your sales team in determining what is “right” for the customer.  In this context, the job of analytics and algorithms is to eliminate the randomness by combing through data to find patterns that help identify consistent characteristics of qualified leads and deliver them to sales.  It’s not utopian to imagine machines and humans cooperating to make better decisions, you just need to have them each focus on the task they are best equipped to handle.

Photo credit: MattHurst via Visualhunt.com /  CC BY-SA      

Using Customer Lifetime Value to Create a Data-Driven Culture

Recent research shows that businesses have made some progress with their Big Data and analytics projects, but success is mostly limited to expense reduction initiatives.  Business transformation efforts and new revenue streams continue to lag.

Analytics Projects Still Expense-Driven

The results from a New Vantage survey of Fortune 1000 executives regarding their Big Data projects shows that “decrease expenses” was an area the showed the highest response (49.2%) for “Started and seen value.”  The responses for “Add revenue” and “Transform the business for the future” received the highest responses for “Not started.”  Interestingly, “Establish a data-driven culture” received the highest response (41.5%) for “Started and not seen value.” 

The report hints at the potential problem:

“In spite of the successes, executives still see lingering cultural impediments as a barrier to realizing the full value and full business adoption of Big Data in the corporate world.”

If one assumes that Big Data or advanced analytics is a major element of any business transformation that will create differentiation and competitive advantage, then removing the impediments to this transformation is paramount for execs. The key to creating a data-driven culture may lie not in focusing on data per se, but on customers and the value they create for your firm, and the value you deliver. Paradoxically, focusing externally on your customers may be the best way to drive internal cultural change.

Using CLV Metrics to Drive Change

MIT’s Michael Schrage talks about how companies can use customer lifetime value (CLV) to bring a more rigorous, data-driven approach to customer relationships focused on long-term relationships. Talking about the value of CLV, he noted:

“By imposing economic discipline, ruthlessly prioritizing segmentation, retention, and monetization, the metric assures future customer profitability is top of mind.”

He also notes the CLV is not enough: “While delighting customers and meeting their needs remain important, they’re not enough for a lifetime.” He argues that CLV metrics should measure how effectively “innovation investment” increases customer health and wealth.  From his workshops, he found that clients talked about how customers become more valuable to a company when “they buy more stuff,” or “they pay more” or “they’re loyal to our brand.”  All of which are traditional CLV type metrics.  He advocates going beyond these measures of value to incorporate more of an “investment ethos,” that looks at customer value created when customers:

  • Share good ideas
  • Evangelize for you on social media
  • Reduce your costs through self-service
  • Introduce you to new customers
  • Share data

By expanding the notion of what constitutes customer value companies can start to rethink segmentation, pricing, and promotions. It might also educate and better align your employees— regardless of their job title—with a complete view of customer value and the importance of measuring and tracking it. This investment view of CLV will help sales understands how new customer introductions create new opportunities; marketing can appreciate how evangelizing on social media drives more leads; product development gets new ideas; and customer support becomes more efficient resulting from greater customer self-service.  Once employees see the potential benefit to them, they just might be more motivated to seek out and use these metrics, thereby creating the data-driven behaviors and decision making that is key to transformation.

Schrage observed in one of his workshops how participants kept interchanging references to the creation of lifetime value as when “we” do something, or when “they”(customers) do something. He noted that there were much broader and deeper discussions around how to engage with and invest in their customers. And more comprehensive CLV metrics are the method for tracking how well the company is engaging and investing.


Cultural change is and has always been a difficult proposition for companies of any size.  Using a broader definition of CLV and the metrics to track it, could help align multiple areas of your organization around customer value that could jump start the data-driven cultural change that will drive transformation.  By clarifying what customer value means, how it is measured, and how each employee impacts these metrics you have a chance of creating a broader sense of purpose -- increasing customer value -- around which your team can rally.

Why Your Customer Profiles Need Behavioral Data

Developing customer profiles and segmentation strategies is essential to delivering personalized and relevant products and services. But too often in the B2B space, segmentation stops at the demographic level (size, industry, geography) and doesn’t include buyer’s behaviors and actions. For companies facing stagnate sales growth, building deeper and broader customers profiles that include a behavioral component may reveal the keys to greater growth and profitability.  B2C companies have led the way on profiling, segmentation, and understanding buyer behaviors. It’s time for B2B firms to catch up.

Buyers as Rational Actors

In the consumer B2C space you will likely find irrational, impulse-purchasing customer decisions driven by emotion. Conversely, business buying decisions in the B2B world are driven by careful, measured, cool-headed analysis, devoid of any emotion. Or so we think. When comparing the two segments, you find one unifying element: humans are involved. And in either the consumer or business context, buyers rarely act as rationally as one would assume. There is plenty of research to back up this notion of irrational human behavior when it comes to evaluating risk, loss, probabilities and other “cognitive biases” associated with decision making.

The most notable work in this field is the Nobel Prize winning research of Kahneman and Tversky, who through decades of research showed there were two competing decision processes that humans engage in:  one is fast, intuitive, and emotional; the other is slower, more deliberative, and more logical.  What they discovered is that when faced with decisions involving high degrees of uncertainty or potential risk, even smart and experienced people can fall prey to biases that lead to bad outcomes.

To develop more meaningful customer profiles, you need to understand how certain types of decision-making biases may be influencing your buyer’s behavior. You need to know how these customer decision biases might influence how and when you offer price discounts, rebates, service contracts, extended warranties and add-on services.

“Loss aversion” is one of the decision-making biases uncovered in their research, and understanding how it influences customer decisions may help you structure your offerings in ways that could improve conversions.  From Kahnemen:

“For most people, the fear of losing $100 is more intense than the hope of gaining $150. We concluded from many such observations that losses loom larger than gains and that people are loss averse.” 

How does loss aversion play out in everyday life?  Homeowners are less likely to sell their home when prices are falling, or investors are less inclined to dump stocks when the market is dropping.  Either action would cause them to recognize the loss, and thus mentally process it. You also find this phenomenon in sports, with professional golfers making a higher percentage par putts (risk of losing a shot) than birdies of equal distance.

Recognizing that behavioral biases impact decisions, you need to ask yourself: Is the way you position and price your products and services mindful of these potential biases?  Are you presenting the “upside” of your offering when the customer is really concerned about downside risk or loss?  Do all your buyers think and behave the same way? Deeper customer profiles and segmentation that incorporates prior actions and behaviors can help you begin to answer these questions.  So what are the core elements of your new, comprehensive customer profile?

Elements of a Customer Profile

To develop a more compressive B2B customer profile, we can borrow somewhat from the B2C world.  In the consumer space, there are six key areas that matter to marketers:

1.       demographic (age, gender, income)

2.       geographic (where they live/roam)

3.       attention (what they concentrate on)

4.       consumption (what they buy)

5.       behavioral (what matters to them)

6.       intentional (what they’re about to do)

Taking these in order, you will likely have acceptable quality and depth of demographic and geographic.  You may also have a basic idea as to “attention” if you are tracking any basic online activity, for example.

Where things get interesting is when you overlay consumption and behavioral data based on prior purchase history. With this data in hand, you have a distinct pattern of purchasing behavior that can lead you to the ultimate end game: intentions. You want to know what buyers are likely to do next—what, when and how much will they purchase—and to be ready with the right combination of offerings when that moment arrives.


The process of developing and using customer profiles is at its core a process of testing assumptions: you structure pricing and offerings targeting a specific segment, and expect certain outcomes. This process begins with understanding the quirks of the human mind (buyer decision making), the depth and breadth of the data about the customer (the profile), the combination of products, prices and promotions you test, the results you see, and the adjustments you make based on those results. This requires digging a bit deeper on your profiles, adding consumption and behavioral data to help you find new opportunities for growth.  

What’s Holding Back Your Digital Transformation

Everyone seems to be on the digital transformation bandwagon.  Recent research by Mulesoft shows that 88% of IT decision makers either have an initiative underway or will within three years.  So, what do they hope to accomplish with these transformative efforts?  Over 60% said that they want to “create great customer experiences,” with 77% looking to improve existing business processes.  But recent experience sheds some light on execution challenges they face, as respondents indicated that only half were able to complete digital transformation projects undertaken in the past year.  The reasons they cited: time constraints and misaligned priorities.

Priorities and Alignment

The top priorities for IT decision makers in the study included the usual suspects:  Security, cloud, application integration, and BI/Analytics.  Although these may be the must-have list for IT, the business leaders may have other priorities. The mismatch can result in priority misalignment and unfunded or stalled projects. A lack of understanding of common objectives across teams, functions, or business units is a result of poor organizational alignment. You see it play out all the time as CIOs who want to focus on securing infrastructure and data, while the business wants just to keep innovating, creating conflicting priorities. Both parties need to be aligned with the notion that security is a priority, or is frankly just important, otherwise you will have constant struggles for resources and budget dollars. The result is often a loss of momentum on projects or misaligned priorities.

To get better alignment, you need start with a shared sense of purpose, and this usually begins at the organizational level.  Understanding your purpose is the foundation for your strategy, business model, operational model, resources, and systems.  You will invest in and pay attention to what is most important, your reason for being.  But before you schedule the company offsite to ponder the question of why you exist, there may be a ready answer.  In his recent letter to shareholders, Jeff Bezos of Amazon described why they exist and hence where they focus: "You can be competitor focused, you can be product focused, you can be technology focused, you can be business model focused, and there are more. But in my view, obsessive customer focus is by far the most protective of Day 1 vitality."  In Bezos-speak, “Day 1” refers to the life stage of a business that is growing and innovating.  Day 2 is the stage when maturity is reached followed by stagnation and ultimately the demise of a business. Amazon’s reason for being is the customer.  In the letter, he expands further on the point: "Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight. A customer-obsessed culture best creates the conditions where all of that can happen."

So, aligning every aspect of your organization around the customer could be the rallying point to develop a shared sense of purpose and solve misalignment issues that can bog down initiatives, or “experiments,” to use Bezos’ terminology.  As you scan the list of technology priorities from the study, one of in particular stands out as having the potential to encourage the alignment necessary for digital transformation.  BI/Analytics could be a great way to align around the customer and prioritize projects that will deliver the greatest impact on customer experience.  Let’s look at are few reasons why.

The Case for BI/Analytics and Alignment

There are three reasons better alignment can be achieved with BI/Analytics:

  1. Creates an external focus on the customer -  Rather than internally focused process improvement, organizations can refocus externally to the data-driven customer journey, moving from efficiency to innovation using data and analytics in new ways that drive value.
  2. Aligns with growth and innovation – By using data and analytics, internal IT is an engine of innovation rather than just the support function.  This puts the IT team on the same side of the table as their business counterparts, developing and executing on new ideas.
  3. Exposes untapped potential –  Existing customer data is one the last untapped resources within organizations. By combing through previous transaction and support data, you can better understand behaviors and actions that can inform decisions regarding new offerings and opportunities to delight your customers.


Proper alignment between IT and business can start by putting the customer first and understanding their needs, wants, and desires through the data they leave behind. You will likely find in there ways to meet the growth of your business and make IT the planter and cultivator of the seeds of growth. 

The New B2B Customer Experience: Table Stakes for the Enterprise

Buried in your mountain of customer transaction data, may lie the key to delivering a vastly better customer experience.  In our latest eBook -- The New Customer Experience: Using Data and Analytics to Drive Digital Transformation -- we talk about how B2B firms that are looking to stay competitive, need to make the transition from a “transaction-based” mindset to one based on deeper customer knowledge driven by data. Data that you already have!  This “data-led digital transformation,” is essential for creating better customer experiences that lead to greater customer trust, loyalty and ultimately profitability.  

Transaction-based Mindset

Organizations that operate with a transaction-based analytics mindset have two primary characteristics: 1. they tend to focus on “what happened”—reporting on historical data, and 2. they are company rather than customer oriented.  What’s lacking with traditional dashboards and reports consumed at the management level, is the understanding of why things happened. Sales have fallen for the last three quarters, customer attrition is growing but management often just sees these events in the aggregate, effecting a monolithic group called customers, without a deeper understanding of what is happening in different customer segments that may disproportionately impact results. The other challenge with a focus on past results is that you have no ability to alter the outcomes, that drop in sales already happened, you can’t go back it time. The easy way to determine whether you are operating in transaction mode is the degree to which you understand and can differentiate your customers. Customer segmentation is where every data-led digital transformation begins.  

Transaction mindset is about managers using data to make decisions after the fact, while digital transformation is about sales reps and customer using analytics making decisions in real-time. This transformation is essential to delivering the new B2B customer experience, and it has now become competitive table stakes for enterprises across industries.

Data-led Digital Transformation

The new B2B customer experience is about helping buyers make smarter, faster decisions by being “context-aware” during every phase of the customer lifecycle. Context is most easily understood as the buyer’s current situation or circumstances. Are they a new customer or repeat? What type of customer (size, location, industry sub-segment, etc.)? What are their specific needs/requirements?  Which products are the best fit? When do they need it? What is their price point? What are comparative offerings?  What is their level of knowledge/experience with the product? What other value-added offerings are appropriate?

B2B companies have thrived in the past by having a deep understanding of their customers’ needs and providing excellent personal service. They delivered this high-touch service through direct customer contact with field and service reps. The new B2B customer experience builds on this notion of personal service but augments the personal touch with a digital en­abler: data and advanced analytics that deliver deeper knowledge of customer needs and buying behaviors.  The new B2B cus­tomer experience means anticipating the information they will need to make informed buying decisions, and predicting when, how and what type of support they will need in the future.

Getting Started

To help you jumpstart your efforts, in the eBook, we drill down deeper on the key enablers of your digital transformation journey:

  • Key elements of the new B2B customer experience
  • Four common barriers to digital transformation using data
  • Your essential B2B analytics toolset

In the final chapter, we spell out the best practices for accelerating your analytics initiatives without taking on too much cost or risk. We hope that you find this insightful and informative.

Gartner Conference Recap: Using Data to Map Your Customer Journey

At the recent Gartner Data & Analytics Summit, AI and machine learning got a lot of attention. While these topics are important for mapping your digital transformation long term, customer analytics are still the most relevant for today.  According to Gartner, customer analytics continues to rank highest in terms of technology investment for customer experience (CX) projects.  Further, they anticipate that by 2020, more than 40% of all data analytics projects will relate to an aspect of customer experience.

So what can you do now to start to understand the different elements of your customer experience?  Start to think about your customer journey, from initial inquiry to customer support. What makes your customers happy, loyal, and repeat purchasers. You need data to help answer these questions.

Moments of Truth

It’s important to understand your customer as much or more than your technology. You need to understand your typical buying cycle and customer buying behavior, and a customer journey map can help.  Per Gartner, there are "Moments of Truth" within activity streams that fluctuate as customers move from buying cycle to owning cycle, and back to buying.  These moments are key opportunities to make a strong, lasting impression.

Gartner describes these moments as three major customer activities that include Explore, Evaluate and Engage.  The graphic below shows how the cycle works, with moments of truth preceding the “off ramps” where customers could abandon your product or service if they have a bad experience.    

Source: Gartner

Source: Gartner


Explore and Evaluate

Customers want transparency and choice. And they want to research products and talk to experts to help them make informed decisions.  This is where trust is established, with the customer determining whether they are being manipulated or empowered, or inundated and confused.  Building customer segmentation data based on records of past purchases across your customer base can help match product and price that is appropriate for each customer. It’s not about manipulation, but helping them quickly and easily finding the right product or service at a price point that meets their expectations.  The more you know about them, the better you can help them make an informed choice. Using segmentation and customer archetypes helps jump start the process by presenting them with the appropriate products, at a price they are willing to pay, that returns acceptable margins to the business.


In the Gartner model, engagement is the most prominent in the “Owning” cycle, where customers consume products and services, and develop impressions of the overall experience. As the graphic above shows, abandonment can be a result of a poor experience at moments of truth. Again, customer analytics can help you understand customer needs and behaviors post sales, as they consume or use your product or service.  Using past customer data, you can look for patterns of use and behaviors that may lead to abandonment.  For example, there may be a spike in support calls from a customer followed by a reduction in purchases. Or perhaps there are seasonal considerations for a certain customer segment that create inventory and staffing challenges that need to be anticipated. 

Source of Customer Data

So where might you find the data to better understand your customers at each leg of the journey?  Gartner suggests the following:

  • Direct feedback surveys such as relationship, transactional or special purpose survey.
  • Indirect feedback such as text, speech and interaction analytics for customer care.
  • Operational data from CRM systems, call center software and marketing analytics to infer customer perceptions.
  • Market research such as marketing department studies gathered to define and understand the target audience.
  • Qualitative research including focus groups, online research communities, and ethnographic research.

For most B2B firms, the operational data from CRM and transaction systems will likely be the best place to start, followed by direct and indirect feedback from surveys and call centers. These data already exist or can be easily obtained and could help accelerate your journey mapping exercise to identify areas of dissatisfaction or missed opportunities.  


In her session, Maximizing Value along the Customer Journey, analyst Melissa Davis offered up the follow recommendations:

  1. Identify high-priority customers – Identify the highest impact customer segments (likely your top 20% of customers that deliver 80% of your revenue and profitability).  This is your starting point for customer analytics.
  2. Identify high-priority moments— Identify places on the buying journey that disproportionately create or destroy customer loyalty and advocacy.  Look at conversion, abandonment and churn around these moments.
  3. Identify high-priority investments in customer analytics— Work with LOB leaders and IT to create an inventory of data analytics competencies and develop a road map of key data analytics projects.

While advanced analytics (AI, machine learning) may be on your road map for the future, make sure you are focused today on customer analytics that will create demonstrable value along the customer journey.