With the start of the New Year, the goals of customers are on my mind and the minds of many customer success managers. It’s no wonder. Goals help customers achieve success. They make it possible to align expectations. They focus efforts on what’s most important. Later, they will help to evaluate a customer’s progress and the value they’ve realized.

Across a customer base, I’ll usually see some customers with very strong, effective goals. They have clear goals for the outcomes they want to achieve, the milestones along the way and the near-term activities. Each of their goals are S.M.A.R.T.: specific, measurable, achievable, relevant and time-bound. They’re likely to be star performers.

At the same time, I’ll see other customers who have weak, ineffective goals. Amazingly, there are customers who don’t even have goals. These customers should be a red flag for customer success managers. Without effective goals, customers won’t be on top of the value they’re getting from your solutions, or the progress they’re making towards realizing value. Later, at a critical moment, they won’t be able to argue successfully for the continued purchase of your solutions. You’re likely to encounter a customer downsell or lose the customer.

The good news is this creates an opportunity for customer success managers to engage customers in setting more effective goals. While it can be challenging to get their engagement, there are various tactics that can be helpful.

Here are some tips that have helped me with goal-challenged customers:

1. Offer to give feedback.
Sometimes, a customer’s goals are unclear. For example, increase product sales and reduce lead generation expenses are too ambiguous to align expectations. When I recognize this, I’ll ask if they would like my perspectives and insights on their goals, based on my experience with other similar customers. I know the results others are getting and how they achieve them. I can tap into customer data to identify benchmarking metrics and benchmarks across similar customers, too. This knowledge gives me credibility and unique insights to share.

2. Ask insightful questions.
When a customer’s goals are too general and unclear, I try to move them from the abstract to the specific. I use a combination of questions (like those below) to help the customer think more critically about the outcomes they need to achieve. I want to help them frame their goals with business value in mind.

• What are you trying to accomplish with our solution?
• How do you see results changing with our solution?
• How will you know you’re successful with our solution?
• How does our solution help your business do what it does better?
• How can our solution impact your company’s business goals and initiatives?

I want the customers to come away with goals that allow for effective expectation management, success planning and measurement of business value.

3. Confirm they have the right set of goals.
Some customers have goals that are incomplete. Often, I’ve found their goals are based on near-term actions to take such as “complete solution training for all sales development reps in Q1.” But, they’re missing goals for the business outcomes they need to achieve while using a solution. Other times, they have effective goals for these desired outcomes, but they’re missing goals for the actions to take to get there. I always look for goals for business outcomes, milestones along the way, and the near-term actions. If they’re missing any of these, or if they’re not connected, I know their path to value realization and success is at risk.

4. Let them know what’s realistic to achieve.
On occasion, customers will share their ’dream’ or ‘wish list.’ I appreciate their value for driving innovation and excellence, but unless goals are achievable, people will become disenchanted. Then, they lose the motivation to even set goals. This is where benchmarking with your own customer data can help. Share what’s best, worst and average for customers like them during the applicable stage of their journey with your business. Offer benchmarks relevant to their desired outcomes, as well as leading indicators which could be product adoption, product usage, key feature usage, program engagement and so on. I’ve found that helping customers develop realistic goals in this way strengthens relationships.

5. Tackle resistance to goal setting.
There’s always a cabal of customers that avoid setting goals. While many people credit their success to setting goals, there are others that don’t see the value or reason to do so. They’ll resist your efforts. Trying to engage these customers to discuss goals can feel like pouring gasoline on a fire. I remember a tech company CEO once told me “Jim, you’re not going to pin me down.” I knew then that neither he nor I would be successful. Several months later, the board replaced him.

I don’t back away from these resistors. I try to gain an understanding of their viewpoint by asking insightful ‘why’ questions. I hope to find common ground from where we can start and grow from there. In some cases, they believe they don’t have enough information to set a goal. You know what? Sometimes they’re right. Their business leaders haven’t set clear goals. Or, they bought the product without defining the desired business impact. When this is missing, I’ll try to persuade them to commit to a goal of gathering information that’s needed to set goals afterwards. Another tactic I’ve used is to encourage setting 1 or 2 ‘micro goals’ for a 3 to 6-month period. It can be easier for the customer to accept. It helps to build their skills and confidence which leads to success.

Wherever these customer conversations go, I emphasize what I’ve learned from experience: customers who set effective goals have more success than those that don’t.

Jim Berardone

How is your county doing, where could it do better, what’s been changing, and among similar counties, who does best?OnlyBoth has launched a County Benchmarking Engine as its newest showcase application and public service, leveraging the availability of federal as well as private-sector data on all 3,143 U.S. counties. We collected data on 104 county attributes by these topics:

• obesity and diabetes from the CDC
• population, migration, national origin, veterans, race, age, education, households, home ownership, income, poverty, jobs, unemployment, land area, oil and gas production, land area, and various county classifications, all from the USDA’s Economic Research Service
• apartment rents from HUD
• religious affiliations from ARDA (Association of Religion Data Archives)
• health-plan enrollments through HealthCare.gov
• water usage from USGS
• farmland from DHHS’s Health Resources and Services Administration
• arrests from the DOJ’s FBI (archived at Univ Michigan’s ICPSR)

Here’s an example of an “Only, both”-style insight about Bronx County in New York City:

Only Bronx County in NY has both as high a median rent for a 3-bedroom unit ($2,293) and as low a median household income ($33,687).

The flip side, for a 1-bedroom apartment this time, is seen in Audubon County, Iowa (population 5,773)  about 1,200 miles away:

Only Audubon County in IA has both as high a per-capita income ($30,714) and as low a median rent for a 1-bedroom unit ($474.00).

Notably, San Francisco County in CA is among the top-15 nationwide in all these 9 categories, in order of their appearance:

1. 3rd-highest Asian population (33.0%)
2. 10th-least male obesity (14.8%)
3. 10th-most residents employed in services (71.8%)
4. 8th-highest foreign-born population (35.5%)
5. 11th-highest per-capita income ($49,986)
6. 11th-least obesity (15.5%)
7. 10th-biggest advantage, relative to its home state, in per-capita income (+67.1%)
8. 12th-least female obesity (16.1%)
9. 13th-least owner-occupied housing (36.6%)

but residents pay for it with the highest median rents in the whole country, for a 0-bedroom (studio/efficiency) unit ($2,072), a 1-bedroom unit ($2,610), and a 3-bedroom unit ($4,250).

Re-visiting New York city, let’s consider this time New York County (i.e., Manhattan). It’s doing well on many dimensions, although unsurprisingly its rents are high. But there’s a residual problem:

Of the 103 counties that have at least $75,459 in median household income, only New York County in NY has decades-long substantial child poverty.

Let’s close out on a high note. Many of the data attributes involve changes over time, so the engine benchmarks not only on characteristics during a single time period, but on changes or trends from one period to a next. Elsewhere we’ve called that space-time benchmarking. Here’s a good outcome on the populous Fairfax county in Virginia, adjacent to Washington DC:

Fairfax County in VA is one of only 3 counties that improved or maxed out on all the obesity and diabetes change metrics (there are 4 of these, and each county needs at least 3 with actual values to qualify).

Fairfax County in VA improved or maxed out on these:

change over 4 years in the prevalence of female obesity = -2.2% (from 22.5% to 20.3%)

change over 4 years in the prevalence of male obesity = -0.5% (from 20.4% to 19.9%)

change over 4 years in the prevalence of obesity = -1.4% (from 21.5% to 20.1%)

change over 9 years in the prevalence of diabetes = -0.4% (from 6.6% to 6.2%)

As we’ve seen, it’s now possible, after a few days of software configuration, to create significant economic or social value by generating numerous performance insights on thousands of benchmarked entities, and later update these insights by figuratively pushing a button to update the insights with updated data.

Try the County Benchmarking Engine yourself at county.onlyboth.com.

Raul Valdes-Perez

“If you’re not getting better, you’re getting worse.”

What do executives mean when they use this phrase? They know that if their company stays as they are, eventually they’d get worse compared to their competitors.  Their competitors’ success forces them into changing how they perform, and vice versa. Hence, their company has to keep getting better, even if it isn’t sick.

Take a look at Salesforce.com in 2004. They had 20,000 customers of their cloud-based CRM software, up from about 6,000 a few years earlier. They were rapidly on their way to reaching unicorn status of a $1B market cap. But they were losing 8% of their subscribers each month. Yikes! That’s a big problem when a recurring revenue business depends on growing revenue from its customer base. Salesforce responded by changing from
reactively to proactively managing its existing customer relationships. They eventually succeeded in reducing customer churn to 1% per month and the loss of customers to their competitors. Their competitors had to follow suit to keep up. Now, proactive customer success management is one of the hottest movements in business today.

What does this mean for your Customer Success?
All of your customers need to keep improving, too, or they’ll fall behind. And your customers know this. Consequently, they need to know how they’re doing and where they can improve versus others like them. These comparative insights motivate improvement actions inside their companies, which leads to achieving their desired business outcomes — including staying competitive.

If your company offers cloud-based, recurring revenue solutions such as a SaaS subscription, then you have data that’s a byproduct of your customer relationships and their usage of your solutions. As your customers are buying the same solutions for similar business reasons, this data contains a rich source of comparative insights for each customer – insights they can’t get anywhere else.

Your customers can use these unique insights to improve. That’s important to you, too. If they’re not improving, then their outcomes, health and loyalty won’t improve, and you won’t achieve the retention and upselling rates that your company needs to grow.

Comparative insights help Customer Success Managers do their job better.
Today, your CSMs likely use customer data to gain a full picture of an account and to evaluate a customer’s likelihood for renewing, buying more or advocating your solution. This customer insight serves your company well, whereas comparative insights serve your customers well. With unique comparative insights, CSMs can deliver more value to each customer in each interaction. They’ll get a better response to their proactive outreaches, have more strategic conversations with key stakeholders during quarterly business reviews and, most importantly, provoke customer actions to improve their behaviors with your solutions.

Generating comparative insights
The process of identifying areas where one can improve by comparing to others is called benchmarking. With your own data, your CSMs can use benchmarking, too. You can create scorecards with a spreadsheet or CSM software product to rank customers and do simple performance comparisons on individual metrics. If you have relevant benchmarks or targets, CSMs can draw additional insights. But the really noteworthy, action-provoking insights are much harder to find. You’ll need data analysts or data scientists who can perform deep, comparative analysis involving complex correlations, comparisons and clustering across multiple dimensions. Unfortunately, they’re in short supply and CSMs have to wait in line to get what they need.

As a result, CSMs settle for rankings and benchmarks without the deeper analysis, which has limited value. It’s like they have Consumer Reports’ published rankings and performance ratings of cars but without the written analysis for each.

An easy, scalable way to get comparative insights
I’m quite excited that, just last week, my company OnlyBoth announced a new customer analytics solution that can help CSMs take full advantage of the unique insights that exist in their company’s data. OnlyBoth’s Customer Benchmarking Engine uses artificial intelligence to completely automate the searching, analysis and reporting of comparative insights in data. CSMs can simply enter a customer name and get numerous comparative insights in seconds. The software performs a massive, sophisticated data analysis that would take many data scientists many months to do.

Here’s an example of a deep, comparative insight the engine found in the data we gave it. This insight is written up by the software using its natural language generation technology.

Rhynyx is amongst a group of 55 customers who are using a vendor’s HR software suite more actively than their other customers. However, Rhynyx is not keeping pace with their use of the recruiting module, a core sticky function of the vendor’s HR suite. A CSM can use this insight to proactively engage Rhynyx to examine the root causes and take action so they can achieve similar outcomes to those 55 peer customers.

You can see more examples and learn about our software here.

As someone who sits at the intersection of customer success leadership and benchmarking innovation, I hope to share more about this important but under-discussed topic in future posts. If you have experiences and lessons learned with using benchmarking and comparative insights for Customer Success, I’d love to hear from you.

Jim Berardone

Today OnlyBoth launches as a public service what is likely the largest benchmarking analysis ever conducted, as measured in terms of readable language output.

The U.S. has about 1.4 million residents of nursing homes and 15,665 Medicare or Medicaid certified nursing homes. The federal government, through its regulatory powers and reimbursement function, collects performance data on all of these nursing homes, which cry out for comparison in order to understand how each is doing and where each falls short, compared to all peers or their subsets, without bias.

We downloaded data from the federal Nursing Home Compare website and spent a couple of days consolidating the information and configuring our Benchmarking Engine, then pushed a button (figuratively!) and waited just a day and a half. The output consists of 642,192 insights, totaling more than two Encyclopaedia Britannicas in terms of English words contained in grammatical, to-the-point sentences and paragraphs. Enter a nursing home and browse the insights at http://nursing.onlyboth.com.

What did the benchmarking engine find?  At one extreme, the engine found the most things to say about Signature Healthcare at Saint Francis in Memphis, TN although most of these were not complimentary. There is clearly room for improvement there.

We often say that a massive analysis, as only an automated benchmarking engine can do, can find specific areas where even the best can improve. We discussed such a case – Stanford University Hospital – while launching our earlier hospitals benchmarking engine.

Let’s revisit sunny California. The US News & World Report lists Edgemoor Hospital in Santee, CA as the top nursing home in California, due to its top five-star rating in all the major categories. Could even Edgemoor improve? The engine reveals several areas for improvement, this one for example:

Edgemoor Hospital in Santee, CA has the most facility-reported incidents (9) of all the 200 nursing homes that have the top rating in each of overall, health inspection, quality measures, staffing, and registered-nurse staffing (5 total). Those 9 represent 18.8% of the total across the 200 nursing homes, whose average is 0.2.

Now let’s consider Bridgepoint Sub-Acute and Rehab Capitol Hill in Washington, DC which is the nursing home nearest Capitol Hill, where Congress meets. This facility does especially well in bladder and bowel control among low-risk, long-stay residents, as compared to other for-profit facilities that locate within a hospital. The engine found six specific areas for keen improvement, the first of which is this:

Bridgepoint Sub-Acute and Rehab Capitol Hill in Washington, DC has the 3rd-most high-risk long-stay residents with pressure ulcers (24.8%) among the 1,982 Mid-Atlantic nursing homes. That 24.8% compares to an average of 6.4% across the 1,982 nursing homes.

Another noteworthy insight it that the facility has “the most severe deficiencies on the health survey (5) of the 718 nursing homes that have the top rating in each of quality measures, staffing, and registered-nurse staffing.”

This application is launched as a public service as well as a technology showcase, benchmarking well over triple the number of entities that were benchmarked in our previous largest application, to 4,803 hospitals. The relevance to business is this:  the advent of cloud services, internet of things, and other means for collecting customer performance data will enable the automated benchmarking of business processes, generating tremendous economic value by benchmarking 10,000 entities with the same amount of work as benchmarking 10 entities.

Our goal is universal betterment by providing persuasive, motivating insights that pinpoint what is going well and where improvement is sorely needed and is achievable. Benchmarking Engines will do for business benchmarking what Search Engines did for information seeking, assigning to computers what they do better – massive comparisons – and to people what they do better – evaluating and following up, as appropriate – on benchmarking insights.

Raul Valdes-Perez

Comparing yourself to peers – also known as benchmarking – lets you understand how you’re doing, identify performance gaps and opportunities to improve, and highlight peer achievements that you could emulate, or your own achievements to be celebrated. As long as data is available, peer comparison can potentially accomplish all of these. The opportunities for peer comparison are greatly increasing due to cloud and other services that generate data as a by-product of serving customers.

The problem is that peer comparison as generally practiced suffers from Tunnel Vision and so misses a lot, to everyone’s detriment. To understand why, let’s first consider an analogy to search engines.

An information seeker, before there were search engines, might have gone to consult a librarian on, say, computers and heard “That’s technology, so look in the Technology books section, over in the back, by the right.” But there’s plenty of material on computers that’s catalogued elsewhere, e.g., automation’s impact on employment and job training, the philosophical question of whether computers in principle could do everything that people do, cognitive modeling of human reasoning using computers, computer history, and so on. The point is that looking only in the Technology section is an example of Tunnel Vision, or maybe bookshelf vision. Search engines changed that.

So where’s the Tunnel Vision in peer comparisons? It’s almost universal practice that the benchmarker chooses one or two organizational goals, then picks a few key metrics (key performance indicators) relevant to those goals, and finally selects several peer groups from a limited set. The outputs are then the mean, median, distribution, or high-percentile values for those peer groups on those metrics. The conclusion is that the organization may or may not have a problem, which may or may not be addressable. The flaw in all this is that organizations have many goals and subgoals, and many metrics that could reveal performance gaps, especially if a very large set of peer groups could also be explored. But our human inability to explore many paths in parallel imposes this Tunnel Vision, for the same reason that pre-search-engines information seekers went looking in one or two sections of the library.

As an example of peer-group selection, suppose you wanted to compare the U.S. against other nations. What would be the right peer group?  Here are some that make sense: democracies; the Anglosphere; constitutional republics; large countries; developed countries; OECD or NATO members; the western hemisphere; non-tropical countries; largely monolingual countries; business-friendly economies; and even baseball-playing nations. Moreover, peer groups could be formed dynamically, e.g., countries at least as big as the U.S. in population or territory. And what would be the right metrics? The mind boggles at the number of interesting possibilities, all of which may have available data. As already pointed out, standard practice is to first specify an overarching goal, which then drives the choice of metrics and peer group. (Some web examples of standard benchmarking outputs are here, there, and elsewhere.) But what if the goal is to understand broadly how you’re doing and where you could improve? Tunnel Vision is caused by over-specific goals, limited metrics, and biased peer groups, all part of standard benchmarking practice which is made obsolete in the face of exploring all interesting metrics and potential peer groups that could lead to operational improvements.

Let’s run some numbers to show the scope of Tunnel Vision. Suppose there are 10 attributes with yes/no values and another 10 attributes that can take on any of five different values, plus one attribute that can take on 50 values, e.g., a U.S. state. There are theoretically 2¹⁰ x 5¹⁰ x 50 = 500 billion peer groups. Even if we include only peer groups whose attribute values match those of the specific individual to be benchmarked, the number would be 2²¹ = 2.1 million peer groups.

Let’s move from the abstract to the concrete. Here are two (accurate) peer comparisons that are arguably insightful:

1. St Anthony Community Hospital in Warwick, NY has the lowest average time patients spent in the emergency department before they were seen by a healthcare professional of all the church-owned hospitals in the mid-Atlantic.

2. Macalester College in Saint Paul, MN has the highest total student-service expenses of any big-city private college that doesn’t offer graduate degrees.

Note the peer groups: (1) church-owned; mid-Atlantic; and (2) big-city; private; doesn’t offer graduate degrees. Now consider an imaginary peer comparison that uses four attributes to form a noteworthy peer group:

3. Cumulus Inc. is the most profitable of all the B2B, cloud-based, venture-backed companies that have at least 200 customers.

We see that considering more peer groups leads to uncovering more valuable benchmarking insights. Since the number of possible peer groups is vast, and benchmarking has seen little automation, this means that Tunnel Vision is necessarily widespread.

But the Tunnel Vision gets much worse! Peer groups can be formed, not just by picking non-numeric (aka symbolic) attributes, but also by dynamically determining numeric thresholds. Here’s a revealing (and true) insight that contains a dynamically-formed peer group:

None of the other 344 hospitals with as many patients who reported YES, they would definitely recommend the hospital (85%) as Stanford Hospital in Stanford, CA also has as few patients who reported that the area around their room was always quiet at night (41%).

That is, among those 344 hospitals, it has the fewest patients who reported that the area around their room was always quiet at night.

This is clearly a provocative insight. One can imagine a hospital CEO reacting in one of these ways:

1. We’re profitable, prestigious, and have great weather. What’s a little nocturnal noise?
2. There’s been night-time construction next door for the last year, and it’s almost done, so the problem will solve itself.
3. I can’t think of any reason why we should be at the bottom of this elite peer group. I’ll forward this paragraph to our chief of operations to investigate and report back what may be happening.

This peer-comparison insight wouldn’t be found by today’s conventional benchmarking methods. Instead, what may be found is along these lines: The average value for this quantity among 309 California hospitals with known values is 51.5% with a standard deviation of 9.5%, so Stanford Hospital is about 1 standard deviation below average. The reader can judge which of the two insights is the more action-provoking, not just for the single individual in charge, but for the entire team that needs to be roused to act on and address performance gaps.

So far, we’ve used some math to highlight the Tunnel Vision problem and shown specific examples, real or fictitious, of what is being missed. As our last step, let’s report the results of actual software experiments.

The website hospitals.onlyboth.com showcases the results of applying an automated benchmarking engine to data on 4,813 U.S. hospitals described by 94 attributes, mostly downloaded from the Hospital Compare website at Medicare.gov. A combinatorial exploration of peer comparisons among the 4,813 hospitals turns up 98,296 benchmarking insights that survive the software’s quality, noteworthiness, and anti-redundancy filters, or about 20 per hospital. In this hospitals experiment, insights were required to place a hospital in the top or bottom ten within a peer group of sufficient size.

There appear 522 different peer groups that are formed by combining the hospital dataset’s 24 non-numeric attributes in various ways. As noted above, the number of peer groups is much, much larger if one counts, not the attributes used, but the diverse ways to combine attribute values, e.g., the attribute “state” can either be used or not, so there are two alternatives there, but the number of state values is 50 (or more, including non-state territories), implying many more alternatives. The number of peer groups becomes still larger when accounting for dynamically-formed peer groups based on numeric thresholds.

Of course, the engine explored more peer groups than appear in the end results, which are those found to be large and noteworthy enough to bring to human attention. Also, each peer group appears in many insights by combining them with the available metrics. On average, each of the 522 peer groups enables over 900 individual hospital insights, by further combining each peer group and metric with different hospitals.

Summarizing, Tunnel Vision in peer comparisons, or benchmarking for understanding and improvement, is widespread but misses a vast number of noteworthy and action-provoking insights that could help improve organizational performance. Without automation, there aren’t enough people and time in the world to explore what’s outside the Tunnel, select the best insights, and bring them to human attention. Software automation is the way forward.

Raul Valdes-Perez