Why the Ancillary Insurance Market is About to Boom, and Only Some Will Have Competitive Advantage

Offering a health plan is no longer enough. Today’s employers are adding dental, life, long-term disability and other innovative ancillary benefits in order to attract and retain the best talent in the tightest labor market in years. This means brokers and carriers need to follow suit for immediate and longer-term revenue opportunities. Here are three reasons why:

1) Ancillary benefits are essential to maintaining momentum in the health-products space.

Benchmarks across numerous carriers found that in small and mid-size markets, retention of employer accounts can increase by 10 to 15 percentage points if health is bundled with at least two supplemental products.1 So although ancillary benefits may seem like a very small incremental opportunity for carriers, it’s an important area to drive persistence. The more products offered, the more likely carriers are to retain that relationship and the harder it will be for employers to switch. Like Amazon offering a single source to purchase almost any consumer product, carriers have an opportunity to be a primary source of ancillary benefits insurance, offering core health insurance plus some.

2) Well-rounded (and expanded) benefits are the new standard for employees looking for work. Employers are following suit to attract and retain the best.

New workplace expectations have employers expanding their offerings to attract and retain talent. Employee well-being is increasingly being defined as not just health but financial wellness, mental health, diet, stress and more with the notion that everything is connected.

Employers are taking note of the job market’s demands and are offering a wider selection of ancillary benefit offerings (including student loan repayment benefits if you read our recent report) in efforts to boost employee productivity and happiness. In fact, employers believe so much of expanding their benefits that more than two-thirds of employers (69 percent) believe voluntary benefits will be a very or more-important component of their total rewards strategy in three to five years.2

Employees still want to maintain choice on how those dollars are spent and employers are working hard to provide them with an integrated approach to benefit management as employees are growing more frustrated with the segmentation of health/vision/dental insurance and simply want a singular offering that covers everything.

3) Brokers and non-traditional competitors are gaining strength.

Brokers continue to lead the voluntary sales market; in 2017 brokers accounted for 59 percent of all voluntary sales.3 Brokerage and employee benefit consulting firms are playing an increasingly important role in helping direct employers and employees in the decision-making process.

For those who cover small market employers, ancillary benefits allow them to offer a broader range of products and appeal more to employers. Carriers offering more of these benefits and enabling their brokers with integrated healthcare solutions will reap rewards as brokers choose their primary partners. Almost 40% of brokers have switched carriers in the last one to three years as these brokers look for the best partners in a rapidly changing market.4

With clear growth opportunity in the ancillary benefits market, our analysts sifted through 1,000s of digital conversations from across the web to compile real-time intelligence on a few top carriers, brokers and disruptors to get an idea of what is happening in the marketplace. Grab the Q1 report where we share a few major market opportunities for carriers and highlight how integrated healthcare is an ever-growing demand from consumers.

Get our report for critical Q1 insights needed to carve your nice and combat competition in an increasingly complex ancillary insurance market.

1 Opportunities in Voluntary Benefits. Oliver Wyman Health, 2017.
2 Willis Towers Watson’s 2018 Emerging Trends: Voluntary Benefits and Services Survey 2018
3 Insurance Brokers & Agencies in the US Number of Businesses 2003–2024. IBIS World, 2014.
4 More positive signs for voluntary. Benefitspro, 2018.


6 Step Approach to Reinvigorating Your Competitive Positioning – Part 1

As tech competition heats up, those that innovate and work faster, better or cheaper, may win, yet while they focus on reinventing or optimizing what they bring to market, businesses that focus on how to position their solutions might be more successful in attracting buyers.

In our latest whitepaper, we highlight seven principles of how disruptors win against competition. The first principle, “have simple offerings, messaging, and pricing,” is first for a reason. Truth be told, while your most well-known products are evolving to be a better version of themselves (such as on-prem to cloud-based solutions) your neighbor is following suit. A huge gap in this race to market lies in meeting buyer’s wants and needs in relevant, simple, yet versioned messaging. While consistent, overarching messaging is important , when a specific buyer, like IT, walks in the door, can you address their individual needs and painpoints more accurately and diligently than the competition?

Here are six steps to reinvigorating your competitive positioning at the persona level. Most importantly, our approach focuses on moving beyond “words on paper” (the typical messaging framework one-and-done approach) to create action steps and playbooks for sales and marketers as they encounter various buyers.

Step 1: Establish a Baseline, Build a Fact Base

How are you positioning yourself in the market today? Is it working? Does it differ from where you want to be positioned? Facilitate an internal workshop to gain alignment on the team’s strategic vision for the solution. Your stakeholders know the value of the product better than anyone. From developers to sales leads, they know what features were built and why, and your selling team knows what benefits seal the deal in customer conversations. Tease out what has worked before and what hasn’t. Build a fact base based on internal expertise. Remember, you will ultimately need their buy-in!


  • Where is there a gap in our messaging?
  • What buyer types/personas might we be missing that need to be better addressed?
  • What do stakeholders believe are the most important benefits to highlight?
  • What are the table stakes value messages we have to include, and where can we differentiate ourselves?
  • What are the dos and don’ts of messaging when buyers are considering other vendors?

Step 2: Conduct Agile Market Research

Market research in any form is critical to inform a top-line understanding of key competitors, target audiences, and industry trends. Consider taking a comprehensive approach to market research to collect intelligence across all of these areas. While a comprehensive approach is ideal, understanding the different market research options and outputs may help you prioritize them based on your timeline and you budget. When it comes to primary research for example, different approaches can surface varying levels of insights. With a survey, you can reach a larger number of buyers (e.g. 150+) and gain shallow, yet statistically significant insights to help inform messaging. With 1-on-1 in-depth interviews, you’ll likely reach a much smaller pool of participants (e.g. 10) but you’ll gain deeper insights and specifics. Here are the three suggested types of market research:

  1. Primary Research
    Primary research will help you understand your users and their dynamics – from use cases, to goals and motivations, to needs and pain points. It will surface distinct personas and their journeys from pre-purchase through post-purchase.

    • Online Surveying (150+ survey respondents): Conduct online survey research with a mix of closed-end and free-form response types to gain a broad understanding of customer personas, customer journeys, usage experiences, and competitor perceptions.
    • In-depth Interviews (10+ interviews): Conduct in-depth interviews to probe deeper into survey results and bring to life the user personas, decision-making journey and usage experiences.

Read more about another form of primary research called ethnography, and how watching customers attempt to buy, use, upgrade, and maintain your product can increase retention rates. Click here. >

  1. Competitive Scan
    Understand your competition’s messaging, product positioning and target audiences without copying them. This will allow you to purposely differentiate while at the same time understanding the messaging tablestakes. Imagine if 5 out of 5 of your competitors are using “speed” as their value prop and all the while, you’re thinking that benefit is your “winning ticket”? Clearly, “speed” must be an important benefit to your buyer, so you too need to highlight it, yet you should not try to differentiate on it. On the other hand, if only 1 in 5 competitors are highlighting integration in their suite of benefits, that may be where you can differentiate your messaging.In initiating a competitive scan, identify your top 5 competitors. Document the landscape including similarities, differences, and ID audiences they might be addressing. Beyond their websites, what marketing campaigns are they driving across social channels and web? And that gets to number three…
  2. Digital Listening
    Digital listening techniques can be used to track thousands of industry, buyer, and competitor conversations to surface newsworthy, real-time insights as well as past historical data. Ask yourself “Do you have the eyes and ears out there to really understand what buyers, partners and influencers are saying (daily, weekly, monthly) to craft messaging?” What’s in plain sight is sometimes overlooked. Many people underestimate the value of social data and it’s actionability, but every second 60,532 GB of internet traffic is taking place. As a strong case for digital monitoring, a leading tech client of ours in the document management space was looking to uncover potential new business opportunities. Through in-depth digital listening on their keywords, competitors, and buyers, it was discovered that law firms and legal offices were seeking to use new technologies as a means of reducing filing and document management requirements of their staff so they could instead focus on client-billable hours. Here, a new target audience was identified (law firms) and a messaging course of action could be built.

Read more about how Digital Listening can ID vertical opportunities. Click here >

Step 3: Develop the Messaging and Personas

In this phase, we aggregate insights to build the messaging framework and define the personas.

  1. Develop Foundational Messaging Framework
    Start by developing a messaging framework structure. A typical messaging framework will consist of an overarching Umbrella Message, 3-5 core messaging themes called Pillar Messages and supporting statements accompanied by proof points/evidence. Consolidate your market research insights to understand the most important features and value props in building your messaging framework. What is the highest ranking product feature or benefit? What differentiators set you apart from competitors that were surfaced in the competitive scan? What details were repeated time and time again across one-on-one interviews?
  2. Refine Messaging Framework by Persona
    Once you have a foundational Messaging Framework, it’s time to refine your messaging based on the unique needs and pain points found for each persona. Document key persona characteristics including how different personas adopt, use, purchase and/or manage the product, and their key needs and pain points. For instance, one of your pillars may be more relevant to an IT buyer and in turn, may need additional supporting messages to address their specific needs. This may also be the priority pillar to lead with when developing IT-focused content or collateral. Technology features and benefit preferences will vary by use case and the benefits that are “must have” will be different for each persona.

Example: Let’s say I’m a provider of a cloudbased web-conferencing solution (to the likes of WebEx or GoToMeeting). In gathering and analyzing the results from my online survey and more, I could dissect the data and rank features by buyer type to understand must-have buying considerations.

Survey Q13. Thinking about all the cloudbased web-conferencing options available, what are the most important features to you? In other words, what are your MUST-HAVES? Rank your top three.

Overarching Messaging Takeaways:

– Mobile device accessibility is a priority for all personas

Persona-Based Messaging Takeaways:

– Emphasize cost effectiveness and simplicity when speaking to executive leadership
– Highlight file storage and sharing in conjunction with security to IT professionals

 Now that you have consolidated insights and understand which messaging resonates at all levels, in Part 2 of this blog will discuss making the messaging actionable. First, validate your results and reviewing with stakeholders to ensure organizational wide buy-in. And secondly, turn your architecture into useful battlecards and cheat sheets – i.e., “If I am speaking to persona A, here are exactly the words and phrases I need to say to show we convey their need.” Oftentimes a messaging framework is built simply as an encyclopedia of findings rather than a playbook for sales and marketing action. The goal should be to put all messaging into activation – in your website, marketing collateral, sales messaging, and speaker notes. Your framework should act as a guide for all go-to-market activities. Stay tuned to Part 2 of this blog as we cover:

  • Phase 4: Validation

    Interview external customers to test messaging and positioning framework, identify key areas of strength and weakness.

  • Phase 5: Activating the Output

    Build a playbook of conversation and market-ready copy.

  • Phase 6: Launch/Measurement

    Measure ongoing trends and reaction to messaging, quantify results (sales talk time, lift, average deals size) and continuously optimize in-market.

How Simply “Watching Your Buyers” Can Increase Retention Rates

One of our clients recently came to us with an issue that we immediately diagnosed as a friction problem. A product had been conceived and built which should have been selling well, but uptake was slow. There was nothing functionally wrong with the product; it did what it said it did, and it filled an unmet need in the market. However, the product and its associated distribution had a lot of rough edges. This wasn’t helped by the fact that the product was also a service like so many things are today. Not only did the customer need to purchase and configure the product, but they also had to continually interface with the company and its distribution partners to “refill” it.

The Challenge:

Complexities in the customer lifecycle from buying, using, upgrading and maintaining the product led to slow adoption.

First, the product telegraphed complexity. Features were touted on the front page that didn’t scream “problem-solving.” It was unclear how the product could be purchased. The product, copy, and imagery was totally boring. But perhaps most damningly, it was unclear exactly how I would use the product.

We recommended that instead of attempting to attack these prima facie problems one-by-one, they instead take another step back and watch the customers attempt to buy, use, upgrade, and maintain the product. At first, the client didn’t want to do “more research”—they’d already done lots of focus groups, quantitative surveys, market sizing, and the like. But I explained that what we were going to do was simply watch. The technical term for this kind of research is ethnography; it arises from ethnographic studies of cultures.

Anthropologists embed themselves for months or years with cultures, and do not ask questions or interfere. Their goal is not to pollute the research with questions. This can be hard for business people and marketers—they want to know the answer! However, by asking customers direct questions, too often acquiescence bias distorts the results. Acquiescence bias is well known to salespeople, and memorialized as the phrase “buyers are liars.” Human beings are nice, and they don’t want to make a researcher or a salesperson feel bad, so they will tell that person what they think they want to hear. They will tell you how they should or would like to use a product, or buy a product. They might not even know they aren’t presenting an accurate accounting of the issue.

I told the client that by going to our client (and prospect) offices, across a wide range of industries, and watching them do their jobs and attempt to purchase our product, the friction points would become clear. The skepticism was palpable; “not actionable,” “high risk,” “unclear what we’ll get out of this.” I made the point that several million dollars of sales and renewals had already been lost due to friction. We embarked on the research.

The Solution:

1) An ethnographic study uncovers friction points.

The most important thing we did in this project was selecting the companies for observation. We did thirty 2-hour observations over several weeks. We split these into 15 companies that had purchased the product, and 15 that had not. In each group, we recruited five different industries. All of the companies had between 25 and 250 employees—the target segment for the product. We recruited the companies directly using LinkedIn. We targeted three cities. So, in each city we had five industries, with one company in each that had the product, and five that hadn’t. The screener ensured that the company and the user were good fits—we wanted companies with a lively culture, a clear fit for the product, and a good primary research subject who was engaging and fairly outgoing. Yes, this biases the research a bit, but ethnographies don’t work with reticent introverts and dull companies.

In preparation for our visit, we asked that key users of this solution focus on this task for the two hours that we were there. This was the extent of our pre-visit meddling; we didn’t ask them to do anything else. We didn’t ask them to use a specific product or anything like that. We didn’t ask them to do any homework.

The day of the visit, we arrived with a team of three people, no more: one client representative, a facilitator, and a videographer. Upon arrival, we simply asked the individual to start doing the task that the product was meant to enable. Every once in a while, we’d ask a question. This went on for about an hour. We then asked the individuals who had the product to attempt a “refill,” and watched them. For those who did not have the product, we asked them to shop for the product. Other than that, we didn’t interfere. This can be hard for the client. All they want to do is ask questions, but this isn’t the point. The point is to observe, in the customer’s “native habitat.” Over the last 30 minutes, it was time to ask questions. Why did you do what you did? What else did we miss? Can we see something we didn’t see?

While at the company, we did a lot of looking at other things, too. How were the supplies that the product was meant to complement/replace stored? What applications did the customer and various influencers use? How was the business laid out? What catalogs were on the desk?

Between sessions, we worked together to outline the sources of go-to-market friction we noticed. Because we had many breaks between visits, our picture of the sources of the friction became very detailed. “Did you notice that she couldn’t remember that URL?” “It took him ten minutes to figure out how to scan that contract.” “She couldn’t find any of those emails.” Etc. Whatever the product or service is, no detail is too small.

At this point, we had the “voice of the customer,” but we had more than that; we had lived in the customers’ shoes for 60 hours. There is a huge difference. Steve Jobs famously said, “no one ever knew they needed an iPod.” However, Steve Jobs was a careful observer of people. He built a product no one knew they needed by observing people living their lives.

2) Friction points are prioritized, categorized and agily dissolved.

When we completed the research, we had a list of over 200 sources of friction between new customer acquisition and account retention (in this case, the ongoing replenishment of services required to operate the product.) We first synthesized this list down into around 100 mutually exclusive items. We then grouped the items by theme:

  • Learning / Research
  • Purchase Mechanics
  • Buyer / Influencer Mechanics
  • Competitors for Time
  • Payment
  • Logistics / Fulfillment

At this point, we transitioned to the mode of an agile product team. We now had roughly 100 rough edges that needed sanding down. Instead of throwing the go-to-market process out, we instead started hacking away at friction points in small sprints.

One of the most serious problems we identified was in finding the web page for product replenishment, and once getting there, remembering credentials. Several individuals we observed searched for the site for almost a minute, and did a “forgot password” reset every time they got there. This made them visibly irritated to go to the site and was clearly an emotional deterrent to continued usage. We hit that problem first.

Another key observation was a clear emotional attachment to a “competing” product (not really a product—just a way of doing things.) The client’s product caused the customer to have to abandon another process that they found rewarding. We needed to replace this sense of emotional reward. Harder, but doable. And so on, and so on.


The Result:

Increased annual retention rate by 5 points.

Nothing changes overnight, but some of the first changes enacted—after prioritizing friction points—led to startling results. Fixing the password and website memorability problem drove password resets down by over 50%, and logins up by 10%. This led to an increase in annual retention rate by around 5 points.

The emotional fix was harder to measure, but we heard good things from the sales force, and retention has steadily increased in the approximately one year since the research completed. The best part is, the changes keep coming, as the insights surfaced are still being hit by the go-to-market “agile product team.”

At the company, this has led to a new way to think about the role of marketing, sales, and product. Instead of working in silos, teams have started seeing things holistically from the eyes of the customer, and imagining “how this would feel,” vs. doing things to change a number.


Lessons Learned:

Watch your customers (and prospects).

At MarketBridge, we talk about “outside kids” and “inside kids.” “Inside kids” are content to sit at their desks and play with data. Data is great; I am a data scientist. However, there is no substitute for seeing things with your own eyes. Qualitative research is sometimes disparaged by arrogant direct marketers; I’ve heard many of these comments. And yet, Google spends tens of thousands of hours watching people interact with one screen. Do you think they might know something others don’t?

Download a PDF version of this case study:

9 Requirements for Effective Cross-Selling in Financial Services

As a part of their Future of Financial Services series, the World Economic Forum recently released a comprehensive and far-reaching report on the “New Physics of Financial Services” and the impact that digital transformation and the rise of Artificial Intelligence is having on the financial services ecosystem.

There is no doubt that these trends will change the operating models for certain firms and continue to impact the competitive dynamics of the industry in a number of different ways in the medium-term.  In the short-term, they are creating challenges and presenting opportunities for how traditional FSI’s acquire, grow and retain customers.  This is especially true in the area of cross-selling and retention. If traditional FSI’s aren’t accurately anticipating the needs of their customers or understanding their risk of defection, they are at greater risk for disintermediation by new entrants or competitors who do.

Cross-selling is the fastest, most profitable path to incremental revenue growth, period. Assuming a firm has a 30% wallet share within an account, attaining just 5% more in account share grows account revenues by 17%. And with the cost of customer acquisition generally estimated to run from 3x to 25X more expensive than cross-selling, the economics of cross-selling are very compelling.  Recognizing this, cross-selling has become a strategic priority for many financial services firms in recent years – yet many firms still appear to be far from realizing the potential of cross-selling.

Why is this? Some firms may be hesitant given the number of actions and warning directed towards FS firms contained within the CFPB Enforcement files where the cross-selling culture was perhaps a bit too aggressive – “Detecting and Preventing Consumer Harm from Production Incentives” as they refer to it.

For others, it may be the challenges associated with overcoming organizational complexity that spans multiple lines of business, diverse functional areas and disparate technologies and business processes that must be coordinated to deliver effective cross-sell programs.

In many instances, it may be the fact that cross-selling responsibility is often left to the “last mile” (the end of the buying journey) in that relationship managers or sales resources often simply don’t have the time or skills to effectively implement programs at scale. Or, efforts are driven by product owners who take a product-centric view of cross-selling as opposed to the customer-centric view of successful cross-selling programs.

Here then is our list of nine requirements for building effective and scalable cross-selling programs in the financial services industry:

1) Adopt a customer-centric view.

Too many cross-sell programs are still organized around lines of business and driven by a product-centric view of cross-selling. Effective cross-sellers build a customer-centric view of opportunity and take a longer-term view of customer value. The most effective cross-sell efforts are led by segment marketers who have responsibility for specific customer segments, working with the product marketing teams and sales channels to coordinate on execution.


2) Establish a single view of the customer.

Patently obvious, but many firms still have a difficult time building a unified view of the overall customer relationship. This includes all product usage and transactional history, service and support history, etc., as well as identifying and integrating external data sources that provide additional insights into buyer behavior and attitudes.

Identifying patterns of behavior across products is essential for understanding and anticipating customer needs. In turn, it informs segmentation and personas in #3 below.

But don’t wait for the completion of an expensive, multi-year data warehouse project. Agile firms today are taking advantage of low-cost storage and data lake architectures to quickly build data repositories. This allows data science teams quick access for specific use cases without processing overhead associated with large inflexible data warehouses. Liberate insights from the tyranny of workflow tools and warehouses!

For more information on the Promise of the Marketing Data Platform, Click Here


3) Build actionable buyer segments and personas.

Utilize data from # 2 – supplemented with primary research – to build actionable segmentation and personas. These will allow you to personalize your interactions with existing customers in ways they have come to expect; based on the totality of their relationship with you and reflecting an understanding of their needs. Maintain assignment of segments and personas in your customer database to ensure segmentation is actionable.

For more information on Creating Actionable Segmentations and Personas, Click Here


4) Create a scalable analytical engine targeted to specific, prioritized use cases.

Use an agile, reproducible approach to developing and managing a library of predictive cross-selling/retention models. Consider segmentation, RFM, CLV, next logical product, retention, Marketing mix optimization, among others.

Customer growth, share growth, wallet growth, account expansion—all of these strategic goals beg the same question; how do I get a given customer to buy more, or buy something new? Cross-sell models use data about the current installed base and compare this with data on other accounts that have upgraded. These are a close cousin to market basket models on the consumer side, analyzing how customers’ ”baskets” of products typically evolve as new items are added.

Deploy a disciplined, scalable approach to managing your data science operations to ensure reproducibility, scalability, and accountability of your investments in AI.

For more information on Creating a Product-Centric Data Science Organization, Click Here


5) Listen to your customers, stalk your competition, foresee your disruptors.

Robust cross-sell programs require a deep understanding of your accounts. This includes how they perceive your brand, what competitors are selling into those accounts and what new disruptors are emerging.

Best-in-class FSI’s leverage “always-on” market intelligence capabilities. Those that track customer feedback, competitive movements, and emerging disruptors. This intelligence is fed back into the product development process, innovation centers, marketing messaging, Account-Based Marketing activities, and sales enablement programs.


6) Create content aligned with personas and buyer’s journey.

Today’s FS customers engage with your organization via multiple channels. With 24/7 availability of your content and resources, it can be challenging to provide a consistent experience across every channel. Think of a potential buyer researching new savings and investment options to kick off 2019. This buyer can easily research opportunities online, get side-by-side comparisons from financial providers, chat online or over the phone with an investment expert, and then set up an appointment at the bank or financial institution for a face-to-face deep-dive discussion.

If the user experience across all these channels isn’t integrated – and customers receive different responses across different channels from the same company – the likelihood of successfully cross-selling or even retaining those customers goes down considerably. Utilizing a consistent framework and taxonomy to map content to the buyer’s journey is critical to ensure a consistent, personalized and relevant experience regardless of product, channel or stage in the journey.

For more information on Creating a Consistent Customer Experience, Click Here


7) Implement a disciplined cross-channel contact strategy and cadence.

Disciplined cross-sellers implement and adhere to well-structured contact strategies that are based on analytics and insight (i.e., using the scores developed in # 4 above). These strategies help determine what that cadence should be, which channel should engage, and what the product/solution and message should be.

This requires close coordination between marketing and sales. One of the most successful cross-sell programs we have ever seen actually rescored their entire customer population each week. They did so using updated transactional and market response data, and then prescribing a set of dynamic business rules to determine where the opportunities were to be routed the following week. A well-defined nurture stream for the next logical product was presented or the prospect was routed to a sales agent.

In either event, the contact strategy and cadence were well-defined for each product and each step in the buyer’s journey. Content and messaging for each segment were defined and utilized as “fuel” for each outreach and delivered into the marketing automation tools and the CRM (See #8 below). The business rules were dynamic and could be modified weekly based on underlying business conditions. This forced an interlock each week between sales and marketing and helped develop shared accountability for results.

To view the FS Cross-Sell case study, Click Here


8) Insert insights and content into workflow tools.

The best insights and AI are useless unless they can be easily understood and acted upon by your sales and marketing channels. Delivering predictive analytics, relevant content and personalized messaging into existing customer contact workflow platforms is critical for successful cross-selling at scale. We have found that loosely coupled architectures are dramatically better over the long run than tight integrations with SaaS MarTech platforms.

Most companies are using multiple platforms to manage the end-to-end customer journey. The need to insert insight into each one of these platforms—and to gather feedback from the customer interaction directed by those platforms. This is critical to managing the process holistically, and understanding where the customer is in the process at any given point in time.

Rather than working to integrate multiple systems, the better alternative is to deliver analytics and content via a set of standardized endpoints that any CRM or MarTech platform can use. Then, writing quick integration layers for specific systems. When that next great piece of technology is rolled out—or when Salesforce raises its prices by 20%— it’s no problem.  It just requires updates to an adaptor layer, vs. tearing out a bunch of proprietary APEX code from Salesforce and trying to remember what the developer was thinking.

Fortunately, all CRM and marketing automation systems—including Salesforce—share the same basic architecture. The objects Account, Contact, Lead, Opportunity, Product, etc. don’t really vary, and haven’t for 25 years.

Cross-selling recommendations also share the same DNA. Typically, the API calls for cross-selling include several microservices that, when taken together, form the basis of the contact and content strategy outlined in #7 woven into the CRM / Martech stack.

For insights on the Microsoft-SAP-Adobe Open Data Alliance, Click Here


9) Relentlessly test, measure and track:

Finally, any successful cross-sell program – in fact, any sales and marketing program – requires a relentless focus on test and learn, agile pilots, on-going measurement and optimization from start to finish.

This process starts with establishing a proforma ROI when any new cross-sell model is slated for development. Writing down what you are trying to accomplish, and estimating how its effectiveness will be measured, puts the entire team on much better footing for success. This should be done before a single line of code is written or a query is executed.

To learn more about Measuring Return on Analytics, Click Here

Implement agile pilots using test and learn methods such as A/B testing to quickly gain insight into optimal combinations of factors that drive the best results and then scale.  As every direct marketer’s learning method, the A/B test divides marketing into test and control cells, and the response is then compared using simple z-tests of proportions to pick a winner. This approach is simple and effective, and given sufficient volume, can be turned into a learning factory for the organization.

Finally, develop and deploy a holistic customer-centric marketing analytics framework that will allow you to consistently track, measure, manage and optimize all of the activities occurring with your customers across all products, marketing and sales channels.   This will provide visibility into the overall results and allow you to make more informed decisions on how to effectively grow your installed base. Dont forget to optimize your customer contact strategies and cadence on a continuous basis.

To learn more about building a Marketing Analytics Framework, Click Here.


Remember this: “If traditional FSI’s aren’t accurately anticipating the needs of their customers or understanding their risk of defection, they are at greater risk for disintermediation by new entrants or competitors who do.”

Having an agile plan to go-to-market against market disruptors by building a customer-centric approach to cross-selling will be key to success. Check out our latest whitepaper on “The Last Mile Opportunity,” for 5 transformational principles to scale operations and build revenue success in the “last mile” of the customer buying journey.

Download the whitepaper:

The Retail Revolution: 5 Ways to Adapt, Survive, and Thrive

With the rise of e-commerce and the decline of the shopping mall, the last two decades have seen massive shifts in the world of retail. Consumers are migrating online and demanding more from their purchases: lower prices, faster shipping, sustainable practices. Amazon looms large and threatens players at every stage of the supply chain as both a demanding partner and ruthless competitor. So what can more traditional retailers and manufacturers do to maintain and grow market share in what feels like an entirely new marketplace?

1) Invest in e-commerce

For years, it’s been a sink or swim situation for retailers: get on board the e-commerce train, or get left behind. But how do you keep up with digital giants that have made online transactions their bread and butter?

As Forrester recently put it, “there has not been a time when technology has had a more profound impact on customer experience and revenue performance. By the sheer force of nature, this places CIOs and technology front and center.” To sell in 2019, you need excellent infrastructure supporting e-commerce and merging it with brick-and-mortar processes to create a fluid omnichannel customer experience. Successful retailers are abandoning the traditional model of basing their IT spending on the previous year’s revenues. Instead, they are growing tech spending as a forward-looking endeavor, understanding that state-of-the-art user experience is crucial in both generating sales and making sure customers return. In fact, IHL reported that leading retailers are outspending their weaker competitors sevenfold on IT.

In 2016, Walmart put out a call to technology companies, looking for partners that could offer solutions for its inventory management, cybersecurity, and e-commerce platforms, to name a few. As a result, Walmart is now partnered with Microsoft, using Microsoft’s cloud services to innovate its external services and internal applications through artificial intelligence and data solutions. Walmart’s investment in technology has paid off; according to a benchmark report by SimilarWeb, Walmart is the third most trafficked online retailer. The superstore giant is topped by only Amazon and Ebay, making it the most successful brick-and-mortar retailer in the e-commerce space.

2) But Find Channel Balance

This isn’t to say that retailers should abandon brick-and-mortar entirely and migrate to the web. Omnichannel is the name of the game now, and retailers can no longer pick and choose between digital and brick-and-mortar. Even Amazon, the poster child for online retail, has found a use, and in fact, a need for physical retail in its model. In addition to the 2017 acquisition of Whole Foods that netted Amazon 460 grocery stores, the website that was founded as an online bookstore has built over a dozen Amazon Books stores. Amazon has also recently announced mutually beneficial partnerships that increase its brick-and-mortar presence, such as delivering tires purchased on its platform to Sears Auto Center locations, where the customer can have them installed, or accepting returns at Kohl’s stores, where store-within-a-store space is also set aside to sell Amazon products. At the same time, Amazon is doubling down on its digital presence, for example experimenting with a Snapchat feature that would allow users to search for an item on Amazon based on a picture of an object or barcode. As an industry leader, Amazon demonstrates that if you want to be successful in the current retail space, you can’t be afraid to bring the physical experience online and the digital experience offline.

3) Take Back the Channel

Most manufacturers understand: you can’t rely on your distribution channels to own your relationship with consumers. That’s why the concept of CRM exists. But, in practice how do you get closer to the consumer, and get a better idea of what they want from you?

The Clorox Company has a successful CRM program, where they rely on distributors to sell their products and to provide accurate sales data. However, Clorox also puts heavy emphasis on its Early Performance Indicators. These are signals of performance early in the funnel, drawn from online engagement, that examine how conversation and buzz about the brand could translate into sales performance. While Clorox’s CRM partners manage valuable customer transaction data, EPI’s allow Clorox to assess its brand perceptions and predict sales activity in between CRM reporting cycles.

In this Amazon-dominant retail environment, some manufacturers have taken more extreme measures to regain control over their sales. The decision to become an Amazon wholesaler can be a Catch-22 for manufacturers. You cede control of your pricing, and potentially brand value, but in a world where over half of online shopping traffic goes to Amazon, not being present in the marketplace can be detrimental. Some manufacturers, such as Birkenstock, have taken the calculated risk to pull their product from Amazon altogether, in protest of unpredictable competitive pricing and the presence of counterfeit and knock-offs that dilute brand image.

With omnichannel so ubiquitous, manufacturers need to step back and take stock of the strengths and weaknesses of each channel. In channels where progress can be opaque or where partners require more control, it may be necessary to get creative in order to maintain the caliber of performance your brand expects.

4) Activate Sales Networks

While e-commerce is clearly the direction retail is moving, there are real challenges of implementing e-commerce platforms for more complex services or offerings. Grainger, the largest industrial B2B online retailer, relies on digital transactions. But, Grainger’s notoriously large catalog (over 1.6 million products, most of them highly specialized) can make it challenging for customers, particularly those with larger business accounts, to decide what to order on their own. The company reports that more than 60% of transactions are completed online, and expect that number to rise to 90% in the next several years. However, many of these transactions are aided by a sales rep at some point in the process. These reps may not be closing the final sale, but they are a crucial step in the customer journey.

Industries that have been historically dependent on agents are beginning to feel the threat of digital channels. With the rise of self-service online insurance enrollment, both offered directly by the provider or through third-party aggregation sites, some industry watchers are announcing the death of the agent and broker field. However, brokers and agents are still valuable in some areas. Employers and HR reps report depending on brokers for their group insurance needs and often maintain contact with their brokers for other resources outside of traditional enrollment season. The roles of agents and brokers may be diminishing, but they will not disappear entirely.

To maintain their standing in this shifting landscape, agents are demanding more from their carriers, such as digital tools and technology that bridge the gap between the growing digital and traditional personal customer experiences. Allied has built tools that help its insurance agents develop online content and digital marketing, and has integrated agent-enabled plans into its mobile app, reducing the administrative burden on agents. Transitioning to the digital marketplace, retailers must take stock of where their e-commerce model may fall short, and empower their human salesforce to fill in the gaps.

5) Empower Consumers

Consumers are more demanding than ever and companies have to either move quickly to meet those needs or make way for those who do. Distribution channels are more democratized than ever, allowing consumers to use platforms to curate the resources around them rather than depend on (and frankly, wait for) companies to offer what they want. This gives consumers access to goods and services they didn’t have before due to financial or geographic obstacles. Consumers’ ability to quickly and temporarily find and use anything from a car to a dog-walker has dramatically changed their spending and purchasing habits. This provides both opportunities for companies that get creative and break out of traditional models, and risks for those that can’t adapt fast enough.

Digital channels are making smaller brands more visible to consumers, and that means more competition for established brands. Search engines give consumers easy access to brands they may have never heard of, consumer reports, and customer reviews, allowing them to make more informed purchasing decisions and giving them broader choice than their local store’s shelves. At the same time, digital marketing and the online marketplace is making it easier for smaller, niched brands to get a foothold in market share, following the model of Warby Parker or Dollar Shave Club. These new brands cater to specific, untapped consumer needs or concerns, offering better value, higher quality, health benefits, more sustainable sourcing, or a departure from long-standing oligopoly. Often these emerging brands debut with a direct-to-consumer model, meaning that companies fail to view them as competitors until after they have already established a customer base.

As consumers become more empowered in their purchasing journeys, companies need to adapt to meet them where they are. For larger established companies, agility is key. Your emerging competitors are listening to customer concerns and you need to as well, and develop marketing, product lines, and business models that satisfy these consumers.


Retail is dramatically different than it was just ten years ago, and it continues to evolve at breakneck speed. The shift to e-commerce may be intimidating for businesses, especially those not digitally native, so to speak. But by investing in a strong online channel, leveraging the strengths of existing channels and using technology to patch their weaknesses, and taking cues from consumers, retailers and manufacturers are finding success in the Amazon era.

What Uber Teaches Us About Great Sales and Buyer Enablement

The “last mile” of revenue generation (getting a qualified lead to close) is always the biggest hurdle. Whether B2B or B2C, this ultimately requires some level of personal relationship development and product customization. Yet many times, businesses don’t realize the costs associated in qualifying a lead in the first place – despite whether or not they close!

Consider a real-life scenario:

I recently flew from DC to Atlanta. I needed to get from Point A (DC) to Point B (Atlanta). The total trip cost was about $470 or 74 cents per mile:

Miles Cost Cost/Mile
Airfare DCA to ATL 600 $380  $    0.63
Uber to DCA 12 $40  $    3.33
Uber from ATL 20 $50  $    2.50
TOTAL 632 $470  $    0.74

Notice that the “last mile(s)” – getting me from my specific location in DC to my specific destination in Atlanta cost me 5X more per mile than “bulk” airfare.  Yes, we all intuitively know this – but do we realize the same economics apply to sales? After all, prospects need to go from point A (unqualified, unknown) to point B (closed deal) as well. Bulk demand generation is always measured in “cost per qualified lead”– same as cost per mile above. So say that’s $100 per lead on a $10,000 potential transaction (1%). If you only close 1 in 7 leads that’s $700 (7%) in lead gen cost per deal closed. And that doesn’t even include the cost to-sell (sales rep salary, benefits, commission, and supporting infrastructure).  This math is simple – I want to get to Atlanta at the lowest cost per mile and your business wants to get to close at the lowest cost per lead. But, generate as many leads as you want at whatever cost per lead… if your sales team can’t close the last mile, total cost-to-sell goes through the roof!

What Uber Can Teach Us About Sales Enablement and Buyer Enablement

For the sales rep (the Uber driver), the Uber platform provides pretty effective sales enablement.

Uber’s Driver Experience

  1. Finds the buyer
  2. Ensures the buyer’s ability to pay (credit card or check)
  3. Qualifies the buyer’s integrity (passenger rating)
  4. Maps the route to complete the sale
  5. Completes a friction-less payment process (set price, optional tip)
  6. Rinse, repeat….

Not only has Uber created as a seamless experience for the driver (i.e. sales rep), but for the buyer. Consider what the Uber app does for buyer enablement:

Uber’s Rider Experience

  1. Matches the buyer (rider) with the most convenient seller (driver)  – without worrying about “sales territories!”
  2. Ensures minimum product standards (car quality, safety)
  3. Qualifies seller integrity (driver rating)
  4. Provides transparent pricing before purchase
  5. Offers value-added services (custom music, driver background info)
  6. Completes frictionless payment
  7. Allows immediate vendor rating
  8. Cross-sells other products/services (Uber Eats, Uber credit card)

It’s also important to point out buyers are NOT be forced into negotiations with sellers like traditional taxi drivers. When you pull the pieces together one thing is for sure –

Key Takeaway

While lead generation boasts a hefty budget, with costs at every intersection, the  “last mile” is where you can expect to throw in the extra dollars. When you think about it, Uber did not invent the ride share (i.e. taxing) experience, instead, they reinvented the way in which buyers and sellers interact in the “last mile.” Without investing in technology, process, and a “frictionless” buying experience, well, consider that taxi meter still running!

P.S. I love talking to Uber drivers – very interesting entrepreneurs with diverse backgrounds – but Uber’s buyer enablement is what really sets it apart from traditional last mile transportation services.

How good is your buyer enablement?


Cyborgs Will Beat Robots: Building a Human-AI Culture

There are two competing AI narratives bouncing around the internet. On the one hand, AI is seen as a future scourge, a technology that once unchained will push humanity past a singularity. Past this singularity, we cannot predict what will happen—but many think it won’t be good [1].

The other camp is dominated by AI optimists like Ray Kurzweil, who believe that human-machine integration is inevitable, is a great thing that will usher in a new golden age for humanity, and has been happening for years. Many people don’t realize that their brains have already been rewired with a Google API; when we don’t know something, we’ve gotten incredibly good at opening a browser, executing a pretty optimal search, and finding the answer (if there is one)—dramatically increasing the productivity and intelligence of those who use this API wisely. This camp still sees a singularity on the horizon, but in their view, humans and machines will merge, creating “cyborgs” that integrate the best elements of human intelligence and artificial intelligence, and this is a good thing.

I wanted to write this article is to help companies and executives navigate this coming cyborg transformation. Just like in past technology waves, the companies that succeed will not be the ones with the best algorithms; the algorithms will largely become tablestakes. In this new reality, the winners will do a better job transforming their employees into better “AI interfacers.” In other words, the companies with lots of motivated employees who understand how to use AI—and who are staffed with employees equipped to interface with the technology—will ultimately stand out from competitors by developing better use cases, integrating AI into their value-added business processes, and using AI in concert with human intelligence to drive better outcomes.

Good News: We Are Still Early

Early in the personal computer revolution, the distance between the most advanced computer engineer and a 12-year old kid messing around with his Apple IIe wasn’t really that large. It probably seemed huge at the time, but the reality was that the basics of that machine were still simple, and someone with a soldering iron and a few screwdrivers could actually tinker, maybe upgrading the RAM or adding on a graphics card. Try doing that in 2019 with a MacBook Pro. The components could seen. The circuits could be understood. Programming languages, while clunky by today’s standards, were BASIC. (sorry).

I would argue we’re roughly at the Apple IIe stage right now with artificial intelligence. A hobbyist can download open source software like Python, the SciKitLearn library, Jupyter, and Git, and be off and running building an OCR (optical character recognition) algorithm. In fact, one could argue that AI technology is more democratized than PC technology was in the mid-1980s. At that time, it would cost at least a few thousand dollars to get up and running with a good IBM clone, and programming languages had to be purchased as physical boxes of floppy disks. Learning to program or build hardware required physical books; today, it’s possible to take free courses on AI from Stanford on Youtube, and any error typed into Google returns an immediate solution courtesy Stack Overflow.

In other words, an interested, talented person can achieve basic artificial intelligence literacy today pretty easily, if they put their mind to it, and the distance between there and a self-driving car isn’t insurmountable. Granted, millions of developer hours have been spent tweaking each neural net and environmental sensor on that car driving around Pittsburgh, but a tinkerer can basically explain the theory behind how it all works, if they want to. The net-net is that it’s still possible to build an army of AI citizen scientists at your company who will fully embrace the unknown advancements of the next decade—and that not doing so will put your company at risk of faltering, just as slow movers on technology did in the 1990s.

New Role: The AI Interfacer

Companies that successfully transitioned from offline to digital in the 1990s and 2000s all had one thing in common; they built a strong layer of interface employees. We’ve all been there: Bob is the master of database X. He works 70 hours a week; he can answer any question; people worship him, and he has total job security. However, that database never reaches its full potential. Hundreds of reports are written, but few are used. Integrations happen, but fall down over the last mile. The problem in this scenario is that few people have the skills (or the interest) to meet him half-way. There are no interfacers for Bob.

The company that Bob works at spends millions on expensive proprietary software, and armies of consultants to install and configure. The bare metal servers at this company are just as powerful as the servers at their competitor—but yet, it just never seems to “click.” The competitors pull away, and before you know it, this company is on the trash heap. Sound familiar?

This analogy extends to AI flawlessly. An AI system can be built to (in theory) predict the perfect marketing touch at a given point, or detect fraud with uncanny accuracy, but without human advocates and interfacers feeding the algorithm data, providing improvement suggestions, and driving adoption, these systems will fail—or at the very least, they won’t evolve.

AI interfacers are to 2019 what computer literate employees were to 1989, or what database-literate people were to 1999. They may not be developing machine learning algorithms, but they know what a machine learning algorithm does. They may not be on the team developing the self-driving car, but they can explain how a self-driving car is put together. They are the key to AI’s success over the last mile.

AI Interfacers come in five flavors, not mutually exclusive:

  • User: Can interface with AI endpoints and integrate them into their day-to-day processes;
  • Explainer: Understands how machine learning algorithms are trained and validated, and how these can chain together to form systems, and most importantly, teaches other about them;
  • Product Manager: Can see how systems and processes can be improved by AI, and can prioritize these improvement points;
  • Data Gatherer: Understands how artificial intelligence gets information from the world (IoT, big data, etc.), environmental sensors, users);
  • Prototyper: Can prototype simple AI systems using machine learning algorithms (in other words, tinker).

The AI User is equivalent to someone who liked and was facile in using email in 1989, or an SAP power user in 1999. These are individuals who instead of running away from AI, actually attempt to integrate it into their day-to-day, realizing that it will make their job easier, and allow them to surf to higher value-added activities (and perhaps, get a promotion.)

The AI Explainer is a natural teacher who understands how AI elements are knit together within the core business processes of the company, and evangelizes these stories to others. He is the executive who tells the same story over and over again at staff meetings until it has been internalized; the line manager who explains the sales rep why the AI-based next logical product algorithm works; the new employee who teaches upwards to their 45-year-old supervisor what machine learning really is, using simple, approachable language.

The AI Product Manager might not be an actual product manager, but has that DNA. They are constantly stepping back and seeing how AI does and could improve existing processes. They are passionate about driving better performance and outcomes, and tell the stories across the company that drive innovation.

The AI Data Gatherer sees how information flows through the company—from customers, marketing campaigns, the supply chain, IoT, etc.—and makes connections. They see potential signal for learning algorithms, and they see how AI algorithms can feed data into other systems. For example, this individual might see that internet-enabled cooling units report on energy usage every hour; she surmises that when units spike above two standard deviations for long periods that another chiller might be required. She recommends to the cross-sell AI team that they use these data in their algorithm, along with her hypothesis.

The most advanced non-engineer role is the prototyper—the individual who is comfortable tinkering and messing around with AI technology. This is usually a business power user who is impatient for results. These individuals can frustrate engineering teams (think, stepping on my turf,) but at successful, agile companies, interdisciplinary work is encouraged. We ask AI engineers to understand the business problem; successful companies encourage business leaders to get their hands dirty (in a safe environment, of course.)

Principles for Building Your Bench of AI Interfacers

There were several traits that companies who successfully built up a strong bench of digital natives had in common, and a few traits that struggling companies also shared. There is no reason to expect that the core principles have changed, but I’ve adapted them for AI.

The actions below are all totally doable. None of them require spending millions of dollars on a quantum computer, or hiring 50 new developers to go “do some AI stuff.” Rather, they are mainly HR and management actions. If they don’t get done, it’s probably because, like most things worth doing, they don’t drive immediate ROI. They are cultural changes that must be driven from the top (the first DO below.)


  1. Hire a Lifetime Learner CEO / Exec Team. It all starts at the top. If you have a CEO who won’t take the time to understand AI at a foundational level—how it works, how it learns, existing use cases—then you’ll be toast. Keep in mind, I’m not talking about hiring a programmer data scientist—I’m talking about someone with an insatiable thirst for learning who never gets tired of reinventing her skillset.
  2. Hire New Cohorts, Every Year. Companies who don’t hire young people for prolonged periods of time quickly fall behind new waves. AI is no exception. I first heard the term “digital native” in 2004, from a technology company marketing executive who lamented his inability to make the transformation to digital. This company had kept old managers in seat for years (they were the original crew) and now needed a talent infusion. If he’d hired one or two 22-year-olds every year, he wouldn’t have been playing catch-up.
  3. Have a Citizen-AI Training Curriculum. One thing that didn’t exist ten years ago was the MOOC. If you wanted a marketing manager to learn the basics of ad exchanges, she either had to learn on the job or go take a course at a university. Today, motivated learners can take AI courses from basic to fairly advanced, essentially for free. As a manager, it’s your duty to (1) create a curriculum based on existing MOOCs and post on your intranet / wiki, and, (2) give employees the time and space they need to get up to speed.
  4. Co-Create, Foster Agency. If an AI-based next logical call algorithm is implemented in a call center, don’t allow it to be cynically jammed in with an explanation of “just do it.” This will drive resentment. Instead, train users on how the algorithm was built. What are its inputs? What algorithms were used to train the model? How do we know it works? Involve your employees in co-creating the AI interfaces; you’ll find that they quickly surface problems and blind spots, and will happily use it / work with it. Analogies for this exist all over, but perhaps the most powerful is the Andon Cord used in lean manufacturing whereby any employee can “stop the line” to identify problems with production.
  5. Force Human Interaction Interfaces. If AI algorithms are only allowed to talk to one another, we might actually get to the “grey goo” scenario pretty quickly, and I’m only half kidding. Rather, focus on human understandable interfaces. The Google search example I started with is a good example of a human-AI interface that is mutually reinforcing. Concretely, building out a next logical product algorithm in a CRM system shouldn’t just spit out a SKU. Expose more about the key inputs; the predictive factors; allow the human to adjust parameters and see how the model changed. Perhaps most importantly,
  6. Promote Tinkering. Siloes and a “guild mentality” kill innovation. Most Silicon Valley companies have done a good job promoting a tinkering culture. However, in too many other places, “stay in your lane” dominates, causing people who stick their neck out to get whacked. AI is no exception. If you want people to stay around, let them play around. Make sure you have safe spaces set up where nothing can be broken—but innovation beats parochialism any day of the week.


  1. Don’t Go Build Stuff Just Because AI. Perhaps the fastest way to alienate your workforce, and make them AI opponents rather than AI proponents, is to hit the panic button and go off half-cocked on an AI initiative without a clear business reason. A lot of companies did this last year with blockchain. “We need to do something with blockchain, because… blockchain!” (Guilty. Mea culpa.) So don’t do this with AI. Wait for the real use cases. If your employees are excited about it, it’ll be a lot easier, and it’s a really good indication that it’s worth doing.
  2. Be Cautious of Black Boxes. Proprietary black boxes may be awesome, but even more so than with enterprise software, companies need to use extreme caution before committing to them. AI is, by its very nature, opaque. Buying from a vendor who won’t expose the inner working adds another level of opacity, and will make it much harder for employees to interface and find agency. It’s fine to test out proprietary solutions, but be aware of what you’re committing to.
  3. Don’t Build a Monolith. Finally, don’t build the one AI ring to rule them all. When I see IBM advertising Watson as the solution to everything, I definitely get Lord of the Rings Flashbacks. I guess I get why everything should be centralized, but again, if you’re trying to build a cyborg organization, this seems like a giant mistake. Instead, building smaller AIs that humans can work with directly, that communicate with one another but aren’t a hive mind, seems a safer way to go—in more ways than one.


Companies that successfully navigate the coming AI transformation will build an army of AI Interfacers, made up of power users, product managers, teachers, data plumbers, and tinkerers, who will drive a positive feedback loop between the power of AI and human intelligence. These companies will make the creation of this culture a priority, with concrete management, HR, and technology decisions designed to prioritize the human-AI interface, not the raw power of the algorithms. These “Cyborg Companies” will emerge as the clear winners over the coming decade.

[1] In his book Superintelligence (2014), Nick Bostrum laid out many potential dangerous outcomes for an unchained, general intelligence AI: a “grey goo” of endlessly self-replicating nanomachines that takes over the planet; a resource-consuming algorithm gone awry whose sole goal is factoring prime numbers, eventually building a Dyson Sphere around the sun to achieve its objective; and even more malicious scenarios evoking devious, trickster AIs who fool researchers into mailing it what it needs to build a machine to escape its human prison. This is pretty dark, and while I do think we need to be worried about these dangers, this isn’t the focus of this article.

How to Take an Omni-channel Approach to Sales Enablement

Today’s typical buyer journey was best described as, “…a big bowl of spaghetti,” at the Gartner Sales and Marketing Conference in Las Vegas. Thanks to constant innovation in digital and mobile technology, buyers now have the freedom to self-serve by researching information online and engaging with industry analysts, SMEs, and other influencers.  The real game-changer of these new advances is a buyer’s ability to jump between various buyer stages—all without involvement from a seller.

While incredibly empowering for the buyer, it has created a new level of complexity for the seller. Buyers while moving between various stages, can also move from self-serve digital channels to one-on-one seller-serve channels. Accenture reports, “…around 30 percent of digital sales leads are expected to be finalized by an agent over the phone or by a call center operator. About 27 percent of digital sales leads are likely to be concluded by agents in face-to-face discussions with customers.”

Despite these trends, many companies are taking a siloed approach to implementing Self-Serve and Seller-Serve sales enablement. Why? One reason is simply siloed organizational ownership.  When you have different teams held to different goals with different leadership, there’s no internal catalyst to encourage teams to work together. Another reason is differing systems and technologies.  Marketing teams traditionally manage online and mobile Self-Serve channels, while Sales teams tend to manage CRM-supported Seller-Serve channels.

No matter the reason, a siloed approach creates notable pain points for both buyers and sellers. The buyer is often faced with a disjointed and frustrating experience, while the seller can be ill-equipped and unprepared to support buyer discussions.

How to Take an Omni-channel Approach to Sales Enablement

Successful sales enablement should take an omni-channel approach.  Marketers use the term omni-channel to describe continuity within the user experience. Ensuring continuity in your sales channels requires an omni-channel approach too.

If a buyer starts in Self-Serve and moves to Seller-Serve, are your sellers well-informed on what the buyer has seen, heard and experienced to date? Will they know how to address related questions they may have?

To ensure an integrated experience where everyone (across different selling channels) is equipped to have relevant, valuable interactions with a buyer, one of the most important things you can do is to create a blueprint of the integrated Self-Serve and Seller-Serve buyer journey.  And then use that blueprint on an ongoing basis to educate, plan and enable the team members responsible for managing your sales channels: Self-Serve and Seller-Serve.

The most effective blueprints capture important details and include feedback and insights from a wide cross-section of stakeholders involved in the sales experience. Here are a five quick-tips for formalizing a comprehensive sales enablement blueprint of your Self-Serve and Seller-Serve channels:

1) Talk to your buyers.

The investment you make in this type of qualitative research will deliver a tremendous VOI (value on investment).  The findings will help you improve processes, content, offers, plus uncover other “ah-ha” optimizations not yet on your radar.

2) Consult with your Self-Serve team and Seller-Serve team.

Both independently, as well as together. These discussions will provide you with the step-by-step details you need, as well as flush out journey intersections and key pain points.

3) Detail the specific content and information made available to a buyer at each step of the journey.

Omni-channel continuity is made easier when your sellers have context of the buyer’s experience across all touchpoints.

4) Highlight not only key points of integration but journey milestones.

…such as specific gates a buyer must complete before moving to the next stage. This allows users of the blueprint to easily review the most critical and common of journey interactions.

5) Don’t set it and forget it.

Plan to revisit and update your blueprint a minimum of once a quarter. Treat it as a living document and repeat steps 1 to 4 with an eye toward continued updates and optimizations that can be made to the sales process.

The Last Mile Involves Distribution and Training

Once you have a sales enablement blueprint of your Self-Serve and Seller-Serve channels, you’re ready to put it into action.  This “last mile” of activation is often the hardest part for companies simply because of the many parties involved and the coordination required in driving awareness and usage.

We recommend live and interactive trainings to bring the power of your blueprint to life. Begin with in-person or web-cam trainings and supplement with e-learnings for the best results. And naturally, be sure the blueprint is readily accessible to everyone working across Self-Serve and Seller-Serve sales channels, preferably via a digital portal and/or App.

Is the process to create and activate an integrated Self-Serve and Seller-Serve buyer journey time-consuming?  Yes. But organizations that do so will produce superior return-on-investment (ROI) for sales enablement content – all while delivering a world-class buyer experience.


FinServ: A Look Back at the Top 5 of 2018

Looking back at the top 5 blogs of 2018 validates our observation that there is still a significant amount of friction in the Go-to-Market models in the Financial Services industry today.  Incumbent FI’s must become much more agile at identifying those friction points, and leveraging a combination of data and insight, analytics, content and technology to help eradicate that friction as new entrants look to gain footholds in the market.   Here’s our take:

  1. Six Early Go-to-Market Trends and Tips for 2019

    The top trends that came up again and again in conversations with Chief Revenue Officers and Chief Marketing Officers on how to outperform the competition in 2019. They’re still valid – and all about reducing friction in your 2019 Go-to-Market!

  2. How to Cross-Sell at Scale – Part 1

    Achieving greater scale in your Cross-Sell execution is one sure way to help reduce Go-to-Market friction.  Marketers need to focus on enabling their sales channels – inside, agents and brokers, direct – to be more effective cross-sellers by providing the prescriptive targeting, content and messaging they need to be successful.  This “last mile” of the buyer’s journey is where much of the GTM friction is still concentrated in the customer experience.

  3. It’s Time to Redefine Go-to-Market Strategy

    In today’s digital world, traditional push marketing and sales activities are no longer effective.  Prospects and customers are self-educating and down-selecting potential  vendors online. If you can’t differentiate and solve their needs through compelling “digital channels”, you have very little chance of being in the consideration set, let alone winning a deal.  Again, it’s all about reducing friction across the customer experience.

  4. 10 Step Checklist for Creating Actionable Segmentations and Personas

    So, what does a successful, actionable segmentation look like? One that is adopted – thereby helping to reduce friction in your marketing efforts by accurately anticipating customer needs and wants. Fortunately, there is a checklist that marketers and market researchers can follow to protect against the risk of a six-month segmentation effort ending with a thud (literally, the 100-page PowerPoint hitting the bottom of the shredder bin).

  5. What About Small Data? Part 1

    Big data remains all the rage – and a source of considerable GTM friction within many organizations still struggling to build their capabilities! Learn how small data can help improve speed to market and short-term results! Many of the best problems out there today—the ones that will yield the most incremental fruit, in terms of leads, opportunities, loyal customers, dollars, etc.—have to deal with small data.

We’re excited about 2019 as a year of implementing innovative ways to activate new go-to-market programs at the ground level with our FinServ clients.  Where are your GTM friction points as you head into 2019?


2019: Eliminating Go-to-Market Friction in FinServ

I had the opportunity over the Holidays to reflect a bit on our Financial Services client experience over the past year, and to think ahead to some of the key industry Go-to-Market challenges we see for 2019.

I went through our blogs for 2018 to see which were the most popular and looked for themes across those which resonated most with our readers. I revisited industry and analyst reports on data, AI and analytics, fintech, martech, digital disruption and transformation, etc. to look for consistent themes. I compared these to our work with multiple clients last year across multiple segments of the industry.  I also reviewed my own experiences as a “consumer” of financial services products almost every day.

A lightbulb went on for me while trying to complete my fourth Christmas present return and exchange for one of my kids – there is still a lot of friction in the system today.  My first return was easy (Amazon).  I clicked on the order in my order history, printed the pre-paid return label and dropped the package at the UPS store.  My bank account was refunded as soon as the tracking order was assigned.  Nearly frictionless.

Had a completely different experience on my fourth attempt when I tried to exchange my daughter’s coat (wrong size!) with another vendor.  I actually had to search through the trash to find the receipt from the original shipment as the order was not available online.  No option to print a label, so I had to take the box to the UPS store, fill out the paper form, have them generate a label, and pay the shipping fees.   The vendor said I should expect to wait 7-10 days after the goods were received for a refund to hit my bank account.  Lots of friction. Guess who I will be ordering next year’s jacket from?

This got me thinking about “Go-to-Market Friction” – the amount of resistance that still occurs today between companies and customers as they progress through the buyer’s journey.  The good news is that rapidly advancing technologies in today’s digital world provide significant opportunity to remove friction in FinServ Go-to-Market processes.  The downside is that it also creates great expectations for ever-better customer experiences and provides opportunities for new market entrants to come in and disrupt traditional “high-friction” FI’s with “low-friction” alternatives.

Arguably, the vast majority of investments that FinServ companies are making today in data, analytics, content, and technology are focused on reducing the friction that exists between customers and an organization as they move through their journey.  Firms that reduce or remove friction have to exert less “force” (think reduced marketing and sales investment and resource) to move customers through each journey and encounter less customer resistance (think improved acquisition, cross-sell, retention, customer satisfaction, etc.)

And with nearly $100B in investment in FinTech globally in 2018, there is still a lot of friction in Financial Services business models that FinTech firms and their investors are racing to address with point solutions across the FinServ spectrum.

With Marketing increasingly responsible for leadership in both customer experience and revenue growth in 2019, CMO’s must be able to identify, prioritize and help reduce or eliminate these areas of friction in their Go-to-Market activities.

Technology changes very rapidly.  Customer buying behavior evolves more slowly.  Existing GTM systems are the last to change.  Legacy FinServ firms must become much more agile at identifying and addressing friction in their current Go-to-Market, or risk being disintermediated by more nimble competitors or startups.

So what are your Go-to-Market friction hot spots?

Based on our experience, here are five key leverage points for your consideration to help eliminate Go-to-Market Friction in 2019.

1) Single View of the Customer

Having a single view of the customer that is available to all channels is a key requirement for creating a frictionless Go-to-Market model.  Gathering, managing and maintaining that data from across multiple business units and numerous touchpoints is still a daunting challenge for many financial services organizations today.

According to Dun & Bradstreet’s 6th Annual B2B Marketing Data Report, 56% of the firms surveyed say that aligning sales and marketing data about companies and contacts is very or extremely difficult today.  49% are NOT confident in the current quality of their sales and marketing data.

Integrating this data into a Customer Data Platform, leveraging it and a Marketing Data Platform to support increasing AI and analytics efforts, and making this insight accessible to other GTM applications, processes and channels will be critical for identifying and mitigating GTM friction in today’s multi-channel world.

Small wonder that IBM predicts that Director of Marketing Data becomes the hottest new marketing role in their 2019 Marketing Trends.  Among other responsibilities, this role will “create processes, rules, and procedure to ensure that critical data is collected and integrated into a customer data platform (CDP).”

For more information on the Customer Data Platform, visit the Customer Data Platform Institute here.

For more information on the Promise of the Marketing Data Platform, see here.

2) Marketing Responsibility for the Customer Experience

Removing friction from the customer journey will continue to be a challenge for those organizations that do not have a group responsible for defining, monitoring and improving that experience end-to-end over time.   Increasingly, customer experience is a role for which marketing is assuming leadership –  though interestingly, this area of responsibility for marketing wasn’t even added to the CMO Survey until February 2018.

According to the August 2018 CMO Survey, Financial Services firms already trail all other industries in terms of marketing leadership of the customer experience (33% vs 45% cross-industry average).  The most recent Salesforce State of Marketing report is more optimistic, with 44% of FinServ firms indicating marketing leads CX initiatives.

Identifying areas of friction and providing the personalized and targeted communications that customers expect at each point along the way will require an end-to-end view of that journey. According to Salesforce, 54% of High Performers leverage Marketing as the “Cross-Functional Glue of Customer Experiences.”

Marketing must assume responsibility for the creation of an integrated customer and seller journey framework that aligns each stage of the buying process and the sales process into a unified strategy that identifies and aligns corresponding content and resources – website, customer service, live sales reps, events, etc. – across all channels.  It is also a vital framework for identifying areas of misalignment or friction between the two!

Again, small wonder that IBM predicts customer centricity will drive constant transformation and that companies must develop a cohesive strategic vision for CX “rather than two distinct customer and marketing strategies as independent playbooks.”

For more information on Creating a Consistent Customer Experience, see here.

3) Leverage Digital Listening to Identify Friction Points

Digital listening is a valuable resource for leveraging the “voice of the customer” to identify Go-to-Market friction points in a near real-time way. Many firms still rely on social media primarily for brand tracking and sentiment analysis, and not for the kind of actionable insight that can be garnered about competitors, partner, products and customers.

Today’s digital listening platforms have evolved considerably from the “old days” of social media sentiment tracking.  The depth of insight that digital listening can now deliver is immense. And in the hands of experts, those insights can be translated into near real-time, actionable intelligence that will help identify GTM friction points.

But GTM friction is not just internal or customer-related, so any digital listening exercise must also include visibility into the actions of other industry players, including your distribution partners and your competitors.  Implementing a highly-structured, action-oriented digital listening program will help organizations make continuous improvements in their Go-ot-Market.

For more information on how companies can better leverage digital listening to identify and remove GTM friction, see here.

4) Agile Analytics at Scale

FinServ tends to invest more in analytics than many other industries today.  That said, according to the most recent CMO Survey, the FinServ industry plans to more than double their investments in analytics over the next three years.  To maximize return on these investments, firms will have to become much more agile with their analytics, and more importantly, the incorporation of those insights into their Go-to-Market execution to help reduce friction.

Developing and deploying an agile analytics capability requires developing the right balance between a corporate COE and individual BU teams, and implementing agile methodologies that enable marketers to execute at scale while retaining the ability to shift gears quickly.  Prioritizing activity based on business impact will be crucial. And aligning your analytics teams closely with your marketing teams will be crucial to ensuring a tighter a business alignment and faster time to market.

To learn more about building an agile, results-driven Analytics Organization, see here and here.

To learn more about the Marketing Analytics Family tree, see here.

5) Focus on Enabling the Personal Relationship

Building trusting relationships is one of the highest priorities in Financial Services, and a key differentiator in driving customer acquisition and retention.  For most Financial Services firms, these relationships are personal in nature, and in many instances exist between third-parties and end customers – agents in the insurance industry, or financial advisors in the Asset Management industry for instance.

This can be an area of significant Go-to-Market friction if insufficient investments are made in enabling these relationships.  This “last mile” relationship is where process breakdowns often occur, and customers, channels, and companies are “out-of-sync.”

Eliminating friction in digital channels is much easier given the one-to-many nature of those channels.  In the “emotion” economy, the many-to-many nature of personal relationships is crucial – especially for Financial Services – and ensuring a consistent customer experience across these resources is key to delivering the value proposition.

To learn more about Enabling the Last Mile, see here.

Where will the biggest friction points be in your 2019 Go-to-Market?